This is the fifth and final post in the series “Problem First: AI-Assisted Problem Solving for Organizations That Can’t Afford to Get It Wrong.”

After I published the second post in this series, the one laying out a five-stage problem-solving process, a good friend of mine, Michael Fruhling, asked a question that stopped me cold: Who in the organization is actually responsible for running this process?

I didn’t have a good answer. And that was revealing, because it exposed a gap not just in my argument but in the way most of us think about problem solving. We describe the process. We explain why it matters. We demonstrate what goes wrong when it’s skipped. But we rarely address the most practical question of all: who does this work?

This post is my attempt to answer that question—honestly, including the parts I’m still thinking through.

The Ownership Problem

In large organizations, the question of who owns a structured process is familiar territory—and still contentious. Decades of debate about innovation management have produced no consensus on how to organize innovation teams: should they be embedded in business units or operate independently? Should they report to the CEO or to a functional leader? Should they be permanent or project-based?

Problem-solving ownership faces all the same organizational tensions, and then some. Innovation teams, whatever their flaws, at least have a recognized mandate: generate new ideas, explore new markets, develop new products. A problem-solving team has a harder pitch: slow down, question your assumptions, and make sure you’re working on the right thing before you commit resources. That’s a valuable mandate, but not a popular one.

For large corporations with strategy departments and experienced leadership teams, the ownership question is at least conceivable, even if the answer varies. Someone can be assigned. A team can be formed. Budget can be allocated.

For SMEs and nonprofits, the question is more fundamental. These organizations don’t have spare capacity to dedicate to process ownership. The people who would run a structured problem-solving effort are the same people handling operations, fundraising, client services, and everything else. There is no one left over.

Three Ways to Organize the Work

I don’t think there is one correct answer to the ownership question. But I do think there are three viable models, each suited to different organizational realities.

Model 1: The external facilitator. An outside expert—a consultant, advisor, or specialist in structured problem solving—guides the organization through the process. The organization provides subject-matter expertise: the people who live with the problem and understand its technical, operational, and human dimensions. The facilitator provides process discipline: the structure, the sequencing, the right questions at the right moments, and the resistance to premature solutioning that insiders often can’t maintain.

This model works well when the problem is high-stakes or unfamiliar, when internal biases are strong, or when the organization has never been through a structured diagnostic process before. Its limitation is cost. Hiring an external facilitator is an investment, and for budget-constrained organizations—especially the small nonprofits operating on less than $500,000 a year—it may not be feasible for every problem that arises.

Model 2: The internal problem-solving lead. One person within the organization—not necessarily full-time, but clearly designated—takes ownership of the problem-solving process. This person doesn’t need to be the subject-matter expert. In fact, it’s often better if they aren’t, because their job is to maintain process integrity, not to have the answers. They convene the right people, ensure that each stage is completed before moving to the next, and serve as the organizational conscience that asks, “Are we sure we understand the problem?”

This model is more sustainable for organizations that face recurring problems and want to build internal capability. The challenge is that it requires someone with a specific and uncommon skill set: analytical rigor, the ability to push back on senior leaders, comfort with ambiguity, and the patience to slow a process down when everyone else wants to speed it up. Finding or developing that person is not trivial.

Model 3: Guided self-service. The organization works through the problem-solving process itself, using structured tools and frameworks, with bounded external support at key checkpoints. Think of it as a middle path: the organization does the thinking, but within a scaffolding that prevents the most common mistakes—weak problem framing, unexamined assumptions, premature convergence on solutions.

This model is particularly promising for SMEs and nonprofits, because it builds internal problem-solving capability while keeping costs manageable. The external support is focused where it matters most: at the intake stage (making sure the problem is framed correctly), at the midpoint (validating the direction of root-cause analysis), and at the end (reviewing solutions and helping translate analysis into action). Between checkpoints, the organization drives the process.

Tools for the Journey

The guided self-service model depends on having the right tools—not just AI in the abstract, but structured instruments designed specifically for the problem-solving process.

This is something I’ve been working on for some time. Over the course of my consulting practice, I’ve developed two AI-assisted tools that operationalize the five-stage process described in this series. They sit on the same problem-solving arc but serve different purposes.

The first, RCFinder, is a convergence engine. It guides users through the diagnostic stages of the process: framing the problem as it is experienced, clarifying assumptions and constraints, and generating multiple root-cause hypotheses. Its purpose is to create clarity before solutions are considered—to resist the Tylenol reflex by ensuring that the problem is properly understood before anyone reaches for a fix.

The second, RCSolver, is a divergence engine. It generates diverse, actionable solution options mapped to specific causes identified by the root-cause analysis. It surfaces trade-offs, risks, and constraints. It avoids the best-practice dumping that passes for solution generation in too many advisory contexts.

Each tool can be used independently. But together, they form a complete workflow—one guided journey from understanding to action. The tools don’t replace human judgment; they structure it. They ensure that the process moves through each stage with discipline, producing documented outputs—problem statements, assumption maps, root-cause analyses, solution portfolios—that become part of the organization’s institutional memory.

I offer these tools both through a done-for-you (DFU) consulting engagement, where I manage the process and involve the client at key checkpoints, and through a guided self-service (DIY) option, where organizations receive structured access along with onboarding and expert support at critical decision points. Both formats reflect the same conviction: the process matters more than the tool, and the tool is only as good as the discipline surrounding it.

What I Don’t Know Yet

I want to be candid about what remains unresolved.

The question of who owns problem solving inside an organization is, ultimately, a question about organizational culture. You can designate a lead, hire a facilitator, or deploy the best tools available—but if the culture rewards the Tylenol reflex, if the person who says “wait, are we sure about this?” is seen as an obstacle rather than an asset, the process will be undermined regardless of how well it’s designed.

I don’t have a formula for changing that culture. What I do believe is that the process itself, practiced consistently, can trigger shifting norms over time. When an organization works through a structured diagnostic process once and discovers that the problem it thought it had wasn’t the actual problem—as my paint company clients did, as the food manufacturer did—the experience creates a kind of organizational muscle memory. The next time a problem arises, there is at least a voice in the room that says, “Last time, we assumed we knew. We were wrong. Let’s check.”

That voice, over time, is how cultures change.

Where This Goes Next

This is the final post in the “Problem First” series, and I want to close by looking forward.

Over the course of five posts, I’ve built an argument that moves from the general to the specific: why organizations are bad at problem solving, what a rigorous process looks like, who needs it most, how AI fits in, and how the work gets done. The arc has been deliberately broad because the principles apply across sectors and organizational sizes.

But as regular readers have probably noticed, my center of gravity has been shifting. With each post, I’ve found myself drawn more deeply toward one particular community: nonprofits.

This isn’t accidental. The more I research the nonprofit sector—its scale, its constraints, its extraordinary commitment alongside its structural vulnerabilities—the more convinced I become that this is where structured problem solving can make the most profound difference. 88% of nonprofits in the United States operate on less than $500,000 a year. Many have been serving their communities for decades, led by deeply experienced professionals. They don’t lack dedication or expertise. They lack the resources and infrastructure to apply that expertise systematically to the problems that matter most.

That is the gap I want to help close. My next posts will focus specifically on the nonprofit world: the unique challenges these organizations face in defining and solving problems, the cultural dynamics that make honest diagnosis so difficult, and how AI-assisted tools can be adapted to serve mission-driven organizations on mission-driven budgets.

The series title has been “Problem First.” The principle doesn’t change. The audience gets more specific. And for these organizations—the ones doing the hardest work with the fewest resources—getting the problem right isn’t a competitive advantage. It’s a moral obligation.

This concludes the “Problem First” series. Previous posts: Post 1 — Why We’re So Bad at Solving Problems. Post 2 — The Problem-Solving Manifesto. Post 3 — The Organizations That Need Problem Solving Most. Post 4 — AI as Problem-Solving Partner.

This is the fourth post in the series “Problem First: AI-Assisted Problem Solving for Organizations That Can’t Afford to Get It Wrong.”

I ended the previous post with a claim that deserves scrutiny: that the real value of AI for SMEs and nonprofits isn’t in generating answers but in improving the quality of their questions. That AI, used within a structured problem-solving process, becomes the thinking partner these organizations have never been able to afford.

It’s a bold statement. But what does it mean in practice?

This is the post where the series comes together. In Post 1, I argued that organizations are bad at solving problems because they skip problem definition. In Post 2, I laid out a five-stage process for doing it right. In Post 3, I argued that SMEs and nonprofits face a structural deficit that makes the problem-solving gap especially dangerous for them. Now the question is: how does AI fit into the process—not as a general concept, but stage by stage, as a specific working tool?

The answer, I’ve come to believe, is not the one most people would expect. AI’s equalizing power isn’t that it gives small organizations the same answers as large ones. It’s that it gives them the ability to ask better questions. And that ability, when embedded in a disciplined process, is a game changer.

The Interlocutor, Not the Oracle

The most common way organizations use AI today is as an answer machine. You give it a question, it gives you an answer. Need a market analysis? AI produces one. Need a competitive landscape? Done. Need a strategic framework? Here are three.

This is useful but limited—and, for organizations with a problem-solving deficit, potentially dangerous. If you haven’t defined your problem correctly, AI will efficiently generate sophisticated answers to the wrong question. It will do so fluently, confidently, and at very low cost. The output will look polished, even brilliant on occasions. And it will point you in the wrong direction.

The alternative is to use AI not as an oracle that delivers answers, but as an interlocutor that improves your thinking. An interlocutor pushes back. It asks clarifying questions. It surfaces assumptions you didn’t know you were making. It proposes alternative framings you hadn’t considered. It doesn’t replace your judgment; it sharpens it.

This is the mode that matters for structured problem solving. And it maps directly onto the five-stage process I described in Post 2. Let me walk through what AI-as-interlocutor looks like at each stage.

AI Across the Five Stages

Stage 1: Problem Intake and Framing. The first stage asks: what is actually happening, where it manifests, and why it matters. AI’s contribution here is not to define the problem for you—that would be precisely the premature diagnosis the process is designed to prevent. Instead, AI can help you expand the problem description before you narrow it. You describe the situation; AI asks follow-up questions that probe dimensions you may not have considered. It can suggest alternative ways to frame the same set of symptoms. It can flag when your problem statement already contains an embedded solution—“We need an AI strategy”—and prompt you to separate the symptom from the assumed remedy. The human team brings the lived experience of the problem. AI brings the discipline of not accepting the first framing as the final one.

Stage 2: Clarification, Assumptions, and Boundaries. This is where AI may add the most value for resource-constrained organizations. The central task of Stage 2 is to distinguish facts from assumptions and surface constraints. AI is exceptionally good at this—not because it knows your organization better than you do, but because it has no stake in your assumptions. It doesn’t share the institutional belief that “we can’t change the process” or that “the problem is obviously mechanical.” It can systematically ask: what evidence supports this claim? Is this a fact or a belief? What would change if this assumption turned out to be wrong? For small teams that have been living inside a problem for months or years, this external pressure-testing is invaluable. AI becomes the colleague who wasn’t in the room when the assumption was first formed—and therefore doesn’t treat it as a settled truth.

Stage 3: Root Cause Analysis. Root cause analysis requires generating multiple hypotheses across structural, process-level, human, and strategic dimensions. This is analytically demanding work that small organizations rarely have the bandwidth to do thoroughly. AI can help by systematically exploring causal pathways that a small team might not have the time or expertise to map on its own. It can propose root-cause hypotheses the team hasn’t considered, assign preliminary confidence levels, and—critically—resist the gravitational pull toward premature solutioning that derails most diagnostic efforts. AI doesn’t replace the team’s domain knowledge; it extends the team’s analytical reach.

Stage 4: Solution Generation. Once root causes are identified, AI can generate solution options that are directly mapped to those causes—varied in ambition (incremental to bold), conscious of stated constraints, and honest about trade-offs. This is where AI’s breadth of knowledge becomes genuinely useful: it can draw on approaches from adjacent industries, analogous problems, and diverse disciplines that a small team might never encounter. The key difference from typical AI use is that solution generation happens after diagnosis, not instead of it. Solutions are tethered to root causes, not floating freely.

Stage 5: From Analysis to Action. AI can support the transition from thinking to doing by stress-testing preferred solutions: probing assumptions, modeling implementation scenarios, anticipating barriers, and identifying second-order effects. It can help sequence actions by mapping dependencies and estimating resource requirements. What AI cannot do—and this is important—is assign ownership, allocate budgets, or make the political decisions that implementation requires. Stage 5 is where human leadership is most irreplaceable. AI prepares the ground; people commit to the path.

Building Tacit Knowledge, One Decision at a Time

There is a secondary benefit of using AI this way that goes beyond any single problem-solving exercise.

In the previous post, I discussed the challenge that SMEs and nonprofits face in retaining and systematizing tacit knowledge, the accumulated judgment and institutional memory that large organizations build over decades. Small organizations possess real expertise, often hard-won over years of frontline experience. But their small staff, high turnover, and constant operational pressure make it difficult to capture that knowledge in ways that survive individual departures or scale beyond individual memory.

AI-assisted problem solving creates a partial scaffold for this. When a small team uses AI to walk through scenario analysis, consequence mapping, or assumption testing, it is externalizing reasoning that would otherwise remain implicit. The process produces artifacts—documented assumptions, root cause maps, decision rationales—that persist beyond the exercise. Over time, these artifacts become a form of institutional memory: a record of how the organization thought through its most important challenges.

This doesn’t replace tacit knowledge. Nothing can substitute for the judgment of an experienced professional who has spent years understanding a community or a market. But it creates a structured way to accumulate, share, and preserve the thinking that informs decisions—so that when staff leave (as they inevitably do in small organizations), the reasoning doesn’t leave with them.

Problem First, Tool Second

Everything I’ve described above depends on one operating principle: AI enters the process after the human team has done the initial work of framing the challenge. Not before. Not instead.

This is the “problem first, tool second” rule that I’ve been advocating since the first post in this series, and it applies to AI with special force. The technology-centric trap—starting with the tool and looking for problems to apply it to—is more seductive with AI than with any previous technology, precisely because AI is so capable and so versatile. It can do so many things that the temptation to let it lead is almost irresistible.

Resist it. AI is most powerful when it enters a process that already has direction. It accelerates thinking that has already begun. It deepens the analysis that has already been framed. It pressure-tests conclusions that humans have already reached provisionally. Without that human foundation, AI generates output—sometimes impressive output—but not insight.

To revisit the paint pump story one last time: imagine the engineers had turned to AI before questioning their own assumptions. They would have described a clogging pump, and AI would have generated a dozen redesign concepts, each more sophisticated than the last. The output would have been technically excellent and entirely beside the point. The problem was never the pump. AI couldn’t have known that—but a structured process that began with assumption-testing would have surfaced it in minutes.

The Equalizer, Reconsidered

Early in my thinking about AI and innovation, I called AI “the great equalizer” for small organizations. I later questioned that claim when I recognized the paradox of explicit and tacit knowledge. Now, having worked through this series, I’ve arrived at a more nuanced position.

AI is an equalizer—but not because it gives SMEs and nonprofits the same capabilities as large corporations. It’s an equalizer because it gives them something more fundamental: a disciplined way to think through problems that they’ve never had before.

Large organizations have strategy departments, experienced leadership teams, and decades of institutional learning to draw on when they face a complex challenge. They don’t always use these resources well—as this series has documented—but they have them.

SMEs and nonprofits, for the most part, don’t. AI, embedded in a structured process, begins to close that gap. Not by replacing human judgment, but by giving small teams the scaffolding to exercise their judgment more rigorously.

This is not about making small organizations look like large ones. It’s about giving them the ability to think like well-resourced ones—to define problems with precision, diagnose root causes rather than treat symptoms, and deploy their scarce resources against the right targets.

The series is called “Problem First” for a reason. The tool matters. The discipline matters more. And for organizations that can’t afford to get it wrong, the combination of the two—a structured process, powered by AI, led by humans—is no longer a luxury. It’s the way forward.

This is the fourth post in the “Problem First” series. Previous posts: Post 1 — Why We’re So Bad at Solving Problems. Post 2 — The Problem-Solving Manifesto. Post 3 — The Organizations That Need Problem Solving Most Are the Ones Doing It Least.

This is the third post in the series “Problem First: AI-Assisted Problem Solving for Organizations That Can’t Afford to Get It Wrong.”

The first two posts in this series made a general case. Organizations are bad at defining problems. They reach for solutions before understanding what’s actually wrong. And the structured process that could prevent this, the five-stage framework from framing through root cause analysis to action, is rarely practiced with any rigor.

Everything I’ve described so far applies to organizations of all sizes. Large corporations misdiagnose problems all the time. But large corporations have something that absorbs the cost of those mistakes: scale. They have budgets that can tolerate a failed initiative. They have teams that can regroup and try again. They have institutional memory, however imperfect, that accumulates lessons from prior failures.

Small and mid-sized enterprises and nonprofits have none of these cushions. And that’s what makes the problem-solving deficit not just inconvenient for them, but existential.

The Double Bind

The situation facing SMEs and nonprofits is best understood as a double bind.

On one side, these organizations have severely limited resources for implementing solutions—any solutions. A mid-sized manufacturer doesn’t have the capital to run parallel experiments. A community nonprofit can’t afford to staff two competing programs to see which works. Every dollar, every hour of staff time, every ounce of organizational energy is committed. There is no slack in the system.

This means that when an SME or nonprofit implements the wrong solution—one that addresses a symptom rather than a root cause, or that was designed for the wrong problem entirely—the consequences are disproportionately severe. A large corporation writes off a failed pilot and moves on. A small organization may not recover. The resources spent on the wrong answer are resources that can’t be spent on the right one.

On the other side of the bind, the very resource constraints that make getting it right so critical also make it nearly impossible to invest in getting it right. Structured problem solving takes time, attention, and often external expertise. SMEs and nonprofits lack dedicated innovation departments. They lack strategy teams. They lack the budget to hire consultants who might guide a rigorous diagnostic process. Their problem solving is, as I’ve described it elsewhere, ad hoc at best.

The double bind, then, is this: the organizations that can least afford to solve the wrong problem are also the organizations least equipped to define the right one. Complication over complication.

The Nonprofit Reality

This double bind is acute across the entire SME and nonprofit world, but it hits nonprofits with particular force—for reasons that are both structural and cultural.

Start with the structural reality. According to the National Council of Nonprofits, 88% of nonprofits in the United States operate on annual budgets of less than $500,000; 92% operate on less than $1 million. Only 3-5% have budgets exceeding $5 million. When people think of the nonprofit sector, they often picture large institutions: major universities, hospital systems, and well-known national charities. But the sector is characterized by what the Council calls a “long tail”: a small number of very large organizations account for most of the revenue, while the overwhelming majority are small, community-based operations serving local neighborhoods and specific populations.

These small and very small organizations are the core of the sector in terms of sheer numbers. And they are precisely the ones operating with the thinnest staff, the tightest budgets, and the least capacity for strategic planning of any kind—let alone structured problem solving.

Now add the cultural dimension. Nonprofits operate under mission-driven pressure that creates a unique obstacle to rigorous problem definition. In a corporation, questioning the framing of a problem is uncomfortable but conceptually acceptable—it’s a business decision. In a nonprofit, questioning the framing of a problem can feel like questioning the mission itself.

Consider a homelessness-focused nonprofit that has defined its problem as “insufficient shelter capacity.” Questioning that framing—asking whether the root cause might be something other than shelter shortage, perhaps failures in mental health services, housing policy, or employment support—can feel like an act of betrayal. The organization’s identity, its fundraising narrative, and its staff’s emotional commitment are often bound up in a particular understanding of the problem. Redefining the problem threatens all of that.

The result is that the organizations serving the most vulnerable populations are often the most resistant to the kind of rigorous problem interrogation that would make their work more effective. Not because they lack compassion or dedication—but because the structure of their world makes honest diagnosis feel dangerous.

The Knowledge Paradox

There is a further complication, and it’s one I’ve been thinking about since I first wrote about AI as “the great equalizer” for smaller organizations.

The promise was straightforward: AI tools, particularly large language models, dramatically reduce the cost of domain expertise. SMEs and nonprofits can now access sophisticated analysis, strategic frameworks, market intelligence, and scenario planning that were previously available only to organizations with dedicated strategy teams. The playing field, it seemed, was finally leveling.

But there’s a paradox hidden in that promise. The knowledge that AI makes abundant and cheap is explicit knowledge—information that can be codified, documented, and transferred. Reports, analyses, frameworks, data syntheses. This is exactly what AI excels at generating. And precisely because AI makes this kind of knowledge universally available, it becomes a commodity. If your AI can produce sophisticated market analysis, so can your competitors.’Explicit knowledge, once a source of advantage, becomes a common baseline.

What remains as a differentiator is tacit knowledge, the kind embedded in experience, institutional memory, professional judgment, and organizational culture. It’s the interpretive layer that determines which AI-generated insights matter and which don’t. It’s the accumulated wisdom that knows when to act boldly and when to proceed with caution. It’s the pattern recognition that comes from years of navigating specific markets, communities, or populations.

Large organizations have this layer, however imperfectly, built up over decades. They have experienced professionals who’ve weathered multiple cycles, institutional processes refined through trial and error, and networks of expertise that span functions.

Most SMEs and nonprofits don’t. They’re gaining access to an ocean of explicit knowledge while lacking the interpretive infrastructure to use it well. They’re rich in information but poor in the capacity to systematize and retain it. And the gap between the two is precisely where problem-solving failures live.

The Risk of Automating Dysfunction

All of this converges on a risk that I find genuinely alarming: that SMEs and nonprofits will adopt AI tools enthusiastically, integrate them into existing workflows, and end up automating their dysfunction rather than solving it.

The pattern is already visible. Organizations acquire AI capabilities and immediately ask: “Which of our processes can AI improve?” The question sounds sensible. It’s the same technology-centric trap I described in the first post—but with higher stakes, because these organizations have no margin for error.

A small manufacturer deploys AI to optimize a supply chain that was designed for a market that no longer exists. A nonprofit uses AI to generate more grant proposals without questioning whether its programs are addressing the right problem. A community organization automates its outreach without asking whether the people it’s reaching are the ones who most need its services. In each case, the AI works. The process it’s applied to doesn’t.

The adoption window for AI is compressing rapidly. We are likely to have only a few years until peak adoption. SMEs and nonprofits that spend this time celebrating access to AI-generated knowledge without building the problem-solving discipline to use it wisely will find themselves in a peculiar position: technologically current and strategically adrift. They’ll look modern. They’ll perform no better.

What’s at Stake

For SMEs, what’s at stake is competitive survival. In a world where explicit knowledge is universally accessible, the organizations that thrive will be those that can define their problems with precision, diagnose root causes rather than treat symptoms, and deploy resources, including AI, against the right targets. The agility that has always been the small organization’s advantage only matters if it’s pointed in the right direction. Moving fast toward the wrong destination is worse than moving slowly toward the right one.

For nonprofits, the stakes are different and arguably higher. When a nonprofit misdiagnoses the problem it exists to solve, the consequences extend beyond the organization itself. The communities it serves don’t get the help they need. The donors who fund it don’t get the impact they were promised. And the broader public loses confidence in the sector’s ability to address society’s most pressing challenges.

Consider the arithmetic. If 92% of nonprofits operate on budgets under a million dollars, and if even a fraction of those organizations is solving the wrong problems because they lack the capacity for rigorous problem definition, the aggregate waste—in money, in staff time, in missed impact—is staggering. Not because these organizations are careless. Because the system they operate in gives them no tools, no training, and no incentive to pause and ask whether the problem they’ve defined is the problem they need to address.

A Different Kind of Equalizer

I began this series by observing that organizations are consistently bad at solving problems. In the second post, I laid out what a rigorous process looks like—five stages, each with a clear purpose and a predictable failure mode when skipped. In this post, I’ve argued that the organizations most in need of that process are the ones least likely to have it.

This is where AI re-enters the picture—but not in the way most people expect.

The real value of AI for SMEs and nonprofits isn’t in generating answers. It’s in improving the quality of questions. When used within a structured problem-solving process—not as a replacement for one—AI becomes the thinking partner that these organizations have never been able to afford. It can help surface assumptions, challenge problem framings, map root causes, and stress-test solutions before resources are committed.

That’s the subject of the final post in this series: how AI tools, deployed with discipline, can begin to close the problem-solving gap—not by giving small organizations the same answers as large ones, but by giving them the ability to ask better questions.

Next in the series: “AI as Problem-Solving Partner: Doing It Right.”