The very first course I took as an undergrad turned out to be the most relevant to my career—and I dropped it after one class.

I, like many 18-year-olds, didn’t have a clue what I wanted to do as I entered college. When considering my course of study I did the seemingly rational thing—I took stock of what I enjoyed (read: was good at) and determined what could lead to the best opportunities (read: most lucrative) after graduation. For me, that analysis resulted in studying engineering.

The first classroom I entered, however, was not an engineering class. It was the one non-core elective I was able to squeeze into my freshman year, a Classics course. The first day of class the professor distributed a one-page essay titled “The Useless: What’s the Point?”, an essay on the purpose of a liberal arts education. The essay glorified the concept of understanding, which it defined as seeking to know the great many things in the larger world, a quest with no end that liberated us from the immediate and practical. It contrasted understanding with critical thinking, calling it a “faddish designation for a practical, marketable sort of intellectual activity,” critiquing it for stopping once its purpose, a solution, is achieved. The thesis, in short, was that inquiry matters more than the answer it produces.

(Unfortunately, the professor rather aggressively contrasted the aspiring humanists it expected to be present with the aspiring engineer it didn’t. Feeling out of place, I dropped the class the next day.)

Little did I know that this tension—between questions and answers—would follow me throughout my career. As a young management consultant, I observed large organizations implement policies and procedures that directed their people toward consistent, reliable answers, enabling incredible scale. Years later, as a senior advisor tasked with defining innovative growth strategies, I watched incredibly successful leaders struggle to think expansively about their own businesses—not for lack of intelligence, but because the organizations they led had spent years training inquiry out of them. And in my own role scaling a creative agency through an acquisition, I found a direct tradeoff between growing the business and preserving the inquisitiveness that made it worth acquiring in the first place.

Then, AI arrived and laid the quandary out in plain sight.

Suddenly, there was a technology that could provide answers to nearly anything. Businesses salivated—AI could be fed data and conduct the analytical thinking their organizations run on. Workflows could be automated. Outputs could be generated. Even decisions could be scaled. As for the role of the knowledge workers who currently do this analytical thinking, their future remains up in the air as AI capabilities continue to impress us with each release.

While the adoption of AI at scale by enterprises is new, the organizational dilemma at the heart of this moment is not. As the world has become more unstable and unpredictable over the past few decades, it has required companies to investigate how to make their organizations more adaptive. Most have been unsuccessful because it requires organizational changes that run contrary to how they are successful today.

But I believe AI has now forced their hand. Now that analytical thinking has been commoditized, differentiation can only be found in what analytical thinking can’t do. Organizations, and the people within them, that have operated on answers for decades now need to operate on questions.

The bad news is that this is a much bigger transformation than most are currently considering. It doesn’t simply boil down to AI literacy, which many organizations currently believe. Shifting from answers to questions has implications across the board—how the organization is structured, the people that it employs, and the culture that it promotes. The good news is that the many failed experiments from the past few decades have shown us where the points of failure are. If we can correct for those, we can finally achieve that elusive adaptive organization everyone has been striving for.

Which raises the questions: How do these “Answer Organizations” operate on answers today, and why will AI change that? What are these points of failure, and how can they be addressed? And finally, what does a “Question Organization” even look like?

Let’s discuss.

How Organizations Run on Answers

The evolution of businesses from startup to scaled incumbent follows the same arc so reliably it barely needs explaining. A company is born by some creative, or often accidental, act. To grow, it defines what makes it successful so it can be repeated again and again. To manage its size, it sets rigid structures to ensure consistency and therefore continued success. This works for as long as the conditions that created the success remain stable. But, inevitably, when the world around them changes they struggle to adapt against the very rigidity they built to scale.

Over the last several decades, the pace of change has accelerated. Recognizing this, businesses have been on a journey to become more adaptive. Their goal is to continue performing at their core, scaled business, while simultaneously transforming to be relevant in the future. You would think, given the existential nature of the challenge and the decades spent trying, that businesses would have figured this out by now. But while they’ve certainly tried, they largely haven’t—because it’s exceptionally difficult.

Roger Martin explains why in The Design of Business. Martin describes a “knowledge funnel” in which new knowledge starts as a mystery, evolves to a heuristic, or rule of thumb that narrows the field of inquiry to something manageable, and is codified as an algorithm. He argues once businesses define an algorithm, they exploit it in order to scale and become fixated on reliability to consistently reproduce it. Inventing new businesses, or adapting existing ones, requires the opposite: exploration at the top of the funnel and a search for validity rather than reliability.

Businesses in the algorithm stage run on analytical thinking, because analytical thinking works from what is already known. Whether through deductive reasoning—applying established rules to specific cases—or inductive reasoning—drawing conclusions from observed data—both are backward-looking. This output is reliable because it’s based in data that is known, perfect for optimizing a set algorithm with a documented track record.

Put a different way, scaled businesses run on answers.

While Martin’s dynamic is generally understood, likely well before he was published in 2009, I believe businesses underestimate how deeply ingrained running on answers is. Martin’s examples alone span structures, processes, and culture. He points out that permanent jobs and ongoing tasks are products of running an algorithm reliably; financial planning is a fundamentally reliability-driven process that sets targets based on data from the past; reward systems favor larger revenue and bigger operations—won by optimizing the proven algorithm, not discovering a new one; and cultures see constraints as enemies interrupting reliability rather than opportunities to create something new. Martin also warns of the preponderance of analytical thinking ingrained from professional degrees (including MBAs), the reliability orientation of Wall Street analysts and boards of directors, and the challenges of defending validity in a world obsessed with reliability.

I spent a decade as a part of an “innovation agency” whose remit was to fight against businesses’ natural inclination to get stuck in the algorithm stage. The very existence of agencies like mine is proof of organizations both acknowledging their need to adapt and struggling to do so independently. Given my experience, I think Martin undersells the difficulty of overcoming an answer bias. Throughout the 2010s, I worked with large businesses to develop new innovation structures, cultures, and products—all starting at the top of the knowledge funnel.

Time and again we ran into issues. New structures collapsed as budget was reallocated to core business endeavors that offered a higher return on investment. New cultures clashed with a system that continued to reward those that scaled the algorithm over those that attempted to discover new knowledge. And new innovations themselves were modified to fit the infrastructure that already existed, no matter what it did to the desirability of the final product.

Not only did I see these adaptation challenges in the clients I worked with, but I also experienced them directly when my small, privately owned innovation agency was acquired by a large, publicly traded management consultancy to grow its presence in a new market. While we were a strategic acquisition whose revenue was a rounding error to our acquirer, we were still required to grow at the same percentage as the rest of the company and received a budget based on our size and headcount rather than our objectives and goals. The infrastructure built for our business was replaced by that designed for an organization 4,000 times our size (because…“synergies”). The incentives our people were charged with promoted behavior that ran contrary to why we were acquired.

Even though the intention of our acquirer was to get into a new market, it operated an Answer Organization that prevented it from adapting. The inappropriate growth demands, underfunding due to our relatively small size, infrastructure that did not serve our business, and incentives running counter to our goals were the result of their algorithms intent on optimizing the core business. It wasn’t irrational thinking driving these decisions—it was sound analytical thinking. I knew that every time I pushed in a direction that ran contrary to the algorithm, there was a rational, data-backed answer pushing back. And where there wasn’t data, there was advice: when I raised my hand to start a new venture with the foresight of how AI would disrupt consulting, my advisor discouraged me—starting something new at a company like this was “career suicide.”

The incumbency trap is so challenging to avoid not because organizations are irrational, but because they are entirely rational—built on analytical thinking that supplies solid, data-backed answers. Of course, there is a role for analytical thinking in organizations. Martin argues the key is balancing exploitation of the algorithm with exploration up the knowledge funnel—performing at the core while transforming for the future. But as someone whose job it was to make sure the two coexisted in organizations, I found this nearly impossible because they run so contrary to each other. To make room for exploration you need to be suboptimal at exploitation—and in an Answer Organization that simply cannot be.

But I believe AI will force a reckoning.

How AI Has Changed the Equation

Most knowledge workers are going through an existential crisis right now—and for good reason. They likely work for an Answer Organization, where analytical thinking dominates their tasks. They’ve prided themselves on analyzing data and developing an answer to either implement directly or report up the chain. Their degrees and experience credential them for exactly this kind of analytical work, at their current organization or any other. But they’ve witnessed how excellent AI is at doing the very same analytical thinking and getting exponentially better with each model release.

But what happens when AI is successfully implemented by all organizations? How will companies differentiate themselves if everyone can execute their algorithm perfectly? And what happened to the desire to be more adaptive as an organization in the face of accelerating change? Has change suddenly slowed?

When AI can deliver analytical thinking at scale, the ability to exploit your algorithm reliably becomes a commodity. Suddenly, differentiation moves up the knowledge funnel to those organizations that can validate new algorithms. As I’ve discussed, this need to be more adaptive is not new for businesses. But AI has changed the equation in two ways: it has made reliable performance easier to achieve, and it has made transformation no longer optional.

The initial rollout of AI has been rocky, but the difficulties are largely those of implementation, and, frankly, should be expected. The technology, however, has been proven more than capable of doing the analytical thinking for Answer Organizations. People are also largely embracing it, including in the “shadows” where corporate mandates do not exist.

What is more interesting to me is how AI is already starting to conflict with Answer Organizations.

In early 2025 I was discussing AI implementation with a client in the media and entertainment industry. He mentioned that the most senior people in the company were getting in the way of AI transformation. I asked why—surely senior leaders saw the potential of the technology and wanted to be at the forefront of reaping the benefits. The client chuckled at my political naïveté: implementing AI would mean a reduction in their organization’s headcount, which would correspond to a reduction in budget, and therefore a reduction in power. In other words, if a CMO automated their marketing organization, they would at best undercut their next salary negotiation, and at worst risk their own job security.

While this doesn’t immediately seem to be a dynamic rooted in being an Answer Organization, it very much is. Those who preside over the largest operations, which usually corresponds to the largest headcount and most budget, reap the largest rewards in Answer Organizations. The surest path to that position is overseeing further exploitation of the algorithm, reliably driving growth and meeting forecasts. (This dynamic is also why my advisor at our acquirer thought starting something new would be career suicide.)

This particular issue is not unsolvable, but it does illustrate how deep the Answer Organization paradigm runs. In a future where answers are commoditized, the changes organizations need to make to differentiate themselves are more structural than initially meets the eye.

AI is also exposing a problem at the individual level of Answer Organizations: the inability of employees to ask the right questions.

That problem has a name: “workslop.” Workslop is AI generated work content that lacks the substance to meaningfully advance a given task. While further research adequately outlines the causes of workslop and how to stop it, fixing workslop isn’t what interests me. Simply getting back to “good work” would be a waste of a good crisis.

AI, a superhuman ability to provide answers, is killing productivity in organizations that run on answers. Why? It isn’t the fault of the technology, which is more than capable of delivering quality work. It isn’t a lack of care, as these same employees delivered good work before AI was available. It’s user error. Good AI collaboration requires context, active challenge, and redirection toward the right outcomes—all of which require the ability to ask the right questions.

It has been widely published that “soft skills” are becoming more important, as the half-life of technical skills is expected to soon fall to two years. To date these arguments have been acknowledged and largely ignored—partly because the skills they preach can be challenging to define and teach, but predominantly because they are deprioritized in a world dominated by analytical thinking. The ability to ask questions has atrophied, and organizations have never needed to fix the problem.

In 2009 Martin described a battle between labor and companies that is playing out quite literally today. He noted that out of sheer self-interest, talent keeps their “heuristic shrouded in priestly secrecy” to prevent it from becoming an algorithm that can be handed to a much less expensive person. Today, AI is that less expensive person—capable of running not just the algorithm, but the heuristic too. Hiding a heuristic is no longer an option. The only move is up the funnel.

The path forward for knowledge workers and their organizations is the same. They need to move up the knowledge funnel to generate the new understanding that will differentiate the business. Not because it would be nice to be more adaptive, but because survival in the future now depends on it.

It’s time for Answer Organizations to become Question Organizations.

The Points of Failure in Building a Question Organization

For the longest time I’ve been tortured by trying to explain what is meant by critical thinking while attempting to avoid using the term critical thinking. Everyone has their own definition and their own critique, including that the term is too abstract to be useful.

In search of better language, I turned to David Hitchcock’s Critical Thinking entry in The Stanford Encyclopedia of Philosophy. Helpful, in part, because it confirmed to me that battling over nuances in the definition and similar terms is a complete red herring. Problem solving, higher-order thinking, creative thinking—the through line is the same: careful thinking directed to a goal.

Let’s call it critical thinking, and move on.

While Hitchcock’s focus is education, his work translates directly to business. He describes the origins of critical thinking from philosophers who preached a “scientific attitude of mind.” Terms like “observing,” “experimenting,” and “deciding” are used to describe key components of critical thinking—all of which are common in business contexts. He tangibly defines the seemingly abstract “soft skill,” and cites hard evidence that it can be taught. Most importantly, it lands the value of critical thinking: the means for people to understand.

The objective of organizations built on questions is to move up the knowledge funnel and understand the mysteries of the world for the purpose of adapting their businesses. With understanding as the goal, critical thinking is the cornerstone to what will get them there.

Well, there you go. Organizations need to move up the knowledge funnel to understand the world around them and adapt accordingly; critical thinking enables that understanding; and critical thinking can be both defined and taught. Let’s spin up a couple corporate bootcamps on critical thinking and call it a day!

Right?

If only it were that easy. Advocating for critical thinking in business is not new. I have never found a business leader who has disagreed that critical thinking is a valuable skill. Arguments for critical thinking are only growing louder as people think about the effect of AI on the human workforce. I’ve seen more demand for critical thinking firsthand—clients increasingly asking how to make their workforces more “future-ready,” “strategic,” or “creative.”

The problem isn’t advocacy or demand—it’s that organizations are still built on answers. Answer Organizations fail to enable critical thinking in three ways: they undermine the dispositions needed, they “teach” the skills incorrectly, and they ignore the knowledge required.

Undermining the Disposition

The biggest point of failure for critical thinking in Answer Organizations is how they relentlessly undermine the disposition required.

Hitchcock describes dispositions as “habits of the mind,” or general tendencies to think in particular ways in particular circumstances. There are initiating dispositions that start someone down a path of critical thought, such as habit of inquiry, courage, and willingness to suspend judgement, and internal dispositions that contribute to critical thought once it has started, such as honesty in facing one’s own biases, intellectual perseverance and humility, and anticipating possible consequences. I’ve often thought of dispositions as behaviors—for example, you can’t discover new knowledge without the behavior of curiosity.

Answer Organizations suppress a critical thinking disposition in so many tangible ways I fear I’ll fail to list them all. In this essay alone we’ve touched on permanent job structures that focus people on operating a small slice of the algorithm in a predetermined way; reward systems that make habits of inquiry career suicide; and analysts and boards that require laser focus on delivering the core business as projected, without wasting money and time on exploration boondoggles. All of these are examples of the downstream effects of Answer Organizations that directly discourage a critical thinking disposition.

Instead of attempting to be comprehensive in listing the downstream effects, I’ll instead focus on the source: the answer.

The goal of work in Answer Organizations is to develop reliable answers as efficiently as possible. This runs directly contrary to the dispositions required to think critically. Habits of inquiry and a willingness to suspend judgment take indefinite time and indefinite budget. Facing one’s own biases and anticipating possible consequences lend themselves to unreliable conclusions. The answer, the very basis of Answer Organizations, is incompatible with critical thinking.

Why haven’t we recognized this and rebelled? The truth is our brains are predisposed to love operating in Answer Organizations.

Daniel Kahneman’s System 1 and 2 framework from Thinking, Fast and Slow explains why our brains are comfortable in Answer Organizations. Kahneman famously popularized the two systems in the mind: System 1 operates automatically and quickly; System 2 allocates attention to effortful, deliberate reasoning. Because System 1 operates automatically and cannot be turned off, Kahneman exposes a number of biases, or errors of intuitive thought, that cannot always be avoided. He also describes a “law of least effort”: when multiple paths lead to the same goal, people choose the least cognitively demanding one. In practice, this means System 1 wins by default.

Answer Organizations amplify our laziest thinking. They encourage all sorts of cognitive biases that enable our System 1 thinking to take over, and leave System 2 dormant. For example, Kahneman describes an availability heuristic, in which people judge frequency by the ease with which instances come to mind, a product of System 1 thinking. In an Answer Organization, where what’s recent and familiar is prioritized over what is emerging and unfamiliar, our System 1 tendencies are encouraged. The answer that worked last time is the answer that gets promoted, as it’s deemed reliable and is efficient to recall.

From my business transformation work with clients, I believe undermining the dispositions that are required for critical thinking is, by far, the biggest point of failure. It is the most poorly understood, least invested in, and most undercut by the mechanics of an Answer Organization. Even our own brains are predisposed to work against us in a quest to evolve to a Question Organization.

“Teaching” Critical Thinking

Critical thinking courses designed for businesses already exist, and countless methodologies implicitly encourage critical thinking in business contexts. For example, design thinking, which instructs people to observe, empathize, hypothesize, and experiment, has been popularized in the business world over the last quarter century. Its language mirrors Hitchcock’s definition of critical thinking almost exactly.

So why haven’t our Answer Organizations been taught how to be Question Organizations yet?

In some cases, what is taught as critical thinking is so manipulated to fit in with Answer Organizations that it abandons the purpose of critical thinking entirely. In A Short Guide to Building Your Team’s Critical Thinking Skills, a four-phase approach efficiently outlines how to evaluate critical thinking in a measurable way. This stepwise, measurable approach to teaching critical thinking in businesses is made appealing to Answer Organizations given their demand for efficiency and reliability. The phases of the approach—execute, synthesize, recommend, and generate—all represent analytical thinking, operating from existing information and moving toward a defensible conclusion. While this form of “critical thinking” might be a helpful articulation of how to be successful in an Answer Organization, none of it represents careful thinking directed toward a goal. All of it can be automated by AI today, and thus, will not be helpful in differentiating organizations in the future.

Even if the phases of critical thinking were more helpful in their instruction, this highlights another way in which Answer Organizations fail to teach critical thinking: they efficiently process-ize it. In the same way Answer Organizations turn their business into an algorithm that can be scaled, they attempt to turn critical thinking into an algorithm itself to scale it across its employees.

This is yet another way in which Answer Organizations appeal to our System 1 thinking. Processes allow our System 1 thinking to take over and systematically check all the boxes, creating the illusion that we understand something without truly wrestling with it. Kahneman acknowledges “because adherence to standard operating procedures is difficult to second-guess, decision makers who expect to have their decisions scrutinized are driven to bureaucratic solutions.” Even Hitchcock caveats his section titled “The Process of Thinking Critically” by explaining “checklist conceptions of the process of critical thinking are open to the objection that they are too mechanical and procedural to fit the multi-dimensional and emotionally charged issues for which critical thinking is urgently needed.”

This is often the main critique of design thinking, a methodology that so often masquerades as critical thinking in business. While I wholeheartedly agree with the critiques, it’s important to note that it isn’t necessarily that the intention of the methodology is wrong—as I mentioned before, a lot of the language is one and the same with Hitchcock’s definition of critical thinking. Rather, it is when design thinking is implemented in an Answer Organization that it turns into a feckless exercise that focuses on checking off steps rather than properly thinking critically. I’ve witnessed design thinking break down countless times in Answer Organizations, as the demands of the organization clash with proper exploration for new understanding.

But the most baffling failure is the one hiding in plain sight. Answer Organizations fail to take advantage of their built-in learning mechanism for critical thinking: developing strategies for their business. Hitchcock explains that effective teaching methods for critical thinking have been proven to be dialogue, such as collaboration, anchored instruction, such as applied simulations, and mentoring. There is no better environment to teach critical thinking than a business already running on real problems. Teams are already collaborating on applied challenges with leaders overseeing them. Yet, Answer Organizations pull people out of that environment to sit through classroom-style training that misses the point entirely.

Ignoring Key Knowledge

In addition to dispositions and abilities, Hitchcock explains that there is also required knowledge for critical thinking. But much of this knowledge is not valued by Answer Organizations.

Answer Organizations value expertise, people who have experience operating a given part of the algorithm so they can do so reliably. Kahneman’s illusion of validity is particularly relevant here. Kahneman has proven that experts mistake narrative fluency rooted in expertise for genuine understanding. Expertise becomes the answer that forecloses the question. When this theory was tested by psychologist Gary Klein, the two teamed up and published their failure to disagree. They concluded that in “low-validity” or “wicked” environments—like the world that businesses operate in—expertise produces the illusion of skill rather than genuine skill.

I’m not here to pick a fight with expertise. Even Kahneman admits that this illusion is deeply ingrained in the culture of business and challenging it would threaten people’s livelihood and self-esteem (including mine!). Hitchcock also admits that substantive knowledge of the domain to which an issue belongs is helpful for critical thinking (whew!). This is why, no matter how capable the AI tools, an expert designer will always produce something a non-designer cannot.

But there is other key knowledge that organizations should value beyond expertise, including metacognitive skills.

Metacognitive skills are awareness and control of one’s own thinking processes. It’s thinking about your thinking. Answer Organizations have no use for metacognitive skills. Why would you need to think about your thinking when only the answer that exploits the algorithm is of value? If the answers are defendable, ensuring actual understanding is irrelevant.

But in a Question Organization, understanding is the whole point. When operating at the top of the knowledge funnel, you’re investigating mysteries seeking validity to ensure a new strategic direction will be successful. Therefore, you need to avoid lazy thinking and cognitive illusions that might lead you astray. If a bias makes you believe a strategic direction will yield business success, you might waste valuable resources pursuing something that will not have its desired effect.

Metacognitive skills are just one example. The point is that Question Organizations will require valuing knowledge beyond expertise. Arguments such as this have been made frequently, both in advocacy of “soft skills,” and sometimes framed as generalism versus specialism. Books like Range by David Epstein make arguments that generalist-specialist teams are most productive in business. This argument mirrors Martin’s point that businesses need to balance exploration with exploitation.

To me it’s quite simple: in a future world where we’ll all have access to a superhuman specialist in AI, we should value different things. Knowledge beyond one’s own domain is more valuable than it’s ever been.

Accomplishing the evolution from an Answer Organization to a Question Organization is not an easy task. It has nothing to do with malintent—some that run Answer Organizations preach critical thinking and have actively been attempting to make their organizations more adaptive for decades. But Answer Organizations have a compounding system in place that combats true understanding through critical thinking. They suppress the disposition required. They process-ize the skill. They prioritize narrow expertise. Together, they form a system that defeats itself before the transformation can begin.

AI: A Point Of Failure and An Opportunity

In 2023, at the start of the AI frenzy, the leaders of my consultancy became obsessed with the concept of productizing our professional services. The idea was simple and obvious—now that we had this technology, let’s take what we did manually with teams of people and sell it as a software product. In other words, automate the algorithm.

Our innovation division productized the design-oriented process we typically follow with clients. The tool identified potential market whitespaces, synthesized insights from synthetic consumers, generated relevant concepts, and iterated based on synthetic persona feedback. The tool worked well enough—it produced what it was supposed to produce. But clients found little value in it. Why?

The issue wasn’t in productization—it was in what was productized. The outputs we produce were not what clients valued most. They valued the understanding we developed with their teams, the same teams that would have to execute the strategy once we had left. Accelerating the development of output decreased the level of understanding we offered and therefore decreased the value of our engagements. If anything, we commoditized ourselves by automating what didn’t matter and failing to deliver what did.

For any company not selling AI directly, AI is simply a means of delivering the value you already offer. Critical thinkers in Question Organizations will know this and adapt the business accordingly. Answer Organizations looking to accelerate the algorithm will miss this entirely. In this way, AI provides an additional point of failure.

AI can also directly discourage the critical thinking required to become a Question Organization. Michael Gerlich’s AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking demonstrates this. Gerlich’s study reveals a significant negative correlation between frequent AI tool usage and critical thinking abilities, mediated by increased cognitive offloading. In other words, people who outsource their thinking to AI are worse at thinking critically. Go figure.

But AI isn’t only an additional point of failure. For organizations that evolve into Question Organizations, it becomes an advantage.

From a Kahneman perspective, Gerlich’s conclusion makes perfect sense. The law of least effort can be revised to say just use your System 1 thinking, because AI can do all your System 2 work for you. I would have expected this to sour his view of AI—if it offloads System 2 thinking, what’s left for humans? But in speech at the University of Toronto in 2017, he surprised me. Because he spent his career documenting how inconsistent and biased human judgment is, he welcomed AI as a tool to eliminate that variability reliably.

For Question Organizations, AI can eliminate human bias and inconsistency for routine analytical work that will allow the business to perform reliably at scale. This will free up their critical thinkers to intelligently collaborate with AI to become better at solving the mysteries that lie at the top of the knowledge funnel. Just as AI without critical thinking produces workslop, critical thinking with AI amplifies understanding.

The AI literacy programs I’ve seen in the corporate world today ignore critical thinking skills. They typically provide an overview of how the technology works, some technique around prompt engineering, policies around security and ethical responsibility, and a plug from the IT department on the tools they’re asking people to adopt. The result is an Answer Organization more deeply entrenched than before.

Contrast that with a healthy Question Organization that has avoided the points of failure listed earlier. In a culture that promotes critical thinking, curious practitioners are already playing with AI before leadership initiates a literacy program. Employees use AI to further their critical thinking every day, not through classroom training but through the actual strategic work of the business. They get the most out of AI by directing it with a metacognitive awareness, rather than getting directed by it toward workslop.

For organizations willing to build the capacity, AI is both the greatest risk and the greatest accelerant.

Becoming a Question Organization

The aspiration of becoming a more adaptive organization predates AI by decades. We’ve had many helpful visuals of what I’m calling a Question Organization. The most vivid one I’ve encountered recently is an octopus.

In Become an Octopus Organization, Jana Werner and Phil Le-Brun describe an organization in terms of the intelligent sea creature, whose arms can think and act independently yet work in concert. Werner and Le-Brun contrast today’s rigid organizations with their more adaptive octopus-inspired counterparts. In today’s organization, meetings are centered around answer dissemination; meetings should instead be designed to generate an outcome, where provocative questions are encouraged. In today’s organization, call center agents follow a formulaic script—a set of answers to deliver; agents should instead own the customer’s problem, questioning what’s needed. These examples are helpful in that they illustrate what a Question Organization should look like on the front lines.

Martin offers structural prescriptions for organizations looking to make the shift. Instead of permanent roles blindly turning the algorithm’s crank, form project teams that see the bigger picture and collaborate toward shared goals. Instead of budgeting from past spending data, set budgets around future goals and explicit spending limits—acknowledging that advancing knowledge is tricky to budget for. Instead of a reward system based on overseeing the largest algorithmic operation, reward those who solve wicked problems and generate impact in doing so. Each addresses a structural ill of the Answer Organization.

But while we can visualize the adaptive organization we want to achieve, and recommend structural, process, and cultural tweaks to address points of failure, Answer Organizations have proven stubbornly resistant to all of it. Our answers have been treating the symptoms, when a core question is what we need to cure the disease:

How do you design an organization for people to genuinely want to think?

The core of a Question Organization is a workforce with a genuine love of inquiry. They must be disposed to proactively move up the knowledge funnel and uncover the next mystery that differentiates the business. They must resist the pull toward process-following and tool-dependence, and willingly engage their System 2 thinking—with AI as a collaborator, not a substitute. This is what Answer Organizations have most systematically destroyed, and why they routinely thwart every structural solution. And it is this principle on which to design a Question Organization.

Make no mistake: this is not a failure of people, it’s a failure of organization. People are natural lovers of inquiry (a point Kant observed long before the age of AI). AI is only proving this point in real time, as workers fear the automation of their analytical work far less than the loss of their inquiry work that gives their roles meaning. I’ve seen this in my own work: the most valuable thing I’ve provided clients isn’t strategy or output—it’s the conditions to develop their own understanding, free from the constraints of their Answer Organization.

In this sense, AI has forced a wonderful reckoning. Organizations must evolve into Question Organizations, where their people explore questions about the future while AI runs the algorithm of the present.

In “The Useless: What’s the Point?” my Classics professor argued that understanding has no practical end. He concluded his essay by writing “understanding is not a big business.”

I now know how wrong he was.

The tension between answers and questions turns out to be deeply practical. Not because understanding is philosophically superior, but because in a world where analytical thinking is automated, the capacity to pursue genuine understanding is the most practical business advantage available. The organizations that know how to pursue questions will be the ones worth working for, investing in, and building.

I feel like I’ve finished the course, twenty years later.

Um, this is awkward. I bet you think this is about you, kind reader, don’t you. I don’t blame you for thinking that—after all, why would anyone write something that’s not intended to be read?

I’ve been writing extensively for years and have rarely published anything, despite the opportunity that comes with publishing and the growing ease to write. Since becoming a professional, a plethora of self-publishing platforms have prospered, peers in my field have successfully developed personal brands, and AI is available to ingest my writing style and generate anything I could write, better than I ever could. By modern definitions of productivity, writing privately makes little sense—yet I continue to labor through my writing, keeping it mostly to myself.

Why? For me, writing has a specific purpose: it is my mechanism to learn and understand.

It started when I was in college. I found that the most effective way for me to study was to rewrite my notes. I’d rewrite them in new ways more fitting to my understanding. I’d annotate formulas with full-blown theories. I’d create diagrams that connected thoughts visually. It was effective in reinforcing what I was learning, so I carried that through to my professional life.

Recently, I had a professional learning opportunity leading the integration of my innovation agency with a large management consultancy. There was so much learning coming at me, about organizational dynamics, scaling a creative workforce, the interconnection between purpose, strategy, culture—so much to digest. I wanted to synthesize what I was experiencing into more strategic thinking.

So, I did what I always do in moments like these: I wrote.

Writing pushed me to answer the new questions that developed throughout the integration. To understand the historical roots of the acquiring firm’s culture, I read The World’s Newest Profession, by Christopher McKenna. To understand why creative agencies like mine are both celebrated and dismissed, I read The Cult of Creativity, by Samuel Franklin. To contrast the generalist-led value of my agency against the specialist-led value of the consultancy, I read Range, by David Epstein. To seek problems to solve for a new future value proposition of the combined entity, I read a critique of the entire industry, The Big Con, by Mariana Mazzucato and Rosie Collington. And, of course, I wrote down all that I had learned experientially to bring more clarity to it.

In putting pen to paper, I synthesized my understanding of it all. I connected the histories and the cultures of both firms I was integrating to understand why they were stubbornly how they were. I compared my experiences with the consulting critiques and wrestled with the paradox of agreeing with the critiques and the impact I’m confident I made with clients. Ultimately, it made me think about the future and form a strategic perspective. Months later, satisfied with the perspective I developed, I put down my proverbial pen.

As the writing sat on my desk gathering dust, I began applying my newfound understanding. I guest lectured on how strategy consulting is evolving in the age of AI. I intellectually sparred with clients, drawing us closer and exposing future collaboration opportunities. I started developing a new consulting model that matched my perspective and iterated it alongside clients. The value of my writing was not from the output it produced, but from the deeper understanding that resulted.

You might be asking yourself, well, if writing is for your internal understanding purposes only, why are you publishing this?

Touché, reader.

Over the last few years, there has been a dramatic uptick in the number of clients asking how to make their workforce more strategic. Specifically, I’ve been asked how to make organizations more “future-ready,” “innovative,” “creative,” “paradigm spotting,” and the like. The requesters are undoubtedly preparing for the anticipated effect of AI on their white-collar workforce.

Leaders are recognizing—whether through mandate, observation, or fear—that most organizations are optimized to execute prescribed tasks. As AI makes task execution table stakes, the agency that comes from understanding becomes the differentiator. Smart leaders recognize this isn’t just an individual problem—it’s systemic. Individuals often blindly churn out tasks, but they do so for an organization that trades in output rather than understanding.

When I was first asked, “How do I make teams more strategic?” it took me a while to answer. I dismissed the processes and frameworks commonly taught—those were just tools in a toolkit, suggesting strategy equated to the development of more outputs. I wanted to answer the question at its deepest point. Eventually, aided by reflecting on why I write, I formed my answer.

I distilled it down simply: it’s about being able to identify what needs to be learned, deeply understanding it, and applying that understanding to the task at hand.

For me, writing is a mechanism to do just that—to gain, synthesize, and apply a deeper understanding. Writing may not be what helps you or your teams learn and understand, which is why this isn’t a recommendation for everyone to start their own Substack. But as more organizations get smarter about valuing understanding over outputs, figuring out a mechanism that sparks curiosity and understanding will make you and your team more—strategic, innovative, creative—all of the above.

Upon this reflection, I’ve started publishing my writing to more easily share my understanding, and, if I’m honest, to hold myself more accountable in doing it thoroughly. My first attempt to publish surfaced my writing motive immediately. When I handed the initial draft of my consulting perspective to an editor, it was more than 5,000 words, on an admittedly dry topic, overly academic in style, non-polarizing, and wasn’t titled “5 ways to find meaning in life.” It was optimized for understanding, not output. Rightfully thinking of the reader, my editor immediately cut it down to 1,000 words. But given all the benefit I derived from the thousands of “extra” words I wrote, I’ll never optimize for the reader. No offense to you, of course.

So why do I write? Well, you just witnessed me figuring it out for myself. But I’m publishing it on the infinitesimal probability that it will inspire others to find a similarly impactful mechanism for themselves.

For the short(er)-form version, see my article featured on fastcompany.com. But I warn you, you’ll miss a lot of the fun.

As the AI craze captivated the business world, many have predicted doom for strategy consulting. But while today’s critiques rightly point to the pressures AI poses to strategy consulting’s business model, few are digging into why its core value is threatened, and fewer are proposing how it should evolve. Those that do might see what I see: strategy consulting is failing because clients’ needs have long changed, and the consulting industry simply has not evolved to meet them.

Over the last 15 years, I have benefited from an accidental professional experiment that has given me a unique perspective on strategy consulting. As a bright-eyed, bushy-tailed analyst for a “strategy” consultancy, I learned all the traditional business frameworks and formulas, and diligently annotated my copy of The McKinsey Way in an attempt to become a professional advisor. After several years, I left to join an “innovation” agency, with lineage in consumer insight and design, where I had to unlearn a lot of what I had absorbed and understand a very different way of practicing the same craft. Then, in an interesting twist of fate, my innovation agency was acquired by the very strategy consultancy I had left. Suddenly, I found myself leading the integration, navigating the synergies and tensions that arose between two, mostly different, strategy schools of thought.

What resulted from this experiment was an unrelenting stream of learning, viscerally experienced. Not only from the years of leading the integration, but from studying the origins of the different streams of thought, garnering client feedback, and immersing in critiques of the general profession. Synthesizing all of this learning helped me understand why strategy consulting is failing, and how it should evolve.

The Client Context

Let’s start with some necessary, albeit obvious, context. The biggest strategic challenge facing clients is delivering results today while, simultaneously, adapting for tomorrow. Trying to keep their organizations successful while (largely technological) change constantly breaks the core business conventions on which they are based is, well, a challenge.

The effect on the strategy demanded of the day should, therefore, also be obvious. While “traditional” strategy extrapolated from the past, today’s must anticipate the future to keep pace with the oncoming change. While traditional strategy assumed stable business conventions, today’s must consider new models enabled by new technologies. And while strategy always required evidence to survive the scrutiny of leadership and boards, today’s must defend unprecedented approaches for a less predictable environment.

Even though foresight, criticality, and experimentation have been long understood, businesses are still challenged—culturally and structurally—in developing transformational strategies. Misaligned incentives, organizational silos, pressure to utilize established infrastructure, and general conservatism to protect a scaled core business keep leaders trapped in the “tyranny of today.”

It’s not for lack of trying, hence the efforts over the last few decades to become more “agile,” or, post COVID, commonly referred to as being more “resilient”. Being at an innovation agency, I have had a front row seat to many efforts to develop and embed an innovation function and culture, to varying degrees of success. In the circumstances of failure, the structural or cultural dynamics of the business discussed previously win out in one way or another. But even in the circumstances of success, they are more often than not small wins that do not affect the overarching strategy and direction of the business.

In 2015, I worked with a global brewer to understand who the consumer of 2020 would be, and what impact that would have on the pipeline of products the company should develop. Even back then, we were already seeing signs of what is obvious today: fewer people wanting to drink alcohol. I continued to work with the client to try and adapt the company for what was to come, including developing a separate organizational unit focused on growing and acquiring sub-scale products, and designing and launching new products, many of them non-alcoholic.

Despite these efforts, the company faced a multitude of challenges to adapt. The requirements of the scaled, profitable core business continued to funnel focus and resources away from new unproven efforts. Fantastically skilled brewmasters pushed back on non-alcoholic beer succeeding, given the inability to recreate the exact taste profile of alcoholic beer. And when new products required different raw materials to be successful, formulations changed to use the raw materials that were readily available, even when evidence showed consumers would reject it.

In short, large businesses struggle to adapt to change. However, while it’s challenging for large businesses to maintain an eye toward the future, think outside the conventions that are ingrained in the business, and find the incentive and space for tinkering in unproven directions, external partners are not under any of the same constraints. With the freedom that comes with being outside the organization, strategy consultancies are incredibly well positioned to provide exactly what today’s strategies require.

Yet they are failing to deliver.

The Failure of Strategy Consultancies

In The World’s Newest Profession, Christopher McKenna chronicles the history of management consulting. McKenna highlights how management consulting arose not as an evolution of early efficiency engineers and scientific managers, but as an unintended consequence of the Glass-Steagall Banking Act of 1933. The legislation prohibited rival professional groups (e.g., lawyers, accountants, engineers) from continuing to act as consultants, thus creating a jurisdictional void in professional services that were filled by the first management consultants. This history is valuable in understanding why strategy consultancies are struggling to deliver what today’s strategy requires.

McKenna describes management consultants’ core value as providing “economies of knowledge,” or in a word, expertise. Consultants provide experience and functional specialty that arose from similar cases on previous assignments, therefore acting as institutional conduits of new knowledge to the client organization. These economies of knowledge did not diminish when shared, as providing the same knowledge over and over again did not decrease its value—and at times even increased it.

The historical value of providing economies of knowledge is still the core value management consultancies offer their clients to this day. While the knowledge they broker has changed over time, management consultancies still stress expertise, touting industry and functional specialization and utilizing case studies as proof points. It is for this reason that new waves of information democratization—whether the “big data” push of the 2010s or the AI craze of today—stir up discussions of consulting’s decline.

But this point of view focuses on what consultancies offered in the past, not on what clients need now. For clients needing help developing a strategy in today’s environment, knowledge brokering is not what they need. Today’s strategies require an understanding of the future, not the past. They require the critical thinking to break conventions and orthodoxies, not adhere to them. And they require constant experimentation to learn in the latest version of the world, not rely on expertise formed under previous circumstances.

Businesses grappling with AI are the perfect example. Last year, a newly hired executive from a major media conglomerate approached my consultancy’s strategy division. With a fresh perspective, he realized the company was using AI to modernize within the current business structure—limiting the technology’s potential to drive transformation. When presented with this challenge, our traditional strategists pitched deep media expertise and proposed using a new AI tool developed to evaluate the business against the traditional media value chain. Privately, the executive expressed his disappointment to me. The proposed solution relied on people and tools steeped in the very media orthodoxies he needed help breaking.

For traditional strategy consultancies, changing a century-old value proposition hasn’t been easy—for cultural and structural reasons alike.

McKenna explains that because consulting was not a field innovated by new skills, but rather by institutionalizing professional practices within a protected market, it took measures to legitimize itself as a profession. The industry linked themselves to professions with perceived authority (i.e. engineering, accounting, law), by, for example, creating professional organizations, hiring exclusively from elite universities, employing a challenging “up or out” system of career progression, and even discouraging individuality of outward appearance in favor of collective professional conformity. These measures formed an extremely homogeneous cultural backbone for the profession—one that still exists to this day.

In my first years in consulting, partners required me to be clean-shaven every day as I worked from the client site the traditional Monday through Thursday. I have a thick beard, which is painful to shave daily. As a consulting analyst I spent a whopping $500 on beautiful, but more importantly, professional-looking tortoiseshell glasses in an effort to distract partners from the slight five o’clock shadow that would appear every other day. Unfortunately, it didn’t work. A partner noticed my shadow and told me to go back to the hotel and shave. (I still wear the glasses to this day trying to recoup my investment.)

As innovation and entrepreneurship have become more en vogue, some of the appearance policies may have relaxed, but many of the policies that maintain conformity and homogeneity are still very much present. Decades of brokering the knowledge of their most experienced people has reinforced a firm hierarchical structure, where more junior consultants look to more senior leaders not only for direction, but for the content they base their client recommendations on. Consultants are organized in deeply specialized practices, often industry-based, and therefore mostly work alongside like-minded colleagues with similar experiences. And given the size many consultancies have grown to, bureaucracy and policy act as a deterrent on a culture of curiosity and experimentation.

Just before my relatively small innovation agency was acquired, we had finished a project developing the first transatlantic experience strategy for a major U.S. airline. To generate the insights that would help lead us to our recommendation, we turned a room in our office into the fuselage of an airplane – complete with seats and overhead compartments, food service provided by an actual flight attendant, iPads that functioned as in-flight entertainment seat-back screens, and sounds and lighting consistent with a plane en route to Europe. (It felt so real we had one experiment participant fall asleep in his seat!) We ran iterations of our experience with consumers, from the meal services to the prototype on the “seat-back screens,” using the learning from our experimentation to support our recommendation.

But after we were acquired by the much larger consultancy, there were fewer examples of this type of bold experimentation. Project teams resorted to simpler tactics, such as basic ethnographies or digital A/B testing, whether or not it was the optimal way to gather insights. Upon investigating this phenomenon, we discovered that the effort required to navigate the large consultancy’s procurement procedures was too high, and the unique items we requested for various experiments were often outright rejected for purchase. Needing to stick to timelines, our teams defaulted to experiments that were common and acceptable to procurement, even if less effective in what they taught us.

Strategy consultancies, while theoretically well positioned to help their clients develop strategy fit for the times, are challenged to do so. The core value proposition of offering economies of knowledge, in addition to being weakened by the continued democratization of knowledge through data and AI, is not the value clients require for strategic advice in today’s environment. Furthermore, today’s strategy requires a culture that encourages individuals to depart from the norm, to challenge long-held industry beliefs, to understand a broader picture to make disparate connections, and a structure to be able to experiment with new ideas at speed. With the legacy of conformity, embedded hierarchies, organization by specializations, and structural blockers to doing new things in new ways, strategy consultancies will continue to struggle to become the partners their clients need.

The Failure of Creative Agencies

In The Cult of Creativity, Samuel Franklin investigates the surprisingly recent history of creativity, from its origins post-World War II to its study by psychologists and educators, as well as its applications in the business world. By the middle of the 20^th century, having achieved the productive capacity far beyond what was necessary to meet everyone’s basic needs, the strategies of the business world began shifting away from efficiency and toward marketing and innovation. Now that necessity was no longer the mother of invention, creativity was needed both to differentiate products serving similar purposes and to develop new ones nobody asked for. In addition to the (more well-known) renaissance in Advertising, the adoption of creativity produced Synectics, the first boutique consultancy specializing in applying creativity to the product and R&D space—the predecessor to many design and innovation agencies of the present.

While management consultancies were maintaining conformity and strict professionalism to evolve from their cost accounting roots into organizational and growth strategy, advertising and innovation agencies leveraged creativity to provide marketing and product strategy. Given that today’s strategy work requires leveraging the art and science of foresight to anticipate the future, expansive thinking to break established orthodoxies, and consumer insight to build confidence in new strategic directions, one would think that creative agencies would be well positioned to lead where traditional strategy consultancies have failed.

Creative agencies have, in fact, gained significant ground in helping clients with strategic questions once posed only to strategy consultancies. Evidence of this includes the growth of “innovation consulting” firms in recent decades, and the subsequent acquisition of these firms by strategy consultancies themselves throughout the 2010s. However, creative agencies have struggled to deliver on the needs of today’s strategy, albeit in far different ways from strategy consultancies.

In documenting its history, Franklin highlights the ambiguity of creativity. The same paradoxes that made the term difficult to research and make scientific conclusions for psychologists and educators made for its mass appeal and frequent application. Creativity was both playful and practical, extraordinary and everyday, artsy and technological, exceptional and common, and mental and material. This ambiguity can, in part, explain the inconsistency across creative agencies in delivering on today’s strategic needs of clients. Agencies that lean too heavily on a vague notion of creativity may not understand why aspects of it are valuable for developing strategy, and therefore fail to apply it correctly to client needs. These agencies often become the facilitators of what is negatively described as “innovation theater,” or creativity for creativity’s sake.

For example, while creative agencies are much better at incorporating an understanding of the future into their work, it often remains stuck in the abstract. Flashy utopian or dystopian futures are presented as provocative stories, recycled trends are reported, and predictions are boldly pronounced without humility—a dynamic eloquently chronicled in Nick Foster’s book Could Should Might Don’t. But this lazy futurism often fails to be applied to the client’s business, sometimes being divorced from the present entirely, preventing clients from using the foresight in any meaningful way. In the same way the endless market research of strategy consultancies is chastised as collecting dust on shelf, so too are stories of future technologies or consumer behaviors left stuck in the theoretical.

Creative agencies can also lack a certain criticality in their work. In his history, Franklin exposes the lineage of creative processes and methods, from brainstorming to design thinking. He identifies a couple through-lines across these methods, including a series of replicable steps, signature literature, materials, and tooling, and being linked to quasi-academic centers. These creative processes are certainly helpful in describing and scoping time-definite projects between agencies and clients. But in practice they work to deemphasize the rigor in thinking needed for strategy development. Steps of a process are followed blindly; ideation happens needlessly; things are broken simply for the purpose of breaking things.

While today’s strategy does require “creativity” in a sense, creative agencies must understand the burden of evidence that their clients are under to do something unprecedented, and work to meet that challenge of confidence. They must apply “creativity” where it is needed for strategy development, but not forget to directly apply it within the context of the client’s business. They must follow the well-intentioned principles of their processes, but not blindly follow the process itself expecting rigor to always result.

All of which raises the question: what exactly are the qualities needed to develop strategy today? For clients, what should they look for in a partner? And for strategy consultancies and creative agencies, how should they evolve?

The Qualities Needed for Today’s Strategy

Consultants—traditional strategy consultancies and creative agencies alike—have a knack for using overly complex language when describing what they do. They use processes that may go by different names, but are often the same. They tout useful proprietary data sets and unique tooling, useless if used the wrong way.

To highlight what matters most—beyond any process or tool—I’ve attempted (emphasis on attempted) to distill today’s essential strategy qualities into three:

1. Understanding Why: In an environment of constant change, strategies must incorporate foresight by understanding why things are changing, not just what is changing.

2. Centering on Tomorrow’s Needs: As conventions break, strategies must employ criticality by focusing on tomorrow’s needs rather than adhering to today’s orthodoxies.

3. Experimenting in Pockets of the Future: Facing uncertainty, strategies must generate new learning by testing with future customers, not analyzing past data or relying only on expertise.

Understanding Why

The most valuable piece of strategy work is not the answer it concludes, but rather the questions it surfaces, especially in an environment of constant change. If Eisenhower was right that ‘plans are worthless, but planning is everything,’ the focus should be on what is learned in the process, not the resulting plan itself. And what should be learned in planning is not the fleeting what that already exists in the world, but the evolving why that will direct novel future actions.

Traditionally, this has been the job of market research. Consultants past and present have been expert in exposing what exists today (e.g. markets, competitors, technologies, etc.), within the domain of the scope in question. But what exists today may not exist tomorrow, and what exists tomorrow might enter from outside the domain of today. Market research, knowledge being made much more accessible by AI today, is only as good as the foundational context it provides.

Strategists must work to understand why what is happening is occurring, to recommend what to do going forward. They must understand the breadth of what is happening in the world to better understand causality and capture what is blind from the purview of the immediate domain. They also must understand the depth of what is happening not at the market level, but at the individual level. Markets don’t demand things—people do, when they have problems, needs, or desires.

There is no one technique or process that accomplishes this. I’ve seen successful understanding from more common techniques like strategic foresight and design research, but also less common techniques like philosophical research and design fiction. Frankly, there are as many foolproof processes for understanding as there are fully confident answers found in today’s learning journey. And processes may surface the required inputs, but those inputs must always be synthesized into an understanding.

When a global media and entertainment conglomerate asked us how they should create the theme park of the future, we desperately needed to understand the why. While strategy consultancies would have dove into the theme park market and creative agencies would have started predicting future technology-induced utopias, the first step for us was a deep understanding of why. Why do people come to theme parks today? Why will they come tomorrow? By synthesizing a wide variety of inputs, we concluded the most important part of our work: our perspective on the value the client must provide park-goers of the future. From the richness of that understanding, we were not only able to define how the theme parks should tangibly evolve, but also how the media-entertainment flywheel should be transformed as a whole.

Centering on Tomorrow’s Needs

The breaking of business conventions will not cease, as new technologies emerge, consumer behaviors change, global policies get implemented, and new events occur. So, how does one identify the conventions that will break, or should be broken? Do they list all the conventions that exist in the world and decide if, and if so how, they should be broken? Sounds laborious and inefficient, but how else can one be comprehensive?

Human-centered design enthusiasts will recognize centering around problems or needs as a core tenet to designing new products and services. In developing strategy, I believe centering around needs plays a slightly different purpose: it allows strategists to better apply their foresight and critically identify the conventions worth breaking.

After having developed an understanding of why the world is in its current state and why it is anticipated to be in its future state, we need to apply that understanding to what is relevant to the strategy at hand. Ultimately, people are what is relevant, whether they are people as end-consumers, or people operating a customer business, or people as continuants of a country or locality. Envisioning how their lives will change—and identifying what new needs will emerge—takes foresight from theory to application. And focusing strategies on how to solve for those needs in a future state will naturally ignore the business conventions that restrain today.

This is easier said than done. It needs to balance the expansive mindedness to project needs into the future with the humility of the practicalities and feasibilities that will still exist. It is for this reason that I believe it is important for external advisors to be generalists with respect to the industries in which they work, working hand-in-hand with the ultimate specialists of the client’s business: the clients themselves.

In his book Range, David Epstein details the value of generalists in a specialized world through numerous examples and studies. He highlights the value of the generalist-specialist team, citing studies where optimal teams included specialists that were able to focus on difficult problems and anticipate obstacles, while generalists added value by integrating insights from one domain and applying them to another. To me, the combination of clients as specialists in their business and generalist advisors being appropriately naïve to orthodoxies of the present (that specialists might have ingrained in them) is the right combination for today’s strategy work. (Note: AI, the superhuman specialist, will be ingrained in teams if it isn’t already, further making the case for external advisors as generalists.)

I was initially uncomfortable as I transitioned from the specialist-valuing strategy consultancy to the generalist-valuing innovation consultancy, especially when working in highly regulated fields such as pharmaceuticals. But in my first work with a pharmaceutical company, I saw the value in being unencumbered to the orthodoxies of the industry. My work involved preparing for the launch of a new therapeutic, which was very expensive, required refrigeration, and a physician to administer. In solving for the challenge of coordinating the drug, the patient, and the physician being in the same place at the same time, the client was working on a tracking application. But, having just finished work with a snacking company in which typical vending machines played a role, I naively suggested creating a device that could store pre-prescribed inventory of the drug on site. Our clients initially challenged the idea for several reasons, all valid given how the industry worked to that point. But together we solved the constraints through the application of new technology, and working through legal hurdles. This is the value of generalist-specialist collaboration: generalists unconstrained by industry norms offer new conventions, while specialists leverage their expertise to make them feasible.

Experimenting in Pockets of the Future

There is an inherent level of uncertainty in any strategic decision concerning the future. Yet, business teams need to be able to make informed decisions with confidence, and provide the support needed to convince their stakeholders to make investments—especially those in directions that are atypical for the business. To do this, we must generate new data to support how our strategy will fare in the future.

Of course, data and expertise grounded in the past is valuable information and a wonderful place to start. I’ve also worked with synthetic data generated by AI, but it should be noted that this data is, by definition, extrapolated from the past as well. How can we attempt to understand net new strategic directions, at worst, in the most recent present and, at best, in the future?

Experimentation is not a new concept. Born of the ‘fail fast’ era of lean product development, it enables teams to learn by trying something new. However, I have found that this concept has not been applied to strategy nearly enough. If the ‘future exists, it’s just not evenly distributed,’ then experimenting in pockets of the future is the best tool we have to learn about future strategic directions.

When working with a major shipping and logistics company to understand the future of digital commerce and develop a new strategy to stave off commoditization in shipping services, we employed this type of experimentation to form the strategy with company leadership and legitimize it to the board. After using strategic foresight to develop a perspective of the future of commerce, we designed ‘sacrificial’ concepts meant to test different strategic value propositions the company could offer to differentiate its services. We experimented and iterated these concepts, not with the typical customer but with the customer indicative of the future. We bypassed the client’s biggest current customers for those that were new, digitally native, serving more demanding—read: younger—generations, and in unique and growing industries. By doing so, we exposed the next wave of value sought by the future mass market, rather than surfacing issues of technical or operational debt with legacy services. By experimenting out in the world, we increased our confidence in how to move forward and strengthened our narrative to stakeholders.

I remember when I first became a strategy consultant, decades ago, how much confidence I had in supporting my recommendations with data grounded in the past or present. No longer—I need to provide firm leading indications that a new strategic direction is the right decision. Today’s consultancy or agency must be able to extract what needs to be learned effectively, design experiments that deliver that learning from pockets of the future, and have the means to create the necessary stimulus (physical or digital) to capture how the strategy would manifest tangibly. All in the interest of building confidence in a strategy fit for today’s wild world.

A New Value for Strategy Consulting

Even before the most recent challenge from AI, strategy consultancies and creative agencies failed to provide the value clients needed to form strategies in today’s environment. Strategy consultancies do not provide value by brokering knowledge, nor do creative agencies provide value by being creative. Both can provide value by helping businesses overcome self-imposed limits to transformation: understanding the broader world, challenging long-held assumptions, and learning—outside the comfort of their offices—to shape new strategic directions. Taking a step back, isn’t this what an external strategic advisor should intuitively do?

While AI has forced a reexamination in strategy consulting I wholeheartedly welcome, my fear is that most are missing the point. Instead of reevaluating their offer for what clients need, the focus to this point has been on ‘AI-ifying’ what already exists: faster deck building; automated searches of proprietary knowledge bases; productizing processes for as-a-service offerings; and moving from pyramid-shaped teams to obelisk-shaped teams. These actions are warranted only if you assume a century-old value proposition—one actively challenged by democratizing specialist knowledge—should stay the same.

So then, what should the new value proposition of strategy consulting be?

Even if I were brash enough to formulate a new value proposition for an entire industry it likely wouldn’t change much, as large consultancies, ironically, have the same problems their clients have. Pursuing the relatively massive revenue opportunities afforded by economies of knowledge—in areas where it is admittedly still valuable, such as technology implementations—is more attractive than transforming themselves to excel in the smaller strategy market. But as clients continue to take issue with strategy work that is not visionary enough from traditional consultancies, or applied enough from creative agencies, new propositions will emerge to fill the gap.

Naturally, though, I do have my hunches.

In their book The Big Con, Mariana Mazzucato and Rosie Collington describe many issues with consulting as an industry. At the core of their critique is the idea that because the real value of consulting is difficult to ascertain, consulting is more concerned with creating an impression of value than actually delivering it. Consultants create this impression of value through their powerful networks, with case studies and recycled frameworks, the qualifications of their people and quasi-academic thought leadership doubling as marketing material. I would assume the authors of The Big Con would support the reexamination of the industry’s historical value proposition, as they challenge the industry having valuable and scarce knowledge assets in the first place.

Reading The Big Con as essentially a career consultant was a paradoxical experience for me. On the one hand, I agreed with nearly everything that was written and even found myself applauding some of the authors’ most cutting points along the way. Yet on the other, were all the positive experiences I have had with clients and the impact I believe my work has made. It wasn’t until I read a specific critique of consulting from The Big Con that a resolution to the paradox was triggered: that consulting steals interesting work and learning opportunities from employees, and thus challenges the ability for organizations to take on important skills.

That critique resolved my paradox by revealing what I’d been mistakenly focused on. When searching for my most impactful work, I had been focused on what we delivered (knowledge and insight, the strategy itself, commercial outcomes that resulted) when the real impact came from something else entirely: the effect on individual clients. My greatest impact was when individual clients—freed from their organizational constraints—developed their own new understanding and advocated for a new direction forward.

Simply put, my work is at its best when I successfully create the conditions to develop and apply a new understanding.

Businesses need external partners to help them transform while they perform—this hasn’t changed. Understanding why the world is changing, centering on tomorrow’s needs, and experimenting in pockets of the future remains essential to developing strategy fit for our times. But the only way businesses can sustain transformation at scale is when their people possess the capacity to continuously understand what is new and adapt to change. Therefore, strategy consulting’s new mandate must be two-fold: develop the strategy that’s right for today while simultaneously building the team’s ability to adapt that strategy over time, with new applied understanding.

Consultancies and agencies face a choice in delivering strategy work for clients: fight a losing battle against AI to continue providing economies of knowledge, or undergo the difficult transformation required to offer value clients need.

Clients, meanwhile, must evaluate partners differently. In a new world where specialist knowledge is easily accessible and traditional outputs can be developed with a few quick prompts, they’ll need to evaluate partners based on their ability to combat the constraints of the organization and build a new understanding capacity across their team.

For me? The irony isn’t lost: the threat of artificial intelligence has only accentuated the disconnect between client’s strategic needs and current strategy offerings, and, in its place, exposed a fundamentally human capacity—the capacity to understand—as what is of value.

A value I intend to pursue.