Beyond the Advisory Stack: Why AI Will Reshape the Consulting Firm Itself

By Vladimir Lazarevik

Artificial intelligence is changing the economics of advisory work, but its greatest impact may be on how consulting firms develop, organize and deploy professional capabilities.

Artificial intelligence is transforming management consulting, but the industry’s greatest challenge may not be technological. It may be organizational.

A recent discussion of the emerging advisory stack in The European Business Review raises a deeper question: if AI changes how consulting value is created, must it also change how consulting firms are organized?

The answer, I argue, is a shift from traditional talent leverage toward capability leverage, with smaller junior teams, stronger managerial oversight and greater integration of external capabilities.

Why Is the Traditional Consulting Pyramid Losing Its Foundations?

For decades, management consulting firms have built their competitive advantage on three pillars: proprietary knowledge, internally developed professional capabilities and deep client relationships.

Exhibit 1. AI Challenges Two Pillars of Traditional Consulting

These pillars have been supported by a pyramid-shaped delivery model, with junior consultants forming the analytical base, managers coordinating delivery and partners maintaining client relationships and accountability.

The pyramid is not merely an organizational chart. It represents an economic system built around human leverage, described by David Maister in Managing the Professional Service Firm. Junior analytical work also provides the apprenticeship through which future leaders develop.

AI is challenging this arrangement. Knowledge is becoming more accessible, while research, analysis, modeling and presentation development can be accelerated through AI-enabled workflows. Experimental evidence demonstrates both the productivity gains and limitations of AI-assisted work.

Yet producing analysis is not the same as verifying, interpreting or taking responsibility for it. What is changing is the historical relationship between analytical output and junior headcount.

My first hypothesis is that AI will progressively narrow the base of the traditional consulting pyramid.

In a recent Harvard Business Review article, Duncan, Anderson and Saviano proposed that the traditional pyramid could evolve into an obelisk: a leaner structure with smaller teams and greater AI-enabled leverage.

But a narrower base does not necessarily produce a proportionally slimmer hierarchy. It may fundamentally change the roles, composition and relative importance of the layers above it.

The question is therefore not simply how much the pyramid will shrink, but what organizational structure will emerge as its economic logic changes.

Why Could the Middle of the Pyramid Become More Important?

As AI accelerates analytical production, experienced professionals may become relatively more important.

Clients will continue to require problem definition, interpretation, judgment, quality assurance and implementation support. AI-empowered managers will need to integrate human judgment with AI-generated analysis, supervise complex workflows and translate findings into recommendations that clients can trust.

These capabilities require experience and cannot be developed overnight.

My second hypothesis is that demand for experienced managerial capabilities will grow faster than consulting firms can develop them internally.

This creates two related challenges.

The first concerns professional development. Consulting firms have traditionally combined analytical delivery with apprenticeship. If AI reduces the economic demand for junior analytical labor, particularly as clients become less willing to pay traditional rates for increasingly automated work, firms risk weakening the mechanism through which they develop future leaders.

Training must therefore become more deliberate, combining structured learning, coaching, supervised problem-solving and client exposure.

The second challenge concerns access to capabilities.

As experienced managerial and delivery capabilities become more important, firms may need to reconsider which capabilities they develop internally and which they access externally.

This echoes Ronald Coase’s foundational analysis of why economic activities are organized within firms rather than through markets. AI may change the relative economics of developing, maintaining and accessing professional capabilities.

This creates opportunities for specialized organizations capable of developing and supplying professional capabilities to established consulting firms.

The emerging middle may therefore expand through both internal professionals and integrated external capabilities supported by AI-enabled workflows.

However, the greatest obstacle to this transition may not be technology, but the economic incentives embedded in traditional consulting partnerships.

What Is the Partnership Preservation Paradox?

Consulting partnerships have historically depended on internal talent development, staffing leverage and returns generated through hierarchical delivery teams. Recruitment, promotion, utilization and commercial models have evolved around these arrangements.

AI introduces a fundamental contradiction.

Partners may recognize that AI-enabled delivery can improve productivity and profitability. Yet capturing its full potential may require redesigning the staffing structures, leverage ratios and commercial models that have traditionally supported partnership returns.

I call this the Partnership Preservation Paradox (PPP).

My third hypothesis is that incentives to preserve established partnership economics may delay the organizational changes required to capture the full benefits of AI.

This does not imply that consulting partners oppose innovation. Many firms are investing substantially in AI.

The distinction is between adopting technology within an existing organizational structure and redesigning the structure itself.

The paradox is that consulting firms may embrace AI while resisting the organizational changes needed to realize its full economic potential.

From Pyramid to Spinning Top: What Could the Future Look Like?

If these hypotheses hold, the consulting pyramid may gradually evolve toward a structure resembling a spinning top.

Exhibit 2. From Pyramid to Spinning Top: From Talent Leverage to Capability Leverage

Its base narrows as AI reduces dependence on junior analytical labor. Its middle broadens in accessible capabilities, combining experienced managers, internal specialists, external capabilities and AI-enabled workflows. Its top remains anchored in client relationships, senior judgment, engagement leadership and accountability.

The spinning top is a conceptual representation of an evolving capability structure, not a prediction that all firms will adopt the same organization.

The transition can be understood as a bridge between two models.

On one side stands the traditional pyramid, built around proprietary knowledge, internally developed capabilities and established client relationships.

On the other stands a capability-centric model, in which consulting firms become more selective about which capabilities they develop internally and more effective at integrating those developed elsewhere.

Crossing this bridge requires reconsidering two traditional pillars: proprietary knowledge and internally maintained delivery capabilities.

Exhibit 3. From Traditional to Capability-Centric Consulting: What Changes and What Remains

The third pillar—client relationships, senior judgment, engagement leadership and ultimate accountability—remains under the control of the lead consulting firm.

For consulting leaders, three decisions become increasingly important:

• Capability development: Which capabilities are strategically essential to develop internally?
• Capability integration: Which capabilities can be accessed externally without compromising delivery quality?
• Capability governance: How can firms integrate internal professionals, external teams and AI while retaining client ownership, professional judgment and accountability?

Conclusion

AI is not simply accelerating consulting delivery. It is challenging the economic foundations of the organizational model through which consulting firms have operated for decades.

The debate about recruiting more or fewer junior consultants addresses only one consequence of a deeper transformation.

The more important question is how firms will organize professional capabilities, develop future leaders and preserve the judgment and accountability clients value.

The consulting pyramid may not disappear. Its underlying economic logic, however, can no longer be taken for granted.

About the Author

Vladimir Lazarevik is Founder and Managing Partner of MECE Solutions, a UAE-based Embedded Capability Partner, and Founder of LMC Academy, which develops professional consulting capabilities. Author of You, the Consultant, he has over 25 years of experience in management consulting and organizational transformation, including senior roles at McKinsey & Company and Arthur D. Little.