How Colleges Can Kick Their Addiction to Consultants

American colleges are spending far too much on consulting firms. Recent investigations reveal staggering numbers: $51 million at the University of Wisconsin, $4.7 million at the University of Florida, and similar seven- and eight-figure contracts across major institutions have been signed with consulting firms like McKinsey, Huron, and Gray Decision Intelligence. These expensive engagements, sometime fully in the open but often shrouded in secrecy until exposed by reporters, represent more than just financial waste. They signal a fundamental crisis in higher-education leadership: the belief that external consultants, rather than internal expertise, hold the keys to institutional transformation.

But 2025 will mark the end of this era, as artificial intelligence (AI) emerges as a more-capable, cost-effective alternative. New premium models such as OpenAI’s GPT-4o, Anthropic’s Claude 3.5 Sonnet, and Google Gemini are capable of ever more sophisticated data aggregation, analysis, scenario planning, and interpretation. Adopting such technology will help keep strategic knowledge where it belongs: within the institution itself.

In The Big Con: How the Consulting Industry Weakens Our Businesses, Infantilizes Our Governments, and Warps Our Economies (2023), the economists Mariana Mazzucato and Rosie Collington criticize consulting firms for hollowing out government-sector expertise: “The costs incurred are often much higher than if government had invested in the capacity to do the job and learned how to improve processes along the way,” they write. “Internal expertise all too often gets shunned in favor of contracting a global consultancy.” This creates an expertise deficit, since “the less an organization does something, the less it knows how to do it.”

The big “con” in consulting, Mazzucato and Collington write, is that the expertise consultants sell isn’t the important kind. They sell general cases, not specific applications. About half the slide deck a consulting firm uses in its proposal presentation was developed for previous clients. Such general expertise “should not be confused with the kind of in-depth insight and ‘tacit’ knowledge that employees working within a company, field, or sector are able to build up over a long career.” In fact, consultants’ highly general understanding of the problems they are brought in to address may actually undermine their ability to provide tailored solutions for each client.

A similar dynamic obtains in the higher-education sector. When top administrators hire outside consultants, they risk destroying the leadership bench in their own organizations and leaving themselves less able to prepare for changes ahead. The problem with hiring a firm like McKinsey has always been that its consultants come away better than the client, both as a matter of pocketbook and of wisdom. This is particularly worrisome in higher ed, where the whole point is the creation and development of knowledge. Every university that hires an efficiency or reorganization consultant is effectively announcing publicly that it doesn’t trust its own people.

When colleges outsource their strategic thinking to consulting firms, they systematically hollow out their own capacity for pattern recognition, innovation, and scenario planning. Each time a consultant produces an efficiency analysis or strategic plan, they don’t just walk away with millions in fees, they don’t value that institutional knowledge — local knowledge — that should have been leveraged and developed internally. This expertise exodus creates a vicious cycle: As colleges become more dependent on external consultants, their internal teams become less capable of handling complex challenges, leading to even more consulting engagements.

There is, however, a promising solution to the expertise deficit created by overdependence on consultants: AI. Tossing out high-priced consultants and embracing AI to improve internal processes and adapt for the changes that are coming may help save colleges from their public-confidence death spiral.

In my own experience, colleges do not know themselves well. Data is fragmented and collected (if at all) unsystematically across individual departments, alumni offices, student clubs, the registrar, old catalogs, and oral histories. Unlike consultants who take their insights with them, AI tools can be integrated into the fabric of university operations, helping internal teams leverage crucial institutional knowledge while building upon it over time.

AI can already do everything that consultants do: access, process, and synthesize information from the same vast global database that consultants use. But internal AI algorithms can also allow university leaders to analyze and evaluate FERPA-protected student data on historical student success (and failure), helping to identify patterns and address obstacles long overlooked. Deans and chairs who already know the anecdotal data about departments over time can work with AI to weigh new allocation models, take action, and think through repercussions.

The first challenge is to convince the faculty, who are already skeptical of AI’s expanding footprint on campus, that what AI does is actually “analysis” or “interpretation.” I would urge all faculty members to familiarize themselves with the basics of what AI can do, especially those who are already in the midst of a consultant-led reorganization or restructuring. Put questions to a free or premium model. Ask how it would recommend going about doing the restructuring or achieving the efficiency metrics to be implemented. Ask it to define resource management, allocation formulas, and space utilization. Ask what data it would need to help. Gray DI offers market data by ZIP code, but departments and alumni offices typically know which of their alumni find jobs, and where. Consultants, alas, often do not. At Sonoma State University, for instance, a consultant-led restructuring caused the entire philosophy department to be cut, when in fact those graduates had been getting jobs for decades. When it comes to such consultants, college leaders should ask themselves: What are we paying for, exactly?

Consultants who parachute in for weeks or months do not have the deep institutional knowledge required for true wisdom. They offer standardized frameworks dressed up as customized solutions, while the actual holders of expertise — the faculty, staff, and administrators who understand their institution’s unique culture, constraints, and capabilities — are sidelined. AI can now handle the data analysis and pattern recognition, allowing colleges to better invest in their own people’s capacity to make judgments about their programs and curricula. Who better to navigate that final stretch between data and decision than the people who walk that last mile every day?

Moreover, AI has no need to maintain the polite fiction that it’s providing independent strategic wisdom rather than political cover for outsourcing recommendations for cuts and layoffs. That could lead to more honest corporate behavior in resource management.

The choice facing college leaders in 2025 will be clear: pay premium rates for consultants who extract institutional knowledge while providing political cover — or embrace AI solutions that empower internal teams to prepare for changes to come. The stakes extend beyond mere efficiency. Institutions that successfully transition from consultant dependency to AI-enhanced internal capacity will emerge stronger, more adaptable, and better positioned to fulfill their educational missions. In an era when higher education faces unprecedented challenges, the end of the consultant era might be exactly what colleges need to rediscover their capacity for self-directed innovation and change. The consultants are indeed leaving, and it’s about time.