Colleges and universities are the gas that drives the world’s engines of innovation, advancing discovery, challenging convention, and teaching critical thinking. Yet these institutions trail far behind their corporate peers when adopting artificial intelligence (AI) for their finance teams.
The reasons have nothing to do with intelligence or imagination. They’re structural, cultural, and, to some degree, the byproduct of the same values that make higher education exceptional. Can their CFOs mend the gap? And should they?
Higher education was built for freedom, not for speed
Although they are businesses in their own right, universities were never meant to run like corporations. They’re federations of semi-autonomous units, such as schools, labs, auxiliaries, and hospitals, each with its own budget, systems, and governance. Every new tool, policy, or workflow must pass through layers of review and consensus-building to gain approval. To faculty, this complex process ensures transparency and inclusion. It’s a feature (not a bug) of an ecosystem built on collaboration, deliberation, and trust.
But the same decentralized structure that protects academic freedom also makes institutional innovation painstakingly slow. To finance leaders trying to modernize operations, the process moves like molasses. The challenge is finding technologies that respect that governance model while delivering the speed, accuracy, and resilience modern finance demands.
Maintaining stability as the paradigm shifts to AI
While academia thrives on intellectual disruption, it also relies on operational stability. Tenured positions, long-term research grants, and predictable funding cycles form its backbone. Finance leaders in higher education are stewards of institutions meant to last centuries. Their instinct is preservation. When new technology arrives wrapped in hype and uncertainty, a cautious approach is simply responsible governance.
Yet, as operating costs rise while headcount remains flat, the status quo has become unsustainable. Shrinking budgets, complex compliance rules, manual processes, and overextended staff are increasingly commonplace. There is a growing need to transition toward tools that can relieve those pressures. As the paradigm shifts toward AI-driven finance operations, how do finance leaders turn an instinct for caution into deliberate, well-scoped steps that keep their institutions relevant and competitive?
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Finance AI and ethical caution
The mandate to drive efficiency is also in constant tension with the responsibility to uphold ethical and environmental standards, making even well-intentioned finance modernization feel risky. AI’s reputation has been complicated from the start. Many academic communities worry about its potential to erode independent thought, its energy and water use, and its appetite for intellectual property. Inside universities, where ethical debates take place daily, finance leaders are acutely aware of how adopting AI tools might be perceived.
That same ethical lens is also higher education’s greatest advantage. Where corporations tend to race ahead and clean up later, universities can define what responsible AI adoption looks like, setting the moral and operational standard for everyone else: transparency, explainability, and in alignment with institutional values.
The paradox of progress in academia
There’s another reason the adoption gap persists: universities champion innovation and teach adaptability but resist it internally. They prepare students for a fast-changing economy even as their own systems run on aging infrastructure.
Corporate CFOs justify new technology in terms of profit and efficiency. University CFOs must justify it through mission and compliance. “Efficiency” doesn’t inspire a budget committee the way “student success” does. Updating finance automation systems, despite the potential to strengthen institutional sustainability, often falls short on the priority list.
Finance AI doesn’t directly touch the classroom, so it can feel secondary to the mission of teaching and research. Yet it’s precisely what can fund that mission more sustainably. The more time finance teams spend buried in manual reconciliation, invoice processing, or policy enforcement, the less capacity they have to guide strategic decisions that protect the institution’s future.
AI redefines a finance team’s expertise
AI also challenges the university’s deeper identity as the arbiter of human knowledge. When algorithms can analyze data, interpret policy, and spot fraud faster than humans, it raises uncomfortable questions about what human expertise is worth, and what students are being trained for.
Inside finance offices, that discomfort can manifest as hesitation. Staff worry that automation will replace jobs or devalue their judgment. Leaders worry that retraining on AI tools will lower morale.
But AI can also elevate a team’s professional expertise. It can take on repetitive, error-prone work, such as matching invoices, verifying receipts, and enforcing policy. People can then focus on analysis, insight, and institutional stewardship. It moves human talent up the value chain, turning transactional roles into strategic ones. For staff, it’s the evolution of finance jobs. The key to that evolution, and to ultimately retaining those critical jobs, is something schools do best: teaching and inspiring.
From caution to confidence
Trust is built on direct experience rather than speculation. AI, specifically AI for finance, has matured enough as a field to earn a level of confidence that the discipline of artificial general intelligence (AGI) has not. These AI systems focus on narrow, well defined tasks with clear performance thresholds. Finance teams currently working with AI have processed millions of invoices and expense reports, flagged real cases of noncompliance, and successfully passed internal audits. And the more robust AI analytics framework has improved their financial decision-making.
Modern finance AI systems don’t require risky rebuilds or deep technical ability. They don’t need IT teams to step in and make constant coding adjustments. They work within existing ERPs and procurement stacks, reading PDFs, classifying transactions, and enforcing policy with audit-ready documentation. They maintain guardrails and have layers of safety measures in place that protect data and systems. Overburdened teams find they have more control, visibility, and room to take on career-defining responsibilities.
Institutions that have taken the leap are seeing measurable impact. Georgetown University, for example, reduced its accounts payable cycle time by 76%. Invoices that once took days to clear now move in hours, with full transparency for auditors. The University saved more than 1,600 staff hours by adopting autonomous processing. Today, staff spend their valuable time on more strategic activities.
“When I took over AP, we had eight people, and today we still have eight. We didn’t eliminate any positions, we just transformed their roles. Instead of spending 90% of their time on data entry, they’ve become subject matter experts on the entire procure-to-pay process.”
Jon Hendrix, AVP of Revenue, Receivables, and Payables, Georgetown University
Across higher education, early adopters report similar outcomes: improved data analysis, faster reimbursements, fewer errors, and finance teams with the bandwidth to focus on what truly matters—supporting faculty, students, and research.
As teams learn new skills and new systems, they also elevate their careers. The positions available when workers retire or move on are more attractive to younger workers, who want to join a workforce that will grow as they do and move them up in the world.
These finance teams showcase what colleges do best, supporting, teaching, elevating, and helping their staff to adapt to the world as it will be tomorrow.
Reframing the question
Maybe it’s time to shift the conversation from “Should higher education use AI in finance?” to “How can we use AI to strengthen our mission?”
AI doesn’t have to replace the human judgment that defines academia. It can reinforce it through better data, faster analysis, and a more consistent application of policy. Models with secure, ethical guardrails that are deeply trained on what finance needs most give institutions the ability to steward limited resources with the same integrity they apply to scholarship.
Finance AI is also one of the few technologies that can demonstrate both near-term ROI and long-term mission alignment. Automating repetitive tasks reduces administrative overhead. It accelerates payments. And minimizes compliance risk. This frees resources to invest back into teaching and research.
A responsible path forward for higher ed finance
While higher education’s deliberate pace can feel like a disadvantage, it builds a level of trust many other areas of business struggle to match. The institutions that approach AI with patience, transparency, and a commitment to shared governance will lead the way. By piloting smarter finance workflows in controlled, policy-driven environments, universities can model responsible AI adoption, inspiring talented students, staff, and faculty to join and remain. Training teams to oversee and interpret AI decisions demonstrates the complimentary nature of institutional excellence and technological innovation.
Ultimately, AI doesn’t define the mission of higher education; it is a tool in service of it. Used well, it gives the people who shape that mission the data and clarity to pursue it more fully. Now is the time to mend the finance AI adoption gap and build a more resilient future. The result will be an operation that is faster, more focused, and more precise, while staying closely aligned with the ideals of stewardship, accountability, and the pursuit of knowledge.
