Six Strategic Approaches

Many higher education leaders view AI as a potential way to address the challenges organizations across the country are facing, such as staffing shortages, rising administrative costs, and operational inefficiencies. Adopting AI can help reduce manual work, provide more data for improved decision making, and allow staff to focus on mission-driven priorities. Yet, there is a significant gap between curiosity and adoption. As we found in our AI Transformation: A Look Into an AI-Forward Future webinar, 15% of higher education institutions reported they’re not prepared at all for the future of AI, 46% reported feeling a little prepared, and 29% reported somewhat prepared.

The organizations making the most progress aren’t beginning with technology. They’re starting with strategic questions. They’re looking ahead, into the near and distant future, to understand the problems they’re trying to solve and how they can leverage this technology to render a long-term advantage. Asking the questions below can give your institution a head start.

1. What Problem Are We Actually Trying to Solve?

At most institutions, long-term sustainability, mission preservation, and student outcomes steer strategic conversations. Strategic questions can range from “How can we grow in a shrinking market” to “How can we recruit and retain top talent in an environment with limited resources?” AI should be part of the answer to these questions, not an independent one-off initiative. If your institution is struggling to implement AI, it may be because you’re struggling to tie to your broader strategic landscape.

Once your opportunities and threats are identified, it can be easier to understand where you need the most support. If your institution struggles with operational efficiencies, it may be time to think about how to leverage the technology to automate routine tasks. If you’re more focused on revenue growth, it may be that AI can augment some of the work your teams are already doing in fundraising, enrollment management, and investing.

Staff spend hours compiling reports, reconciling information across multiple systems, responding to routine inquiries, or manually analyzing data. As we found in our webinar, 44% of institutions report that routine operational work such as data entry is something they’d like to offload to an AI agent in the next three years. Eleven percent reported wanting to offload monitoring, controls, & risk management such as fraud and 11% reported wanting to offload analytical, decision-making activities such as forecasting. These pain points create a natural starting point for AI conversations.

2. Where Can AI Quickly Create Value & Help Solve Problems?

Many successful AI initiatives begin by identifying a specific problem rather than an AI platform or tool. Rather than asking, "How can we use AI?" leadership teams should ask, "Where are our people spending time on work that technology could simplify or augment?" The objective isn’t simply to automate tasks. It’s to help reduce administrative burden and create capacity for relationship-building, strategic thinking, program delivery, and other mission-focused activities.

Though many higher ed leaders may assume AI requires a large technology investment, some of the most practical applications build on data, systems, and workflows organizations already have in place.

Across higher ed functions, early use cases often include:

Donor segmentation and personalized outreach
Fundraising analytics and donor propensity modeling
Financial reporting and forecasting
Accounts payable workflow support
Grant compliance monitoring
Internal knowledge management and policy search
Employee onboarding and people analytics
Program review and reporting
Student success tracking and intervention

The common thread is practicality over technological sophistication. Prioritizing solutions is a strong way to get return on investment quickly.

3. Which Use Case Should We Start With?

One common challenge is trying to tackle too many initiatives at once. The institutions seeing the strongest early results tend to start with focused pilot projects rather than large-scale transformations. These pilots are often times designed to address a specific challenge while producing measurable outcomes that leadership can evaluate in a relatively short amount of time. This should also help reduce confusion about how and why you’re using AI on campus.

An initial pilot typically addresses a manual, repetitive process; involves limited operational complexity; relies on existing data and systems; produces results that can be measured clearly; and causes minimal disruption to daily operations.

The experience of one higher education institution offers a useful example. The institution was looking to evaluate their AI readiness and develop a strategic framework for institution-wide AI adoption. After conducting a readiness assessment through 40+ stakeholder interviews, a comprehensive, responsible AI strategy and three-year implementation roadmap was developed, along with identifying and prioritizing AI use cases. The institution was then equipped with a scalable approach to AI adoption that supports student success, workforce readiness, institutional effectiveness, and long-term innovation while maximizing the value of AI investments in alignment with their mission.

For many institutions, beginning with a focused use case, addressing a manual burden, and building on early successes can help create organizational momentum.

This measured approach often builds organizational confidence in addition to these efficiency gains.

4. Do Our People Understand AI & How They Can Use It?

Many leaders identify limited expertise and staff capacity as significant barriers to wider AI adoption. Concerns or confusion about change, unfamiliar tools, and evolving roles can slow progress even when promising use cases exist.

Organizations experiencing sustainable adoption often invest in people and processes alongside technology. That includes explaining why AI is being introduced, communicating how it supports organizational goals, providing role-specific training, encouraging experimentation and feedback, and clarifying where human judgment remains essential. Framing adoption as a strategic imperative, to the extent that’s true, can also be helpful. This is yet another reason to think about AI as supporting your institution-wide strategic plan.

One of the most important messages leaders can reinforce is that AI is not a replacement for employees. AI can support routine tasks so employees can focus on work requiring judgment, creativity, empathy, and relationship-building. Said differently, emphasizing the “why” behind AI initiatives is just as important as the “how.”

5. Do Our Policies Enable Us to Get the Most From AI?

Institutions are already experimenting with AI, while critical policies often remain underdeveloped. To help address potential risks, organizations may want to establish clear guardrails around acceptable AI use, data stewardship and privacy, human review requirements, vendor selection criteria, and accountability.

This does not require building an extensive governance infrastructure on day one. However, organizations can evaluate which data can be used, where sensitive information may warrant additional restrictions, and how outputs can be reviewed before being incorporated into important decisions. Ideally, this task can be delegated to cross-functional governance groups that evaluate opportunities, prioritize use cases, and establish responsible practices as adoption expands.

On a related note, many organizations struggle to get the most from AI technology because their data isn’t clean, managed, or stored properly. Data hygiene and related policies prevent you from having a “garbage in, garbage out” problem down the line.

6. How Will We Measure Success?

Organizations may initially evaluate success based on whether a new tool was deployed on time. A more meaningful question is whether the initiative served strategic goals.

Common measures may include, reduced manual reporting effort, stronger strategic alignment between functional units, faster access to organizational insights, improved financial visibility, more confidence in quick decisions, stronger donor engagement, and indicators aligned with student outcomes.

As in any strategic planning process, the strongest programs establish these measures before implementation and compare results over time. This can help create clarity for leadership teams, boards, and staff and helps organizations determine whether an initiative should be expanded, revised, or discontinued. Most importantly, measurement helps keep AI initiatives aligned with organizational priorities.

Moving From Experimentation to Execution

While AI isn’t a solution to every challenge or a fast-track to realizing every opportunity, it can be a powerful tool when applied thoughtfully to the right problems.

Chasing technology trends may be less effective than identifying specific opportunities to reduce administrative burden, improve decision making, and increase organizational capacity. Organizations should begin with focused use cases, supporting staff through change, establishing appropriate guardrails, and measuring outcomes that make an impact.

As institutions move from experimentation to adoption, experienced advisors, like the team at Forvis Mazars, can help leaders evaluate opportunities, prioritize investments, develop implementation road maps, and create governance structures that align with organizational goals.

Continue Learning

To explore additional resources on AI strategy in higher education or to connect with an AI professional at Forvis Mazars, visit our website. We also invite you to register for our webinar, Leading Into 2027: What Executives Need to Know About AI, coming up on December 10.

Source note: Statistics, findings, and referenced examples are drawn from the AI Transformation: A Look Into an AI-Forward Future webinar.

The information set forth in this presentation contains the analysis and conclusions of the author(s) based upon his/her/their research and analysis of industry information and legal authorities. Such analysis and conclusions should not be deemed opinions or conclusions by Forvis Mazars or the author(s) as to any individual situation as situations are fact-specific. The reader should perform their own analysis and form their own conclusions regarding any specific situation. Further, the author(s)’ conclusions may be revised without notice with or without changes in industry information and legal authorities.

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