Is it possible for trustees to govern a billion-dollar institution while staying informed on every detail they’re responsible for? We like to think so, but the reality is, there are very few (= no) board members with these superpowers.
One must look no further than the daily headlines to understand that higher education governance has never been more challenging—from Harvard’s governance challenges to Columbia’s board oversight issues—boards face a growing matrix of oversight responsibilities that require sophisticated tools and approaches to keep their heads above water.
Meanwhile, artificial intelligence has emerged as a practical solution for many of the information management and strategic alignment challenges that plague modern board governance. Companies like Onboard and platforms such as Diligent’s Governance Cloud and BoardEffect are demonstrating how AI can address longstanding governance pain points while enhancing trustee effectiveness.
If you are a board member or higher education leader, you should understand how AI can improve governance outcomes while recognizing the implementation considerations that ensure institutional protection and board effectiveness.
The Current State of Higher Education Governance
Over the past two decades, higher education governance has evolved significantly due to increased accountability demands, financial pressures, and convoluted stakeholder expectations. Sitting on a higher ed board is probably one of, if not the most challenging board assignment anybody can have. And this is made even more difficult when the sum total of a board member’s experience at a higher ed institution resulted in their bachelor’s degree.
You may chuckle about this, but when you stop and think about how many public entity boards are filled by gubernatorial or legislative appointed members as a “payback” for donating to their political campaign, you can start to understand why some of the boards make the decisions that they make. You would never have someone on Google’s board or Intel’s board who were not familiar with high-tech. Yet, this very thing happens with higher education boards.
And then there is the keeping track of everything.
Higher education institutions operate with the complexity of small cities, managing academic programs, campus security, housing, dining, healthcare, and community relations. This expanding matrix of operations creates an overload of information that trustees must evaluate to make informed decisions about institutional direction and performance.
The traditional approach of distributing board books—sometimes reaching 500 pages or more—two weeks before meetings no longer serves institutional needs or trustee effectiveness. There’s tremendous energy and overhead spent on material gathering, synthesizing, and publishing that detracts from the trustees’ ability to better prepare and digest information.
How AI Addresses Governance Challenges
Here’s where I think boards can utilize AI to gain some of their life back—by helping boards manage information overload while maintaining strategic focus by processing large volumes of institutional data, track strategic alignment over time, and providing trustees with the insights they need for effective oversight.
Well-implemented AI governance tools address three important board functions: information processing, strategic monitoring, and institutional intelligence gathering.
Information Processing
AI can review mountains of board materials and highlight key issues, trends, and decisions requiring trustee attention. Research with 27 board chairs found that effective chairs function like conductors: “The role of the chair is like a conductor in an orchestra, where you don’t actually play—you don’t make any sounds, you don’t play any instruments, but you’re responsible for the final sound.” AI can help ensure that conductor has the right information at the right time.
Strategic Monitoring
AI’s ability to track how board discussions and institutional activities align with approved strategic plans over time is particularly valuable. Drawing from Jack Welch’s approach at GE, which entailed synchronized board and operational planning calendars, AI can identify areas where the institution may be drifting from strategic priorities or where board attention has been lacking.
Institutional Intelligence Gathering
AI can process surveys and feedback from multiple stakeholder groups—faculty, students, staff, alumni, and community members—providing boards with broader institutional perspectives than traditional administrative reports might offer. The voice of all constituents needs to be heard in the university boardroom without passing through filters. This broader stakeholder input should inform agenda development, though board leadership retains authority over meeting structure and strategic timing.
Practical AI Applications in Board Operations
Companies like Onboard are implementing AI in several practical ways for their client organizations. Document management and synthesis allow AI to process board books, committee reports, and supporting materials to create executive summaries that highlight key decisions, red flags, and strategic connections trustees might otherwise miss.
We’re getting close to the point where trustees can interact with board management systems much like ChatGPT, asking to summarize pending quarterly board materials and turning those 500-page board books mentioned earlier into five pages or less. The system can then respond to follow-up questions about the most important subjects requiring board focus based on strategy and past meetings.
Meeting preparation enhancement enables AI to provide trustees with personalized briefings based on their committee assignments and areas of expertise, ensuring more productive board discussions and better-informed decision-making.
Strategic alignment tracking helps boards monitor whether their discussions and decisions consistently support approved institutional goals. AI can flag when boards are spending disproportionate time on operational issues versus strategic priorities, or when key strategic areas have been neglected.
Historical analysis and institutional memory allow AI to analyze years of board minutes and institutional data to identify patterns, track decision outcomes, and provide context that enhances current governance decisions. This capability addresses the challenge of trying to consolidate information across multiple meetings, documents, minutes, and agendas backwards, which is incredibly challenging for humans but manageable for AI.
AI Improved Board Composition and Effectiveness
AI can support better board composition through skills gap analysis that compares current trustee expertise with the competencies needed for effective institutional oversight. When we do work with boards, we use a proprietary board skills matrix—a tool that helps the board (and especially the Nom/Gov committee) suggest the necessary skills that board members should have based on the strategic plan and what are the oversight needs. The Change Leader’s methodology links board skills to institutional strategy, ensuring that trustee expertise directly supports oversight in critical areas such as academic quality, financial sustainability, innovation, and workforce development.
The board composition challenges are significant. I can think of one seven-member board at a land-grant R1 University we encountered that had four lawyers, one nurse, one former military officer who used to be the superintendent of one of the academies, and an accountant, but zero expertise in higher education. (This board was politically appointed based on contributions to the governor’s election campaign.) How can a board of trustees look at what they need to be looking at if they don’t have the proper expertise to do that?
AI-supported analysis can help nominating committees understand what skills the board currently possesses, what capabilities are missing, and how to prioritize recruitment efforts for maximum governance effectiveness. AI can assist in maintaining this matrix, flagging composition gaps in real time and helping board nominating committees visualize what expertise is needed to meet strategic goals, especially as it keeps track of members’ terms and term limits.
Board composition cannot be viewed as a throwaway or a political entitlement. It has to be something that has meaningful impact on the long-term strategy, vision, and health of the institution.
AI Enhanced Strategic Planning and External Data Integration
AI can make strategic planning much easier for boards and institutional performance monitoring. Remember Jack Welch’s approach at GE? AI can do something similar by coordinating academic calendars, strategic priorities, performance evaluations, and board reporting to ensure optimal timing and information flow.
Performance dashboards can be integrated into your existing governance systems, providing real-time insights into academic quality, financial health, student success metrics, and operational efficiency. This enables boards to monitor institutional performance against strategic benchmarks and identify areas requiring attention.
External data integration allows AI to incorporate publicly available information—such as Bureau of Labor Statistics datasets—to help boards assess how well institutional strategies align with external realities. For institutions transitioning to workforce development from traditional academic degrees, AI can help determine if they’re pursuing the right programs based on labor market data.
Transforming the Role of Board Secretaries and Chairs with AI
AI transformation extends beyond trustees to fundamentally enhance the effectiveness of board secretaries and chairs. AI is changing the role of the board secretary to be able to track what’s important, get the board book together, and bring to the chair things that are bubbling up that haven’t been taken care of.
Traditionally, these leaders have managed significant administrative overhead around agenda preparation, follow-up tracking, and strategic alignment monitoring. AI automation of tactical work frees governance leaders to focus on strategic priorities and institutional stewardship.
AI tools can generate executive summaries of complex reports, monitor progress on action items from previous meetings, alert board chairs when discussions drift from strategic objectives, and create draft agendas based on pending issues and strategic imperatives.
AI Implementation Considerations, Privacy, and Data Security
This section covers the key research areas board members and CIOs should explore when evaluating AI governance tools. While keeping the overview concise, the details and resources here will help you ask the right questions and make informed decisions about AI adoption at your institution.
Boards considering AI governance tools should address several important factors. Data security and privacy require careful attention since AI platforms will process sensitive board materials, strategic documents, and institutional information. Most AI tools run on commercial cloud infrastructure, and if vendors use U.S.-based providers like AWS, that data falls under federal surveillance laws.
Amazon, for example, hosts classified environments for the CIA and NSA on the same infrastructure that powers many AI governance tools. In some cases, the government can compel access to data without informing the institution.
The USA PATRIOT Act and the CLOUD Act (2018) allow U.S. agencies to demand access to data held by American companies, and cases like Microsoft Ireland have exposed how murky jurisdiction becomes when data is stored across international boundaries.
Palantir’s ICE contracts and AWS’s “Secret Region” cloud demonstrate how closely tied commercial cloud services are to federal surveillance infrastructure. If your board is using AI, don’t just ask about features. Ask where your data goes, who can access it, and what happens if that access is compelled by law.
Vendor selection and governance involve choosing AI platforms that align with institutional values and governance requirements. Boards should establish clear policies for AI use, including data handling protocols and boundaries on automation that preserve human decision-making authority.
Key Questions to Ask Your AI Governance Vendor
Before implementing any AI governance platform, boards should conduct thorough due diligence. Here are essential questions to evaluate potential vendors:
Data Security and Privacy
- Where is our board data stored, and who has access to it?
- What happens to our data if government access is compelled by law?
- Can you guarantee our data will not be used to train AI models for other clients?
- What specific encryption and security protocols protect our sensitive governance materials?
Functionality and Integration
- How does your platform integrate with our existing board management systems?
- Can the AI be trained on our specific strategic plans and institutional priorities?
- What level of customization is available for our governance workflows?
- How does the system handle confidential or privileged communications?
Vendor Stability and Support
- What is your company’s track record with higher education institutions?
- Who provides ongoing support and system maintenance?
- What happens to our data if your company is acquired or goes out of business?
- Can you provide references from similar institutions using your platform?
Implementation and Training
- What is the typical implementation timeline, and what support do you provide?
- How do you train board members and staff to use the system effectively?
- What ongoing education do you provide about AI governance best practices?
- How do you measure and report on the system’s impact on board effectiveness?
The Private LLM Alternative: Complete Control at a Cost
There is a way to get all the AI governance benefits we’ve discussed without worrying about government access or vendor data mining. For institutions where data security and privacy concerns outweigh convenience, building a private large language model (LLM) represents a viable alternative to cloud-based AI governance tools.
Instead of sending your sensitive board materials to someone else’s servers, you keep everything in-house. A private LLM can be trained specifically on your institution’s historical board materials, strategic plans, policies, and operational data, providing AI insights tailored to your specific institutional context.
Since the system operates entirely within your campus network, there’s significantly reduced risk of government surveillance, vendor access, or data breaches through third-party cloud services.
But this approach isn’t for everyone. The upfront costs can be substantial, including specialized hardware, software licensing, and technical personnel who know how to build, train, and maintain these systems. Unlike commercial solutions that you can start using next week, private LLMs require months of development, training, and testing. But building a private LLM means your entire institution can access and train the secure model, from academic departments and student services to faculty research and administrative functions.
The decision comes down to your institution’s risk tolerance, technical capabilities, and resources. For boards handling highly sensitive materials or operating where data sovereignty isn’t negotiable, the private LLM route may justify the additional complexity and investment.
Human Governance Remains Non-Negotiable
All this AI capability comes with an important caveat: some board functions require human judgment that no algorithm can replace. The research with board chairs mentioned earlier revealed that certain board functions—particularly those involving strategic judgment and relationship management—should remain under human control even when AI assistance is available. The study identified “power-conduit tasks” that may appear routine but are absolutely necessary for enabling leaders to exert their influence.
Agenda Setting
AI suggestions may be helpful, but final decisions about meeting focus and discussion priorities should rest with board leadership, who understand institutional dynamics and strategic timing. The Toronto Stock Exchange research found that around 30% of chairs said agenda management was a major way they exercised influence, involving “shaping the entire flow of the conversation: where each part goes, what gets emphasized, and what’s not discussed at all.” While board leadership should retain control over meeting structure and timing, this authority should not become another filter that limits stakeholder input.
Discussion Facilitation and Summarization
AI can enhance these functions, but board chairs should maintain control over how conversations are framed and concluded. While AI can summarize conversations, the research noted it can miss nuances, “such as the things people aren’t saying or shifts in mood, that a good chair can pick up on and use in their summary.”
Board Member Evaluation and Committee Assignments
These involve complex judgments about personalities, skills, and institutional needs that require human insight and relationship management capabilities that AI cannot replicate.
Final Thoughts
Being a trustee isn’t getting any easier, and the expectations keep growing. As I’ve mentioned throughout this article, AI should be used as a tool to assist, not substitute judgement and keen oversight of all board members, as they are the fiduciary, not AI.
About the Author
Dr. Drumm McNaughton is the founder, CEO, and Principal Consultant at The Change Leader, Inc. A highly sought-after higher education consultant with 20+ years of experience, Dr. McNaughton works with leadership, management, and boards of both U.S. and international institutions. His expertise spans key areas, including accreditation, governance, strategic planning, presidential onboarding, mergers, acquisitions, and academic realignment. Dr. McNaughton’s approach combines a holistic methodology with a deep understanding of the contemporary and evolving challenges facing higher education institutions worldwide, ensuring his clients succeed in their mission.
