When Grant Writing Isn’t the Problem

Posted on June 10, 2026 by Eugene Ivanov

Earlier this spring, I wrote about the structural pressures bearing down on the nonprofit sector. The numbers are not comfortable. According to the State of Nonprofits 2026 report, a national survey of 380 nonprofit leaders published by the Center for Effective Philanthropy, nearly three-quarters of nonprofit CEOs report that demand for their services has increased since January 2025. Almost 60% say it has become harder to secure foundation grants. More than a third have seen reduced government funding. About 30% have cut staff.

These are not outlier findings. They describe a sector caught in a widening gap between what communities need and what organizations can provide. And the tools available to close that gap are becoming less reliable.

One of those tools is the foundation grant. And it is precisely here that I want to slow down, because I think the difficulty many nonprofits are experiencing with grant funding is not quite what it appears to be.

A Fictional Case — and a Real One

What follows is a diagnostic exercise built around a fictional organization: Harbor Bridge Family Services, a human-service nonprofit with an 18-person staff, an annual budget of approximately $2.8 million, and a 25-year record of serving low-income families, immigrants, and older adults in its community.

Harbor Bridge is not real. But the situation it represents is.

I constructed the case to reflect conditions that appear with increasing regularity in the sector: rising service demand, a development function under strain, a grant success rate in visible decline, and leadership already working beyond capacity. The State of Nonprofits 2026 data suggest that the narrative I created is not a composite of edge cases. It is damn close to reality.

My purpose in building the case was not to demonstrate that I can describe nonprofit problems accurately. It was to test whether a structured diagnosis process — the kind of rigorous, assumption-interrogating problem definition I have been writing about recently — could produce genuine insight when applied to a sector-specific situation. Not whether the process could just name the problem, but whether it could reframe it.

It could. And the reframe matters.

The Pattern Harbor Bridge Tried to Escape

Harbor Bridge’s data tells us that it has a problem with the grant application process:

YearApplicationsAwardsTotal Requested  Total Awarded
2022     14     8  $920,000     $610,000
2023     16     7$1,100,000     $575,000
2024     19     5$1,350,000     $420,000
H1 2025     11     2  $760,000     $145,000

More applications each year. Fewer awards. Smaller amounts. Harbor Bridge was, as its staff described it, running harder and falling further behind.

The organization had not been passive in response. It had tried five distinct interventions: submitted more applications, brought in an outside grant writer, added more client stories to proposals, built an internal impact dashboard, and launched a monthly donor campaign. Each response was reasonable. None of it helped much.

That pattern — multiple intelligent attempts, no sustained improvement — is one of the clearest diagnostic signals suggesting that the stated problem may not be the real one. The organization has been solving something accurately described but not accurately defined.

The Trap Inside the Data

The structured diagnosis, conducted using RCFinder — an AI-assisted tool that establishes a neutral problem statement before any analysis begins, then generates and deepens a structured hypothesis set — identified two primary root causes. They were not independent. They formed a loop.

Funding pressure and rising service demand were pushing Harbor Bridge to submit more applications. More applications consumed the time and relational bandwidth needed to qualify funders carefully, cultivate relationships, and position the organization as a genuine mission partner rather than a grant applicant. That reduced capacity produced lower-fit applications. Lower-fit applications produced lower win rates and smaller, more restricted awards. Smaller awards intensified the funding pressure, which pushed the organization to submit still more applications.

Every response Harbor Bridge had tried was operating entirely within this loop. Bringing in a grant writer improved proposal quality but did not address funder fit or relationship depth. Submitting more applications increased volume — and made the capacity problem worse. The loop was self-reinforcing, and every attempt to escape it through the obvious responses was, in effect, tightening it.

The core finding: Harbor Bridge did not have a grant-writing problem. It had a funding strategy problem — one that had been progressively hidden behind a grant-writing problem for three years.

Why This Matters Beyond Harbor Bridge

I want to be precise about what this diagnostic finding does and does not claim.

It does not claim that the external environment is irrelevant. The State of Nonprofits 2026 data is real, and the tightening of foundation grant markets is a genuine structural shift. Harbor Bridge was not imagining this difficulty.

What the diagnosis reveals, however, is that the organization’s response to external pressure can create a secondary problem that compounds the original one — and that the secondary problem, once established, can become the dominant driver of poor outcomes. Harbor Bridge’s declining win rate was not caused only by a tighter grant market. It was being actively worsened by a volume-driven response that was consuming the relational capacity on which grant-making depends.

This distinction matters because it changes what the right response looks like.

If the problem is purely external — funder priorities have shifted, the market is harder — then the appropriate response is largely strategic repositioning: find different funders, develop alternative revenue streams, adjust program offerings. These are real and necessary conversations.

But if the problem is partly internal — a self-reinforcing loop that is destroying the relational infrastructure that grants require — then the most urgent response is to stop the loop before anything else.

And stopping the loop requires first seeing it clearly.

What Diagnosis Made Possible

Once the loop was visible, the path forward became considerably more tractable. A second tool, RCSolver, generated a set of recommendations organized by implementation readiness.

The immediate priority was not to write better grants. It was to stop the capacity drain. That meant implementing a Go/No-Go qualification gate before any new application was begun — a simple filter based on funder fit, relationship status, and strategic alignment. It meant protecting existing funder relationships above all else, because renewals are won on relationship depth, not proposal quality. And it meant building a systematic process for learning from recent declines, to understand what differentiated funded applications from unfunded ones.

Over the following weeks and months, the work would shift toward restructuring the pipeline: prioritizing renewals and warm relationships over cold outreach, repositioning the organization as a funder partner rather than a grant applicant, and gradually rebuilding the relational infrastructure that had been consumed by volume-driven grant-seeking.

None of these recommendations required Harbor Bridge to become a fundamentally different organization. They required it to stop doing one thing — treating grant volume as a response to grant pressure — and start doing something else: treating funder relationships as the asset that makes grants winnable in the first place.

The Diagnostic Question Worth Asking

Earlier in this series, I wrote about the double bind facing resource-constrained nonprofits: the organizations that can least afford to solve the wrong problem are also the least equipped to invest in defining the right one. The Harbor Bridge case is, among other things, an illustration of what that bind looks like from the inside.

Harbor Bridge’s leadership knew something was wrong. They had tried to fix it, repeatedly, with reasonable tools. What they lacked was not effort, intelligence, or commitment, but a structured way to step outside the problem and ask the question that the pressure of the situation made nearly impossible to ask: Are we solving the right problem?

That question — asked before more resources are committed to the existing approach — is worth more than any number of improved proposals.

If your grant success rate is declining despite increased effort, the instinct to work harder is understandable. But before you hire the grant writer, submit more applications, or redesign your impact metrics, it is worth pausing long enough to ask: is this a grant-writing problem? Or is it something else, wearing a grant-writing problem face?

The answer will determine whether the next round of effort moves you forward — or tightens the loop.

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When Confidence Becomes the Problem: The Most Dangerous Step in Problem Solving

Posted on June 2, 2026 by Eugene Ivanov

A few years ago, a large Midwest-based paint and coatings manufacturer asked me to help them crowdsource a redesign of a new industrial pump. The new pump worked beautifully in the lab, but clogged constantly when used outdoors. The engineers were convinced they had a mechanical design problem. They wanted to put a challenge out to a crowd of external solvers to get a better pump.

Before we started to design a crowdsourcing campaign, I asked the engineers one question: were the indoor and outdoor testing conditions identical?

They weren’t. Lab testing took place in summer, with indoor temperatures often in the mid-seventies. Outdoor testing happened in autumn, with temperatures rarely exceeding sixty degrees. The paint loaded into the pump was becoming viscous with even a small drop in temperature — choking the device. The engineers fixed the problem by adjusting the paint formulation. No redesign. No crowdsourcing. No new pump.

What they were about to spend was tens of thousands of dollars on service fees and prize money, at least six months running the campaign, and additional months (and money) testing and validating new designs. The actual cost to fix the problem? Essentially nothing.

The engineers had not made an engineering mistake. They had made a diagnostic one. And it is the most common kind.

The Step Nobody Examines

Here is the move that almost derailed this project — and that derails far more projects than most organizations ever realize.

The engineers observed a failure: the pump clogged outdoors but not indoors. That observation was accurate. Their next step, however, was not an investigation. It was a classification. They decided, immediately and confidently, that they were dealing with a mechanical design problem.

That leap — from “we observe a failure” to “we know what type of failure this is” — is the most dangerous step in problem solving. It is dangerous not because it is always wrong, but because it feels indistinguishable from reasoning. It presents itself as a logical inference when it is actually an assumption.

Once that classification is made, everything that follows becomes coherent. If you have a mechanical design problem, you need better mechanical design. You bring in external solvers. You evaluate proposals. You test prototypes. Every subsequent step is rational, given the premise. The premise itself is never revisited, because it was never recognized as a premise in the first place.

This is how organizations spend months and significant resources solving the wrong problem with great discipline and intelligence.

Why Confidence Makes It Worse

There is a particular irony in the pump story. The engineers were not careless. They were experienced professionals who had designed, built, and tested the pump themselves. That expertise is precisely what made the misdiagnosis so easy to make and so hard to question.

When you know a domain deeply, pattern recognition accelerates. You have seen pump failures before. You know what mechanical problems look like. The clogging fit a familiar category, and the familiar category triggered a familiar response. Confidence was not a character flaw here — it was the natural output of genuine competence applied one step too early.

This is worth reflecting on, because it means the problem is not solved by hiring smarter engineers or more experienced consultants. It is solved by building in a deliberate pause between observation and classification — a moment where the question is not “what should we do about this?” but “what kind of problem is this, and how do we know?”

That pause is uncomfortable. It can feel like doubt, hesitation, or a lack of decisiveness. In reality, it is quite the opposite: it is the discipline to avoid committing resources to a direction before the direction has been validated.

This Is Not a Manufacturing Problem

The pump story is industrial and concrete, which makes it easy to follow. But the diagnostic trap it illustrates is not specific to manufacturing, or to engineering, or to any particular domain.

Consider an organization that has tried to fix a recurring operational problem three times, with three different interventions, none of which have helped. The instinct after the third failure is usually to look harder for a better version of the same type of solution — a different vendor, a different process, a different team. The question that is rarely asked is whether the problem type was correctly identified in the first place.

Consider a leadership team preparing to invest in AI tools because competitors are moving quickly and the pressure to act is real. The stated problem is often something like: “We need to be more efficient” or “We need to automate more of our workflow.”

Those are directions, not diagnoses. The classification — this is an automation problem, this is a technology problem — has already been made, usually before anyone has examined what is causing the inefficiency or where automation would genuinely create value versus simply accelerating a flawed process.

In both cases, the leap from symptom to assumed cause type has already happened. By the time the solution is being designed, the diagnostic window has effectively closed.

Intervention Is Simpler Than You Think

The question that stopped the crowdsourcing campaign was not sophisticated. It did not require advanced analytical tools or weeks of investigation. It required only a willingness to treat the problem classification as provisional rather than settled.

“Were the testing conditions identical?” is a question about assumptions. It surfaces a factor, temperature, that had been invisible not because it was hidden, but because it had never been looked for. The engineers were looking at the pump. Nobody was looking at the conditions around the pump.

Structured problem diagnosis does not replace domain expertise; it works alongside it. The goal is not to slow down good engineers or second-guess experienced teams. The goal is to insert a brief, rigorous examination between the moment of observation and the moment of classification — to ask, before resources are committed: “What are we assuming about the problem, and what would it take to test that assumption?”

That question, asked early enough, is worth more than almost any solution.

Before You Fund the Fix

The pump story ended well and quickly. One question, asked before the campaign launched, redirected months of effort and significant cost. Not every story ends that cleanly — sometimes the diagnostic question comes later, after some of the wrong path has already been traveled. But the principle holds regardless of timing.

The most expensive mistake an organization can make is not choosing the wrong solution. It is choosing the right solution to the wrong problem. Because the right solution to the wrong problem can be executed brilliantly, on time and on budget, and still fail to change anything that matters.

Before you fund the fix, redesign the system, or automate the workflow — make sure you’re solving the right problem.

That is not analysis paralysis. It is the precondition for genuine progress.

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A Way Through (What structured problem-solving offers the nonprofit sector)

Posted on May 23, 2026 by Eugene Ivanov

Three articles in, the picture is not a comfortable one. A sector under compounding pressure — rising demand, shrinking capacity, exhausted leadership. Three structural problems that make the crisis so difficult to escape: the double bind that tightens with every resource constraint, the mission trap that makes honest diagnosis feel dangerous, and the nature of the work itself, which resists clean solutions almost by definition. And an apparent technological rescue — AI — that, without the right foundation, risks making things faster without making them better.

If that were the whole story, there would be little reason to continue writing. But it isn’t.

There is a discipline that addresses precisely these conditions. It is not new, it is not complicated in its core logic, and it does not require resources that small nonprofits don’t have. What it requires is a different way of thinking about problems before attempting to solve them. That discipline is structured problem-solving, and this article is about why it fits the nonprofit context specifically — and what it looks like at its core.

The Fundamental Shift

Most organizations, when confronted with a problem, move quickly toward solutions. This is understandable. It feels like action. It looks like leadership. In a resource-scarce environment, where every day of inaction has a cost, the pressure to do something is almost physical in its intensity.

But moving fast toward a solution before the problem is correctly defined is not action. It is the efficient pursuit of the wrong objective. And in a sector where resources are already stretched to the limit, a well-executed initiative aimed at the wrong target is not just ineffective — it is harmful. The resources it consumes are resources that could have addressed the real problem. The time it takes is the time the people who depend on these organizations don’t have.

Structured problem-solving begins with a different question. Not “what should we do?” but “what is actually happening, and why?” It treats the presenting problem — the symptom that triggered the conversation — as a starting point for investigation, not a destination for solutions. It insists on distinguishing between what is known and what is assumed. And it recognizes that the assumptions most worth questioning are precisely the ones that feel most certain, because those are the ones that have been unquestioned the longest.

Organizations treat assumptions as facts, especially when those assumptions are long-held, widely shared, or endorsed by senior leadership. The longer an assumption goes unquestioned, the more solid it appears — and the more dangerous it becomes.

What the Process Actually Does

At its core, structured problem-solving moves through a sequence that any nonprofit, regardless of size or budget, can follow. It begins with problem definition — not the problem as it has always been described, but the problem as it actually is, examined freshly, with the willingness to discover that reality differs from the inherited narrative. This step alone is where most organizational problem-solving fails. Not because people are careless, but because the inherited narrative feels like the truth.

It then moves to root cause analysis — the disciplined pursuit of why the problem persists, rather than what to try next. Root cause analysis doesn’t ask what we should do differently. It asks why this keeps happening. The answer is rarely comfortable. It often points to something structural, something long-established, something that implicates decisions made years ago by people who are no longer around. But it is the only answer that leads to something genuinely new.

From there, the process moves to the development of options — deliberately in plural. A process that produces only one solution hasn’t explored the solution space. It has merely confirmed a prior preference. Real options come with real trade-offs, and presenting those trade-offs honestly is what allows an organization to make a genuine decision rather than ratify an assumption.

Finally, the process moves to implementation — but implementation grounded in clear problem definition and chosen from a real set of alternatives, not grabbed at in the heat of operational pressure.

Why This Fits Nonprofits

The objection is predictable: this takes time and capacity that nonprofits don’t have. It’s a fair concern, and it deserves a direct answer.

Structured problem-solving, properly adapted for small organizations, does not require a strategy department or a consulting budget. It requires protected time — not much, but some — and the intellectual honesty to ask hard questions before committing resources. Many nonprofits already do pieces of this instinctively. What they rarely do is put the pieces together in a sequence, with enough discipline to ensure that the definition of the problem is genuinely interrogated before the solution is chosen.

The adaptation for nonprofits is not about simplifying the process until it loses its rigor. It is about making the rigor accessible — building it into the rhythms of how an organization thinks about its work, rather than treating it as a special project requiring special resources.

And this is precisely where AI, used correctly, becomes genuinely valuable: not as a replacement for thinking, but as a tool that makes the thinking faster, more thorough, and more affordable than it has ever been before.

An organization that knows what problem it is solving, and why, and has chosen its approach from a real set of options — that organization can use AI to remarkable effect. One that hasn’t done that work first will use AI to produce sophisticated answers to wrong questions instead.

Discipline comes first. Everything else follows from it.

What Comes Next

This series will spend the next several articles making this concrete. Not in the abstract, but in the specific context of the nonprofit world — its cultural dynamics, its resource constraints, its relationship with problems that are messes rather than puzzles. There are aspects of this process that require adaptation for mission-driven organizations, and those adaptations are worth examining carefully.

The goal is not to import a corporate framework into a world where it doesn’t fit. The goal is to offer something that has been largely unavailable to this sector: a rigorous, honest, practical approach to the most important question any organization can ask. Not what should we do next — but what is actually going on, and what would genuinely help.

The people this sector serves deserve organizations that ask that question. And ask it well.

This is the fourth and last article in a series on nonprofits, problem-solving, and what it takes to help organizations become more effective at achieving their missions.

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The AI Paradox (Why the most powerful tool in the room isn’t enough)

Posted on May 19, 2026 by Eugene Ivanov

There is a moment in many conversations about the nonprofit crisis when someone says, “But what about AI?”

The question is reasonable. Technology is real, the capabilities are growing fast, and the appeal to resource-stretched organizations is obvious. If AI can write grant proposals, summarize research, handle administrative tasks, and analyze program data — all at a fraction of traditional cost — then surely it offers some relief to a sector that is under pressure.

It does. And it doesn’t. The distinction matters enormously, and it is worth being precise about it.

The State of Nonprofits 2026 report surveyed 380 nonprofit leaders across the country and found that one in five is currently considering using AI tools to reduce operational costs. That is not surprising. When funding is tightening, staffing is strained, and burnout is accelerating — the same report found that nearly half of nonprofit CEOs describe their own burnout as a serious personal concern, up from under a third just a year ago — any tool that promises efficiency is going to attract attention.

The question is not whether nonprofits should use AI. Many already do, and most of them will. The question is what they are using it for, and whether that use is making them more effective or simply more efficient at what they were already doing.

Those are not the same thing.

The Efficiency Trap

AI is extraordinarily good at a specific category of tasks: processing, generating, and organizing explicit knowledge. It can draft documents, surface patterns in data, synthesize research, answer questions, and accelerate workflows. Applied to the right problems, it is genuinely transformative. Applied to the wrong ones, it produces something more insidious than failure. It produces the appearance of progress.

Consider two scenarios that are playing out in the sector right now. A nonprofit uses AI to generate more grant proposals — faster, better written, more professionally formatted than anything its small staff could produce alone. The proposals go out. Some are funded. The organization grows. But no one has paused to ask whether its programs are addressing the root causes of the problem it exists to solve, or whether years of accumulated assumptions about who needs help and why have quietly hardened into unexamined certainties. The AI has accelerated the operation. It has not improved the diagnosis.

Or consider a community organization that uses AI to automate and scale its client outreach — more contacts, faster response times, broader reach. The metrics look good. But the people being reached most efficiently are not necessarily the people most in need of services. The algorithm optimizes for what it can measure. What it cannot measure — the neighbor who doesn’t respond to digital outreach, the family that doesn’t know help exists, the community that distrusts institutions — becomes invisible precisely because the tool is working so well.

In both cases, the AI works. The process it is applied to doesn’t. This is what automating dysfunction looks like. It is not dramatic. It doesn’t announce itself. It simply makes organizations faster and more productive at pursuing objectives that were never rigorously defined in the first place.

If you haven’t defined your problem correctly, AI will efficiently generate sophisticated answers to the wrong question.

The Knowledge Gap

There is a second, less obvious reason why AI underdelivers in the nonprofit context, and it has to do with the nature of the knowledge these organizations possess.

AI operates on explicit knowledge — information that has been articulated, documented, and made available in a form the system can process. What it cannot access, by definition, is tacit knowledge: the hard-won expertise that lives in the heads of experienced staff, accumulated through years of frontline work, refined through thousands of interactions with clients and communities. This knowledge — who shows up and why, what language lands and what doesn’t, which interventions work in this neighborhood with this population under these conditions — is often the most valuable thing a nonprofit has. It is also almost entirely uncodified.

Most nonprofits don’t have a solid body of documented institutional knowledge. High turnover, constant operational pressure, and minimal administrative capacity mean that expertise walks out the door with every staff departure and has to be rebuilt from scratch. AI cannot compensate for this gap — it can only work with what has been made explicit. And in many cases, what has been made explicit is the surface of the organization’s knowledge, not its depth.

This gap — between the ocean of AI-accessible information now available to nonprofits and the interpretive infrastructure required to use it well — is precisely where problem-solving failures live.

What AI Actually Offers

None of this is an argument against using AI. It is an argument for using it correctly, which means using it within a structured process rather than as a substitute for one.

The real value of AI for nonprofits is not in generating answers. It is in improving the quality of questions. When deployed inside a rigorous problem-solving process — one that begins with honest diagnosis, interrogates assumptions, and defines the problem before designing solutions — AI becomes something these organizations have rarely been able to afford: a thinking partner.

It can help surface assumptions that have gone unexamined. It can challenge problem framing. It can map potential root causes and stress-test proposed solutions before resources are committed. It can do in hours what would otherwise take weeks, if the process directing it is sound.

Without that process, it does something else entirely. It accelerates. It scales. It makes organizations look modern while leaving the underlying dysfunction untouched.

Looking modern but performing no better is not progress. It is a more expensive version of standing still.

The sector is under enormous pressure, and the temptation to reach for powerful tools is completely understandable. But the organizations that will navigate this moment most effectively will not be the ones that adopt AI fastest. They will be the ones that define their problems most clearly — and then use every tool available, including AI, in the service of that clarity.

That is what the next part of this series is about.

This is the third article in a series on nonprofits, problem-solving, and what it takes to help organizations become more effective at achieving their missions.

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Three Problems Hiding Inside One Crisis (Why good diagnosis matters more than ever)

Posted on May 15, 2026 by Eugene Ivanov

The previous article drew on a Massachusetts white paper describing a workforce crisis in the human services sector: vacancy rates four to six times the state average, wages running $17,000 below the median, capacity shrinking as demand rises. A legitimate question follows: Is Massachusetts an outlier? Is this a regional story?

The answer, according to a separate national survey published this spring by the Center for Effective Philanthropy, is no. The State of Nonprofits 2026 report surveyed 380 nonprofit leaders across the United States and found a sector under compounding pressure on every front simultaneously. Nearly three-quarters of nonprofit CEOs report that demand for their services has increased since January 2025. Almost 60% say it has become harder to secure foundation grants. More than a third have seen reduced funding from federal, state, or local government sources. About 30% have reduced their staff size, most by more than 10%.

And then there is the human toll at the leadership level, which the report documents with unusual candor. The proportion of nonprofit CEOs who describe their own burnout as “very much a concern” jumped from 29% in 2025 to 46% in 2026 — in a single year. One CEO, quoted in the report, describes the situation with a precision that no statistic can match: “When funding revenue is insufficient or insecure, the staff feels it every day. They are the ones who must look into the eyes of someone seeking help and tell them that we cannot provide services for them. It is demoralizing.”

Two reports, two methodologies, two geographies. The same picture. Rising need, shrinking capacity, exhausted people doing essential work with inadequate tools.

This is the landscape. The question this series is asking is not whether the crisis is real — it clearly is — but why it persists so stubbornly, and what it would take to address it. That requires looking beneath the surface at the structural problems that make the crisis so difficult to escape. There are three of them, and they are worth examining carefully.

The Double Bind

The first is a trap with no obvious exit.

Nonprofits operate with severely limited resources — not as a temporary condition, but as the permanent architecture of their existence. Every dollar is committed. Every hour of staff time is taken. There is no slack in the system: no reserve capacity set aside for thinking, no innovation budget, no strategy team.

When an organization is running this lean, the consequences of misallocating resources are not merely inconvenient. They can be fatal. A small nonprofit that spends a year implementing the wrong solution — one aimed at a symptom rather than a root cause, or designed for a problem that was never correctly defined — may not recover. The resources spent on the wrong answer are resources that cannot be spent on the right one.

This is already painful. What makes it a bind is the other side of it. The very resource constraints that make it so critical to get things right also make it nearly impossible to invest in getting things right. Structured problem-solving — the kind of rigorous diagnostic work that distinguishes root causes from symptoms, that interrogates assumptions before committing resources — takes time, attention, and often outside expertise. Large organizations have strategy departments for this. Nonprofits, particularly small ones, don’t.

The organizations that can least afford to solve the wrong problem are also the organizations least equipped to define the right one. That is the bind. And the tighter the resource constraints become — as the State of Nonprofits 2026 data suggest they are — the tighter the bind becomes.

The Mission Trap

The second structural problem is more delicate, and it requires careful framing.

In a corporation, questioning the definition of a problem is uncomfortable, but acceptable in principle. It is a strategic conversation.

In a nonprofit, the same act can feel entirely different. The organization’s identity, its fundraising narrative, its board’s commitments, and its staff’s emotional investment are often bound up in a particular understanding of the problem it exists to solve. Questioning that understanding doesn’t feel like a strategy. It feels like a challenge to the mission itself.

This is not irrationality. It is, in fact, a completely rational response to the situation. When your ability to raise funds, retain staff, and maintain community trust depends on a coherent and stable story about what you do and why, destabilizing that story carries real costs. Leaders who protect that narrative are not being defensive — they are managing genuine risk.

But here is the quiet difficulty at the center of this: the very protection of the mission can become the thing that puts the mission at risk. When the problem definition goes unquestioned for long enough, it stops feeling like a definition and starts feeling like a fact. Assumptions harden into certainties. The question “are we solving the right problem?” becomes not just uncomfortable, but unaskable. And organizations continue to deliver services that may be well-executed, compassionately delivered, and aimed, very precisely, at the wrong target.

Puzzles and Messes

The third structural problem adds a final layer of difficulty, and it has to do with the nature of the problems themselves.

There is a useful distinction in problem-solving between what I’d call puzzles and messes. A puzzle is a problem that is well-defined, bounded, and solvable with the right method. A mess is something else entirely — a tangle of interacting conditions, shifting variables, and human behaviors that resist clean definition and rarely yield to any single intervention. Messes don’t get solved. At best, they get managed, improved, or navigated.

The problems nonprofits work on are almost always messes. Poverty, addiction, family dysfunction, mental illness — these are not puzzles. They don’t have solutions in the way that engineering problems have solutions. They require ongoing, adaptive responses that learn and adjust over time. They require honest diagnosis, not just determined effort.

The cruel irony is this: messes demand more rigorous problem-definition than puzzles do, precisely because they are so resistant to quick answers. The temptation is always to move fast, try something, and adjust later. But with limited resources and high stakes, moving fast toward the wrong destination is worse than moving slowly toward the right one.

Taken together, these three problems — the bind, the trap, and the nature of the work itself — explain a great deal about why capable, dedicated people working in well-intentioned organizations so often find themselves running hard and falling behind. It is not a failure of commitment. It is a failure of diagnosis. And diagnosis, it turns out, is precisely what the current moment makes most difficult.

In the next article, we will look at the obvious modern answer to all of this — and why, applied without care, it makes things worse rather than better.

This is the second article in a series on nonprofits, problem-solving, and what it takes to help organizations become more effective at achieving their missions.

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A Sector Under Pressure (On nonprofits, rising need, and a widening gap)

Posted on May 12, 2026 by Eugene Ivanov

In April 2026, the Providers’ Council, a Massachusetts human services membership association, and the UMass Donahue Institute published a white paper. It is titled Stretched to Capacity: The Workforce Crisis in Human Services. The title is not a metaphor. It is a diagnosis.

The report focuses on Massachusetts, but its findings describe a condition that is national in character. The numbers are worth reflecting upon for a moment before we move on to anything else.

As of January 2026, one in six client-facing positions in Massachusetts human services organizations was unfilled. Clinicians with independent licenses — the people most needed for mental health, substance abuse, and behavioral care — carried a 22% full-time vacancy rate. Direct support professionals, the frontline workers who assist people with disabilities in their daily lives, had a vacancy rate of 18% to 19%. The case and social workers were running at 18% full-time and 23% part-time vacancy.

To put those numbers in context: the statewide job-opening rate across all Massachusetts industries was 3.3% at the end of 2025. Human services organizations are operating with vacancy rates four to six times the broader economy — not as a temporary disruption, but as a persistent structural condition.

The wages explain part of it. The median income of a human services worker in Massachusetts runs $17,000 below the state median. And these are not simple jobs. They require emotional resilience, clinical training in many cases, and a tolerance for conditions that exhaust people quickly. The sector is asking a great deal and paying considerably less than comparable work elsewhere. State agencies — the Departments of Developmental Services, Mental Health, Children and Families — offer higher wages and defined career ladders. Community-based nonprofits cannot compete on those terms — and they don’t; they lose people instead.

And then there is the immigration dimension, which the Stretched to Capacity report documents with unusual candor. Foreign-born workers make up 24% of the human services workforce overall, and a significantly higher share in direct support roles: 35% of home health and personal care aides.

In 2025 alone, three Massachusetts providers collectively lost 205 staff members due to changes in work authorization. One-third of surveyed providers reported being greatly or somewhat affected by federal immigration and work-authorization policy changes. The consequences run in two directions simultaneously: organizations lose staff, and clients — many of them immigrants themselves — withdraw from services out of fear. The sector’s capacity contracts on both ends.

The overall picture the report paints is not of a system straining under temporary pressure. It is of a system that has been operating past its structural limits for years and is now confronting the compounding effects of workforce losses, demographic change, pandemic-era burnout, and policy turbulence all at once. More service delivery sites are opening — providers are responding to rising community need — but employment is falling at the same time. The lines are moving in opposite directions, and the gap between them is filled with underserved need.

What Numbers Say

This is the context in which nonprofit organizations do their work. And it is worth pausing here to describe what most of those organizations look like in real life, because the public image of the nonprofit sector tends to be shaped by its most visible exceptions.

According to the National Council of Nonprofits, the largest network of nonprofits in North America, 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% to 5% have budgets exceeding $5 million.

The large, professionally staffed, nationally recognized organizations are real, but they are not representative. The sector is overwhelmingly composed of small and very small organizations, most of them deeply embedded in specific communities, most of them running on thin margins with minimal administrative infrastructure.

These are the organizations absorbing the workforce crisis described above. They don’t have HR departments to manage the vacancy problem. They don’t have strategy teams to think through the structural issues. They don’t have the budget to hire consultants. They have people — usually not enough of them — working hard to keep programs running for the populations that depend on them.

This is not a critique; it is a description. The people who work for and lead these organizations are, in most cases, doing remarkable work under genuinely difficult conditions. My point is that the resources available to the sector, especially at the small-organization level, are not commensurate with the problems it is being asked to solve. The gap between mission and capacity is not a management failure. It is a structural feature of the landscape.

The Question We Need to Answer

None of this would matter quite as much if the problems nonprofits were trying to solve were simple ones. But they aren’t. They involve human behavior, systemic poverty, mental illness, housing instability, addiction, and family breakdown — conditions that resist clean solutions and rarely yield to any single intervention. The sector is navigating extraordinarily complex territory with extraordinarily limited tools.

This gap — between the complexity of the problems and the resources available to address them — is where things get genuinely difficult. Not just operationally difficult, but intellectually difficult. When an organization is stretched to capacity just keeping services running, it has very little left over for the harder and more important work of asking whether those services are addressing the right problems in the right ways.

That question — whether resources, already scarce, are being aimed at the right targets — is what this series will explore. It is not an abstract question. It has real consequences for real people.

In the next article, we will look at three structural problems that make it so difficult for nonprofits to answer the above question well. The first is a bind that tightens the more you try to escape it.

This is the first article in a series on nonprofits, problem-solving, and what it takes to help organizations become more effective at achieving their missions.

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In Defence of AI

Posted on April 30, 2026 by Eugene Ivanov

There is a well-known framework for understanding how people respond to loss. Elisabeth Kübler-Ross identified five stages of grief: denial, anger, bargaining, depression, and acceptance. It was meant to describe how individuals cope with death and dying. It turns out it also describes, with uncomfortable precision, how societies respond to disruptive technology.

With artificial intelligence, we quickly moved through the denial stage. For a brief moment, the consensus was that AI was a clever parlor trick — impressive at chess, useless for anything that actually mattered. That window closed fast. Now we are firmly in the anger stage, and by all appearances, we intend to stay there.

The anger takes two forms, and it is worth distinguishing between them.

The first is trivial, though it consumes a disproportionate amount of energy. It is the sport of finding what AI cannot do. When an AI system miscounts the letter “r” in “strawberry,” or produces a garbled map of Europe, a certain kind of observer erupts in applause. These failures are real. They are also beside the point. No one cancelled the space program because early rockets exploded on the launchpad. No one concluded that surgery was a failed experiment because early anaesthesia killed patients. Every powerful technology is, in its early stages, also a failing one. The correct response to an imperfect tool is to improve it — or, in the meantime, to understand its limitations and work around them. It is not to declare the entire enterprise fraudulent.

The second form of anger is more legitimate and deserves a more serious response. The concerns about AI’s societal impact — large-scale displacement of workers, erosion of cybersecurity, the amplification of disinformation — are not invented. They are real risks, and anyone who dismisses them entirely is not paying attention. But here, too, perspective matters.

Consider what we chose to do with nuclear technology before we learned to use it for clean energy and medicine: we dropped two bombs on civilian populations, killing hundreds of thousands of people. Consider recombinant DNA technology, which gave us insulin, cancer therapies, and vaccines for diseases that once killed millions — but not before it was weaponized to produce more lethal biological agents. Consider the internet, which has done more to advance commerce, education, and human connection than almost any invention in history — and which was also, from its earliest days, a vector for fraud, hate speech, and the organized manipulation of public opinion.

In each case, society did not conclude that the technology was irredeemable. In each case, we developed — imperfectly, slowly, often reactively — the regulatory frameworks, cultural norms, and institutional safeguards needed to contain the worst and amplify the best. We will need to do the same with AI. That work is urgent, and it’s doable. But it is not an argument against AI. It is an argument for taking it seriously.

So why does AI attract a quality of suspicion that we did not, in hindsight, extend to nuclear fission or genetic engineering? Partly, perhaps, because this technology feels more intimate. It does not require a reactor or a laboratory. It sits on a laptop. It writes, reasons, and converses. It mimics — sometimes uncannily — what we have always thought of as distinctly human. That proximity is unsettling in ways that a uranium centrifuge is not.

But unsettling is not the same as dangerous, and dangerous is not the same as irredeemable.

The Kübler-Ross framework ends, eventually, in acceptance — not passive resignation, but the kind of clear-eyed reckoning that makes constructive action possible. We are not there yet with AI. But the path there does not run through a catalogue of its failures. It runs through an honest accounting of what it can already do, a rigorous effort to address its risks, and the intellectual honesty to tell the difference between a tool that is imperfect and one that is unwelcome.

Those are not the same thing. And until we stop confusing them, we are mostly just grieving.

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From Process to Practice: Who Runs It and How

Posted on April 27, 2026 by Eugene Ivanov

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.

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AI as Problem-Solving Partner: Doing It Right

Posted on April 22, 2026 by Eugene Ivanov

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.

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The Organizations That Need Problem Solving Most Are the Ones Doing It Least

Posted on April 13, 2026 by Eugene Ivanov

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.”

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