Why AI Companies Need Management Consultants to Solve Their Adoption Problem

AI Adoption Is an Organizational Crisis, Not a Technology Problem

The relationship between artificial intelligence and management consulting has taken an unexpected turn. Rather than displacing consultants as many predicted, AI companies now find themselves dependent on these very professionals to achieve their growth ambitions. This development reveals more about the nature of business transformation than it does about technology itself.

Allison Pohle’s recent Wall Street Journal article documenting partnerships between OpenAI, Anthropic, and major consulting firms exposes a fundamental tension in enterprise technology adoption. While AI capabilities advance at remarkable speed, organizational capacity to absorb and utilize these tools lags considerably behind. This gap represents both a challenge and an opportunity that merits closer examination.

The Adoption Crisis Hiding Behind the Hype

The statistics Pohle cites paint a sobering picture. McKinsey’s survey of nearly 2,000 employees found that two-thirds of organizations haven’t begun scaling AI across their enterprises. More tellingly, PricewaterhouseCoopers reported that over half of 4,500 surveyed CEOs have seen no significant financial benefit from AI investments.

These figures demand interrogation. They suggest that the primary barrier to AI value creation isn’t technological capability but organizational readiness. This mirrors patterns observed in previous technology adoption cycles, from enterprise resource planning systems in the 1990s to cloud computing in the 2010s. Each wave of technology promised transformation but required extensive organizational redesign to deliver results.

Research from MIT Sloan Management Review supports this interpretation. Their 2024 study on AI implementation failures found that 70% of AI projects fail to progress beyond pilot stage. Critically, these failures stem primarily from organizational change management issues rather than technical limitations. The technology works; companies struggle to integrate it into existing workflows, cultures, and decision-making processes.

This context makes the OpenAI–Anthropic consulting partnerships less surprising and more inevitable. Colin Jarvis, OpenAI’s global head of forward-deployed engineering, describes working with a large European bank to evaluate eight use cases for their Frontier platform, including credit risk functions and voice capabilities. This work requires deep understanding of banking operations, regulatory constraints, risk management processes, and organizational politics. No amount of algorithmic sophistication substitutes for this domain expertise.

The Consulting Industry’s Precarious Renaissance

Tom Rodenhauser of K2 Consulting Research reports that global consulting grew 5.5% in 2025, double the prior year’s rate. Accenture disclosed $2.2 billion in new AI bookings in their most recent quarter, a $400 million increase from the previous quarter. These numbers represent genuine growth, not merely rebranded existing services.

However, characterizing this as a renaissance requires acknowledging its potentially temporary nature. The article quotes Bill Achtmeyer, chairman at Acropolis Advisors, noting that “the near-term gains might be short-lived.” This assessment deserves serious consideration.

The consulting business model faces structural pressure from AI in several ways. First, clients increasingly resist paying for armies of junior associates to collect and synthesize data when AI tools can perform these tasks faster and cheaper. Ben Ellencweig of McKinsey’s QuantumBlack unit acknowledges that the classic team model has changed to include more engineers. This shift represents margin pressure as highly compensated engineers replace leveraged junior staff.

Second, the move toward outcome-based pricing fundamentally alters consulting economics. Traditional time-and-materials billing allowed firms to profit from project scope expansion and duration. Outcome-based arrangements transfer risk to consultants and reward efficiency over effort. While this better aligns incentives with client interests, it pressures consulting profitability.

Harvard Business Review’s 2025 research on AI adoption emphasizes that successful implementation requires three elements: technological integration, workflow redesign, and cultural transformation. Consultants currently add value across all three dimensions. But as AI tools become more sophisticated and user-friendly, and as companies develop internal capabilities, the technological integration component becomes commoditized. This leaves workflow redesign and cultural transformation as the remaining defensible consulting territory.

The Trust Factor and Human Accountability

Mo Koyfman of Shine Capital offers perhaps the most insightful observation in Pohle’s article. Companies want “a throat to choke,” someone to hold accountable when implementations fail. This colorful phrase captures something fundamental about organizational decision-making under uncertainty.

Senior executives face asymmetric career risk when adopting transformative technologies. Success may earn modest recognition, but failure can prove career-ending. External consultants serve multiple risk-mitigation functions. They provide air cover for controversial decisions, offer scapegoats if projects fail, and supply the specialized expertise that justifies major resource commitments to boards and investors.

This dynamic explains why Achtmeyer believes company bosses will always want input from senior partners on pressing decisions, regardless of AI capabilities. The psychology of executive decision-making hasn’t changed, even as the tools available have transformed.

Yet this trust factor has limits. Gartner’s 2025 research on management consulting predicts significant industry consolidation and specialization. As AI handles more analytical work, the consulting value proposition increasingly centers on judgment, relationships, and accountability rather than information processing. This suggests a smaller, more elite consulting industry rather than its wholesale displacement.

Lessons for Business Leaders

This situation offers several insights for executives navigating AI adoption:

• Treat implementation as an organizational challenge. Technology acquisition represents perhaps 20% of successful AI implementation. The remaining 80% involves organizational change, process redesign, and capability building. Budget and plan accordingly.
• Be deliberate about building internal capability. Partnerships with consulting firms provide faster access to expertise but may create dependency. JPMorgan Chase’s multi-year development of their COiN contract analysis platform illustrates the investment required to build internal AI capabilities—along with the resulting proprietary advantage and organizational learning.
• Scrutinize consulting partnership structures. The shift toward outcome-based pricing can align incentives, but agreements should specify measurable outcomes, include provisions for knowledge transfer, and avoid vendor lock-in.
• Lead the cultural change. The most sophisticated algorithms produce limited value if employees don’t trust them, don’t understand how to use them, or see them as threats rather than tools. Address these human factors explicitly.

The Broader Strategic Context

The OpenAI and Anthropic consulting partnerships reveal something important about the current state of artificial intelligence. Despite breathless media coverage of AI capabilities, actual business value creation remains nascent. The technology has outpaced organizational capacity to absorb it.

This gap creates opportunities for intermediaries who can bridge the divide between technological potential and operational reality. Management consultants currently occupy this space, but their tenure isn’t guaranteed. As Dylan Bolden of BCG notes, these partnerships aim to “help clients move from isolated AI pilots to full-scale reinvention of workflows.” Once that reinvention occurs and companies develop internal capabilities, the consulting role diminishes.

Unilever’s AI-enabled supply chain transformation, executed in partnership with Accenture, provides an instructive example. The project required three years and touched virtually every aspect of supply chain operations. Consultants played crucial roles in process redesign, change management, and technical integration. However, Unilever emerged with significantly enhanced internal AI capabilities. Future AI initiatives will rely less on external support as organizational learning compounds.

This pattern suggests a boom-bust cycle for AI consulting. Current demand reflects a one-time transformation as companies retool operations around AI capabilities. Once organizations complete this transformation and build internal expertise, consulting demand should moderate. The question facing consulting firms is whether they can create sustained revenue streams from AI or whether they’re experiencing a temporary spike before inevitable decline.

The Path Forward

Several scenarios could unfold over the next five years:

• Optimistic scenario for consultants: AI capabilities continue expanding faster than organizations can absorb them, creating sustained demand for implementation support. Consultants also evolve toward higher-value strategic work that AI cannot address.
• Pessimistic scenario: AI tools become sufficiently user-friendly that companies implement them with minimal external support. At the same time, AI-native leaders emerge who intuitively integrate these tools into operations, reducing consulting demand after the initial transformation wave.

The most likely outcome probably lies between these extremes. Consulting will transform rather than disappear. Firms will employ fewer junior associates and more specialized engineers. Revenue models will shift from labor leverage to outcome-based partnerships. The industry will shrink in headcount but potentially maintain revenue through higher-value, higher-margin work.

For AI companies like OpenAI and Anthropic, these partnerships represent pragmatic recognition that technological capability alone doesn’t guarantee market success. They need consultants to drive adoption, at least temporarily. The strategic question facing these AI companies is how to reduce this dependency over time—perhaps by making their tools more accessible or by building their own professional services capabilities.

Conclusion

The irony of AI companies needing management consultants to drive adoption shouldn’t obscure the fundamental insight this reveals: technology implementation remains primarily an organizational challenge rather than a technical one. This has been true for decades and shows no signs of changing despite AI’s transformative potential.

Business leaders should approach AI adoption with appropriate sophistication. The technology offers genuine value, but realizing that value requires sustained organizational effort. Partnerships with consultants can accelerate this process, but they work best when structured to build internal capabilities rather than create dependency.

The consulting industry faces both opportunity and threat. AI creates near-term demand for implementation support while simultaneously pressuring the traditional business model. Firms that successfully navigate this transition will focus on judgment, accountability, and strategic guidance rather than information processing and analysis.

Ultimately, both AI companies and consulting firms serve as intermediaries helping organizations navigate technological transition. The question isn’t whether AI will replace consultants or vice versa. Rather, it’s how both industries will evolve as businesses develop greater internal capability to adopt and utilize advanced technologies independently. That evolution has just begun, and its ultimate destination remains uncertain. What is certain is that successful organizations will focus less on the tools themselves and more on the organizational capabilities required to use them effectively.