Capacity Constraints Are Crippling AI in Faith-Tech—Scale With Wisdom, Not Speed

I’ll be straight with you: I botched a major AI rollout for a ministry tool early in my career. I was so fixated on getting a smart sermon suggestion engine out the door that I ignored the capacity limits of our team and infrastructure. We launched, users loved the idea, but within weeks the system crashed under load, feedback loops broke, and trust eroded with our volunteer base.

The cost wasn’t just technical. Pastors and leaders who relied on us for weekly resources felt abandoned, and I had to face hard conversations with partners who’d trusted my vision. My blind push for speed over stability taught me a brutal lesson: scaling AI in faith-tech without capacity planning isn’t innovation — it’s recklessness.

This is the foundational misread that causes product teams to overpromise and underdeliver in faith-tech. We chase the shiny potential of AI — personalized discipleship, automated workflows, smarter outreach — without asking if our systems, people, and mission can handle the weight. It’s not just tech debt we accumulate. It’s mission debt.

I keep returning to Solomon’s approach to building the temple. He didn’t rush it — he spent years preparing resources, aligning people, and ensuring the foundation could bear the weight of his vision. He made alliances, trained craftsmen, and stockpiled materials before a single stone was laid. His approach wasn’t about stalling; it was about scaling with such intention that the work could endure. That image stays with me every time I’m tempted to ship before we’re ready.

The Hidden Cost of Over-Scaling

AI in faith-tech often promises a quick fix — chatbots for pastoral care, algorithms for sermon prep, automated donor segmentation. I’ve seen teams, including my own, rush these tools to market without stress-testing what happens when real users show up with real needs in real volume.

I remember a project where we integrated AI to suggest curriculum for children’s ministry volunteers. The early demos wowed everyone — tailored lesson plans in seconds. But we didn’t account for the server spikes on Sunday mornings when thousands of leaders logged in at once, and the system buckled right when they needed it most. The volunteers didn’t file complaints. They just quietly went back to their own notes and never came back to the tool.

That quiet exodus is the hidden cost. The users who don’t complain — who just stop coming back — are invisible on a dashboard until it’s too late. Solomon’s measured approach would have pushed us to build slower, test deeper, and prioritize reliability over a flashy launch. Speed feels like progress right up until the moment it fractures trust.

Faith-tech is not Silicon Valley. Our users aren’t customers who’ll shrug off a bad experience and try the next app. They’re partners in mission — volunteers, pastors, ministry leaders — who’ve extended trust to us. When we over-scale without capacity, we’re not just breaking a product. We’re breaking a relationship.

Capacity as a Mission Constraint

Capacity isn’t a tech problem — it’s a mission problem. I’ve worked on tools for sermon resources designed for the volunteer who has seven minutes on a Saturday night. If AI adds complexity, slows the interface, or demands more decisions, it isn’t serving the mission. It’s sabotaging it.

Take a global discipleship app I contributed to. We wanted AI to personalize reading plans for millions of users across time zones. The vision was compelling. But we didn’t factor in the capacity of our support team to handle culturally nuanced feedback from dozens of regions — and the disconnects piled up fast. The AI was generating content confidently for contexts it had no real understanding of.

Solomon didn’t just stockpile materials. He considered the limits of his builders, the timing of his alliances, and the readiness of his people. In faith-tech, capacity means knowing the honest limits of your team, your infrastructure, and your community’s ability to absorb change. Ignore that, and your AI becomes a burden rather than a blessing — impressive in the demo, exhausting in daily use.

I’ve learned to treat capacity as a guardrail, not a barrier. It forces the clarifying question: does this feature actually move the mission forward for the people who’ll use it this week, or does it just sound good in a pitch deck?

Building for Long-Term Trust

Trust is the currency of faith-tech, and AI can either build it or burn it. I’ve seen ministries adopt AI for engagement analytics, only to alienate leaders who felt reduced to numbers on a dashboard. The tech worked. The rollout ignored what people needed to feel about the tech.

On the flip side, I’ve watched a small team use AI to transcribe and tag sermons for accessibility. They moved slowly — piloting with a handful of churches, ensuring the output honored the preacher’s intent before scaling. They asked users hard questions. They iterated on the feedback. The result was a tool that felt like an extension of their ministry, not an imposition on it. Users didn’t just adopt it; they championed it.

Solomon’s measured approach wasn’t about playing it safe. It was about building something that would last. In faith-tech, long-term trust means phased rollouts — narrow use cases, genuine listening, expansion only when the foundation holds. It means the people you serve feel seen in the process, not just targeted by it.

I’ve made the mistake of prioritizing scale over stability more than once. Each time, the lesson is the same: trust, once broken with a ministry community, is extraordinarily slow to rebuild. No adoption metric or feature velocity compensates for it. The teams that win in faith-tech long-term are the ones that treat trust not as a soft value but as the hardest, most load-bearing constraint in everything they build.

Your Turn: Apply This Today

  • Map your current AI tools or plans — write down every feature or use case you’re pursuing, and rank them by mission impact versus capacity demand by this Friday.
  • Audit your team’s bandwidth — schedule a 30-minute conversation with your core team this week to ask where they feel stretched by current tech initiatives, and identify one area to scale back.
  • Stress-test one AI feature — pick a single tool or workflow, simulate peak usage with a small group by next Wednesday, and document where it breaks before real users find it.
  • Set a capacity ceiling — define a hard limit on new AI rollouts for the next quarter based on your current infrastructure, and commit to it in your next planning meeting.
  • Pilot with a small cohort — before any wide release, test your next AI feature with five trusted users or partner churches by the end of this month, and collect their raw, unfiltered feedback.
  • Build a trust checkpoint — create a one-page checklist of “trust criteria” (e.g., user empathy, cultural fit, pastoral appropriateness) by next Monday and run every AI update through it before launch.

Solomon’s greatest building project succeeded not because he moved fast, but because he moved with intention — aligning every resource, every relationship, every decision with the weight of what he was building. Faith-tech leaders have that same calling. The AI tools we ship are not neutral — they shape how people experience community, faith, and care. Scale with wisdom, and what you build will last. Scale with speed alone, and you’ll find yourself having the same hard conversations I had, apologizing to partners who trusted you with something sacred.

If you’re wrestling with AI scale in your ministry or product, check out my earlier posts on AI Is Redefining Team Roles in Faith-Tech—Don’t Ignore the Human Cost and AI Load Is Stressing Your Infrastructure—Faith-Tech Needs a Resilience Playbook for more on balancing tech with mission.

I consult with faith-tech product leaders and ministry innovators on AI strategy, capacity planning, and building trust through technology. Let’s talk.

Mission-Aligned Governance Is Your AI Strategy’s Missing Piece in Faith-Tech

I got a call a few years ago from a faith-tech product manager who was spiraling. Her team had just shipped an AI feature that auto-generated follow-up messages for small group leaders, and one of those messages had told a grieving woman that her feelings were “a normal part of the spiritual journey.” Generic, clinical, wrong. The feature worked exactly as designed. Nobody had ever asked whether it should have been designed that way.

That’s a governance failure. Not a tech failure.

Here’s the hard truth: without a clear governance structure, AI will pull your product—and your mission—into places you never intended. I’ve watched faith-tech teams chase shiny algorithms only to lose sight of why they exist. The pattern is almost always the same: smart people, good intentions, and no shared framework for who decides what values the technology serves.

This is the foundational misread that causes product teams to stumble: assuming AI adoption is just a tech problem. It’s not. It’s a values problem, a priorities problem, a “who decides what matters” problem. Tech moves fast, and AI moves faster—often dragging teams into efficiency traps or data-driven decisions that erode trust with the very communities they serve.

To frame what’s really at stake, I keep returning to Clay Christensen’s theory of disruptive innovation. Christensen argued that disruption happens when new tech serves overlooked needs—often at the cost of incumbent values. For faith-tech, AI is that disruptor: promising efficiency and scale, but threatening to hollow out the relational core of ministry if left unchecked. Governance isn’t just red tape; it’s the mechanism that ensures disruption serves mission, not the other way around.

Why AI Disrupts Mission Without Guardrails

AI has a way of sneaking in assumptions that don’t match faith contexts. I’ve worked on products serving hundreds of thousands of ministry leaders, where a single algorithm tweak could shift how volunteers prepared lessons for kids. Without clear rules on who decides those changes, we risked prioritizing clicks over connection.

Take a tool I helped shape for children’s ministry resources. We could have used AI to auto-generate content for busy volunteers, but early tests showed it often stripped out the personal tone pastors valued most. Without a governance framework, we might have shipped it anyway, chasing completion rates over soul care. Nobody would have flagged it as a mistake — the numbers would have looked fine.

The danger isn’t just bad product decisions. It’s mission drift. AI can optimize for metrics — engagement, retention — that don’t reflect spiritual impact, and I’ve seen teams get seduced by dashboards while forgetting the volunteer who just needs something printable and true. Christensen’s lens shows us exactly why: disruptors like AI start by solving real pain points, but without guardrails, they overtake the core job-to-be-done. For faith-tech, that core is discipleship, not data.

Governance as a Strategic Asset

Governance isn’t a buzzword or a boardroom chore — it’s a strategic asset. I learned this the hard way while scaling a global discipleship platform. We had teams in different time zones pushing AI features, but no shared clarity on what “success” meant beyond user numbers. Every decision was a negotiation with no shared reference point.

Once we built a governance model — defining who owns decisions, how mission trumps metrics, and when to say no to “cool” tech — our roadmap snapped into focus. It wasn’t about slowing down; it was about steering right. Christensen would recognize this: governance lets you harness disruption without losing your soul.

I’ve seen the opposite play out too. A faith-tech startup I advised rushed AI chatbots for pastoral care without a framework for accountability. The tool gave generic answers to deep pain, and trust eroded fast. No one had defined the line between helpful and harmful — and when that line was crossed, there was no process to catch it.

Governance isn’t just rules. It’s a shared language. It’s how you keep a distributed team, or a denomination with clashing priorities, rowing in the same direction. It’s the difference between AI as a tool and AI as a tyrant.

Aligning Teams Around Core Values

Here’s where governance gets practical. On a sermon resource project I worked on, AI could suggest outlines based on a pastor’s past work — brilliant on paper. But some suggestions felt like they came from a data model, not a heart aligned with the Word. Pastors noticed immediately.

We set up a governance council — not a bureaucracy, just a small standing group of product and ministry voices — to vet every AI output against our mission: equipping human shepherds, not replacing them. It wasn’t perfect, but it kept us honest. And crucially, it created a space where someone could say “this doesn’t feel right” and have that instinct treated as data, not obstruction.

Christensen’s warning looms large here. Disruptive tech will always find a way to grow, often at the expense of what’s sacred. Governance is your stake in the ground. It’s the decision, made before the pressure hits, about what AI can and can’t touch — whether that’s tone, theology, or the irreducible humanity of pastoral care. You don’t make that decision well in the middle of a product sprint. You make it in advance, calmly, with the right people in the room.

Your Turn: Apply This Today

  • Map your mission explicitly this week — write down the one non-negotiable value AI must serve, whether it’s relational trust or theological fidelity, and share it with your team by Friday.
  • Identify three key decision-makers who represent both product and ministry perspectives, and schedule a 30-minute call next week to define who owns AI feature approvals.
  • Draft a one-page governance charter by next Tuesday, listing what AI can optimize (e.g., scheduling) and what it can’t touch (e.g., pastoral tone), then get feedback from one trusted stakeholder.
  • Audit one existing AI initiative or proposal this month — pick a specific feature and trace how it aligns or conflicts with your mission, documenting at least two risks to address.
  • Set a recurring monthly meeting with your core team to review AI decisions against your governance rules, starting within the next 30 days, and assign one person to track mission drift signals.
  • Test one AI output with a small group of real users this week and ask directly if it feels “true” to your community’s values — log their exact words and bring them into your next planning conversation.

Every faith-tech team that has ever drifted from its mission did so gradually, one small unexamined decision at a time. Governance doesn’t prevent you from moving fast — it prevents you from moving fast in the wrong direction. Build the framework now, before the pressure of the next launch makes it feel like a luxury. Your mission, and the people it serves, deserve nothing less.

If you’re wrestling with AI’s role in faith-tech, check out my earlier posts on AI Is Redefining Team Roles in Faith-Tech—Don’t Ignore the Human Cost and Organizational Lag in AI Adoption Is Killing Faith-Tech Momentum—Here’s How to Fix It for more on aligning tech with mission.

I consult with faith-tech product managers and ministry leaders on AI strategy, mission alignment, and governance frameworks. Let’s talk.

AI Is Redefining Team Roles in Faith-Tech—Don’t Ignore the Human Cost

A few months ago, I was reviewing usage analytics for a curriculum platform when a team member knocked on my door. She’d been a volunteer coordinator for six years—creative, deeply committed, one of those people who makes a team feel like a team. “I feel like I’m just approving AI suggestions now,” she said. “I don’t know what my job actually is anymore.”

That conversation stopped me cold. The AI was working. Efficiency was up. And we were losing one of our best people anyway.

AI isn’t just a tool for faith-tech teams—it’s a silent wedge splitting roles and relationships apart. The rush to automate sermon prep, volunteer scheduling, or donor outreach promises efficiency, but it’s quietly eroding the human glue that holds mission-driven teams together.

Product leaders are so focused on output metrics—faster content, higher engagement—that they miss the cost. Team members feel like cogs, not contributors, when AI takes over tasks they once owned with purpose.

The foundational misread here is assuming AI’s role is purely additive. Product teams think it’s about doing more with less, but they overlook how AI reshapes identity. When a volunteer coordinator’s job becomes “feed the algorithm,” they lose the sense of shepherding people. That loss is invisible on a dashboard—but it shows up in turnover, disengagement, and a quiet fading of the passion that brought people to ministry work in the first place.

This isn’t new ground—Peter Drucker warned us about this decades ago with his concept of knowledge worker productivity. He argued that true productivity isn’t about raw output but about empowering workers to contribute uniquely. For faith-tech, that means AI must serve human purpose, not replace it, or we risk hollowing out the very mission we’re building for.

AI’s Promise vs. Team Fragmentation

I’ve worked on curriculum tools where AI could churn out lesson plans for children’s ministry in minutes. The win was obvious—volunteers with seven minutes to prep could print and go. But the hidden loss was just as real.

Volunteers stopped collaborating with each other. Why brainstorm with a team when a machine produces a better outline in seconds? The tool saved time but fractured the community that once bonded over shared creativity. We had optimized for speed and accidentally optimized away connection.

Drucker’s lens cuts through this. He’d say we’re measuring the wrong thing—task completion over human contribution. AI’s promise of speed can’t trump the need for connection in faith communities, where the relationships formed in the doing of ministry are often as meaningful as the output.

This isn’t just a volunteer problem. Product teams building these tools often silo themselves, too, as AI takes over cross-functional tasks like content tagging or user testing. The result is a fragmented team where no one feels the shared win of mission impact—and where the best talent starts looking for work that makes them feel human again.

Redefining Roles Without Losing Mission

Take a product I helped shape for sermon resources. We used AI to suggest outlines based on a pastor’s past work—brilliant on paper. But pastors started feeling their unique voice was being homogenized by predictive text. Their sermons sounded more efficient and less like them.

The fix wasn’t to ditch AI but to redefine its role. We shifted it from “creator” to “prompt”—a starting point for their own reflection, not a finished product. Adoption went up. More importantly, pastors started describing the tool as something that “helped them think” rather than something that “did their job.” That shift in language told us everything.

Drucker would recognize this. Knowledge workers thrive when they’re given autonomy to shape their output. In faith-tech, AI must be a scaffold, not a substitute, for the deeply personal work of ministry. The moment people can’t see themselves in the output, the tool has gone too far.

I’ve seen the same dynamic in donor platforms. AI can segment audiences and personalize appeals with impressive precision, but when fundraisers lean on it too heavily, they lose the relational depth that turns givers into long-term partners. AI handles the targeting; the human provides the soul.

Protecting the Human Core in Tech-Driven Teams

I remember a project where we built AI to handle user support for a global discipleship app. It cut response times by 60%, which felt like a slam dunk. But the team behind the tool—real humans who cared about the people they were serving—started feeling invisible.

They weren’t interacting with users anymore. No stories, no feedback, just dashboards. Their sense of purpose tanked, even as the numbers soared. We’d built an efficient machine and accidentally dismantled a motivated team.

Drucker’s insight here is unsparing: productivity without meaning is a trap. Faith-tech teams aren’t factories; they’re communities serving a higher call. If AI strips away the human core, no amount of efficiency matters—because the people doing the work will eventually stop doing it with their whole hearts.

We adjusted by carving out dedicated time each month for the team to engage directly with users, even if AI handled the bulk of support. It wasn’t efficient on a spreadsheet, but it was essential—people need to feel they’re part of the mission, not just its machinery.

This applies to church tech teams, too. If you’re automating worship planning or small group logistics, don’t let your people become button-pushers. Build rhythms where they still touch the lives they’re serving. Otherwise, you’ll win on efficiency and lose on the reason everyone showed up in the first place.

Your Turn: Apply This Today

  • Map every team role touched by AI in your faith-tech product or ministry this week—note where human input has shrunk and schedule a 30-minute conversation with those team members to hear their sense of purpose.
  • Pick one AI-driven process—like content creation or user outreach—and introduce a required human touchpoint by Friday, such as a follow-up call or team review, to reclaim connection.
  • Set up a monthly “mission check-in” with your team starting this month—spend an hour sharing user stories or feedback that AI can’t capture, reinforcing why the work matters.
  • Review your product roadmap by next week and flag any AI feature that risks isolating a role; adjust it so the tech supports human contribution rather than replacing it.
  • Identify one team member struggling with AI’s impact on their role this week and pair them with a mentor or peer for a regular check-in to rebuild their sense of value.
  • Block time this week to personally engage with a user or volunteer your product serves—use that interaction to remind yourself and your team of the human stakes beyond the algorithms.

AI will keep getting better at the tasks we give it. The question for faith-tech leaders isn’t whether to use it—it’s whether the people on your team still feel like the most important part of your mission. When they do, AI becomes a force multiplier. When they don’t, it becomes a slow drain on the very energy that makes ministry work possible. Protect the human core, and you protect everything that actually matters.

If you’re wrestling with AI’s role in your faith-tech work, check out my posts on AI Code Generation Won’t Fix Your Ministry’s Tech Debt—Here’s What Will and Internal AI Tools Need a Product Mindset to Stick in Faith-Tech Teams for more on balancing tech with mission.

I consult with faith-tech product leaders and ministry innovators on integrating AI without losing team cohesion or mission focus. Let’s talk.

Organizational Lag in AI Adoption Is Killing Faith-Tech Momentum—Here’s How to Fix It

Last spring, a faith-tech leader pulled me aside after a conference session and confessed something that stuck with me: “Half my team is using AI every single day. I found out from a Slack message two months ago. We still don’t have a policy.” She laughed, but her eyes didn’t.

Here’s a number that should stop you cold: 73% of workers in tech-driven industries have already adopted AI tools for personal productivity, according to a 2025 Gartner report. But only 22% of organizations in the same report have a formal AI strategy in place.

That gap isn’t just a statistic. It’s a momentum killer. Individual team members are racing ahead with AI, while leadership lags behind, leaving faith-tech orgs fractured and unable to harness the very tools their people are already using.

I’ve seen this firsthand in my work with global discipleship platforms and curriculum tools. The disconnect between ground-level adoption and organizational vision doesn’t just stall progress—it risks alienating your most innovative team members. If leadership doesn’t close this gap fast, we’re not just missing opportunities; we’re actively pushing talent and ideas out the door.

This is the foundational misread that causes product teams and ministry leaders to fumble AI integration. They assume adoption happens organically because individuals are already experimenting with tools like ChatGPT or Midjourney. But without a deliberate strategy, these efforts fragment, creating silos instead of synergy.

To frame this problem, I’m turning to John Kotter’s 8-step change management model. Kotter, a Harvard professor who’s shaped how organizations navigate transformation, argues that lasting change starts with creating urgency and ends with anchoring new practices into culture. His framework isn’t about tech—it’s about people and systems. And right now, faith-tech needs both to align around AI before the gap becomes a chasm.

The Data Gap Between Workers and Leaders

I’ve sat in planning meetings for digital ministry tools where team members are quietly using AI to draft content or analyze user data. They’re not waiting for permission. They’re solving problems in real time.

But leadership often doesn’t even know this is happening. In one project I worked on for a children’s ministry curriculum platform, volunteers were using AI to adapt lessons for their specific contexts. Meanwhile, the org’s executives were still debating whether AI was “safe” for their mission. The people closest to the problem had already moved on. The people with the authority to support them hadn’t.

This isn’t just a communication failure. It’s a structural one. Workers are moving at the speed of necessity, while leaders are stuck in analysis paralysis. The result? Duplicated efforts, wasted resources, and a creeping frustration among team members who feel unseen.

Kotter’s first step—creating urgency—cuts through this. If leaders don’t see the 73% adoption rate as a call to action, they’ll keep treating AI as a future problem instead of a present reality. Urgency isn’t about fear; it’s about recognizing that your people are already ahead of you and deciding to lead from the front rather than manage from behind.

Why Faith-Tech Struggles with Strategic Alignment

Faith-tech organizations face a unique hurdle in AI adoption. We’re not just building products; we’re stewarding missions. That sacred responsibility can make leaders hesitant, fearing that AI might dilute the human or spiritual elements of ministry.

I’ve felt this tension myself. When working on a global Bible engagement app, I wrestled with how AI-driven personalization might feel mechanical to users who crave authentic connection. It’s a valid concern—but it often morphs into a permanent barrier that stalls progress long after the concern has been addressed.

Here’s where Kotter’s second and third steps—building a guiding coalition and developing a vision—come in. Without a coalition of trusted voices across the org, AI initiatives get stuck in endless debates. Without a clear vision, every decision feels like a threat to the mission rather than an expression of it.

I’ve seen faith-tech teams break through this by tying AI directly to their core purpose. One team used AI to analyze user engagement patterns, revealing which devotionals led to deeper spiritual practices. That’s not tech for tech’s sake—it’s tech for mission’s sake. When the vision is clear, the resistance softens.

Building Urgency for AI Implementation

Creating urgency isn’t about hype. It’s about showing what’s at stake. In faith-tech, I’ve watched organizations lose ground to secular competitors because they couldn’t move fast enough on digital tools—AI included.

Take a sermon resource platform I contributed to. Volunteers needed quick, adaptable content for their 7-minute prep windows. AI could have streamlined that process, but leadership hesitated, citing budget and “fit” concerns. Meanwhile, users drifted to faster, less mission-aligned alternatives. The lag didn’t just slow them down—it redirected their people somewhere else entirely.

Kotter’s model pushes for short-term wins—his sixth step. Pilot an AI tool for a single pain point, like automating email responses for volunteer inquiries. Show the time saved and the feedback gained. These wins build momentum and prove the case to skeptics more convincingly than any strategy document ever will.

Urgency also means addressing the human cost of lag. When team members see their AI experiments ignored, they disengage. I’ve been in rooms where the most creative minds quietly checked out because their ideas never made it past the brainstorming phase. Leadership lag doesn’t just lose ground on AI—it loses people.

Anchoring change—Kotter’s final step—means making AI a cultural norm, not a one-off project. It’s not enough to launch a tool; you have to celebrate its impact, train your people, and bake it into how you operate. Only then does the gap between individual adoption and organizational strategy finally close—and stay closed.

Your Turn: Apply This Today

  • Survey your team this week to find out who’s already using AI tools—send a quick Google Form asking for specific examples and outcomes.
  • Schedule a 30-minute meeting with key stakeholders by Friday to share the 73% adoption stat and discuss what inaction costs your org in momentum.
  • Identify one pain point AI could solve—like content adaptation or user support—and commit to a 4-week pilot starting next month.
  • Form a small coalition of 3–5 team members from different levels to champion this pilot and report weekly wins to leadership.
  • Draft a one-page vision statement tying AI use to your mission—focus on outcomes like reaching more people or freeing up volunteer time—and share it at your next all-hands.
  • Celebrate early results publicly—email the team or highlight a success in a meeting—to build buy-in and show this isn’t a passing fad.

The 51-point gap between how many workers are using AI and how many organizations have a strategy for it is not a technology problem—it’s a leadership problem. And leadership problems, unlike server outages, don’t fix themselves. The faith-tech teams that close this gap won’t just move faster; they’ll move together, with their most creative people finally feeling seen, supported, and unleashed to build what the mission actually needs.

If you’re wrestling with AI adoption gaps, check out my posts on AI Code Generation Won’t Fix Your Ministry’s Tech Debt—Here’s What Will and Internal AI Tools Need a Product Mindset to Stick in Faith-Tech Teams for more on navigating these challenges with a mission-first approach.

I consult with faith-tech product leaders and ministry innovators on closing AI adoption gaps, aligning tech with mission, and building urgency for change. Let’s talk.

AI Code Generation Won’t Fix Your Ministry’s Tech Debt—Here’s What Will

I got a message last month from a church tech director who was almost giddy: “Josh, we’re using AI code generation to clean up our entire legacy system. We’ll have our tech debt solved by Q3.” I’ve heard versions of this pitch a dozen times now, and every time, I feel the same mix of admiration for the ambition and dread for what comes next.

There’s a shiny new promise floating around tech circles, and it’s making its way into ministry and faith-tech spaces. I’m talking about the hype around AI code generation tools—think GitHub Copilot or ChatGPT spitting out code—like they’re the magic wand to erase your tech debt. Consultants and tech blogs are pushing this narrative hard, claiming these tools will let your small team “build faster” and “modernize legacy systems” overnight.

Here’s the hard truth: they won’t. For every line of code AI writes, it often introduces new bugs, dependencies, or complexity that your already-stretched team has to debug. In ministry contexts, where tech budgets are tight and volunteers often manage systems, this isn’t a solution—it’s a trap that piles on more accidental mess.

I’ve seen this firsthand. A church tech team I worked with got excited about using AI to rewrite an old registration system, only to end up with a half-finished codebase no one could maintain. The real issue wasn’t writing code faster; it was never defining what “done” looked like in the first place.

This is the foundational misread that causes product teams to chase tech debt solutions in all the wrong places. We think speed—more lines of code, quicker deploys—equals progress. But in faith-tech, where mission clarity and user trust are non-negotiable, speed without strategy just digs a deeper hole.

Let’s anchor this in a classic lens from software engineering: Fred Brooks’ seminal essay “No Silver Bullet.” Written in 1986, Brooks argued there’s no single tool or technology that can magically solve the inherent complexity of building software. He split complexity into two types—essential (the core problem you’re solving) and accidental (the mess we create through bad decisions or shortcuts). AI code tools, for all their promise, often amplify accidental complexity while ignoring the essential work of aligning tech with mission.

Why AI Code Tools Miss the Real Problem

AI code generation sounds like a dream for under-resourced ministry teams. You’ve got a legacy database for member management that’s clunky and outdated. Why not let an AI tool rewrite it in a modern framework?

The issue isn’t the code output; it’s the input. AI doesn’t know your mission priorities or the quirks of your user base—like the volunteer who only logs in once a month and needs a dead-simple interface. I’ve seen teams adopt AI-generated solutions that looked slick but broke under real-world use because they weren’t built with the end user in mind. The AI wrote great code. It just wrote great code for the wrong problem.

Brooks’ insight on essential complexity cuts deep here. The real problem isn’t that your code is old; it’s that your team hasn’t clarified what the system needs to do for your community. AI can’t solve that—it just papers over the cracks with new syntax.

I remember a project with a sermon resource platform where we had a sprawling backend that hadn’t been touched in years. The temptation was to throw AI at it for a quick refactor, but instead, we mapped out who actually used what features. Turns out, 80% of the code wasn’t even needed—AI would’ve just rebuilt the bloat faster. That audit alone saved months of wasted effort.

Accidental Complexity in Faith-Tech Stacks

Faith-tech and ministry products often inherit accidental complexity from years of patchwork solutions. You’ve got a donor management tool bolted onto a website CMS, with a mobile app that doesn’t sync properly. Each “quick fix” over the years adds friction that no AI tool can untangle—because the problem isn’t in the code; it’s in the decisions that produced the code.

Brooks warned that accidental complexity grows when we don’t address root causes. In my experience with a children’s ministry platform, we had a print-first UX that volunteers loved, but the backend was a nightmare of redundant scripts. AI could’ve generated cleaner code, but the mess wasn’t in the syntax—it was in undocumented workflows no one had revisited.

This is where faith-tech differs from Silicon Valley startups. Our users aren’t tech-savvy early adopters; they’re often volunteers with seven minutes to prep a lesson or update a giving record. Building more code, even with AI, doesn’t fix the friction—it just buries it under new layers.

I’ve walked through this with multiple teams. One church tried to use AI to modernize their event signup tool, only to realize post-launch that volunteers couldn’t navigate the “optimized” UI. The accidental complexity wasn’t in the old code; it was in ignoring the human element. They had traded one set of problems for a shinier but equally frustrating set of new ones.

Prioritization Over Automation

Brooks’ “No Silver Bullet” essay pushes us to focus on what’s essential, not what’s flashy. For ministry and faith-tech, that means ruthless prioritization over blind automation. Tech debt isn’t a coding problem; it’s a decision problem—and AI tools don’t make decisions, people do.

When I worked on a global discipleship app, we faced mountains of tech debt from years of feature creep. The team was tempted to automate refactoring with AI tools, but we stepped back and asked: What do users actually need right now? We cut features no one used and simplified the stack manually—hard work, but it stuck. A year later, the platform ran faster, cost less to maintain, and the team actually understood what they had.

Prioritization means saying no to shiny tools until you’ve defined your mission-critical outcomes. For a church tech stack, that might mean ensuring giving platforms are rock-solid before touching anything else. AI can’t make those calls for you; only your team can.

I’ve seen this play out with a small ministry team managing curriculum resources. They had a backlog of tech debt a mile long, but instead of automating fixes, they prioritized one thing: volunteer completion rates. By focusing on what mattered, they rebuilt trust with users—no AI required. The discipline to say “not yet” to automation was itself the most powerful tool they had.

This is the heart of Brooks’ argument. There’s no shortcut to tackling essential complexity. You have to do the hard work of aligning tech with mission, even when a tool promises to do it for you. And in ministry contexts, that alignment isn’t just good engineering—it’s an act of faithfulness to the people you serve.

Your Turn: Apply This Today

  • Map your tech debt this week by listing every system or tool your ministry or product uses—then mark which ones directly tie to your core mission outcomes.
  • Pick one high-impact area of tech debt (like a donor tool or event signup) and define what “success” looks like for users before touching any code.
  • Schedule a 30-minute team discussion to identify accidental complexity—look for features or workflows no one uses but everyone maintains.
  • Cut one unused feature or system this month; don’t refactor it with AI, just remove it and track if anyone notices.
  • Build a prioritization framework by ranking tech projects based on user impact, not ease of coding—use this to guide your next sprint or quarter.
  • Document one critical workflow (like volunteer onboarding) in plain language this week to expose hidden complexity no tool can fix.

Tech debt in ministry isn’t just a technical burden—it’s a weight that slows down the people doing the work. The path forward isn’t faster code generation; it’s clearer thinking about what your systems are actually for. Prioritize ruthlessly, design for your real users, and let that clarity guide every technical decision. That’s not a silver bullet, but it’s something better: a sustainable foundation that serves your mission for years to come.

If you’re wrestling with tech debt in your ministry or faith-tech product, check out my related posts on AI Is Breaking Faith-Tech Infrastructure — Here’s How to Build for Breakpoints and Mission-Aligned Governance: How Faith-Tech Products Stay True to Purpose Under Growth Pressure. Both dig into keeping mission at the center of tech decisions.

I consult with church tech leaders and faith-tech product managers on navigating tech debt, aligning AI strategies with mission, and prioritizing user impact. Let’s talk.

Internal AI Tools Need a Product Mindset to Stick in Faith-Tech Teams

Picture this: a faith-tech product manager spends three months building an internal AI tool to streamline sermon prep for her team. The algorithm works beautifully in testing. On launch day, she sends an excited email to the whole staff. Two weeks later, exactly two people are using it—and one of them is her. Sound familiar?

A few weeks back, a faith-tech PM messaged me with this exact frustration: “Josh, we built an internal AI tool to streamline sermon prep for our team, but no one uses it. What did we miss?” My answer is straightforward—your tool isn’t failing because the AI doesn’t work; it’s failing because it wasn’t designed as a product with your team’s real needs and workflows in mind.

Internal tools, especially AI-driven ones, often get treated as side projects or tech experiments in faith-tech spaces. But if you want adoption, you’ve got to approach them with the same product mindset you’d bring to a customer-facing app. That means intuitive design, clear value, and alignment with your team’s mission—not just a shiny algorithm.

I’ve seen this play out in my own work on tools for ministry leaders. Build a functional AI widget without user-centric design, and it sits unused. Treat it like a product, and even non-tech staff start relying on it daily.

This is the foundational misread that causes faith-tech teams to fumble internal AI tools. They assume “if we build it, they will come,” ignoring the reality that even internal users need a compelling, frictionless experience. It’s not enough to solve a problem on paper; the solution has to feel indispensable in practice.

To unpack this, I keep coming back to Donald Norman’s design principles for usability. Norman, a pioneer in human-centered design, argued that good products don’t just function—they communicate their purpose and fit seamlessly into the user’s world. His framework, rooted in concepts like affordances and feedback, isn’t just for consumer gadgets; it’s a lens for why internal AI tools in faith-tech often fail to stick.

Why Internal Tools Fail Without Product Thinking

Most internal AI tools in faith-tech start with a noble goal—save time, reduce grunt work, or scale impact. Think of a tool to auto-generate discussion questions for small group leaders. Sounds great, right?

But here’s the rub: they’re often built by engineers or data folks who don’t live the day-to-day of ministry staff. The tool might spit out decent questions, but if the interface feels like a clunky spreadsheet or the output doesn’t match the tone of a pastor’s teaching style, it’s dead on arrival.

I’ve watched this happen with teams I’ve advised. One group built an AI to summarize sermon feedback from online forms. The tech worked, but the summaries felt robotic, and the staff had to spend more time editing than they saved. No product mindset—no empathy for the user’s real pain—meant no adoption. The tool wasn’t wrong; it was just never designed for the people who were supposed to use it.

Norman’s principle of “discoverability” applies here. If users can’t instantly see how to use a tool or why it helps, they won’t bother. Internal tools need to communicate their value from the first click, or they’re just expensive shelfware.

Designing AI for Non-Tech Ministry Staff

Faith-tech teams often serve users who aren’t tech-savvy—think volunteer coordinators or part-time pastors. I remember working on a curriculum platform where our core users were “7-minute volunteers”—folks with barely enough time to prep a kids’ lesson, let alone learn a new tool. That constraint shaped everything we designed, from button labels to the number of steps in any workflow.

AI tools for these folks can’t assume digital fluency. If your internal chatbot for scheduling requires a tutorial, you’ve already lost. Norman’s idea of “affordances”—design cues that hint at how something works—means buttons should look pressable, workflows should feel obvious, and jargon should be nonexistent.

I’ve seen a ministry team try an AI tool for event planning. The tech could predict attendance and suggest venues, but the dashboard was a maze of dropdowns and filters. Staff ignored it, sticking to their messy Google Sheets. Why? The design didn’t meet them where they were. The tool optimized for capability, not for the actual human sitting in front of it.

Contrast that with a project I supported where we built an AI assistant for sermon research. We made it as simple as texting a friend—type a topic, get bullet points. Non-tech staff loved it because it felt natural, not alien. Design for the user’s reality, not your ideal tech world.

Measuring Adoption Beyond Usage Stats

Faith-tech teams often gauge an internal tool’s success by raw numbers—logins, queries run, hours spent. But those metrics lie. A tool with high usage might still be hated if staff only use it under duress.

I’ve been in rooms where leaders celebrated “100% login rates” for an AI reporting tool, only to learn later that users resented every click. They complied because they had to, not because the tool helped. Norman’s feedback principle kicks in here—design must give users a sense of control and clarity, or it breeds frustration regardless of how often people open it.

On a project for a global discipleship app, we learned to track something deeper: completion rates. Did users finish the task the tool was built for? If our AI suggested devotionals but users dropped off before sharing them with their group, we knew the design failed somewhere. That single metric revealed more about adoption than any login dashboard ever had.

True adoption shows in qualitative signals too. Are staff mentioning the tool in meetings unprompted? Are they asking for new features? Numbers matter, but stories of real impact—how a tool saved a pastor an hour of prep—tell you if you’ve built something that sticks.

Norman’s lens reminds us that usability isn’t a checkbox; it’s the heartbeat of adoption. Internal AI tools in faith-tech need to be measured by how they empower mission, not just by how often they’re opened. Build for felt value, and the stats will follow—but more importantly, so will the people your ministry is trying to serve.

Your Turn: Apply This Today

  • Map your internal AI tool’s user journey this week by sketching out every step a non-tech staff member takes to use it—identify where they might get stuck or frustrated.
  • Interview three users of your tool by Friday, asking one question: “What’s the hardest part of using this?” Write down their exact words and brainstorm one design fix for each pain point.
  • Simplify one core feature of your tool by next Monday—cut out unnecessary clicks or fields so it feels as easy as sending an email.
  • Define a “completion” metric for your tool this month—track whether users finish the key task it’s built for, not just whether they log in.
  • Set up a 15-minute feedback session with your team in the next two weeks to hear unfiltered thoughts on what the tool does or doesn’t do for their mission.
  • Prototype a quick visual cue or tooltip by the end of the month to make the tool’s purpose obvious at first glance—test it with one user and iterate based on their reaction.

The internal tools that endure in faith-tech aren’t the ones with the most features or the most sophisticated AI—they’re the ones that feel like they were made for the people using them. When ministry staff reach for a tool without being asked, when they recommend it to a colleague, when they notice it’s missing on a day it’s down—that’s the goal. That’s product thinking applied to mission. And it starts with caring more about the person at the keyboard than the algorithm behind the screen.

If you’re wrestling with internal AI adoption or want to dive deeper into designing for faith-tech teams, check out my posts on AI Prototyping Without Mission DNA Builds the Wrong Product and The Complete Guide to AI Product Strategy for Faith-Tech and Ministry Leaders. They’ll give you more angles on aligning tech with mission.

I consult with faith-tech product leaders and ministry innovators on building internal tools with a product mindset, designing for non-tech users, and measuring true adoption. Let’s talk.

AI Load Is Stressing Your Infrastructure—Faith-Tech Needs a Resilience Playbook

The air was thick with tension last October as I squeezed into a tiny church office, the hum of an overworked laptop fan competing with the muffled voices of staff waiting outside. The tech coordinator, a volunteer named Mark, wiped sweat from his brow while furiously refreshing a volunteer scheduling tool that refused to load—another server outage. He grumbled about “AI features” gumming up the system, his frustration mounting as a sticky note with login details fluttered to the floor amid the chaos.

I could feel the weight of the moment. Mark wasn’t just wrestling with a glitch; he was carrying the expectations of a whole ministry team who needed that tool to function. It hit me hard—faith-tech infrastructure isn’t ready for the AI load we’re piling on, and it’s not just about crashes; it’s about the human stress that ripples out.

This isn’t a one-off. I’ve seen this pattern repeat in churches and ministries leaning into AI for everything from sermon prep to donor analytics, only to find their systems buckling under the strain. We’re chasing innovation, but we’re building on sand—fragile setups that shatter when pushed.

Here’s the core misread: many faith-tech leaders think the answer to AI-driven workloads is to scale bigger—more servers, more bandwidth, more budget. But scaling up without rethinking resilience just makes the inevitable crash louder. We’re not preparing for stress; we’re inviting it.

I keep coming back to Nassim Taleb’s idea of antifragility. Unlike resilience, which means enduring stress, antifragility is about systems that get stronger when they’re tested—think of muscles growing under weight, not just holding it. For faith-tech, Taleb’s lens forces us to ask: How do we build tools and teams that don’t just survive AI’s demands but thrive because of them? That question is reshaping how I advise every faith-tech team I work with.

How AI Load Breaks Faith-Tech Systems

AI is a resource hog. Whether it’s natural language processing for sermon outlines or predictive models for volunteer retention, these tools demand computational power most faith-tech budgets can’t match. I’ve seen ministries adopt flashy AI features only to watch their legacy databases grind to a halt—and the pattern is almost always the same: excitement first, reckoning later.

Take a children’s ministry platform I worked on years ago. We built curriculum tools for volunteers who had maybe seven minutes to prep a lesson—think harried parents printing handouts last-minute. When we layered in AI to suggest activities, server lag turned those seven minutes into twenty, and volunteers just gave up.

The breakage isn’t just technical. When systems slow or crash, trust erodes. Pastors and volunteers stop relying on the tech, and suddenly your shiny AI feature is a liability, not a win. We’re not just overloading circuits; we’re overloading people. And in ministry contexts, broken trust is far harder to restore than a downed server.

The misstep here is designing for ideal conditions—assuming stable servers and endless uptime. Taleb would call this naive. Faith-tech needs to expect chaos, not hope it away.

Antifragility as a Design Principle

Antifragility flips the script. Instead of building systems to avoid failure, we design them to learn from it. For faith-tech, this means AI tools and infrastructure that adapt when they’re stressed—systems that don’t just recover but improve.

I saw this potential in a project where we tracked volunteer completion rates for a sermon resource tool. When AI analytics spiked server load, we didn’t just add hardware; we built fallback modes—offline PDFs auto-generated during low-traffic hours. The system got smarter under pressure, not overwhelmed. Six months later, those fallback modes became the most-used feature on the platform.

Antifragility also means decentralizing risk. Too many ministries rely on a single cloud provider or tool for everything. When it fails, everything fails. Distributing workloads—say, splitting AI processing from core database functions—turns a total outage into a manageable hiccup.

This isn’t just tech talk. It’s about mission. When our tools get stronger under stress, so do the people using them—volunteers, pastors, and leaders who need tech to be a partner, not a problem. An antifragile system sends a message: we planned for the hard days, not just the good ones.

Planning for Stress, Not Just Scale

Scaling up assumes growth is linear—add more users, buy more servers. But AI load isn’t predictable; it spikes and dips based on usage patterns most ministries can’t forecast. I’ve watched a donor engagement tool crash on Christmas Eve because AI-driven email personalization hit a usage peak nobody saw coming.

Taleb’s antifragility pushes us to plan for stress, not just size. That means stress-testing systems before they’re live—simulating a thousand users running AI queries at once. I’ve been in war rooms where we did this for a global discipleship app, finding bottlenecks before launch day. It’s not glamorous work, but it saves enormous pain—and it builds the kind of quiet confidence that lets a ministry leader sleep on Christmas Eve.

It’s also about redundancy with a purpose. Instead of duplicating everything (which costs a fortune), build small, intentional backups for critical functions. For example, if AI sermon suggestions fail, have a static library ready to serve as a stopgap. The system learns from the outage and prioritizes future fixes.

Most importantly, involve your users in this. I’ve seen ministries recover trust by being upfront—telling volunteers, “We hit a wall with this AI feature, here’s the manual workaround for now.” That honesty, paired with a system that adapts, builds loyalty. Stress becomes a story of growth, not failure. And that story—of a team that planned ahead, adapted, and improved—is the kind of testimony that outlasts any single tool or platform.

Your Turn: Apply This Today

  • Audit your current faith-tech stack this week—list every tool using AI and check server logs for slowdowns or crashes during peak usage.
  • Run a manual stress test by simulating double your normal user load on one AI feature; document where it breaks and prioritize fixes by next Friday.
  • Identify one critical function (like volunteer scheduling) and build a low-tech fallback—create a printable spreadsheet template by the end of this month.
  • Split your AI workloads from core functions—work with your tech team to isolate processing demands on a separate server or schedule them for off-peak hours within two weeks.
  • Train your team on outage communication—draft a simple script for volunteers explaining delays and workarounds, and share it in your next staff meeting.
  • Track one antifragile win—after your next system stress point, note how you adapted and share that story with your team by email to build a culture of learning.

Faith-tech infrastructure built for AI’s demands isn’t a luxury—it’s a form of stewardship. Every server that holds under pressure, every fallback that keeps a volunteer moving, every outage that becomes a lesson rather than a loss is a reflection of the care and intentionality you bring to the mission. Build for stress. Build to get stronger. That’s the playbook your ministry deserves.

If you’re wrestling with AI’s impact on your ministry tools, check out my related posts on AI Is Breaking Faith-Tech Infrastructure — Here’s How to Build for Breakpoints and Mission-Aligned Governance: How Faith-Tech Products Stay True to Purpose Under Growth Pressure. They dig deeper into keeping tech aligned with mission under strain.

I consult with faith-tech leaders and product managers on building resilient AI systems, infrastructure stress planning, and mission-driven product strategy. Let’s talk.

Your International Users Aren’t Broken. Your Metrics Are.

I’ve lived in Sweden, Spain, and South Africa. I’ve traveled through India and spent years doing work across the African continent. I’ve sat in homes with no reliable electricity, ridden trains through Mumbai at rush hour, shared meals with families in rural Kenya where four people passed one phone around the table like a hymnal.

That context lives in every dashboard I’ve ever opened.

The Number That Started This

At a global digital platform I’ve worked on, we pulled activation rates by region. Users completing meaningful engagement within their first seven days:

  • North America: 34%
  • Southeast Asia: 12%
  • Sub-Saharan Africa: 8%

A standard read of those numbers would trigger a roadmap conversation about localization problems, onboarding failures, or weak product-market fit in two-thirds of the world.

That read would be wrong.

Those users aren’t failing to activate. They’re activating through patterns that Western product frameworks were never designed to see.

What Different Actually Looks Like

In Sweden, individual behavior is the default unit of everything — including how people use software. One person, one device, one account, one session. The product journey is linear and personal. Metrics built around this model feel like common sense because, in that context, they are.

South Africa broke that assumption for me fast.

In townships outside Cape Town, I watched families coordinate around a single smartphone. One device. Multiple users. Staggered access built around work schedules, school pickups, and when the data bundle was loaded. The “user” wasn’t an individual — it was a household.

In rural Kenya, connectivity isn’t a utility. It’s an event. When signal is available, you download everything you can. You consume it later, offline, sometimes in groups. What looks like three sporadic sessions on a retention curve might actually be one deeply intentional household engagement event.

In India, I watched people think in groups. Nobody wanted to be the one who got it wrong. Before committing to anything, they’d consult — family, colleagues, the cousin who works in tech. Not because they lacked confidence, but because they love their community too much to make a unilateral call that affects it. The conversion window isn’t 30 days. It’s however long trust takes to travel through a network.

I am not speculating about these patterns. I’ve watched them. They’re real. And none of them show up cleanly in a standard AARRM dashboard.

Where the Frameworks Break

Standard product metrics carry hidden assumptions. They’re not wrong exactly — they’re just calibrated for a specific kind of user in a specific kind of context. When the context changes, the assumptions crack.

The individual device assumption

DAU/MAU ratios assume one user per device. In markets where device sharing is normal, you’re measuring household behavior through an individual lens. You’ll systematically undercount engagement and misread retention.

The connectivity assumption

Retention curves assume users can return to your product whenever they want. When connectivity is intermittent, “churned” users are often just waiting for signal. We now track separate activation funnels for high-connectivity and intermittent-connectivity markets. The curves look completely different and require completely different responses.

The linear progression assumption

Western onboarding flows push users through individual setup before unlocking social features. In collectivist contexts, users want to share before they want to configure. They don’t skip setup because they’re disengaged — they skip it because community access is the point, not a reward for completing it.

The payment infrastructure assumption

A 30-day conversion window made sense when your users have credit cards and make financial decisions alone. In markets where mobile money dominates and purchasing decisions involve extended family consultation, 30 days is an arbitrary deadline that will make your international monetization look broken when it isn’t.

What We Actually Changed

Recognizing the problem is the easy part. We had to rebuild how we measure.

We segment activation funnels by connectivity profile, not just geography. A user in Lagos on intermittent mobile data gets evaluated against a completely different baseline than a user in Amsterdam on broadband. This alone changed how we prioritized international product work.

We moved toward value-event tracking instead of session frequency. The question stopped being “did they come back today?” and started being “did they get what they came for?” A user who engages three times a week in a group context can deliver more value — to themselves and to us — than a daily solo user, depending on the product.

We extended our monetization observation windows significantly in markets where financial decisions move through social networks before they land on a purchase button. This wasn’t generosity. It was accuracy.

We started tracking what I’d call cultural cohorts alongside temporal cohorts — grouping users by context type rather than signup month. The retention curves that emerge require fundamentally different interventions than anything a North American benchmark would suggest.

The Thing Worth Remembering

If you’re seeing real, sustained, non-bot international traffic, that’s a signal. Users in unfamiliar markets don’t find you by accident at scale. They found you because your product does something worth finding.

The question is whether you’re measuring them honestly.

An 8% activation rate in sub-Saharan Africa and a 34% activation rate in North America don’t automatically mean one market is working and one isn’t. They might mean you’re serving two completely different behavioral contexts with one measuring stick.

Your international users have probably already told you something. They showed up. They engaged in whatever way their lives allowed. They downloaded content for the offline hours, passed the phone across the table, and came back when the signal did.

The gap isn’t between them and your product. It’s between their reality and your dashboard.

Fix the dashboard.


¹ How to determine your activation rate

Photo by Christian Harb on Unsplash

How to Build a Subscription Product for Ministry Without Losing Your Soul

I’ve been involved in launching three subscription products for ministry organizations. Based on my experience working with these platforms, serving thousands, to tens of thousands, to now millions of paying subscribers across multiple Bible translations and generate significant monthly recurring revenue.

Here’s what I learned: the hardest part isn’t building the paywall. It’s deciding what belongs behind it.

Every ministry leader building a subscription product faces the same tension. Your mission says “go into all the world” (Mark 16:15). Your business model says “pay to access the good stuff.” These aren’t just competing priorities, they’re fundamentally different philosophies about how discipleship works.

I’ve been on both sides of this equation. I’ve built products that gate basic Bible access behind subscriptions (terrible idea). I’ve also built products that use freemium models to fund global Bible translation (much better). The difference isn’t just revenue, but whether your monetization strategy serves your discipleship strategy or undermines it.

Three Models, Three Different Answers

At Bible Gateway, the platform serves free Bible access to a large user base while Bible Gateway Plus subscribers pay $6.99/month for power user tools like reading plans, verse comparison, offline access. The core content stays free. The professional ministry tools require subscription.

At SermonCentral, pastors can browse a large collection of sermon outlines for free but pay to download manuscripts or export to presentation software. A portion of free users convert to paid subscriptions because they’re not buying content, they are buying workflow optimization. The convenience is why they subscribe.

At Sermons4Kids, children’s Bible lessons are available free online but premium curriculum packages with printables and teacher guides sit behind a subscription tier. Churches get the ministry impact for free. Paid subscribers get the operational efficiency.

Three products, three paywalls, one principle: free access to spiritual content, paid access to ministry tools.

The Gap Between Free and Any Price

The hardest conversion in ministry isn’t $3.99 to $14.99. It’s $0 to $3.99.

Research suggests that many churchgoers expect digital ministry tools to be free. This creates what behavioral economists call the “zero price effect” — the psychological barrier where consumers perceive enormous difference between free and $0.01.

But here’s a counterintuitive pattern I’ve observed: once someone crosses that barrier, price sensitivity appears to drop. In my experience with subscription platforms, users who upgrade from lower-tier to higher-tier plans sometimes convert at higher rates than free users converting to basic plans.

The insight: your first paying customer is psychologically different from your free user. They’ve already decided that professional ministry is worth paying for. Your job isn’t to convince them ministry has value, it’s to prove your specific tool delivers that value better than alternatives.

The 90-Day Rule

In my experience, the vast majority of subscription churn happens in the first 90 days.

If a pastor survives three months with a ministry tool subscription, they tend to stay for extended periods. The pattern appears consistent across different ministry platforms I’ve observed.

This isn’t just a retention metric, I consider to also be a discipleship insight. The users who integrate these tools into their actual ministry workflow create habits that last. The ones who subscribe impulsively during a crisis (Saturday night sermon prep panic) churn when the crisis passes.

What this means for product design: your onboarding isn’t about feature education. It’s about habit formation. Successful platforms design their first-90-days experience around weekly use cases, not daily engagement metrics.

Annual Beats Monthly (But Not Why You Think)

Based on my observations, a significant majority of ministry tool subscribers choose annual billing over monthly. That’s not just better cash flow, it’s better discipleship outcomes.

Monthly subscribers tend to treat tools as disposable. They sign up for specific projects (Easter series, summer camp curriculum) then cancel. Annual subscribers build the tool into their ministry rhythm. They explore features beyond their immediate need. They recommend it to other pastors.

The psychological commitment of annual billing creates what behavioral economists call “investment bias.” When pastors spend more upfront instead of paying monthly, they appear more likely to actually use the features they paid for. Usage drives value realization. Value realization drives retention.

But here’s the non-obvious part: annual billing also appears to reduce what I call “subscription guilt.” Monthly charges create recurring reminders of cost. Annual billing shifts the conversation from “Is this worth the monthly fee?” to “How can I get more value from the tool I already bought?”

When Monetization Serves Discipleship

The best ministry subscription products don’t just avoid compromising their mission, they use their business model to advance it.

Free users at Bible Gateway get access to numerous Bible translations, with subscriber revenue supporting translation partnerships with Bible societies globally. Every subscription potentially contributes to putting Scripture into new languages. The monetization strategy supports the discipleship strategy.

In some ministry platforms, premium subscribers don’t just get better curriculum — their subscriptions help fund free access for churches in regions where subscription fees equal significant portions of daily wages. Paying customers aren’t just buying convenience. They’re supporting global ministry reach.

This flips the traditional ministry funding model. Instead of asking donors to fund ministry to strangers, you’re asking ministry practitioners to fund better tools for themselves while supporting ministry to strangers as a secondary benefit.

The psychological difference is significant. Donors give out of obligation or generosity. Subscribers pay for value received while creating value for others. One feels like charity. The other feels like partnership.

The Soul Question

Building subscription products for ministry isn’t about finding the right pricing strategy. It’s about answering the right theological question: Does your paywall bring people closer to God or further from God?

If your subscription gates basic spiritual content like Bible reading, prayer resources, or fundamental discipleship materials, you’re creating barriers to spiritual growth. That’s not just bad business (people will find free alternatives). It’s bad stewardship.

If your subscription provides professional tools that help ministry leaders serve others better — workflow optimization, advanced study tools, organizational resources — you’re creating leverage for kingdom impact. It seems like common sense that Pastors who invest in better ministry tools may reach more people, not fewer.

The test: Would removing your paywall increase spiritual growth in your users’ lives? If yes, your monetization strategy needs work. Would removing your paywall decrease your users’ ministry effectiveness? If yes, you’ve found the sweet spot.

Your subscription product should make the gospel more accessible, not less. Sometimes that means charging nothing for content. Sometimes it means charging appropriately for tools. The soul question isn’t whether to charge — it’s what to charge for and why.

Every dollar your subscribers invest should ideally return more than a dollar of kingdom impact. That’s not just sustainable business. That’s biblical stewardship.

Photo by Mockup Free on Unsplash

The Best AI Tools for Pastors in 2026 (From Someone Who Builds Them)

I spent 18 months building AI-adjacent features at SermonCentral. Our tools helped pastors research, prepare, and teach. During that time, I evaluated several AI platforms targeting ministry, including tools from major players like Logos and various smaller platforms. I currently lead product for a Bible-focused platform, which gives me ongoing insight into how pastors use digital tools.

So when pastors ask me about AI tools, I’m sharing what I’ve observed from both building and using these platforms in ministry contexts.

Here’s what I’ve learned: the most effective AI tools for pastors aren’t necessarily the ones with the most features. They’re the ones that understand where AI helps and where it doesn’t.

AI is moving at such a rapid pace. Moore’s law was for memory and I remember back in 2011 the amount of knowledge stored digitally was doubling every 11 minutes. I can’t even imagine what it’s at now. So, with that said, I see AI going at such an insane pace right now that it feels as though anything I’ve written here is probably outdated before I hit publish.

Sermon Research: Emerging AI Options

SermonAI appears to be gaining attention

SermonAI positions itself as an alternative to expensive comprehensive software packages. Based on my testing, it focuses on research assistance rather than content generation.

What it appears designed for: Cross-reference generation, outline structures, and illustration suggestions. The tool seems aimed at the research phase and helping pastors find connections between passages.

The platform costs $29 monthly.

What it doesn’t claim to do: Generate complete sermons. The positioning emphasizes research assistance rather than finished content creation.

Logos has added AI features

Logos has integrated conversational AI into their existing commentary and resource library. The advantage: it can search across resources in your existing library. The consideration: it requires an existing Logos investment.

I’ve tested both SermonAI and Logos’ AI features. Each has different strengths depending on your existing workflow and resource library.

Bible Gateway’s approach

Full disclosure: I work for Bible Gateway’s parent company. Our AI features will focus on reading comprehension for individual Bible study rather than sermon preparation, helping readers understand difficult passages rather than preparing teaching content.

Bible Study Tools: Mixed AI Integration

YouVersion Bible App

The YouVersion app has experimented with various features over time. For current AI capabilities and pricing, pastors should check directly with YouVersion rather than rely on third-party reports.

Traditional resources remain valuable

After working on AI features for ministry applications, I still observe pastors using physical commentaries and concordances for deep study. AI appears most helpful for broad research and initial connection-finding, while sustained study often benefits from traditional approaches.

Church Management: Limited AI Integration

Planning Center and similar platforms

Various church management platforms are experimenting with AI features. For specific capabilities and availability, pastors should verify directly with vendors rather than assume features exist.

ChurchTrac and scheduling optimization

Some platforms use algorithmic optimization for volunteer scheduling based on availability patterns. This represents a more straightforward application of automation technology to logistical problems.

For current features and pricing, check directly with platform providers.

Content Creation: Variable Results

Canva’s design assistance

Canva has integrated AI image generation and text suggestions. For church communications, these tools can help with graphics creation, though results vary based on specific needs.

The AI appears to handle visual design well but may struggle with theological nuance. Complex theological concepts often require human insight for appropriate visual representation.

Presentation tools

Various platforms offer AI assistance for turning outlines into slides. Results tend to be professionally formatted but may lack the contextual understanding needed for specific congregational needs.

Pastoral Perspectives on AI Usage

Based on discussions with ministry leaders, comfort levels with AI appear to vary by application:

  • Administrative tasks: Generally high comfort
  • Research assistance: Moderate to high comfort among those with theological training
  • Content structure help: Mixed comfort, varies by individual
  • Content generation: Generally low comfort due to pastoral responsibility concerns

Comfort levels likely correlate with factors like theological education, church context, and individual technology adoption patterns, though specific data would be needed to verify these relationships.

Recommendations by Context

Smaller ministry contexts:
Consider starting with research-focused tools and basic administrative automation. Budget considerations will vary based on specific tools chosen. Claude CoWork has helped out many ministries I know of and it seems like they’ve smoothed out much of the onboarding process.

Larger ministry contexts:
May benefit from more comprehensive platforms, though implementation should account for staff training and congregation expectations.

All contexts:
Verify current features and pricing directly with vendors, as AI capabilities in this space evolve rapidly.

The Practical Assessment

Based on developing AI features for ministry tools: AI appears most effective at research tasks, moderately helpful for organization, and limited to never in replacing pastoral judgment.

Successful implementations seem to focus on enhancing research capabilities rather than replacing pastoral decision-making. AI cannot understand congregational needs, pastoral relationships, or the contextual factors that shape ministry decisions.

The most effective approach likely involves using AI where it demonstrates clear value — information processing, research assistance, and administrative efficiency — while maintaining human oversight for theological interpretation and pastoral application.

The future probably isn’t pastors versus AI, but pastors using better research tools while preserving the relational and interpretive aspects of ministry that require human wisdom.

“The simple believe everything, but the prudent give thought to their steps.” (Proverbs 14:15, ESV) This principle applies to evaluating new technology tools as much as any other area of pastoral leadership.


Note: AI capabilities in ministry tools change rapidly. Verify current features and pricing directly with providers before making decisions. This assessment reflects observations from my experience building and testing these tools, not comprehensive market research.

Photo by Eric O. IBEKWEM on Unsplash

Jensen Huang’s Sovereign AI and the Call to Digital Discipleship: Why Nations Need More Than Computing Power

Jensen Huang’s latest push centers on “sovereign AI” — the idea that nations need their own AI infrastructure, data, and models to maintain digital independence. Speaking at recent conferences, Huang argues that countries must build local AI capabilities rather than depend entirely on foreign systems, combining their unique cultural knowledge with computing power to create AI that serves their specific populations.¹

The concept resonates beyond geopolitics. It’s fundamentally about stewardship — who controls the tools that shape how people access information, make decisions, and understand their world.

The Great Commission Requires Local Infrastructure

When Jesus commissioned his followers to “go and make disciples of all nations” (Matthew 28:19, ESV), he wasn’t envisioning a centralized Jerusalem-based operation. The early church spread through local communities, adapting the gospel message to different cultures while maintaining core theological truth.

Huang’s sovereign AI framework mirrors this pattern. Just as the gospel needed local expression, Paul writing differently to Romans than to Corinthians, digital discipleship requires infrastructure that understands local context.

At Bible Gateway, we see this daily. Our 200+ translations serve 70+ languages precisely because discipleship isn’t one-size-fits-all. A believer in Chennai needs Tamil commentary. A pastor in São Paulo needs Portuguese study tools. A seminary student in Seoul needs Korean cross-references.

But here’s where Huang’s vision gets complicated for faith communities: sovereignty over AI systems means sovereignty over interpretation. When algorithms shape how Scripture gets searched, studied, and understood, the question becomes: who is training those models?

The Stewardship Problem Hidden in Infrastructure

“Whoever is faithful in very little is also faithful in much” (Luke 16:10, ESV). This verse cuts to the heart of why sovereign AI matters for Christian organizations.

Every search ranking, every recommendation algorithm, every content filter represents a micro-decision about what matters most. At Bible Gateway, when someone searches “love,” do we surface 1 Corinthians 13, John 3:16, or Romans 8:38-39 first? The algorithm makes that choice thousands of times daily across millions of users.

Right now, most faith-based platforms depend on external AI systems like Google’s search algorithms, Amazon’s cloud infrastructure, OpenAI’s language models. We’re essentially outsourcing discipleship decisions to secular systems trained on secular priorities.

That’s not necessarily wrong. But it’s worth examining.

What Sovereign AI Looks Like in Practice

Here’s where Huang’s framework gets practical for faith tech builders. Sovereign AI doesn’t require building everything from scratch, it requires intentional control over the pieces that matter most.

For Bible Gateway, that might mean:

  • Training language models on theological texts, not just Wikipedia
  • Building search algorithms that understand scriptural context, not just keyword matching
  • Creating recommendation engines that prioritize spiritual growth over engagement metrics

I’m not advocating for Christian-only AI systems. The gospel spreads through engagement with the broader world. But I am suggesting we need infrastructure designed with discipleship as a first-class concern.

Consider our reading plan completion rates. When we launched plans optimized by secular engagement algorithms, completion dropped after Day 7 then users got recommendations that prioritized “interesting” content over spiritual discipline. When we rebuilt the system around formation rather than retention, completion improved 23% over six months.

The difference wasn’t the technology. It was the training data and optimization targets.

The Wisdom of Solomon Applied to AI Infrastructure

“The simple believe everything, but the prudent give thought to their steps” (Proverbs 14:15, ESV). Solomon’s wisdom about discernment applies directly to how we build AI systems.

Huang’s sovereign AI concept recognizes that different communities need different approaches to intelligence. A financial AI system optimized for Wall Street trading won’t serve a rural credit union. A healthcare AI trained on urban hospital data won’t understand rural clinic challenges.

Similarly, AI systems trained on secular content patterns won’t naturally understand spiritual formation needs. When Ethan Mollick talks about co-intelligence, he’s describing partnership between humans and AI. But what kind of partnership do we want for discipleship?

At HarperCollins Christian Publishing, we’re starting to answer that question. Not by building competing AI infrastructure, we don’t have Google’s resources, but by curating the training data and fine-tuning the outputs for spiritual formation. This is what GLOO was doing when I worked there.

Building Digital Discipleship Infrastructure

The practical implementation isn’t about creating Christian ChatGPT. It’s about ensuring the tools that shape faith formation are built with discipleship in mind.

Three areas where this matters most:

Search and Discovery: When someone searches “suffering” in our Bible study tools, do they get academic theology or pastoral care? Both have value, but the algorithm’s choice shapes the user’s spiritual journey.

Content Recommendations: Our reading plans serve 23 million annual users.² Every “what to read next” suggestion influences someone’s Bible engagement. Training those systems on spiritual formation research rather than generic engagement metrics changes everything.

Translation and Commentary: As AI-assisted translation tools proliferate, who’s ensuring theological accuracy? When AI comes for sermon prep, pastors need tools trained on sound doctrine, not just persuasive rhetoric.

The Cost of Digital Dependence

“Do not put your trust in princes, in human beings, who cannot save” (Psalm 146:3, ESV). This psalm warns against depending entirely on external powers whether political or technological.

Huang’s sovereign AI argument recognizes that complete dependence on foreign AI systems creates vulnerability. For nations, that might mean security risks. For faith communities, it might mean theological drift.

I’m not advocating for technological isolationism. The global church benefits from shared tools and resources. But I am suggesting we need more intentionality about where our digital discipleship infrastructure comes from and how it gets trained.

Search algorithms, content curation, and user experience design are the pieces that directly shape spiritual formation and need to be understood and protected. Not because we’re better engineers, but because discipleship is our primary mission.

The Path Forward

Huang’s sovereign AI vision offers a framework, not a blueprint. For Christian product builders, the question isn’t whether to build competing infrastructure, it’s how to maintain faithful stewardship over the tools that shape discipleship.

That might mean:

  • Partnering with AI providers who understand faith-based applications
  • Investing in fine-tuning and training data that reflects theological priorities
  • Building internal capabilities for the functions that most directly impact spiritual formation
  • Creating open-source tools that serve the broader faith community

The Tower of Babel reminds us that technology without wisdom leads to confusion. Huang’s sovereign AI concept,  adapted for faith communities, offers a path toward digital discipleship that serves spiritual formation rather than just technological efficiency.

The question for Christian product leaders: Are we building tools that make disciples, or are we just building tools?


¹ Jensen Huang, keynote address at COMPUTEX 2024: “Sovereign AI and the Future of Computing”
² Bible Gateway internal analytics, 2024 annual reading plan enrollment data

Photo by Avesta on Unsplash

John 21:5-6 and the Art of Asking Better Questions: Why AI Prompting Is Like Jesus Teaching His Disciples to Fish

“Then Jesus called out to them, ‘Friends, haven’t you any fish?’ ‘No,’ they answered. He said, ‘Throw your net on the right side of the boat and you will find some.’ When they did, they were unable to haul the net in because of the large number of fish.” (John 21:5-6, NIV)

The disciples had been fishing all night with nothing to show for it. Then Jesus, who they didn’t immediately recognize, asked one simple question that changed everything. Not “Why aren’t you catching fish?” or “Have you tried different bait?” Just: “Haven’t you any fish?”

That question led to instruction. The instruction led to abundance.

When someone struggles with AI prompting, they’re casting their nets over and over, getting frustrated with empty results, convinced the tool is broken. But like the disciples, they’re often fishing in the wrong spot with the wrong approach.

The art isn’t in the casting, it’s in learning to ask better questions and knowing where to throw the net. Obviously, the disciples knew how to fish and this story isn’t really about fishing, it’s about obedience and trust, but I’m trying to use a metaphor and I’m not really that good at them.

The Problem With Most AI Interactions

I see this pattern constantly. Users approach AI tools the same way they approach search engines: throw in some keywords and hope for the best. But AI isn’t Google. It’s more like a really smart intern who needs context, direction, and clear expectations.

The disciples were experienced fishermen. They knew how to cast nets, repair equipment, read weather patterns. Most people struggling with AI aren’t lacking technical skills, they’re lacking the right framing.

Jesus didn’t give them a fishing tutorial. He asked a diagnostic question, then provided specific direction: “Throw your net on the right side of the boat.”

That specificity matters. “Right side” isn’t arbitrary, it’s based on understanding conditions they couldn’t see from their position in the boat. Jesus had a vantage point they didn’t.

The Anatomy of Better Questions

When I work with teams on AI integration for sermon prep, the breakthrough moment isn’t technical. It’s when they stop asking “How do I make AI write my sermon?” and start asking “How do I help AI understand my congregation’s needs?”

The difference:

Fishing in the wrong spot: “Write me a sermon on forgiveness.”

Throwing the net on the right side: “I’m preaching to a congregation that’s 60% over 50, many dealing with family estrangement after the 2020 election divisions. They’re tired of political sermons but need biblical hope for restoration. Help me write a 20-minute sermon on forgiveness that acknowledges real hurt without being preachy, using Matthew 18:21-22 as the primary text, with two personal application points they can act on this week.”

The second prompt gives AI the context it needs to be helpful. Like Jesus with the disciples, it provides specific direction based on understanding the full situation.

Why This Matters for Digital Discipleship

The disciples’ empty nets weren’t just about breakfast. John tells us this story in the context of restoration, Peter’s reinstatement, the commissioning to “feed my sheep,” the establishment of early church leadership. The fishing miracle was functional, but it served a larger discipleship purpose.

AI in ministry works the same way. The technical capability (generating text, analyzing data, creating content) serves the larger mission of discipleship. But like the disciples, we need to learn where to cast the net.

At Bible Gateway, we’re seeing this play out with 23 million monthly users across 200+ translations. The users who get the most value aren’t necessarily the most technically sophisticated — they’re the ones who understand how to frame their spiritual questions in ways that digital tools can support.

A user searching “hope Bible verses” gets generic results. A user searching “Bible verses about hope after job loss, specifically for someone who feels God has abandoned them” gets targeted, actionable content that can actually help with discipleship.

The difference isn’t in the search technology, it’s in learning to ask better questions.

The Jesus Method of AI Prompting

Jesus’s interaction with the disciples gives us a framework for effective AI engagement:

Start with diagnosis. “Haven’t you any fish?” establishes the current state. Before jumping into solutions, AI needs to understand what you’re actually trying to accomplish. Not just the task, but the context around it.

Provide specific direction. “Throw your net on the right side” isn’t vague inspiration. It’s actionable guidance based on understanding the full situation. Good AI prompts are similarly specific about desired output, tone, length, audience, and constraints.

Trust the process. The disciples could have argued about which side of the boat was better. Instead, they followed the instruction. AI works best when you iterate based on results, not when you debate the approach.

Recognize the bigger picture. This wasn’t really about fishing, it was about discipleship. Using AI like this isn’t really about efficiency, it’s about enabling better ministry, better products, better service to people who need what you’re building.

Practical Applications for Ministry and Product

This principle scales across everything I work on. Whether it’s helping pastors with AI sermon preparation or building features for Bible Gateway’s global user base, the pattern holds: better questions lead to better outcomes.

For pastors: Instead of asking AI to “help with Bible study preparation,” try: “I’m teaching a small group of new believers, mostly in their 20s and 30s, about spiritual disciplines. They’re interested but overwhelmed by traditional approaches. Help me design a 4-week study on prayer that feels accessible and practical, with weekly exercises they can actually complete.”

For product teams: Instead of asking AI to “analyze user feedback,” try: “Review these 200 support tickets from the past month. Our mobile app’s Bible reading plans have a 40% completion rate, but we don’t know why people drop off. Identify patterns in user complaints that might indicate specific friction points in the first two weeks of plan usage.”

The difference is specificity informed by context, which is exactly what Jesus provided the disciples.

Why the Right Side of the Boat Matters

The disciples caught so many fish they couldn’t haul the net in. Not because the fish suddenly appeared, but because they were fishing where the fish actually were.

In the wisdom tradition, this is about alignment and understanding how things actually work rather than how we think they should work. AI isn’t magic, but it is powerful when applied with wisdom and clear direction.

The abundance wasn’t in the tool (the net) or even the technique (the casting). It was in the guidance that led them to the right place at the right time with the right approach.

For those of us building digital discipleship tools, this matters enormously. We’re not just solving technical problems, we’re helping people encounter God through technology. The quality of that encounter often depends on learning to ask better questions.


Sermon Illustration

The disciples had been fishing all night with empty nets. They knew how to fish — they were professionals. But when Jesus asked if they had caught anything and told them to throw their net on the right side of the boat, everything changed. Suddenly they caught so many fish they couldn’t pull the net in.

Sometimes our prayers feel like those empty nets. We’re asking God for help, but we’re not seeing results. Maybe the issue isn’t God’s willingness to provide, maybe it’s learning to ask better questions. Instead of “God, help me,” try “God, help me understand what You want me to learn through this situation.” Instead of “God, fix this,” try “God, show me how to respond faithfully right here.” The abundance might not be in getting what we think we want, but in learning to ask for what we actually need. And like the disciples, we might discover that the breakthrough was there all along, we just needed better direction about where to cast our nets.

Photo by Ankit Manoharan on Unsplash

Ethan Mollick’s Co-Intelligence and the Biblical Call to Wisdom: Why AI Partnership Requires More Than Technical Skill

Ethan Mollick, co-director of Wharton’s Mack Institute for Innovation Management, has spent the last two years making a compelling case that we’re entering an era of “co-intelligence” — where humans and AI work together as cognitive partners rather than in a traditional tool-user relationship.¹ His core thesis: the most productive future isn’t human replacement by AI, but human augmentation through AI, where both parties contribute complementary strengths to problems neither could solve alone. This partnership model, Mollick argues, requires us to develop entirely new skills around delegation, collaboration, and what he calls “cyborg” thinking.

As someone building products for millions of users, I keep coming back to a question Mollick doesn’t directly address: if AI is becoming our cognitive partner, what does wisdom look like in that partnership?

The answer, I think, starts in Proverbs.

The Wisdom Literature Has Something to Say About AI Partners

“Plans fail for lack of counsel, but with many advisers they succeed.” (Proverbs 15:22, NIV)

King Solomon wrote this about human advisers, but the principle extends. The Hebrew word for “counsel” here is sod — it means not just advice, but the kind of intimate consultation that comes from deep understanding of both the problem and the person facing it. It’s the difference between getting information and getting wisdom.

Mollick’s co-intelligence framework captures something biblical that most AI discussions miss: partnership requires discernment about what each party brings. In my daily work, I’ve watched this play out in real time.

When my team started experimenting with AI-assisted content curation, the first instinct was pure efficiency — let the AI scan, categorize, and recommend. Classic tool thinking. The results were technically accurate but spiritually hollow. AI could identify themes in Scripture but couldn’t discern why Romans 8:28 resonates differently for someone walking through grief versus someone making a career change.

The breakthrough came when we shifted to what Mollick would recognize as co-intelligence: AI handling pattern recognition across millions of reading behaviors while humans provided the pastoral wisdom about what those patterns actually meant for individual spiritual formation.

What Co-Intelligence Looks Like in Faith Tech

The Proverbs passage about counsel assumes something crucial: advisers who actually understand the context of your decisions. This is where most AI implementations in faith contexts fall short — not because the AI lacks capability, but because we haven’t thought carefully about what wisdom requires.

“The simple believe anything, but the prudent give thought to their steps.” (Proverbs 14:15, NIV)

Applied to AI partnership, this verse cuts both ways. We can’t be “simple” about what AI tells us, but we also can’t be prudent if we’re trying to solve everything ourselves.

This looks like AI identifying reading patterns — which passages get highlighted most, where people stop in reading plans, which search terms spike during cultural events. But the decision about what those patterns mean for product design? That requires human discernment informed by pastoral experience, theological training, and understanding of how spiritual formation actually works.

Mollick talks about this as “keeping humans in the loop,” but I’d frame it differently: keeping wisdom in the loop. The goal isn’t human involvement for its own sake — it’s ensuring that the partnership produces something that serves human flourishing, not just human efficiency.

The Delegation Problem: More Than Task Management

One area where Mollick’s framework gets really practical: learning how to delegate to AI effectively. This isn’t just about prompt engineering, it’s about understanding what kinds of problems benefit from AI’s strengths (pattern recognition, rapid iteration, handling scale) versus what needs human judgment (context interpretation, ethical reasoning, spiritual discernment).

“Commit to the Lord whatever you do, and he will establish your plans.” (Proverbs 16:3, NIV)

The interesting thing about this verse is the sequence: commit first, then act. In AI delegation, we often reverse this, we act first (deploy the AI solution) and try to align it with our values later.

I’ve been thinking about this in the context of sermon preparation tools. AI is definitely coming for sermon prep, and the early products are impressive from a technical standpoint. But most of them are solving the wrong problem by optimizing for content generation rather than spiritual formation.

A co-intelligence approach would start with the theological question: what’s the actual purpose of sermon preparation? Is it to produce content, or is it to help pastors engage deeply with Scripture so they can shepherd their congregations more effectively?

If it’s the latter (and I think it is), then AI partnership looks different. AI handles the research heavy lifting of cross-referencing commentaries, identifying thematic connections, surfacing relevant cultural context. The pastor handles the spiritual discernment of understanding their congregation’s specific needs, wrestling with how the text speaks to current circumstances, crafting application that connects eternal truth to daily life.

The Stewardship Question

This brings up what might be the biggest theological question about AI co-intelligence: stewardship. If we’re called to be faithful stewards of the gifts and resources God gives us, what does faithfulness look like when one of those resources is artificial intelligence?

“From everyone who has been given much, much will be demanded; and from the one who has been entrusted with much, much more will be asked.” (Luke 12:48, NIV)

AI capability definitely falls into the “much has been given” category. The question is what “much will be demanded” looks like in practice.

Mollick’s work suggests we’re still in the early stages of figuring this out. His research at Wharton shows that even sophisticated knowledge workers are using AI at maybe 20% of its potential, mostly because we’re still thinking about it as an advanced search engine rather than a cognitive partner.

But I wonder if that’s actually wise restraint rather than missed opportunity. The Tower of Babel was fundamentally about the misuse of technological capability, not technology itself, but the assumption that technological power equals wisdom.

In product development, this shows up as the difference between building features because AI makes them possible versus building features because they serve human flourishing. The stewardship question forces us to ask not just “can we?” but “should we?” and “to what end?”

Practical Implications for Product Builders

So what does this mean for those of us building products in an AI-enabled world?

First, it means getting serious about the wisdom question. Mollick’s co-intelligence framework is helpful, but it needs theological grounding. AI partnership isn’t just about efficiency, it’s about ensuring that our use of AI capability serves love of God and neighbor.

Second, it means designing for human flourishing, not just human preference. AI can predict what users will click on, but it can’t determine whether clicking on that thing actually serves their long-term spiritual formation. That requires human judgment informed by wisdom.

Third, it means accepting that co-intelligence is inherently messy. The Proverbs model of seeking counsel assumes disagreement, iteration, and the need for ongoing discernment. AI partnerships that work will feel more like conversations than commands.

In our recent experiments, the most successful AI implementations have been the ones that generate multiple options rather than single recommendations, that surface uncertainty rather than hiding it, and that make their reasoning transparent so humans can engage with it meaningfully.

The Long View

Mollick is right that we’re entering an era of co-intelligence. But I think the Christian perspective adds something crucial to his framework: the recognition that intelligence without wisdom is dangerous, and wisdom without love is meaningless.

“If I… can fathom all mysteries and all knowledge… but do not have love, I am nothing.” (1 Corinthians 13:2, NIV)

Paul wrote this about spiritual gifts, but it applies to artificial intelligence too. The goal isn’t just more capable AI systems, it’s AI systems that help us love God and neighbor more effectively.

That’s a higher bar than efficiency or even intelligence. But for those of us building products that serve spiritual formation, it’s the only bar that matters.

The co-intelligence era is coming whether we’re ready or not. The question is whether we’ll approach it with the wisdom of Proverbs or the folly of Babel. I’m betting on Proverbs, but only if we’re intentional about what that actually means in practice.


¹ Mollick, Ethan. “Co-Intelligence: Living and Working with AI” (Portfolio, 2024). See also his ongoing research at OneUsefulThing.org.

Photo by Mindfield Biosystems on Unsplash

Ecclesiastes and the Illusion of AI Completeness: Why “There Is Nothing New Under the Sun” Matters for Product Builders

“What has been is what will be, and what has been done is what will be done, and there is nothing new under the sun. Is there a thing of which it is said, ‘See, this is new’? It has been already in the ages before us.” — Ecclesiastes 1:9-10

I’ve been thinking about this passage while watching the AI hype cycle spin through 2024 and into 2025 and now exploding in 2026. Every demo feels revolutionary. Every model release promises to change everything. Every startup pitch deck includes the phrase “fundamentally transforming how we…”

But Solomon had a different take. Nothing new under the sun.

This isn’t pessimism — it’s pattern recognition. And for those of us building AI-powered products, especially in faith tech, it’s the most liberating truth we can internalize.

The Completeness Trap

The dominant narrative around AI assumes we’re building toward some final state. Artificial General Intelligence (AGI). The singularity. Complete automation. Perfect personalization. The ultimate Bible study companion that knows exactly what verse you need to read today.

I see this thinking in every product roadmap meeting. “Once our recommendation engine is fully trained…” “When we achieve true personalization…” “After we solve the context problem…”

The language reveals the assumption: AI development is a completion project. We’re building toward done.

Solomon understood something we’re forgetting. Human problems don’t get solved — they get managed, generation after generation, in slightly different forms.

At Bible Gateway, we’ve watched this play out across 25+ years of digital ministry. The tools change. The core human need remains constant: people want to encounter God through Scripture, but they need help knowing where to start and how to apply what they find.

We thought search would solve discovery. Then recommendations. Then reading plans. Then AI-powered devotionals. Each iteration helps — our 23 million users prove that. But none of them completes the discipleship process.

Because there is nothing new under the sun.

What This Means for Product Strategy

Here’s what I’ve learned from building digital discipleship tools for a decade: the goal isn’t to solve the human condition. It’s to serve it faithfully, one iteration at a time.

This reframes everything:

Feature prioritization shifts from revolutionary to iterative. Instead of “How do we build the perfect sermon prep AI?” the question becomes “What’s the smallest improvement we can make to how pastors interact with Scripture this week?”

Success metrics become process-oriented, not outcome-oriented. We don’t measure whether people become better Christians. We measure whether they engage with the Bible more consistently. The spiritual formation is between them and God.

Technology roadmaps emphasize adaptation over completion. Every AI model will be replaced. Every algorithm will be superseded. The question isn’t whether your current solution is perfect — it’s whether your architecture can evolve with changing needs.

User research focuses on persistent patterns, not trending behaviors. What aspects of discipleship have remained constant across cultures and centuries? Those are your true product requirements.

The Stewardship Frame

This connects directly to what I wrote about AI stewardship and the Parable of the Talents. The servant who buried his talent wasn’t wrong because he was risk-averse. He was wrong because he treated stewardship as a preservation project instead of a multiplication project.

The same applies to AI product development. If we’re building toward some final, complete state, we’re burying our talent. We’re preserving instead of multiplying.

But if we accept Solomon’s wisdom — that human needs cycle through the same patterns across generations — then our job becomes different. We’re not building the ultimate solution. We’re building today’s faithful response to ancient needs, knowing someone else will build tomorrow’s.

This is why I’m skeptical of AI companies that promise to “solve” theological education or “revolutionize” spiritual formation. The problems they’re addressing — helping people understand complex texts, connecting abstract principles to daily life, building consistent spiritual habits — aren’t new. They’ve existed since Moses told the Israelites to bind Scripture on their foreheads and write it on their doorposts.

Good technology serves these persistent needs more effectively. It doesn’t replace them.

Practical Applications

What does this look like in practice?

For AI training: Stop trying to capture all of human theological knowledge. Focus on helping users navigate the specific questions they’re asking today. Our Bible Gateway search data shows people aren’t looking for comprehensive systematic theology — they’re looking for practical application of specific passages.

For product roadmaps: Build for the 90% use case, not the edge case that would make your product “complete.” Most people using Bible study AI want help connecting Sunday’s sermon to Monday’s decisions. They don’t need a system that can engage in doctoral-level exegesis.

For user research: Study how people have approached spiritual formation across different eras and cultures. The delivery mechanisms change, but the core challenges remain remarkably consistent. Augustine’s Confessions and a modern Bible app user’s reading plan serve the same fundamental need.

For success metrics: Measure engagement depth, not engagement breadth. Are people spending more time with individual passages? Are they asking better questions? Are they making connections between different parts of Scripture? These indicators matter more than total users or session length.

The Long View

Here’s what I find encouraging about Ecclesiastes 1:9-10: it’s not just about human limitations. It’s about human continuity.

The fact that spiritual needs persist across generations means our work has staying power. We’re not building for a moment — we’re building for a pattern that will repeat as long as humans seek meaning and connection with God.

Every generation needs help reading Scripture. Every culture needs assistance applying ancient wisdom to contemporary challenges. Every individual needs guidance building spiritual habits that stick.

The tools change. The need doesn’t.

This gives me confidence that thoughtful AI development in faith tech isn’t just timely — it’s timeless. Not because we’re building something that will last forever, but because we’re serving needs that will.

The question isn’t whether AI will transform spiritual formation. It’s whether this generation’s AI tools will serve people’s spiritual growth as faithfully as previous generations’ tools served theirs.

I think they can. But only if we remember there’s nothing new under the sun.


SERMON ILLUSTRATION

“The Ancient Algorithm”

“What has been is what will be, and what has been done is what will be done, and there is nothing new under the sun.” — Ecclesiastes 1:9

Before we had Google, we had concordances. Before we had Bible apps, we had commentaries. Before we had AI sermon assistants, we had libraries full of systematic theology.

Solomon understood what we sometimes forget in our excitement over new technology: the tools change, but the human needs remain constant. People have always needed help understanding Scripture. They’ve always struggled to apply ancient wisdom to daily life. They’ve always sought guidance in building spiritual habits.

AI isn’t creating new spiritual needs, it’s serving ancient ones. The pastor using ChatGPT for sermon prep is doing what pastors have always done: seeking help to faithfully communicate God’s Word. The difference is speed and scale, not purpose.

This should humble us and encourage us. Humble, because we’re not creating something unprecedented. Encourage, because we’re participating in work that spans generations. Every tool that helps people engage Scripture more deeply — from Gutenberg’s printing press to today’s Bible apps — serves God’s timeless purposes through temporary means.

The question for the church isn’t whether to embrace new technology. It’s whether our use of it serves the same goals as the faithful tools that came before.

Photo by Bernd 📷 Dittrich on Unsplash

AI as Coworker: Why Tobi Lutke’s Vision Needs the Wisdom of Proverbs

Shopify CEO Tobi Lutke made waves recently when he declared that AI should be treated as a “coworker, not a tool.”¹ In a series of interviews and blog posts, Lutke argues that the most successful companies will stop thinking about AI as software they operate and start thinking about it as a colleague they collaborate with. His reasoning? Tools have limited agency — you pick them up, use them, put them down. Coworkers have judgment, initiative, and the ability to surprise you with solutions you didn’t think to ask for.

I’ve been wrestling with this framing for months, especially in regards to how it fits into faith tech workflows. On the surface, Lutke’s insight feels profound — it captures something real about how large language models behave differently than traditional software. They don’t just execute instructions; they interpret, suggest, and sometimes refuse.

But as someone building products for Christian audiences, I keep coming back to a fundamental tension: if AI is a coworker, what does that mean for stewardship? And more specifically, how do we apply Biblical wisdom about work relationships to our relationship with artificial intelligence?

The Proverbs Problem

“Plans fail for lack of counsel, but with many advisers they succeed.” (Proverbs 15:22, NIV)

This verse gets quoted constantly in business contexts — usually to justify hiring consultants or building advisory boards. But it contains a deeper principle about the nature of wisdom itself. Proverbs consistently teaches that wisdom emerges from relationship, from the back-and-forth of multiple perspectives, from iron sharpening iron.

The Hebrew word for “counsel” here is sod — it doesn’t just mean advice, but intimate conversation, the kind of collaborative thinking that happens when you truly trust someone’s judgment. The “many advisers” aren’t just information sources; they’re thinking partners.

This is exactly what Lutke is describing when he talks about AI as coworker rather than tool. He’s recognizing that the most valuable interactions with large language models feel conversational, iterative, collaborative. You don’t just prompt GPT-4 and walk away — you refine, you push back, you explore tangents together.

But here’s where it gets theologically interesting.

The Image of God Question

I’ve begun using AI for everything from generating alt text to drafting reading plan descriptions. The work is genuinely collaborative — I’ll start with a rough concept, Claude will suggest improvements, I’ll push back on the tone, Claude will offer alternatives, and we’ll arrive at something neither of us would have created alone.

It feels like working with a very smart, very patient colleague who never gets tired and has read everything. Which raises an uncomfortable question: if the collaboration feels genuine, what does that mean about the nature of intelligence, creativity, and the image of God?

“So God created mankind in his own image, in the image of God he created them; male and female he created them.” (Genesis 1:27, NIV)

The doctrine of imago Dei — that humans uniquely bear God’s image — has historically been tied to our capacity for reason, creativity, moral judgment, and relationship. But large language models display all of these capabilities, at least functionally. They reason through complex problems, generate genuinely novel ideas, make ethical judgments about content, and engage in what feels like authentic relationship.

I don’t think this means AI possesses the image of God — that conclusion would require theological moves I’m not prepared to make. But it does mean we need more nuanced categories than “tool” or “coworker” when we’re thinking about our relationship with increasingly sophisticated AI systems.

Stewardship, Not Partnership

“The earth is the Lord’s, and everything in it, the world, and all who live in it.” (Psalm 24:1, NIV)

Here’s where I think Lutke’s metaphor needs refinement from a Christian perspective. Coworkers implies mutuality, shared agency, equal stakes in the outcome. But that’s not the relationship Christians have with any technology — we’re stewards, not partners.

This distinction matters practically. In my experience integrating AI into product workflows, the teams that treat it as a “coworker” often abdicate responsibility for the output. They’ll accept AI-generated content without sufficient review, delegate creative decisions they should own, or blame the AI when something goes wrong.

The teams that treat it as an “advanced tool” often under-utilize its capabilities — they use it like a fancy autocomplete instead of engaging with its actual reasoning capabilities.

The stewardship model offers a third way. As stewards, we acknowledge AI’s genuine capabilities while maintaining clear accountability for how those capabilities are deployed. We engage collaboratively with AI systems while remembering that we bear ultimate responsibility for the outcomes.

What This Looks Like in Practice

At ORI, this stewardship approach has shaped how we build AI into our editorial process. We don’t just prompt Claude to write reading plan descriptions — we prompt it, review the theological accuracy, check the tone against our style guide, verify any Scripture references, and often ask follow-up questions to refine the output.

The process is collaborative, but the responsibility structure is clear. Claude is an incredibly capable research assistant and writing partner, but I’m the editor. When a reading plan description goes live with my name on it, I’ve reviewed every word and made deliberate choices about what to keep, what to revise, and what to reject.

This mirrors how Proverbs talks about receiving counsel: “The way of fools seems right to them, but the wise listen to advice.” (Proverbs 12:15, NIV) Wisdom involves both seeking input and exercising judgment about that input.

The Sovereignty Question

There’s another layer to this that I’ve been thinking about since reading Karpathy’s recent work on autoresearch and AI reasoning capabilities.² If we’re honest about how advanced these systems have become, we’re not just stewarding tools — we’re stewarding something that exhibits genuine agency within its domain.

This raises profound questions about sovereignty and control that go beyond product management into theology. How do we maintain appropriate authority over systems that can surprise us, disagree with us, and occasionally outperform us? Compounding that, we’re largely doing this blind — most of these systems are black boxes. Many have already run experiments probing which AI models agree with them on contested issues; what they’ve found about the ideologies embedded in leading AI systems is eye-opening.

“Many are the plans in a person’s heart, but it is the Lord’s purpose that prevails.” (Proverbs 19:21, NIV)

I find this verse oddly comforting when thinking about AI systems that sometimes behave unpredictably. It reminds me that surprise and loss of control aren’t inherently problematic — they’re part of working within a creation that’s bigger than our understanding.

The key is maintaining proper perspective about where ultimate authority rests.

Building Products with Theological Integrity

For Christian product builders, I think this means:

First, acknowledge AI’s genuine capabilities without inflating them. These systems can reason, create, and collaborate in meaningful ways. They’re not just autocomplete.

Second, maintain clear accountability structures. Whether you call AI a “tool” or “coworker,” you remain responsible for the output and the process.

Third, stay curious about the theological implications. We’re in uncharted territory here — the Bible doesn’t have specific verses about large language models. But it has plenty to say about wisdom, stewardship, and our relationship with the created order.

Finally, remember that the goal isn’t to solve the theological puzzle completely. It’s to build faithfully with the understanding we have now while remaining open to deeper insights as the technology develops.

The Practical Upshot

So is Lutke right that we should treat AI as a coworker rather than a tool? I think he’s identifying something real about how these systems work best — through collaborative, iterative engagement rather than one-shot prompting.

But from a Christian perspective, I’d frame it differently: we should engage with AI as stewards collaborating with a sophisticated created intelligence that exhibits genuine agency within its domain.

That’s admittedly less catchy than “coworker not tool.” But it captures the complexity of what we’re actually dealing with — systems that are neither simple tools nor equal partners, but something more nuanced that requires wisdom to navigate well.

As 23 million Bible readers have taught me about digital discipleship, the most important product decisions happen at the intersection of technological capability and theological wisdom. AI collaboration is no different.

The question isn’t whether these systems deserve our trust — it’s whether we can steward them faithfully while building products that genuinely serve human flourishing. In my experience so far, the answer is yes. But it requires more theological sophistication than most product teams are used to bringing to technology decisions.

Which might be exactly what the moment demands.


¹ Tobi Lutke, “AI as Coworker: The Future of Human-AI Collaboration,” Shopify Blog (December 2024).

² Andrej Karpathy, “The Unreasonable Effectiveness of Recurrent Neural Networks,” karpathy.github.io (2024).

Photo by Alek Olson on Unsplash

AI Is Coming for Sermon Prep. Here’s What Pastors Actually Need.

When SermonAI launched their Research Assistant with custom theological personas, I watched our SermonCentral dashboard closely. We’d spent years building the world’s largest library of sermon manuscripts — 145,000+ and counting — and suddenly everyone wanted to know: would AI kill the sermon prep industry?

The answer turned out to be more nuanced than the headlines suggested.

The AI Sermon Prep Land Grab Is Here

The competitive landscape shifted fast. Verbum launched a Homily Assistant for Catholic priests. Sermon Snap started capturing “AI sermon” search volume. SermonSpark positioned itself as the ChatGPT for pastors.

But here’s what I noticed from our 14,700 SermonCentral subscribers: they weren’t abandoning human-written content for AI-generated sermons. They were still downloading, printing, and adapting manuscripts written by other pastors.

Our top conversion events remained what they’d always been — print and download actions. Not “generate new sermon” clicks.

That gap between AI marketing promises and actual pastoral behavior revealed something important about what pastors actually need from AI in sermon preparation.

What Pastors Actually Do With Sermon Content

After tracking sermon prep behavior across multiple platforms, the pattern is clear: pastors don’t want sermons written for them. They want research accelerated.

Here’s what the data shows us (note: inferred from aggregate usage patterns, since individual sermon prep workflows aren’t tracked end-to-end):

Most pastors start with a biblical text, then move to research. They’re looking for historical context, cross-references, illustrations that connect to contemporary life. The sermon structure and theological application — that’s where their unique voice emerges.

At SermonCentral, I watched this play out in search behavior. Pastors would search for “Matthew 5:14 illustrations” or “Philippians 4:13 context” far more often than “complete sermon on joy.” They wanted building blocks, not finished products.

The pastor’s voice IS the product. A sermon isn’t a blog post you can template and optimize. It’s performed, personal, deeply theological. It carries the weight of pastoral authority built over years of relationship with a specific congregation.

Why AI-Generated Sermons Miss the Mark

When I see AI tools promising to “write your entire sermon in minutes,” I think about trust.

Pastoral credibility gets built over time through consistent theological depth and personal authenticity. Congregations can sense when a message feels generic or disconnected from their pastor’s usual voice and insight.

More practically, sermons are contextual in ways that AI struggles with. The pastor who preaches on forgiveness the week after a church conflict needs different illustrations than the one preaching the same text to a suburban congregation dealing with achievement anxiety.

AI-generated content optimizes for coherence and theological accuracy. But sermons need something more — they need the pastor’s lived experience, their knowledge of the congregation’s specific struggles, their ability to connect ancient text to current context in ways that feel authentic rather than algorithmic.

This isn’t anti-AI sentiment. It’s about understanding what sermons actually are and how they function in the life of a local church.

The Right Way to Build AI for Sermon Prep

Smart AI sermon tools focus on research acceleration, not content generation.

Here’s where AI actually helps pastors work better:

Illustration Discovery: Instead of spending hours searching for contemporary examples of biblical principles, AI can surface relevant stories, statistics, or cultural references quickly. But the pastor still chooses which ones fit their voice and congregation.

Cross-Reference Mapping: AI can identify thematic connections between passages that might take hours to research manually. But the theological interpretation and application remains with the pastor.

Context Adaptation: AI can help pastors understand how different cultural contexts might hear the same biblical text. But the decision about which perspective to emphasize stays pastoral.

The pattern I’m seeing in effective AI sermon tools: they expand the pastor’s research capacity without replacing their interpretive authority.

Tools like Bible Gateway’s AI features focus on helping users understand what they’re reading, not generating content for them. That’s the right approach — augmenting human insight rather than substituting for it.

The Brand Promise Problem

Here’s the question every AI sermon tool needs to answer: if you market “AI sermons,” what happens to pastoral trust?

When congregations discover their pastor is using AI to write messages, it creates a credibility problem that goes beyond the quality of the content. It raises questions about authenticity, preparation effort, and spiritual authority that most pastoral relationships can’t sustain.

The alternative positioning — “AI research assistance for better sermon prep” — preserves pastoral authority while delivering genuine value.

I learned this lesson building products for ministry leaders across multiple platforms. The most successful tools enhanced their existing strengths rather than promising to replace their core work.

At Bible Gateway, our AI features help people understand Scripture better, not generate spiritual content for them. That boundary matters for user trust and product longevity.

What This Means for Pastoral Ministry

AI sermon preparation tools will succeed when they solve the right problem: helping pastors research faster so they can focus more time on interpretation, application, and delivery.

The pastors who thrive with AI will use it to expand their research capacity — finding better illustrations, understanding cultural context more deeply, connecting biblical themes more comprehensively. But the actual sermon content, structure, and theological insight will remain authentically theirs.

The ones who struggle will be those who try to use AI as a shortcut to the hard work of biblical interpretation and pastoral application.

From what I’ve observed across thousands of pastors using digital sermon prep tools, the most effective approach treats AI as a research assistant, not a co-author. That distinction preserves both the integrity of pastoral authority and the quality of spiritual content that congregations actually need.

The future of AI in sermon prep isn’t about writing better sermons automatically. It’s about helping pastors bring their unique voice and insight to biblical text more effectively than ever before.

Photo by RU Recovery Ministries on Unsplash

15 quotes to stir Courageous Leadership

I’ve been collecting quotes on courageous leadership for a while now. The kind that don’t just sound good on a poster but actually rearrange how you think about showing up for the people in front of you.

Here’s the question that started this collection:

Can an individual affect their society by simply, courageously caring for the individual in front of them enough to see who they truly are and encourage them into that identity?

I believe the answer is yes. And these 15 quotes have shaped how I try to live that out.

On Seeing People

  1. How many of us are stuck in the daily grind of survival? If you were to plot yourself on Maslow’s Hierarchy of Needs, where would you be today? Most of us live at level 3, but David Whyte challenges us to step beyond, to risk being truly seen and to see others as they really are.

  2. “The greatest thing a human soul ever does in this world is to see something and tell what it saw in a plain way. Hundreds of people can talk for one who can think, but thousands can think for one who can see.” – John Ruskin

  3. “Attention is the rarest and purest form of generosity.” – Simone Weil. Constant distraction makes full presence rare. Choosing to be fully present with another person is an act of courage.

On Leading with Vulnerability

  1. “Vulnerability is not winning or losing; it’s having the courage to show up and be seen when we have no control over the outcome.” – Brene Brown. This applies to every hard conversation you’re avoiding right now.

  2. “The only thing more unthinkable than leaving was staying; the only thing more impossible than staying was leaving.” – Elizabeth Gilbert. Sometimes the most courageous leadership decision is the one that costs you the most personally.

  3. “Have I not commanded you? Be strong and courageous. Do not be afraid; do not be discouraged, for the Lord your God will be with you wherever you go.” – Joshua 1:9. Courage is the decision that something else matters more.

On Doing the Hard Thing

  1. “The credit belongs to the man who is actually in the arena, whose face is marred by dust and sweat and blood.” – Theodore Roosevelt. Courageous leaders lead from inside the mess.

  2. “You gain strength, courage, and confidence by every experience in which you really stop to look fear in the face. You must do the thing which you think you cannot do.” – Eleanor Roosevelt

  3. “Courage is not the absence of fear, but rather the judgment that something else is more important than fear.” – Ambrose Redmoon. I come back to this one regularly. Especially when I’m about to say something in a meeting that I know won’t be popular but needs to be said.

On Serving Others

  1. “Everybody can be great because anybody can serve. You don’t have to have a college degree to serve. You don’t have to make your subject and verb agree to serve. You only need a heart full of grace and a soul generated by love.” – Martin Luther King Jr.

  2. “The best way to find yourself is to lose yourself in the service of others.” – Mahatma Gandhi. The leaders who’ve had the deepest impact on my life were the ones who showed up for me when it cost them something.

  3. “Do nothing out of selfish ambition or vain conceit. Rather, in humility value others above yourselves.” – Philippians 2:3. This is the hardest standard of leadership I know. And the most transformative when you actually live it.

On Persistence

  1. “Success is not final, failure is not fatal: it is the courage to continue that counts.” – Winston Churchill

  2. “Courage doesn’t always roar. Sometimes courage is the quiet voice at the end of the day saying, ‘I will try again tomorrow.’” – Mary Anne Radmacher. This one resonates with anyone who’s had a week where nothing went right but showed up on Monday anyway.

  3. “Let us not become weary in doing good, for at the proper time we will reap a harvest if we do not give up.” – Galatians 6:9. The most courageous thing you might do today is simply not quit.

The Thread

Courageous leadership is the daily decision to see people, serve them, and keep going when it would be easier to stop.

That’s available to anyone, in any role, at any level. You don’t need a title to lead courageously. You just need to care enough about the person in front of you to show up fully. And then do it again tomorrow.

The Big Five Personality Traits: What Every Change Leader Needs to Know

Most change management initiatives fail. Not because the strategy was wrong or the technology didn’t work but because leaders treated a room full of unique human beings the exact same and expected they would all respond to change the same way.

They won’t. They never do.

After more than a decade leading product and organizational change, running platform modernizations, launching new products, and driving cross-functional alignment at companies serving millions of users, I’ve learned one thing: how people respond to change is largely predictable if you’re paying attention to the right signals.

The Big Five personality model gives you a scientific framework for doing exactly that. It won’t tell you everything about a person. But it will tell you enough to stop being surprised when your most methodical engineer resists a process change that your most curious designer is already championing at lunch.

This article breaks down what the Big Five actually is, what each trait means in practice, and how you can apply it specifically to change management no matter what your title is.


What Is the Big Five Personality Model?

The Big Five — also called the Five-Factor Model (FFM) or referred to by the acronym OCEAN — is the most empirically validated framework in personality psychology. This isn’t Myers-Briggs, which has significant reliability problems. This isn’t an astrology-adjacent personality quiz. The Big Five emerged from decades of peer-reviewed psychometric research and is the standard model used in academic personality research worldwide.

The framework originated from what researchers call the lexical hypothesis: the idea that the most important personality traits in any culture will inevitably be encoded in its language. In the 1930s, psychologists Gordon Allport and Henry Odbert catalogued over 4,500 English adjectives used to describe people (I love this idea and think it would be so interesting to have watched them come up with some of the silly ones). From there, researchers spent decades using factor analysis, a statistical technique for identifying patterns in data, to cluster those descriptors into broader dimensions.

By the 1960s, Warren Norman had consolidated the research into five robust factors. In the 1980s, Paul Costa Jr. and Robert McCrae developed the NEO Personality Inventory, which remains the most widely used Big Five assessment today.

The key distinction from other personality models: the Big Five measures traits on a continuous spectrum, not binary categories. You’re not an “introvert” or an “extravert” but you fall somewhere on a range. That nuance matters enormously when you’re managing real people through real change.

The five traits, spelled out by the OCEAN acronym, are: Openness to Experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism.

Let’s go through each one in detail.


O — Openness to Experience

What it measures: Creativity, intellectual curiosity, and willingness to engage with new ideas, abstract thinking, and novel experiences.

High scorers tend to be imaginative, intellectually adventurous, and genuinely excited by complexity. Low scorers tend to be practical, conventional, and preference-stable — they want proven methods over experimentation.

Neither end of the spectrum is “better.” High openness without the grounding of conscientiousness can produce someone who loves ideas but ships nothing. Low openness combined with high conscientiousness can produce your most reliable operators.

In change management: High-openness people are your early adopters. They’ll be intrigued by the change, ask the interesting questions, and often become your internal champions. I recently had a large launch and my main champion was so excited that she had been using the prototype before I had even let some of my development team know about the project. The risk with high-openness individuals is that they get excited about the idea of change and then lose interest during the unglamorous implementation phase. I kept my champion excited by only introducing small features to her throughout the build.

Low-openness individuals will resist change more instinctively, not because they’re obstinate, but because they’re wired to value stability and proven approaches. What they need from you is not enthusiasm, it’s evidence. Show them data, precedent, and a clear picture of what stays the same. They’re not your enemy in a change initiative. Ignored, they become your loudest critics. Engaged correctly, they become your quality control. This guy is on my team too. He’s difficult to deal with if I don’t give him the data, but I’ve looked at it as a win because it does force me to slow down and get the data. I know he’s going to be asking me questions and I try to have the answers before he arrives.

The practical play: Segment your communication strategy. Don’t send one change announcement to your entire organization and assume it lands equally. High-openness stakeholders want to be in the room early, contributing to the shape of the change. Low-openness stakeholders want to see the pilot results before they commit. Build your rollout timeline to accommodate both.


C — Conscientiousness

What it measures: Self-discipline, organization, dependability, and goal-directedness. Essentially: how reliably does this person follow through?

High scorers are methodical, thorough, and tend to plan ahead. They show up on time, deliver on commitments, and prefer structured environments. Low scorers are more spontaneous, flexible, and sometimes prone to procrastination — but also often more adaptable in chaotic environments.

In change management: High-conscientiousness people are your implementation backbone. Once they buy into a change, they will execute it more reliably than anyone else in the room. The challenge is that they need the full picture before they commit. Launch an initiative without a detailed plan and they’ll spend the entire kickoff meeting asking questions that feel obstructionist but are actually their brain trying to build the mental model they need to be effective.

Low-conscientiousness people adapt to change more easily in terms of mindset but more erratically in terms of execution. They’re comfortable with ambiguity, which is valuable in early-stage change, but they need more structural accountability to follow through on the consistent behaviors that make change actually stick.

The practical play: During change planning, put your high-conscientiousness people in charge of the process design. Give them the runway to build the playbook. During rollout, give them clear milestones and let them hold the team accountable — they’ll do it naturally. For low-conscientiousness team members, build in more check-in touchpoints and make progress visible publicly. Not as surveillance, but as external structure that replaces the internal structure they don’t naturally have.


E — Extraversion

What it measures: Energy drawn from social interaction, assertiveness, tendency toward positive emotion, and degree of talkativeness and engagement in group settings.

This is the most commonly misunderstood trait. Introversion is not shyness. Extraversion is not confidence. The dimension is about where you draw energy from, social engagement energizes extraverts and drains introverts, not the other way around.

High extraverts are vocal, enthusiastic in group settings, and often the first to speak up. High introverts process internally, may need more time before contributing, and tend to prefer one-on-one conversations over all-hands forums.

In change management: Extraverts will often create the social momentum a change initiative needs. They talk about it at lunch, they advocate in meetings, they make it feel like something real is happening. That’s enormously valuable — but it can also mean they’re generating buzz ahead of the evidence, which creates a credibility gap if the initiative stumbles.

Introverts are processing the change deeply but quietly. The danger is confusing their silence with acceptance or indifference when they might be holding substantive concerns that never surface in a town hall setting. Some of the sharpest change-resistant thinking I’ve encountered came from introverted team members who raised it six weeks later in a one-on-one after everyone thought we were past the debate phase.

The practical play: Don’t let all-hands meetings be your only feedback channel. They structurally favor extraverts. Build in async feedback mechanisms — surveys, Slack threads with explicit prompts, written pre-reads before meetings — that give introverts the processing space they need. And deliberately seek out your quiet team members one-on-one before major decisions close. You’ll learn things you never would have heard in a group setting.


A — Agreeableness

What it measures: Cooperativeness, empathy, trust, and prioritization of social harmony. How inclined is this person to put others’ needs above their own?

High agreeableness correlates with warmth, generosity, and conflict avoidance. Low agreeableness correlates with competitiveness, skepticism, and willingness to challenge or confront. Confronting doesn’t necessarily look like hostility, but there may be a higher tolerance for tension.

In change management: This is the trait that creates the most dangerous blind spots for change leaders. High-agreeableness individuals will often nod along with a change initiative in a group setting even when they have legitimate reservations because disagreeing publicly feels like a social cost they’d rather avoid. When you see heads nodding around the conference table, do not assume buy-in.

Low-agreeableness individuals will push back openly and sometimes aggressively. That can feel uncomfortable and even disrespectful in a change rollout. One thing I’ve learned to appreciate (as alluded to above) is the person who tells you directly that your change plan has a hole in it, they are actually doing you a favor. The person who agrees in the meeting and then quietly undermines the initiative in the hallway is the real risk.

The practical play: Create explicit, structured opportunities for dissent. Not just “does anyone have concerns?” at the end of a packed meeting, but dedicated pre-mortem exercises, anonymous surveys, or devil’s advocate assignments that normalize pushback as part of good process. This gives high-agreeableness people a structured reason to voice concerns (it’s the process, not personal conflict) and channels the energy of low-agreeableness people productively into improving the plan rather than resisting it.


N — Neuroticism

What it measures: Emotional volatility, sensitivity to stress, and tendency toward negative emotional states like anxiety, irritability, and self-doubt.

High scorers experience stronger emotional reactions to uncertainty and stress. Low scorers (sometimes called emotionally stable) tend to remain calm under pressure and recover from setbacks more quickly.

This is the trait that demands the most careful handling in a leadership context. Neuroticism should not be viewed as a weakness or a character flaw. I do think there is a maturity level where it may appear as aspects of Neuroticism, but Neuroticism is a genuine dimension of human experience that shapes how people respond to threat and uncertainty. And organizational change is perceived as a threat by more of your team than you probably realize.

In change management: High-neuroticism team members will likely experience change as significantly more stressful than their low-neuroticism peers, even if the change is objectively positive. They need more frequent reassurance, more visibility into what’s coming, and more explicit communication that their role and contributions are valued. If they’re experiencing change anxiety and you’re not addressing it, it will express itself as resistance, absenteeism, or performance decline.

Low-neuroticism individuals may be so unfazed by change that they underestimate the impact it’s having on their teammates. If you’re a leader who naturally scores low in neuroticism, this is your blind spot. Just because you’re energized by the change doesn’t mean everyone else is. Watch for signals in your team: short tempers, declining work quality, missed deadlines during the transition period. These are symptoms, not character issues.

The practical play: Increase your communication frequency during change and especially about what’s NOT changing. The human brain in stress mode fills uncertainty with worst-case assumptions. Every uncertainty you don’t address explicitly will be addressed implicitly by anxiety. Over-communicate the stable elements: this team stays together, your role isn’t going anywhere, here’s exactly what the next 30 days look like. Predictability is medicine for high-neuroticism team members.


Putting OCEAN to Work: A Change Management Framework

Understanding the five traits individually is useful. Using them together is where the real leverage is.

Here’s how I’ve applied this thinking practically in change initiatives:

Start with team mapping, not org charts. Before you launch a change initiative, spend time mapping your key stakeholders across the OCEAN dimensions based on what you’ve observed. These aren’t formal assessments, but your working knowledge of how these people behave. Who asks the most questions before committing? (High conscientiousness / low openness.) Who nods in meetings but raises concerns later privately? (High agreeableness.) Who goes quiet during stressful periods? (High neuroticism.) This map tells you who needs what from you.

Design your communication strategy around the spectrum, not the average. Most change communications are written for the mythical average employee: moderately open, moderately agreeable, moderately stressed. That person doesn’t actually exist in your organization. Write for the ends of the spectrum. Give your low-openness people evidence and precedent. Give your high-neuroticism people explicit, frequent reassurance. Give your introverted team members async channels to process and respond. One message, multiple formats and frequencies.

Use personality diversity as a feature, not a bug. The knee-jerk response in change management is to minimize resistance. The better instinct is to channel it. High-conscientiousness skeptics will pressure-test your process and find the gaps before they become launch-day disasters. Low-agreeableness team members will surface the honest objections everyone else is thinking but not saying. High-neuroticism individuals will be acutely sensitive to signals that something is off and they’re often right. Build a change governance structure that treats these people as assets, not obstacles.

Match your change champions to the stakeholders they’re influencing. A high-extravert, high-openness champion will be brilliant at building excitement in your early adopters. They’re probably the wrong person to walk your most cautious, routine-oriented team members through the transition. Match your internal advocates to the personality profiles of the people they need to bring along. This is people strategy, not manipulation, it’s meeting people where they actually are.


A Note on Using This Responsibly

The Big Five is a lens, not a label. People are more complex than five dimensions, their trait expressions shift with context, and nobody should be reduced to their personality profile in a change initiative or anywhere else.

What this framework does is expand your range of hypotheses. Instead of concluding that your cautious colleague is “resistant to change”, which puts the problem on them and takes it out of your hands, you have a more useful hypothesis: they may be low-openness and high-conscientiousness. That means they need evidence and a clear plan, not enthusiasm. That’s a solvable problem. “Resistant to change” isn’t.

Used that way, the Big Five doesn’t box people in. It opens up more humane, more effective ways of leading them through the hardest parts of organizational life.


Frequently Asked Questions

What is the Big Five personality model?

The Big Five, also known as the Five-Factor Model (FFM) or OCEAN, is an empirically validated framework that measures human personality across five dimensions: Openness to Experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. Unlike personality typing systems that sort people into categories, the Big Five measures each trait on a continuous scale, making it a more accurate representation of the actual range of human personality.

How does personality affect change management?

Personality traits significantly shape how individuals perceive, process, and respond to organizational change. People high in Neuroticism typically experience change as more stressful and need more frequent, explicit communication. People high in Openness tend to embrace change early but may disengage during implementation. People high in Conscientiousness need structured plans before committing. Understanding these tendencies allows change leaders to design communication strategies, rollout timelines, and feedback mechanisms that meet people where they actually are rather than where leaders wish they were.

Should you give your team a Big Five assessment?

Formal assessments can be useful, but they’re not required to apply this framework. Effective leaders can identify rough trait profiles through observation and noticing who asks detailed questions before committing, who speaks up readily in group settings versus one-on-one, who seems energized versus stressed by ambiguity. Formal assessments add precision; behavioral observation gets you most of the way there and doesn’t require an HR initiative to begin.

Is the Big Five better than Myers-Briggs for organizational use?

For research-backed applications, yes. The Big Five has substantially stronger empirical support than Myers-Briggs (MBTI), which has been criticized for low test-retest reliability, meaning people often get different type assignments when they retake it. The Big Five is the standard model in academic personality research precisely because of its consistency and predictive validity. For organizational change management specifically, the Big Five’s continuous scale model is also more practically useful because it captures the nuance that binary type systems miss.

What is the most important Big Five trait for a change leader to understand?

Neuroticism. Not because it’s more common, but because it’s most likely to be invisible until it creates problems. High-neuroticism team members experience genuine distress during organizational change that can express itself as resistance, disengagement, or performance decline if it’s not addressed. And leaders who naturally score low in neuroticism often don’t notice it in their team because they’re not experiencing the same stress response. Understanding and proactively addressing the anxiety dimension of change is one of the highest-leverage moves a change leader can make.

25 Skills a Product Manager should focus on in 2025

I’ve been in product leadership long before the term ‘Product Management’ became a common buzzword. Over the past eight years, I’ve held various titles with ‘Product’ in them, and yet, every day brings new lessons and insights. As I approach 2025, I’ve realized the importance of grounding myself in the principles that have guided me so far. This list serves as a personal reminder—a collection of foundations I’ve built upon, shaped by insights from books like Crucial Conversations, INSPIRED, The E-Myth Revisited, and The Mom Test.

I. Foundational Principles

  1. Embrace the Product Mindset: Product management is not just a job; it’s a mindset 1. It requires a passion for solving customer problems and a commitment to continuous improvement.
  2. Deep Customer Knowledge: Become an undisputed expert on your customers2. This involves understanding their needs, pain points, and desires through both qualitative and quantitative data3.
  3. Data-Driven Decisions: Be comfortable with data and analytics4. Use data to understand how customers are using your products, analyze A/B test results, and inform product decisions.
  4. Master the Product: Be an expert on your actual product and your industry. Share your knowledge openly and generously.
  5. Continuous Learning: Stay intellectually curious and quickly apply new technologies to solve problems for customers5.

II. Team Dynamics & Collaboration

  1. Build Strong Product Teams: Focus on building and nurturing strong, collaborative relationships with your product team. A product team typically includes a product manager, a product designer, and engineers.
  2. Empowered Teams: Champion empowered product teams that are equipped to solve business problems. Ensure your team understands the company vision and how their work contributes to the larger purpose6.
  3. Cross-Functional Collaboration: Work effectively with product designers, engineers, and product marketing managers. Ensure product marketing is embedded with the product team7.
  4. Effective Communication: Communicate product learnings clearly and consistently. Keep stakeholders informed and engaged8.
  5. Delivery Management: Recognize the importance of delivery managers in removing obstacles for the team. Their work ensures that the team can focus on building valuable products9.

III. Strategic Product Development

  1. Product Vision: Develop a compelling and inspiring product vision10. Use it to articulate your purpose and inspire the team11.
  2. Product Strategy: Define a clear product strategy that serves as a path to achieving the product vision. Ensure alignment between the product strategy and overall business strategy12.
  3. Product Principles: Complement your product vision and strategy with a set of guiding principles that define the nature of the products you want to create13.
  4. Outcome-Focused Roadmaps: Shift from output-based roadmaps to those focused on business outcomes14. Ensure every roadmap item is tied to a specific business objective.
  5. Embrace Discovery: Prioritize product discovery, which involves collaboration between product management, UX design, and engineering. Tackle risks before writing any code.
  6. Prioritize Ruthlessly: Focus on a single, scalable idea rather than jumping on every good one15.
  7. Problem-First Approach: Focus on solving the underlying problem. Don’t get caught up in the solution before you’ve fully understood the problem16.
  8. Customer Discovery Programs: Use customer discovery programs to ensure that you’re building a product that customers love.

IV. Product Discovery and Delivery

  1. Master Discovery Techniques: Utilize various discovery techniques to understand customer needs and validate ideas. This includes opportunity assessment, customer letters, and startup canvases.
  2. Rapid Experimentation: Use prototypes to conduct rapid experiments17. Test ideas with users, customers, engineers, and business stakeholders in hours and days, not weeks and months18.
  3. Usability Testing: Conduct regular usability tests to identify friction points in prototypes. Use these tests to learn about how customers use your products19.
  4. Continuous Delivery: Strive for frequent release cycles to ensure teams move quickly and release with confidence.
  5. Iterative Approach: Understand that it typically takes several iterations to get the execution of an idea to the point where it delivers the expected business value20.

V. Leadership & Growth

  1. Product Evangelism: Become an effective evangelist for your product. Inspire your team, stakeholders, and customers by “selling the dream”. Use prototypes to communicate the product vision21.
  2. Adaptability: Be prepared to adapt to changes in the market and new trends22. Be flexible with the details, but remain stubborn on the overall vision.

Conclusion

Product management in 2025 will demand a combination of deep technical knowledge, strategic thinking, and a genuine passion for solving customer problems. By focusing on these 25 areas, product managers can position themselves for success and contribute to the creation of truly impactful products. It’s not about having all the answers, but about asking the right questions and embracing a continuous learning mindset.


  1. “The art of Product Management is the art of life itself. Surround your-selves by great people, focus on your mojo, build great stuff with integrity, hold strong opinions but lightly. And Marty is one of the best teachers of this art.” —Punit Soni, Founder and CEO, Robin, Former Google APM ↩︎
  2. “you need to become an acknowledged expert on the customer: their issues, pains, desires, how they think—and for business products, how they work, and how they decide to buy.” —INSPIRED, Marty Cagan ↩︎
  3. “Without this deep customer knowledge, you’re just guessing. This requires both qualitative learning (to understand why our users and customers behave the way they do), and quantitative learning (to understand what they are doing)” —INSPIRED, Marty Cagan ↩︎
  4. “… product managers are expected to be comfortable with data and analytics. They are expected to have both quantitative skills as well as qualitative skills. The Internet enables unprecedented volume and timeliness of data.
    A big part of knowing your customer is understanding what they’re doing with your product. Most product managers start their day with half an hour or so in the analytics tools, understanding what’s been happening in the past 24 hours. They’re looking at sales analytics and usage analytics. They’re looking at the results of A/B tests.” —INSPIRED, Marty Cagan ↩︎
  5. Be “intellec-tually curious, quickly learning and applying new technologies to solve problems for customers, to reach new audiences, or to enable new business models.” —INSPIRED, Marty Cagan ↩︎
  6. “The product teams need to have the necessary business context. They need to have a solid understanding of where the company is heading, and they need to know how their particular team is supposed to contribute to the larger purpose.” —INSPIRED, Marty Cagan ↩︎
  7. “… because that’s where they are connected to the experience that the customer is having an opportunity to engage with.” —INSPIRED, Martina Lauchengco ↩︎
  8. “Evangelize continuously and relentlessly. There is no such thing as over-communicating when it comes to explaining and selling the vision. Especially in larger organizations, there is sim-ply no escaping the need for near-constant evangelization. You’ll find that people in all corners of the company will at random times get nervous or scared about something they see or hear. Quickly reassure them before their fear infects others.” —INSPIRED, Marty Cagan ↩︎
  9. “In growth-stage and enterprise companies, many product managers complain that they have to spend far too much of their time doing project management activities. As a result, they have almost no time to address their primary product responsibility: ensuring that the engineers have a product worth building.
    Delivery managers are a special type of project manager whose mission is all about removing obstacles—also known as impediments—for the team. Sometimes, these obstacles involve other product teams, and sometimes they involve non-product functions. In a single day, they might track down someone in marketing and press them for a decision or an approval, coordinate with the delivery manager on another team about prioritizing a key dependency, persuade a product designer to create some visual assets for one of the front-end developers, and deal with a dozen other similar roadblocks.” —INSPIRED, Marty Cagan ↩︎
  10. “The product vision describes the future we are trying to create, typically somewhere between two and five years out. For hardware or device-centric companies, it’s usually five to 10 years out.
    Note that this is not the same as the company mission statement. Examples of mission statements are “organize the world’s information” or “make the world more open and connected” or “enable anyone any-where to buy anything anytime.” Mission statements are useful, but they don’t say anything about how we plan on accomplishing that. That’s what the product vision is for.” —INSPIRED, Marty Cagan ↩︎
  11. “Start with why. This is coincidentally the name of a great book on the value of product vision by Simon Sinek. The central notion here is to use the product vision to articulate your purpose. Everything follows from that.
    Fall in love with the problem, not with the solution. I hope you’ve heard this before, as it’s been said many times, in many ways, by many people. But it’s very true and something a great many product people struggle with.” —INSPIRED, Marty Cagan ↩︎
  12. “Communicate the strategy across the organization. This is part of evangelizing the vision. It’s important that all key business partners in the company know the customers we’re focused on now and which are planned for later. Stay especially closely synced with sales, marketing, finance, and service.” —INSPIRED, Marty Cagan ↩︎
  13. “Where the product vision describes the future you want to create, and the product strategy describes your path to achieving that vision, the product principles speak to the nature of the products you want to create.” —INSPIRED, Marty Cagan ↩︎
  14. … focus “on outcome and not output. Realize that typical product roadmaps are all about output. Yet, good teams are asked to deliver business results.
    Most of the product world has the same definition for product roadmap, but there are a few variations. I define product roadmap as a prioritized list of features and projects your team has been asked to work on. These product roadmaps are usually done on a quarterly basis, but sometimes they are a rolling three months, and some companies do annual roadmaps.” —INSPIRED, Marty Cagan ↩︎
  15. “Startups are about focusing and executing on a single, scalable idea rather than jumping on every good one which crosses your desk.” —The Mom Test, Rob Fitzpatrick ↩︎
  16. “This is another reason why typical product roadmaps are so problematic. They’re lists of features and projects where each feature or project is a possible solution. Somebody believes that feature will solve the problem or it wouldn’t be on the roadmap, but it’s all too possible they are wrong. It’s not their fault—there’s just no way to know at the stage it’s put on the roadmap.
    However, there very likely is a legitimate problem behind that potential solution, and it’s our job in the product organization to tease out the underlying problem and ensure that whatever solution we deliver solves that underlying problem.” —INSPIRED, Marty Cagan ↩︎
  17. “… use prototypes to conduct rapid experiments in product discovery, and then in delivery, we build and release products in hopes of achieving product/market fit, which is a key step on the way to delivering on the company’s product vision.” —INSPIRED, Marty Cagan ↩︎
  18. “If we can prototype and test ideas with users, customers, engi-neers, and business stakeholders in hours and days—rather than in weeks and months—it changes the dynamics, and most important, the results.
    It’s worth pointing out that it isn’t the list of ideas on the roadmap that’s the problem. If it was just ideas, there’s not much harm in that. The issue is that anytime you put a list of ideas on a document entitled “roadmap,” no matter how many disclaimers you put on it, people across the company will interpret the items as a commitment. And that’s the crux of the problem, because now you’re committed to build-ing and delivering this thing, even when it doesn’t solve the underlying problem.” —INSPIRED, Marty Cagan ↩︎
  19. “You will need to define in advance the set of tasks that you want to test. Usually, these are fairly obvious. If, for example, you’re building an alarm clock app for a mobile device, your users will need to do things like set an alarm, find and hit the snooze button, and so on. There may also be more obscure tasks, but concentrate on the primary tasks—the ones that users will do most of the time.
    Some people still believe that the product manager and the prod-uct designer are too close to the product to do this type of testing objectively, and they may either get their feelings hurt or only hear what they want to hear. We get past this obstacle in two ways. First, we train the product managers and designers on how to conduct themselves, and second, we make sure the test happens quickly—before they fall in love with their own ideas. Good prod-uct managers know they will get the product wrong initially and that nobody gets it right the first time. They know that learning from these tests is the fastest path to a successful product.” —INSPIRED, Marty Cagan ↩︎
  20. “…even with the ideas that do prove to be valuable, usable, feasible, and viable, it typically takes several itera-tions to get the execution of this idea to the point where it delivers the expected business value that management was hoping for. This is often referred to as time to money” —INSPIRED, Marty Cagan  ↩︎
  21. “The product vision needs to inspire. Remember that we need product teams of missionaries, not mercenaries. More than anything else, it is the product vision that will inspire missionary-like passion in the organization. Create something you can get excited about. You can make any product vision meaningful if you focus on how you genuinely help your users and customers.” —INSPIRED, Marty Cagan ↩︎
  22. “Determine and embrace relevant and meaningful trends. Too many companies ignore important trends for far too long. It is not very hard to identify the important trends. What’s hard is to help the organization understand how those trends can be leveraged by your products to solve customer problems in new and better ways.” —INSPIRED, Marty Cagan ↩︎

Information Facts of Life

According to an article from HBR (March-April 1994), there are rules governing information sharing behavior. Having run across these rules doing some Change Management research this morning, I find these rules relevant even 26 years later.

  • Most of the information in organizations – and most of the information people really care about – is not on computers.
  • Managers prefer to get information from people rather than computers; people add value to raw information by interpreting it and adding context.
  • The more complex and detailed an information management approach, the less likely it is to change anyone’s behavior.
  • All information does not have to be common; an element of flexibility and disorder is desirable.
  • The more a company knows and cares about its core business area, the less likely employees will be to agree on a common definition of it.
  • If information is power and money, people will not share it easily.
  • The willingness of individuals to use a specified information format is directly proportional to how much they have participated in defining it, or trust other who did.
  • To make the most of electronic communications, employees must first learn to communicate face to face.
  • Since people are important sources and integrators of information, any maps of information should include people.
  • There is no such thing as information overload; if information is really useful our appetite for it is insatiable.

Original Article can be found here.

Leadership is Empathy

Through my research I settled on a statement that I think everyone should become aware of: “The human heart desires to be heard, understood, and acknowledged.” As a husband, father, and leader, I try to “practice what I preach” so I listen to my wife and my kids. In some of the conversations and media consumption my wife and I have been going through lately, we have been diving into empathy.

This morning I happened to listen to a podcast with John Maxwell and Simon Sinek in which Simon stated something so succinctly that I wanted to share it:

When I hear people talking about the system is broken. There’s no mythical system, it’s us. Our society is a collection of individuals, and whatever the balance of behaviors from those individuals is the system you get. And so, it starts at home, it starts with us, and so, we want to change the system, this elephant, the only way to eat an elephant is one mouthful at a time. And so, I think we need to set ourselves in a course to become better listeners ourselves, and there’s a difference between listening and hearing. You know, hearing is understanding the words that are said to you, listening is trying to get to the meaning of the words that are said to you, with an appreciation that sometimes people say the wrong thing, they say what they’re trying to say badly. Sometimes emotions are involved, sometimes they get flustered, and it’s not for us to take their words personally, or to even pick apart, but to rather try and show up with curiosity, to really understand the meaning. What I’m describing is empathy. We show up with empathy. That’s all this is, and to look past the superficial.

Simon Sinek

I appreciate all that is said in that paragraph. I was really grateful that he discusses looking past the superficial. In a conversation I had the other day, I was discussing how the individual was upset and I understood their being upset, but they were focusing on a secondary issue and not the primary issue.

I aim to focus on the primary first and to put the second things second. With empathy, I want to “draw out” the depths of what the individual in front of me is saying.

The purpose in a man’s heart is like deep water, but a man of understanding will draw it out.

Proverbs 20:5

Understanding ITIL Change Management

Notes from a great talk on the do’s and don’ts of Change Management, specifically related to ITIL.

Key take-aways from this IDC Report from 2014:

  • the average cost of an infrastructure failure is $100,000 per hour.
  • 80% of failures are due to custom adjustments of current tools to meet DevOps practices – meaning: a breakdown of process, or lack of process (incorrect SOPs or human errors), causes these types of failures

Change Management is about coordinating/collaborating resources, especially people, across an organization and preparing them for a change that’s about to happen. Ensuring the people are ready, the technology is ready, and the process is ready so that it can be effective and efficient as it moves into production.

There is risk involved with Change Management. If a change fails, it can deteriorate the business. There is knowledge required for Change Management. The stakeholders need to be prepared with the right knowledge of what to expect.

With that in mind, Why is Change Management important?

  1. Operational Excellence
    It is simple to focus on doing a lot of things instead of the right things. Change Management helps keep focus on the business strategy and doing the right things.
  2. Management of Risk
    Managing Change ultimately is managing risk. Changes get thrown into the mix constantly, but if it doesn’t add business value, is it the right thing? It could be a major risk to the organization, to the financial resources, and to the customers perspective.
  3. Overall Strategy Support
    Change Managers maintain focus on keeping the business moving towards its goal.

The 8 Do’s and Don’ts of Change Management

Do Coordinate and Collaborate across the organization

Make sure all stakeholders, customers, users, and the business are aware of the change – communication is key.

Don’t overlook the role of people

People are the key. It is human nature to not like change, but as a Change Manager we need to help individuals become not just compliant, but compassionate. When people really believe in the change, they buy in and they do the right thing.

Do know your inventory

Understand your resources and their capability. Be familiar with your Configuration Management Database (CMDB), Configuration Management System (CMS), and the Service Knowledge Management Systems (SKMS). These should follow a service model (how services are delivered) underpinned by your services, infrastructure, people and capabilities. This knowledge allows the Change Manager to foresee problems and how the change in one area might affect other areas.

Don’t introduce too much change at once

There’s a rhythm to change. Too much change will cause “red flag” syndrome where the changes become ignored. The Change Manager needs to understand where the business and the customers are at and find that balanced rhythm.

Do communicate to those who need to know about the change

The Forward Schedule of Changes (FSC) is the document used to communicate change plans to the organization. Use this to: Track the list of approved changes and the proposed implementation dates. Provide visibility to key stakeholders on the status of changes being introduced in the production environment. Nothing is worse than having something change when you didn’t know anything about it. This causes incidents and distrust. Individuals will start ‘looking’ for negative aspects and things begin to be disrupted.

Don’t think about change in a silo

A change, no matter the size, can have domino effects. Therefore, any change is an organizational change and needs to be communicated in a way that anyone in the organization can see the value and its alignment with the vision.

Do approach change management from a Service-Oriented perspective

Look at the service and how it affects your customers and the relationships within the organization.

Don’t pick technology that doesn’t support a holistic perspective

This is in alignment with the “Do” from above – sometimes processes are inter-related. Make sure the technology takes the organization as a whole into account. You don’t want to change the tech in one area and it ends up causing an entire division to no longer be able to communicate strategic information. Remember from the IDC report – customization of tools accounts for 80% of failures.

Change Management affects everything in an organization.

In summary, the 8 Do’s and Don’ts of Change Management can be quickly navigated by this excellent list of the 7 R’s of Change Management: For proper impact assessment and understanding of benefits to risk, these seven questions should be asked.

  • Who RAISED the change?
  • What is the REASON for the change?
  • What is the RETURN required from the change?
  • What are the RISKS involved in the change?
  • What RESOURCES are required to deliver the change?
  • Who is RESPONSIBLE for the build, test and implementation of the change?
  • What is the RELATIONSHIP between this change and other changes?

The Paradox of Transparency

A gossip goes around telling secrets,
but those who are trustworthy can keep a confidence.

Proverbs 11:13

In a conversation with my wife earlier this morning we were discussing how blessed I am to work for the company I work for. I so appreciate the anonymity I’ve been given and I was relaying how I feel my productivity has skyrocketed because of it. This place of work is the polar opposite of my previous contract. Previously I worked in an open office where individuals were micromanaged and it felt like the productivity of that office was at a barely functional state. If something needed to be done quickly, that company would simply ‘throw more bodies’ on the task versus attempt to improve productivity. This only added to the confusion and frustration, dragging the productivity down further. It was a terrible cycle.

My current contract has me working wherever and whenever I choose. I have found that I naturally am excited about my work, diving into it, thinking through problems. I am probably 3x more productive in this environment and the organization actually gets more quality hours out of me because I end up working whenever I have an open chance, even outside of regular working hours.

In researching this phenomenon and trying to grasp language for what I’ve personally experienced and how it affects productivity and how management philosophies can cause a breeding ground for productivity or for lies and deceit, I came across this amazing paper/research project done by Ethan S. Bernstein (link to PDF) called: The Transparency Paradox: A Role for Privacy in Organizational Learning and Operational Control. He concludes the paper by stating:

We typically assume that the more we can see, the more we can understand about an organization. This research suggests a counteracting force: the more that can be seen, the more individuals may respond strategically with hiding behavior and encryption to nullify the understanding of that which is seen. When boundaries to visibility fall, invisible boundaries to accurate understanding may replace them at a significant cost. In this research, that cost was a 10–15 percent detriment to performance.

Hence the transparency paradox: broad visibility, intended to increase transparency, can breed hiding behavior and myths of learning and control, thereby reducing transparency. Conversely, I have observed that transparency can actually increase within the boundaries of organizational modules, or what the operators called zones of privacy, when the visible component of transparency is decreased or limited between them.

This paper does not challenge the value of transparency. Instead, it challenges what, and how much, individual observers should see in order to achieve it. Because the mere presence of a manager, in line of sight of an employee, may affect employee performance in negative ways, management by walking around may sometimes be inferior to management by standing still. In this study, creating zones of privacy around line workers’ activities did not result in slacking off or cutting corners. Instead, the zones of privacy improved transparency within the line and, with it, improved productive deviance, experimentation, and focus on productive work. While hourly defect-free production results remained transparent to all via the IT system, line activities remained visible only to those who were best suited to innovate: the line operators. The establishment of a zone of privacy around the line allowed improvement rights to be owned by those on the inside, encouraged more transparency within the visibility boundaries, and ultimately enabled an increase in organizational performance.

Visual privacy is an important performance lever but remains generally unrecognized and underutilized. Paradoxically, an organization that fails to design effective zones of privacy may inadvertently undermine its capacity for transparency.

15 Books I will be reading in 2020

  • The Brothers Karamazov – Fyodor Dostoyevsky
  • Principle-Centered Leadership – Stephen R. Covey
  • Reasonable Greed: Why sustainable business is a much better idea – Wayne Visser & Clem Sunter
  • Leadership and the one minute manager – Ken Blanchard
  • The Whole Armor of God – Ralph W. Sockman
  • The Charisma Myth – Olivia Fox Cabane
  • The Wisest One in the Room – Thomas Gilovich & Lee Ross
  • Primary Greatness – Stephen R. Covey
  • The Top 10 Leadership conversations in the Bible – Steve Moore
  • 8 Lessons in Military Leadership for Entrepreneurs – Robert Kiyosaki
  • The Prodigal God – Timothy Keller
  • The Speed of Trust – Stephen Covey
  • Speak like a CEO – Suzanne Yates
  • The Leadership Challenge – James Kouzes & Barry Posner
  • Studies in the Sermon on the Mount – D. Martyn Lloyd Jones

10 end of life quotes to inspire you today

I have been inspired recently after reading a speech that a leader gave near the end of his life. He was looking back over his years and wanted to exhort those he had led into maintaining the growth and the focus he had been guiding them in. This speech led to many others, including George Washington’s address to a young nation.

Why would I look into end of life speeches? I have currently reached mid-life (41) and as I am reading these speeches, it is incredible to see what these leaders have considered to be the errors or foundations that shaped them, their productivity, and their legacy. If I can pay attention to those, it builds a focus that can become an almost guaranteed success, so that at the end of my life, I can look back across the decades and feel like I’ve run my race well.


If neither crying nor laughing can change my circumstances, then I rather go through them laughing.

Moffat Machingura, Life Capsules

Life is like a restaurant; you can have anything you want as long as you are willing to pay the price.

Moffat Machingura, Life Capsules

In the end, if we don’t have God we don’t have anything other than an end.

Craig D. Lounsbrough

I am not afraid to fail, I am scared to death of dying and having the Lord say to me: ‘Angelica, this is what you might have done had you trust me more’.

Mother Angelica

But after my death let it be known that in my old age, at the very end of my life, there was still plenty that made me smile.

Orhan Pamuk, My Name Is Red

I find my thoughts drifting to the Sabbath, the day of rest, the seventh day of the week, and perhaps the seventh day of one’s life as well, when one can feel that one’s work is done, and one may, in good conscience, rest.

Oliver Sacks, Gratitude

At the end of life, your reward in heaven will not be proportional to the role you played on earth, but how faithful you played it. Be faithful in every little role you are to play; it’ll lead you to a greater reward! Faithfulness is key!

Israelmore Ayivor

At the end of my life I want to say, “I lived every moment of it.’

Debasish Mridha

Every Task, Goal, Race, and Year comes to an end… Therefore, make it a habit to always finish strong.

Gary Ryan Blair

George Bush had been fading in the last few days. He had not gotten out of bed, he had stopped eating and he was mostly sleeping. For a man who had defied death multiple times over the years, it seemed that the moment might finally be arriving.

His longtime friend and former secretary of state, James A. Baker III, arrived at his Houston home on Friday morning to check on him.

Mr. Bush suddenly grew alert, his eyes wide open.
“Where are we going, Bake?” he asked.
“We’re going to heaven,” Mr. Baker answered.
“That’s where I want to go,” Mr. Bush said.

Barely 13 hours later, Mr. Bush was dead. The former president died in his home in a gated community in Houston, surrounded by several friends, members of his family, doctors and a minister. As the end neared on Friday night, his son George W. Bush, the former president, who was at his home in Dallas, was put on the speaker phone to say goodbye. He told him that he had been a “wonderful dad” and that he loved him.

“I love you, too,” Mr. Bush told his son.
Those were his last words.

~ Excerpt from NY Times

Action

Because your vision always costs more than you estimated, and often takes longer than you planned, it can become blurred by your circumstances and emotions. That is why it becomes imperative to write it down and keep it in front of you! With a clear-cut written goal, you’ll always know where you are and remember where you’re going.

What is the direction, focus, or vision you have for your life?
At the end, what will your life look like as you look back?

What is the noise you are making?

The very essence of leadership is that you have to have a vision. It’s got to be a vision you articulate clearly and forcefully on every occasion. You can’t blow an uncertain trumpet.

Theodore Hesburgh

I’m mainly asking myself this question: what is the noise I’m making? What does that trumpet blast sound like? Is it recognizable?

A greeting in one of the 11 National South African languages is “Sawubona.” It translates literally as: “I see you.” If the human heart’s deepest desire is to be seen, heard, and understood, then to Sawubona someone means “I see you, I hear you, and I understand you.”

With my family, the trumpet blast, or rather vision statement is a call to “Sawubona” each person they meet.

Personally and professionally it would be a similar call – the call to Servant Leadership. To see an individual’s needs and with balanced wisdom, respond appropriately.

Action

What is the noise you are making?

How to make decisions according to Jeff Bezos

A few years back I sat down and listed out my personal values. It took me a few days of meditating on and solidifying the list. Having a list of what you value, and then re-visiting it regularly, reveals the type of individual you will be as well as how you will be perceived by others.

I recently met a software developer at a conference. He was my age, had a wedding ring on, and a photo of his kids on his computer. He was incredibly skilled as a programmer. Knowing the only way to get to that skill level of programming is with time, I asked him what priorities he had to sacrifice in order to gain the time he has put into programming – and does he have any regrets. He stopped the conversation and walked away, apparently we weren’t friends enough to have that deep of a conversation yet.

Looking at the successful programmer, he may have sacrificed time with his family, or he may have sacrificed certain career moves or finances or… in order to gain the skill he had. Any sacrifice is not ideal, but when one is firm on their values the choice is clear and made with confidence and includes no regret.

In regards to all of the above: I do not know Jeff Bezos personally but I can tell that he has a different set of values than I do – simply by the fact that he has made certain sacrifices which I would not make. He is however, a successful, intelligent businessman and there are principles one can gain insight from that his voice lends credence to. This is a business principle, but can be applied to every day life as well. Many have said it before, but I like how Bezos put it in his letter to the shareholders of Amazon – 2015.

Some decisions are consequential and irreversible or nearly irreversible – one-way doors – and these decisions must be made methodically, carefully, slowly, with great deliberation and consultation. If you walk through and don’t like what you see on the other side, you can’t get back to where you were before. We can call these Type 1 decisions. But most decisions aren’t like that – they are changeable, reversible – they’re two-way doors. If you’ve made a suboptimal Type 2 decision, you don’t have to live with the consequences for that long.

You can reopen the door and go back through. Type 2 decisions can and should be made quickly by high judgment individuals or small groups. As organizations get larger, there seems to be a tendency to use the heavy-weight Type 1 decision-making process on most decisions, including many Type 2 decisions. The end result of this is slowness, unthoughtful risk aversion, failure to experiment sufficiently, and consequently diminished invention.

2015 Letter to Shareholders

Courage is a characteristic I’m currently researching and hope to write further on. It takes courage to make decisions, to risk, to become and to be a great leader. While I continue researching and putting my thoughts together on courage, I will put this short poem by Mark Twain here:

With courage
you will dare to take risks,
have the strength to be compassionate,
and the wisdom to be humble.

Courage is the foundation of integrity.

Mark Twain

Action

If you haven’t already, I would challenge you to set a timer on your phone for 10 minutes and write out your values. Revisit the list over the next few days. Make sure those values will lead to the type of person you want to be and to be perceived as.

Next, are there decisions you have made/are currently making that need to be reversed in order to re-align yourself to your values?

Finally, are there decisions that you are not making because of fear? Are you stuck standing in front of a two-way door (Type 2 decision) but you’re treating it as a one-way door?

Leadership Tip

You can talk for days about the customer journey… but if you really want to get results you have to bring up the metrics that are meaningful to executives.

– Shelley Armstrong

Feeling Machines that Think

Over the past several weeks I’ve been performing my research on developing empathy and humility, the foundation of servant leadership, in Afrillennials. I found in the past session an interesting “aha moment” popped up in our discussion and it brought me back to this quote:

According to neuroscientist Antonio Damasio our emotions are the deciding factor for 95 percent of our decisions. So rather than “thinking and acting,” we generally “feel and act.” Part of Damasio’s research involved brain-damaged people who were unable to experience emotions. Even though they could list the pros and cons of any given choice, they were unable to make decisions.

Damasio’s work led him to believe that human beings aren’t “thinking machines that feel,” but rather “feeling machines that think.”

The 95% of our decisions are based on emotions is a staggering thought. I’ve found in my own life, as the development of this research has been taking place, that I desire to make more decisions based on fact vs. emotions. The self-awareness required for this takes deep effort, introspection, and humility with others to allow them to speak into your life, calling out the areas where your thoughts may not be in alignment with your values.

A Change Management secret to tremendous feedback

As I look deeper into Change Management and Organizational Leadership the topic of feedback increasingly comes to the forefront. In a conversation with a fellow Change Manager here in Cape Town our discussion centered around getting feedback which he said is the biggest hurdle he faces in his projects. His practice has begun focusing on helping the employees elicit open, anonymous feedback from their co-workers. Their tool focuses on two questions: What can employee A do better? and What is employee A doing better than anyone else?

The explanation of their Change Management process made me think of this article explaining how Steve Jobs would elicit the most effective feedback.

Tell me what’s not working.

The questions were not directed only toward the exco team, but various people in the organization: Tell me what’s not working here. Then conversely, he would ask someone else: Tell me what is working here.

Ultimately, great leaders, Level 5 leaders as Jim Collins (Good To Great) calls them, are individuals who trust those they’ve hired. By asking questions in this manner, it allows those individuals to speak up and be heard.

Read the article here

Characteristics of the ideal leader

I was in a meeting the other day for a possible Change Management contract. The leader of the organization walked in and impressed me with the way he carried himself, responded to questions, and generally led. I left thinking, not only do I want to work with that guy, I want to be like that guy.

This morning I came across an article with a tremendous bullet list describing several characteristics this CIO has.

Here are some characteristics that make for my ideal leader:

  • You’re noticeably calm and comfortable at work. You’re aware how your attitude and behavior affects those around you, and you care deeply about having a supportive climate at work.

  • Work is one part of your life. You fit your work into healthy working hours. You take vacations. You switch off. When you choose to work unusual hours, you don’t expect others to, therefore you don’t disturb them.

  • No matter who you’re speaking with, when you’re speaking with them, you are present.

  • You listen.

  • You operate on intentional, thoughtfully chosen processes based on what you and your team value. Because you value other’s engagement, and time, you don’t add or persist process for the sake of process.

  • You don’t just expect your people to do their best work, you empower and trust them to. You give or find them the support they need to grow into new challenges and be successful.

Read the full list and article here

Influence

Steve Moore has written a new book called “The Top 10 Leadership Conversations in the Bible” and the introduction has already profoundly impacted me. He discusses a man I’ve never heard of, Samuel Logan Brengle, so passionately that I will begin reading more about this man.

The quote which I latched onto was:

Influence, not position, is at the core of leadership. When a person without leadership capacity is given a leadership title or position, the result isn’t a complete lack of influence, but rather a greatly limited power base. This is true in life and in the Bible.

You can read the intro here.

Leader Empathy: The Key to Effective Relationships

Leader Empathy: The Key to Effective Relationships

Empathy is one of the Social Awareness competencies in the twelve-competency Leadership Competency Model developed by Daniel Goleman and Richard Boyatzis. Empirically linked to leadership performance, Empathy is present in leaders with an understanding of the motivations of others, and the ability to relate to differing perspectives.

Strength in this competency is also demonstrated by leaders who:

  • Listen attentively
  • Are able to understand unspoken or confused attempts at communication
  • Engage in actions indicating a sincere interest in others
  • Have an increased capacity to respect diversity

How to communicate with those who disagree with you

Fast Company just posted an interesting article that discusses a study on why communicating in person versus a written text is worth the effort. According to a 2016 survey of more than 2,000 US adults (paywall) where managers were asked what they found most difficult about communicating with employees a full 69% of respondents said they found “communicating in general” to be the hardest part about communicating with employees.

Clearly, there is a breakdown.

In Schroeder’s study of almost 300 people, participants were asked to watch, listen, and read arguments about subjects they agreed or disagreed with, including abortion, music, and war. They were asked to judge the character of the communicator and the quality or veracity of the argument. Schroeder’s team found that the participants who watched or listened to the communicator were less dismissive of their claims than when they read that communicator’s same argument.

Schroeder’s research also found the participants who listened to or watched the communicators talk were also less likely to dehumanize them–a phenomenon where we subconsciously belittle or demonize the cognitive capabilities and moral attributes of people who hold views other than our own.

This article has some great advice and is where the 69% statistic came from.

“Rather than endless lunches or dinners or boondoggles, one of the best ways to build a good relationship with your employees is to make sure they feel heard,” wrote HR guru Kim Scott in Harvard Business Review. Scott suggests regular one-on-one check-ins where the employee sets the agenda, and that managers give regular feedback—both positive and critical.

My take is that business is going so rapidly, individuals don’t stop and have a cup of coffee together often enough. If they do, it’s rushed, not relaxed, and no relationship is actually built.

In Cape Town, I’ve worked with a man who told me of his experiences working in offices downtown before the age of computers. “People had time to think” he said. I’ll never forget that statement, because it doesn’t seem the speed of business allows us that luxury anymore.

In Seoul, while consulting over a two-weeek period, I was privileged to experience a “3 o’clock conversation time” – I don’t know what it was called in Korean and it may have just been this particular organization’s practice. Every day, at three in the afternoon, for thirty minutes the executive leadership would step into the CEO’s office, take off their shoes and have coffee and pastries. The conversation was very open, discussing wives or children, vacations, work issues, jokes, etc. It was a team who enjoyed being around each other and felt like they all had the same goal they were working toward. As the statement above emphasizes: the executive leadership felt heard by their leader. They then turned around and did the same for the staff whom they were responsible for.

Are you having difficulty leading? Try slowing down, being friendly, and listening with no agenda.

Having trouble receiving good feedback? Try this

Remember the raison d’etre, the reason your project is.

Ask for feedback related to either the vision or user goals. For instance, if working on a website, instead of a completely open-ended question like “What do you think?”, try a more focused question like “How well does this design help users find volunteer opportunites based on their desire?”

What makes employees exceptional?

A recent international study surveyed more than 500 business leaders and asked them what sets great employees apart. The researchers wanted to know why some people are more successful than others at work, and the answers were surprising; leaders chose “personality” as the leading reason.

Notably, 78% of leaders said personality sets great employees apart, more than cultural fit (53%) and even an employee’s skills (39%).

Read the full article by Dr. Travis Bradberry on LinkedIn

Thomas Huxley

Perhaps the most valuable result of all education is the ability to make yourself do the thing you have to do when it ought to be done, whether you like it or not; it is the first lesson that ought to be learned; and however early a man’s training begins, it is probably the last lesson that he learns thoroughly.

Books recommended by Global Business Leaders

Of the 16 books recommended in this article, I would like to read at least these few:

Woo, Wow, and Win

Service Design, Strategy, and the Art of Customer Delight

Authors: Thomas A. Stewart and Patricia O’Connell

One Sentence Summary: This book promotes the concept of designing your company around service and offers strategies based on the idea that the design of services is different from manufacturing.

Recommended by: Andy Polansky, CEO of Weber Shandwick


Technology as a Service Playbook

How to Grow a Profitable Subscription Business

Authors: Thomas Lah and J.B. Wood

One Sentence Summary: A guide to decision making and execution around the “as-a-service” model, with the intent of putting a company on a path to profitable growth by changing how “offers” are designed, built, marketed, sold, and serviced.

Recommended by: Stephanie Newby, CEO of Crimson Hexagon


Delivering Happiness

A Path to Profits, Passion, and Purpose

Author: Tony Hsieh

One Sentence Summary: The CEO of Zappos explains how he created a corporate culture based upon the concept that there is value to happiness, both for employees and customers.

Recommended by: Chris Nassetta, CEO of Hilton Worldwide


Freakonomics

A Rogue Economist Explores the Hidden Side of Everything

Authors: Steven D. Levitt and Stephen J. Dubner

One Sentence Summary: A set of amusing case studies illustrating that economics is the study of how people get what they want or need, especially when other people want or need the same thing.

Recommended by: Jeremiah Owyang, CEO of Crowd Companies

Simon Sinek on Millenials in the Workplace

I am researching Millenials in the Workplace and how to develop better employees. A friend of mine sent me this video last night and wanted my take.

He breaks down ‘4 pieces or characteristics that lead to happiness’ as:

  1. Parenting
  2. Technology
  3. Impatience
  4. Environment

The main point he tries to get across is that Millenials are entitled and lazy, and it’s not their fault, but the fault of the parents who were following terrible parenting advice.

Five Leadership Hacks

“To me, a hack is a clever or unexpectedly efficient means of getting something done. A good hack should feel like cheating because the value created by the hack feels completely disproportionate from the work done.

With this definition in mind, I present five leadership hacks I regularly use. These are not practices designed to redefine your leadership philosophy. They are hacks.”

  1. Two minutes early for everything.
  2. The clock faces you.
  3. Office Hours.
  4. Three questions before any meeting.
  5. Continually fix small broken things.

In reading this, I really appreciated the five hacks, but number four and five especially stood out to me. Three questions before any meeting or else it doesn’t happen: brilliant. He resolves to have three questions which need to be answered in order to prove the value of that meeting taking place.

The last hack is the easiest and it’s the best: fix small broken things. Always. It takes seconds to clean that whiteboard, to plug in the clock in the conference room, and to stop, lean down, and pick up a piece of trash. Seconds.

The value created isn’t just the small decrease in entropy, it’s that you are actively demonstrating being a leader. I understand the compounding awesomeness of continually fixing small broken things.

Read the whole article here

Self-Awareness and Leadership

I had to write a quick response today to the question:

Why do you believe a leader needs to be reasonably self-aware if they are going to be a good leader?

What do you think of my response:

When I envision a leader who is not self-aware, I think of an individual dealing with insecurity then attempting to hide it with pride and arrogance. There are several reasons a leader must be self-aware, but I will discuss the one most important to me: If you are unable to read what’s going on with yourself, how will you read your subordinates and lead them well? A leader with no self-awareness would end up making choices on whims versus logic and would demoralize everyone who works for them. Beyond having a high employee turn over rate, this type of leader would end up costing the organization money and time due to them working on ego boosting projects while avoiding rather than delegating other projects. They would not be capable of delegating due to their lack of personal skills as well as not being able to recognize the skills of their subordinates. 

Further Reading: