AI, Accreditation And The Future Of Faculty & Staff Roles

In an earlier piece, we argued that higher education has long struggled to align its core human capital systems. Faculty are treated as instructional labor rather than strategic talent. HR units are focused more on compliance than capability-building. Accreditation is often a bureaucratic exercise rather than a framework for mission and purpose. These silos limit innovation, adaptability, and strategic clarity.

Today, the arrival of generative AI makes that misalignment impossible to ignore.

AI is transforming how students learn, how teaching happens, how research is conducted, and how administrative work is performed. The disruptions are real and accelerating. The risk is not that AI will replace the people who make universities work. It is that institutions will fail to redefine and support the roles of those people in ways that preserve meaning, capability, and continuity.

Yet AI also presents a powerful opportunity. Used intentionally, it can help faculty and staff return to the work that matters most: designing and redesigning effective and engaging learning experiences, mentoring students, advancing scholarship, and cultivating intellectual community.

Whether AI becomes a threat or an enabler depends on how institutions respond today.

THE NATURE OF ACADEMIC WORK IS CHANGING

AI is being integrated across nearly every academic and administrative domain:

• Teaching: lesson development, instructional support, and feedback modeling.
• Research: literature scanning, conceptual outlining, and data-assisted synthesis.
• Student services: advising triage, scheduling, and information support.
• Administration: workflow-heavy tasks around student recruitment, enrollment, and HR.

AI automates tasks, not relationships. And higher education has long been overloaded with tasks that pull people away from the human core of their work: students, scholarship, and collaboration.

Used well, AI can finally allow faculty and staff to operate where they add the most value. But that outcome will not happen automatically.

THE TEXAS LESSON: DON’T EAT YOUR SEED CORN

There is a saying in Texas ranching folklore: “Don’t eat your seed corn.” You do not consume the resources meant to ensure the next generation’s harvest.

That wisdom applies directly to universities in the age of AI.

Across the United States, the tech sector has slowed or halted entry-level hiring, not because the work is gone but because AI can now perform much of the early-career workload that once built human capability. The same risk now looms over higher education.

Junior staff roles often center on data entry, documentation, scheduling, research preparation, and advising support, the exact functions most exposed to automation. Eliminating these roles may deliver short-term efficiency, but it destroys the very pipeline that sustains institutional vitality.

Instead, universities should redesign these positions as developmental pathways for the skills that matter most in a human-centered institution: communication, empathy, collaboration, and problem-solving. These are the “soft” but indispensable capabilities that ensure excellent student service, create welcoming campus environments, and translate institutional mission into lived experience. AI can process information, but only people can build trust, connection, and belonging — and that is what sustains both learning and loyalty.

If universities “eat their seed corn,” they will lose the next generation of skilled staff and faculty leaders who carry forward institutional mission, culture, and adaptability.

DON’T SQUAT WITH YOUR SPURS ON

As Texans like to say, “Don’t squat with your spurs on.”

In other words, think before you act. For universities, that means avoiding the temptation to rush into AI adoption or rewrite strategic plans without first ensuring that human capital systems, accreditation standards, and mission are aligned. Implementing new technologies or frameworks without foresight can cause more pain than progress.

AI promises tremendous potential for teaching, research, and operations, but only when deployed with care, coherence, and a clear sense of purpose. It must be aligned with the institution’s strategy, mission, and values, not adopted simply because it is fashionable or to meet compliance expectations. Doing something because everyone else is doing it will not bring meaningful or sustainable benefits. That is where accreditation can become a guiding framework rather than a bureaucratic hurdle, helping universities ensure that every AI initiative supports their distinctive goals and reinforces their long-term vision.

ACCREDITATION AS A STRATEGIC LEVER, NOT A COMPLIANCE EXERCISE

Too often, business schools and universities treat accreditation such as AACSB or EQUIS as an administrative hurdle or periodic audit. But accreditation, used strategically, can be a blueprint for aligning people, purpose, and performance, especially now that AI is transforming how academic work gets done.

Nearly every institutional strategic plan focuses on four familiar pillars: growth, quality, research, and partnerships.The challenge is that these goals often live in separate silos and are disconnected from how human capital is cultivated or how technology is adopted. Accreditation standards can help bridge that gap:

• Growth: Accreditation standards ask institutions to demonstrate mission-driven innovation. AI can enhance data-informed recruitment, student success analytics, and personalized learning at scale, helping institutions grow while staying true to their mission.
• Quality: Continuous improvement is at the heart of AACSB and similar frameworks. AI can support faculty by automating administrative work and providing learning insights, enabling deeper teaching quality and measurable outcomes.
• Research: Accreditation requires impact and intellectual contribution. AI tools can accelerate synthesis, collaboration, and discovery, but accreditors should push institutions to clarify why their research matters and how it aligns with mission and societal impact.
• Partnerships: From industry collaborations to community engagement, AI can create new opportunities for shared innovation. Accreditation can guide these partnerships to remain ethical, globally minded, and student-centered.

When woven into strategic planning, accreditation becomes not a checklist but a strategic compass that helps universities ensure that AI adoption strengthens, rather than fragments, institutional purpose.

EMBEDDING VALUES, ACCOUNTABILITY & HUMAN CAPITAL

What often goes unmeasured in strategic plans are the deeper values embedded in modern accreditation frameworks: societal impact, ethics and integrity, diversity, equity, inclusion, agility, and global mindset.

These are not “add-ons.” They define how institutions sustain trust and relevance in a world reshaped by AI.

The next frontier is to connect these values directly to faculty and staff evaluations and rewards. Too many universities articulate lofty goals but assess individuals only on teaching, research, and service without linking those outputs to the unit’s strategy or mission.

This is why so many schools struggle with societal impact. They compile and report everything being done rather than focusing on what matters most. AI can help measure and visualize impact, but it cannot create strategic alignment where none exists.

Accreditation can drive that alignment by tying strategic goals, AI initiatives, and human capital systems into a coherent framework that rewards the work that truly sustains academic excellence.

DESIGNING A MORE HUMAN FUTURE

AI will not replace the people who make universities work. But it will reshape how their value is defined and supported.

Accreditation offers a roadmap for this transition if institutions use it to:

• Establish joint AI and human capital councils that bridge faculty, staff, and leadership.
• Redesign entry-level roles to focus on coordination, collaboration, and data literacy.
• Rebalance faculty evaluation to reward mentorship, innovation, and societal impact.
• Build tiered AI literacy across all levels.
• Rethink assessment models toward process, reflection, and applied problem-solving.

As the Texas saying reminds us, “Don’t eat your seed corn.” AI should not be used to shrink talent pipelines or erase developmental pathways. It should be used to strengthen the human core of academic work.

Done poorly, AI adoption will narrow roles, reduce opportunity, and weaken resilience.

Done well, and guided by purposeful accreditation frameworks, it will restore meaning to faculty work, expand staff capability, and strengthen universities as vibrant communities of learning and inquiry.

The future of academic work is not less human. It is more human. But only if we design and accredit it that way.

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Benjamin Stevenin is special adviser to Poets&Quants and former Director of Business School Solutions and Partnerships at Times Higher Education. Geralyn McClure Franklin is an executive search consultant with Higher Education Leadership Search and a retired business school dean who led schools in Texas, Florida, Louisiana, and the United Arab Emirates. 

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