The AIHE Moment: Why Funding Is the Start, Not the Finish

Lilly Endowment’s Artificial Intelligence in Higher Education (AIHE) initiative is one of the most significant investments in the future of Indiana’s colleges and universities in a generation.

With up to $500 million committed, Indiana institutions have two funding opportunities in Phase 2 of the initiative. Phase 2 implementation grants range from $5 million to $25 million each, depending on student enrollment, with a grant period of up to three years.

For Phase 2 collaboration grants, the endowment allocated up to $200 million. Each collaborating group identified a lead institution to submit a concept paper, and groups with compelling concepts are invited to submit full proposals by noon EST on October 2, 2026.

It’s a remarkable opportunity — and it’s also a test. Because here’s the reality of AI in higher education: Securing funding is an important milestone, but the longer-term work begins as institutions move from planning into implementation.

Activity isn’t the same as value

Walk onto almost any campus today and you’ll find a wide range of AI activity and opinion. Some faculty are experimenting with generative tools and building them into their courses. Others are deliberately holding back, concerned AI use can short-circuit critical thinking, and many students share that same concern.

Environmental questions about the energy and resources behind large models have become a live topic of campus debate, as well. This mix of enthusiasm, skepticism, and genuine ethical tension is exactly what makes AI in higher education different from a typical technology rollout, and it’s why activity and value aren’t the same thing.

We see a familiar pattern play out. Faculty and staff begin experimenting. Use cases multiply faster than leadership can prioritize them. Data gaps, governance questions, and unclear ownership slow everything down. And eventually leaders find themselves unable to connect all that activity to student outcomes or institutional value. The pilots stall in what many have started calling pilot purgatory.

This is why the strongest AIHE proposals may not be the ones with the longest list of tools. They’ll be the ones with a credible plan: Shared AI literacy, use cases tied to mission, prepared and trusted data, practical governance, and a clear operating model for how the institution moves from pilot to scale. Funders are looking for evidence an institution can turn dollars into durable capability.

Treat AI as an operating model, not a project

The most important mindset shift for higher education leaders is to stop thinking of AI as a technology project with a start and end date, and start thinking of it as an organizational capability compounding over time. A single successful pilot is nice. A repeatable way to identify, govern, build, and scale use cases across the institution can be transformational.

Curriculum modernization sits at the heart of this shift, and it’s a large part of what motivated the AIHE initiative in the first place. One of the initiative’s central goals is to help institutions develop new or enhance existing strategies to improve students’ educational opportunities and outcomes and their preparation to prosper in a workplace and life increasingly shaped by AI.

That work should begin with a clear view of how employers expect graduates to use AI within their fields. Institutions can draw on employer, alumni, career services, and industry networks to identify emerging expectations, translate them into discipline-relevant competencies, and determine where those competencies belong in existing programs.

Importantly, this doesn’t necessarily mean creating a new AI degree or certificate. In many cases the opportunity is embedding appropriate AI use, critical evaluation, and responsible decision-making into existing programs.

That capability rests on a few foundations unrelated to a particular model or platform:

AI literacy across functions

AI literacy isn’t a single, institution-wide curriculum. Institutions need a shared foundation for responsible use while tailoring training and application to the needs of different disciplines, functions, and learner populations. That approach may look different from one institution to the next, and literacy for an English major isn’t the same as literacy for an engineer.

Use cases anchored to mission

Efforts should connect to retention, student success, teaching, research support, or administrative efficiency, and be prioritized by value, feasibility, and readiness.

Trusted data foundations

For many institutions, the highest-value starting point is a more connected view of the student lifecycle, from prospective student through enrollment, progression, graduation, and alumni engagement. This doesn’t require replacing every existing system. It means identifying the data needed for priority decisions and establishing a trusted foundation around those use cases. Data modernization is a substantial opportunity in its own right.

Governance that enables, not blocks

Practical, FERPA and GLBA-aligned guardrails clarify what’s allowed, what needs approval, and who owns the risk, so teams can move with confidence.

An operating model for scale

Scaling requires ownership, change management, adoption planning, and measurement, not just a better tool.

Where AI creates real value on campus

When these foundations are in place, the opportunities become clearer and easier to defend. Rather than a narrow list of point solutions, the highest-value work tends to cluster in a set of broader strategies, each tied to a measurable institutional outcome.

None of these depend on exotic technology. They depend on trusted data, clear governance, and people ready to adopt them.

A disciplined path from grant to results

Turning AIHE funding into value doesn’t require a giant transformation program on day one; it requires discipline. Start by assessing where you are and identifying high-impact, low-friction opportunities.

Prioritize them honestly against value, feasibility, and readiness. Prove a small number of wins to build momentum and confidence. Then put the governance and operating model in place to scale what works and retire what doesn’t.

This is the essence of an assess, adopt, accelerate approach, and it’s deliberately unglamorous. It favors stacking small, credible wins over betting everything on a moonshot. It treats AI initiatives as a managed portfolio rather than a collection of disconnected experiments. And it keeps people, not technology, at the center, because adoption is where most AI value is won or lost.

An advisor down the road, not across the country

Executing on that path is easier with an advisor who knows your institutions and is close enough to sit at the table. CLA digital brings strategy, data science, and AI engineering together from our Indianapolis digital hub.

That proximity matters. It means faster cycles, in-person relationships, and people who understand the realities of Indiana’s colleges and universities rather than a team parachuting in from another region.

It also means you get more than advice. Our teams do the work, from early strategy and readiness through solution design, engineering, and ongoing operation. We help institutions:

• Define the strategy — Assess readiness, prioritize opportunities, establish governance, and develop an actionable roadmap.
• Prepare faculty, staff, and leaders — Develop role- and discipline-specific AI literacy, training, and enablement.
• Align curriculum with workforce needs — Gather employer input, define AI competencies, and advise on curriculum modernization while institutions retain academic ownership.
• Modernize data and decision-making — Connect priority data, improve the visibility of the student lifecycle, and develop analytics supporting enrollment, retention, financial aid, and advancement.
• Design and engineer solutions — Prototype and build targeted AI, analytics, automation, and software solutions.
• Deliver and sustain change — Provide project delivery, organizational change management, adoption planning, measurement, and continuous improvement.

Because CLA brings CPAs, consultants, and technologists under one roof, we connect AI strategy directly to finance, operations, and the measurable outcomes your board cares about. We proved this approach inside our own firm first, as our own client zero, before bringing it to the institutions we serve. And as a firm with roots across Indiana and the Midwest, we are invested in the same communities your graduates will go on to strengthen.

The window is open now

Students are arriving on campus with AI habits already formed. Employers increasingly assume AI fluency in graduates. And for a limited time, Indiana institutions have funding available to meet this moment deliberately rather than reactively.

The colleges and universities treating AIHE as a catalyst for building lasting capability, and not simply as a budget to spend, can help strengthen student outcomes, modernize operations, and distinguish themselves for years to come.

The funding is the start. The results are the point. And the advisor to help you get there is right here in Indiana.