Why learning agility, disciplined inquiry and intelligent use of AI will define the next generation of consulting.
AI will not eliminate consulting. It will expose the difference between performing consulting tasks and creating consulting value.
For generations, consultants built part of their value on access: access to expertise, information, analytical capability, established frameworks and experience accumulated across clients.
AI is rapidly making much of that access available to everyone. A client can now research a market, compare approaches, analyze documents, generate strategic options and produce a credible first draft without calling a consultant. The question is therefore no longer whether consulting will survive AI. The more interesting question is: which consultants will survive?
The answer may depend on something rarely included in a job description and difficult to infer from seniority alone: the consultative gene.
Consulting’s old advantage is disappearing
When information was scarce, access itself created value. When analysis was slow, the ability to mobilize capable people created value. When frameworks were proprietary, knowing the model created value. Those advantages have not vanished, but they are becoming easier to copy and harder to charge a premium for.
The value that remains is more demanding. It lies in framing the right problem, making sense of competing evidence, exercising judgment under uncertainty, challenging assumptions without damaging trust and helping people act on what they learn.
Research with 758 Boston Consulting Group consultants illustrates the shift. For tasks within AI’s capability frontier, consultants using GPT-4 completed 12.2 percent more tasks, worked 25.1 percent faster and produced results rated more than 40 percent higher in quality. Yet on a task outside that frontier, AI users were 19 percentage points less likely to reach the correct solution.
As Cármen Lúcia, a justice of Brazil’s Supreme Federal Court, recently put it at Brazil’s FLIP literary festival:
“I am not afraid of artificial intelligence. I am afraid of natural unintelligence.”
The conclusion is neither that AI makes expertise irrelevant nor that consultants should resist it. It is that value increasingly depends on knowing when to use the technology, how to question it and where human judgment must lead.
What is the consultative gene?
The consultative gene is a metaphor, not a fixed trait. It is not something a person either inherits or lacks. It is a set of observable, developable behaviors that determine whether expertise becomes useful to a client.
Consultants with this gene are curious before they are certain. They distinguish a request from a diagnosis. They remain close enough to understand the client and independent enough to notice what the client cannot easily see. They do not need to dominate the room, but they are willing to improve the quality of its thinking.
Above all, they treat consulting as a learning process—not a performance of already knowing.
Learning agility: the first survival capability
In Learning Agility: The Key to Leader Potential, David Hoff and Warner Burke shift attention from what people know to how they behave when they do not know. For consultants, that distinction is decisive. Our clients often call us when the answer is not obvious, the context is shifting or yesterday’s solution has stopped working.
Experience still matters. It gives us patterns, context and a sense of consequence. But experience creates value only while it remains teachable. Learning agility prevents expertise from hardening into certainty.
Information Gathering. Looking beyond the brief and understanding the system before committing to an answer.
Feedback Seeking. Inviting evidence that may refine—or overturn—our interpretation.
Flexibility and Speed. Changing direction quickly without confusing movement with progress.
Experimenting. Trying unfamiliar methods, including AI, in thoughtful and controlled ways.
Collaborating. Combining expertise across the client and consulting team instead of protecting territory.
Interpersonal Risk Taking. Raising the question that matters when silence would be more comfortable.
Reflecting. Examining our own contribution to the outcome, including what we may have missed.
These behaviors are not signs of inexperience. They are what allow experience to remain relevant.
Burke’s seven stages: an operating system for the consultative gene
Learning agility describes how a consultant learns. The seven-stage process consulting model developed by Columbia University professors Warner Burke and Debra Noumair gives that learning a disciplined structure: entry, contracting, diagnosis, feedback, planning change, intervention and evaluation.
Entry. Understand the initial concern, the context and the people involved.
Contracting. Clarify outcomes, roles, boundaries, mutual commitments and how the work will proceed.
Diagnosis. Gather evidence and test what may be driving the presenting problem.
Feedback. Return the emerging picture to the client and learn from the response.
Planning Change. Develop options and build ownership of the chosen direction.
Intervention. Act on the plan while continuing to observe and learn.
Evaluation. Assess whether the intended outcomes occurred and what the system now needs.
The phases are not rigid gates. Burke and Noumair emphasize that they overlap. Intervention begins at entry because a consultant’s questions already influence how the client thinks. Evaluation begins there too because the client is already judging the consultant’s credibility and usefulness.
The model reminds us that execution is one part of a larger value-creation process. It protects consultants from mistaking activity for progress and a presenting request for the whole problem.
It also widens the idea of the client. Burke came to see the true client not merely as one sponsor or team, but as the relationships and interfaces among people and units. This systems view asks a better question: what must change between people—not only what must be delivered to them?
What the consultative gene looks like in practice
Across consulting disciplines and industries, the difference between completing a task and creating consulting value often appears in the first few questions.
A coalition. A task response brings organizations together. A consultative response asks what shared outcome the coalition exists to achieve, whose decisions it must influence and what each member must contribute.
A workshop. A task response begins designing the agenda and slides. A consultative response first asks what participants must understand, decide or do differently afterward.
A communications campaign. A task response produces content. A consultative response asks which behavior, decision or relationship the communication is intended to influence.
A report. A task response clarifies the format and deadline. A consultative response also asks what decision the report must enable and whose confidence it must earn.
A proposed solution. A task response builds the implementation plan. A consultative response tests whether the solution addresses the real problem, then proceeds with greater confidence or offers a better option.
None of these responses requires theatrical disagreement. Sometimes the client’s requested activity is exactly right. The consultative contribution is to establish why it is right, connect it to an outcome and help the client own the reasoning.
Yet in my own work in strategic communications, the sentence I fear most is: “The client wants this.”
Not because what the client wants is unimportant. It is essential data. I fear the sentence because it can be used to end inquiry at the precise moment consulting should begin. A client’s request should command our attention; it should never suspend our judgment.
AI fluency: the third survival capability
AI can extend the consultant’s reach. It can search more widely, compare perspectives, surface patterns, generate hypotheses, test scenarios and accelerate early drafts. Used well, it creates more time for the conversations and judgments that matter most.
But AI fluency is more than prompt-writing or text editing. It includes protecting confidentiality, checking sources, recognizing plausible errors, understanding the limits of the technology and retaining responsibility for the recommendation.
The weak extremes are easy to identify: rejecting AI to protect familiar work, or trusting it so completely that judgment is outsourced. The learning-agile consultant does neither. They experiment, verify and adapt.
AI should make a consultant more curious, not merely faster.
A self-test for consultants
The consultative gene becomes visible in choices made under pressure. Before the next engagement, or the next deliverable, it may be worth asking:
Outcome. Have I clarified what must change, or only what must be produced?
Diagnosis. Have I tested the presenting problem before committing to the solution?
Evidence. Am I seeking information and feedback that could challenge my preferred view?
Independence. Can I question an assumption without turning the relationship into a contest?
Technology. Am I using AI to expand inquiry while verifying its contribution?
Ownership. Will the client become more capable through this work, or merely more dependent on us?
Reflection. When my expertise is challenged, do I become more curious—or more certain?
The consultants who will remain valuable
AI will not eliminate the need for consultants. But it will remove some of the protection that information, credentials and proprietary methodologies once provided.
The consultants who remain valuable will not necessarily be those with the most experience or the most answers. They will be those who uncover the better question, diagnose before prescribing, challenge without alienating, use technology without surrendering judgment and help clients become capable of changing.
They will combine experience with learning agility.
That is the consultative gene. The question is not simply whether we possess it. It is whether we are continuing to develop it.
Sources and further reading
Burke, W. W., & Noumair, D. A. (2015). Organization Development: A Process of Learning and Changing (3rd ed.). Pearson.
Hoff, D. F., & Burke, W. W. (2017). Learning Agility: The Key to Leader Potential. Hogan Press.
Dell’Acqua, F., et al. (2023). Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality. Harvard Business School Working Paper 24-013.
Queiroz, J. (2026). “‘A arte é libertária por sua essência’, diz ministra do STF Cármen Lúcia em mesa lotada na Flip 2026.” Terra/Estadão.
