AI is Disrupting Consulting, but It’s Not (Just) About Analysis

Introduction

The consulting industry initially responded to the rise of artificial intelligence (AI) with a blend of hubris and anxiety. Some senior leaders confidently claimed that “machines could never replicate what we do,” while others warned that AI would soon perform most consulting tasks—analyzing data and generating insights—faster and more efficiently than human consultants ever could.

Several years into this transformation, it’s worth reflecting on the state of the management consulting industry. While many have focused on AI’s ability to automate analysis, the reality is more nuanced. Consulting is being disrupted, but not in the way most had imagined. The real question is not whether AI can replace consultants but how consulting practices must evolve to remain relevant in a world driven by AI power.

Dispelling the Myth: Consulting Isn’t Just About Analysis & Insight

In a 2017 Harvard Business Review article, Megan Beck and Barry Libert argued that AI could pressure even elite consulting work. That remains a useful warning, but the stronger lesson is not that AI automatically replaces consultants; it is that firms must be clearer about where human judgment, context, and decision support create value.

Management consulting is a large global professional-services industry filled with smart people and strong traditions. But like other skilled services, it is vulnerable to the market forces created by AI: easier access to information, faster synthesis, and lower-cost analytical support.

The typical view of consulting follows a familiar pattern: gather data, analyze it, derive insight, and present recommendations. AI can streamline many of these tasks, but in my 15 years as a strategy consultant, I’ve rarely seen a successful project follow this formula to the letter. Insights, without a deep understanding of the client’s context and the ability to drive decisions, are not enough to create lasting value. The NIST AI Risk Management Framework is a useful reminder that these tools also require governance, context, and human oversight. The HBR framing of AI as augmentation is also a useful safeguard against overstating replacement claims.

In the early days of our careers, many of us didn’t fully grasp this. However, experience taught us that the true value of consulting lies not in the insights themselves, but in how we work with clients to apply those insights to decision-making. This realization is key to understanding why the industry was largely unprepared for AI’s impact.

The Traditional Consulting Model: Team Leverage

Throughout our careers, we ran successful projects that helped clients accelerate their decision-making process and commit to actions that launched new businesses or significantly increased their return on capital. We largely operated on the “team leverage” model, in which teams of consultants gather data, conduct research, and analyze a client’s business environment. The goal was to provide detailed insights and recommendations, often at a significant cost to the client.

While this model has produced successful outcomes, it is fraught with inefficiencies. Common risks include:

• Scope creep: As new information is discovered; the project scope can expand beyond its original boundaries.
• Churn: Misunderstanding client issues or failing to break them down appropriately can lead to wasted efforts.
• Late engagement: Client input often arrives too late, complicating decision-making and creating alignment risks.

Clients also face significant risks with this model. Misalignment with their key issues can lead to cultural challenges, while the high cost of large consulting teams puts this approach out of reach for many organizations.

AI and the Future of the Team Leverage Model

Introducing AI tools can change the cost structure and reduce the client-side risk created by large teams and expensive research cycles. For example, some work that once required a larger pyramid of consultants can now be handled by a smaller team using AI-enabled transcription, synthesis, and research support. The general flow of this kind of project requires the team to sync at the point of divergence, process the initial research in parallel, and then split into focused workstreams to evaluate client context, constraints, and the problem-solving work that still requires senior judgment. OECD AI Principles emphasize human-centered, trustworthy AI, which fits this article’s central point.

While this approach can reduce labor hours and project cost in some settings, the exact economics depend on the client problem, data access, tool maturity, and how much senior judgment is still required. Over time, competition may push prices down further, making some forms of consulting more accessible to a broader range of clients. However, the team leverage model itself may be on the verge of obsolescence.

The Leader Leverage Model: A More Efficient Approach

The other model we used for consulting projects is the Leader Leverage Model. The goal here is to couple a breadth of client relationships with a deep understanding of the client’s business to gain an advantage in selling to clients. We were adept at this, and by the time we left our old firms, we were among the few running accounts on retainer in this model. We also adopted this model out of necessity as we increasingly saw top talent from generalist “tier-1” firms use this to win proposals even when we had a distinct advantage or got feedback that our proposal was more complete.

This model looks different than the team leverage model and presents a very different set of execution risks:

• It depends on your consulting leader’s problem-solving and structuring ability precisely to rapidly and efficiently diagnose client issues, propose hypotheses, and use that insight to add value to client meetings—even when they weren’t yet paid for work.
• The insights and ideas are being discussed with the clients in advance of the team doing the work, meaning that the team is confirming a limited hypothesis rather than looking expansively at possibilities.
• Failure of the team to do good research means that the client is likely to feel that the consulting team is not being thorough and “reading their watch to tell them the time,”—and increased focus means that your research output is not likely to impress with hundreds of appendix pages.

The client-side risk of not bringing a novel insight to the table is mitigated by this approach's speed, repeatability, and lower cost. The value proposition here becomes about helping clients make decisions faster than before and creating a “product” they can feel confident “hiring” to solve other problems in their business. This is because:

• Alignment precedes execution, meaning that clients agree on the problem being solved and what the potential solution set looks like before engaging the team.
• ‍Knowledge can be leveraged for future projects as they are built around a client’s business, external business context, and culture.‍
• The process is hypothesis-driven and, therefore, always limited in scope and largely immune to project churn, so the timelines are quick and predictable.

In the classic definition, consultants using this model were well along the journey of disrupting the traditional consulting firms using the team leverage model. They enable clients to trade off the depth of research and “thunk” factor of an appendix they don’t need for work done to similar levels of quality in less time, at less cost.

AI’s Impact: Empowering Solo Consultants

Perhaps AI’s most significant impact is its ability to empower individual consultants. What once required an entire team can now be accomplished by a single consultant using AI tools in the Leader Leverage Model. This shift is already happening. Many consultants have told me they no longer expect to manage large teams. Instead, they can complete projects in a fraction of the time as a solo consultant, reducing the total labor required by an order of magnitude vs. a team operating in the team leverage model.

Specific benefits of AI that can be used today include:

• ‍LLMs can rapidly summarize publicly available reports, allowing a leader to consume information an order of magnitude faster than before. While this needs to be checked, it makes executing the Leader Leverage Model much easier.‍
• AI notetaking and transcription to support initial client meetings.‍
• AI research tools can reduce time spent on first-pass research, synthesis, and hypothesis checking. The work still needs review, source validation, and senior judgment, but the research cycle can become materially faster than a traditional junior-consultant workflow.

AI is not replacing senior consultants. Instead, some consulting engagements can become more efficient by reducing the need for large teams and lowering labor requirements in certain engagement models. The economics still depend on the problem, the data, the tooling, and the level of senior judgment required. As a result, consulting firms will need to rethink their business models and training programs to prepare the next generation of consultants for a world where AI is a central part of many engagements.

Conclusion

Over the last few months, we’ve caught up with others doing the same thing: using AI in a model that revolves around a single skilled consultant. We’ve largely heard the same thing: “I don’t expect to use large teams.” “I expect never again to have a team with more than just me.” “Work that used to take a team 8 weeks, I can do in 2.”

AI is undoubtedly transforming the consulting industry, but its impact goes far beyond data analysis and insight generation. AI is reshaping how consultants deliver value to their clients by enabling models like the leader leverage approach. The challenge for today’s consulting firms is to adapt quickly, embracing AI’s potential to drive efficiency while finding new ways to cultivate the next generation of talent.