Heralding The Next Chapter Of AI Governance For The Public Sector

AI Governance In The Public Sector

As a long-time and active AI consultant to public sector entities, I often find that one of the first questions I get from those organizations revolves around the weighty matter of how AI governance applies to them. This is a great question. Why so? Because they are usually exceedingly eager to just dip into using AI. They’ve heard about AI, plus many of the entity’s personnel likely use generative AI and large language models (LLMs) in their personal lives and firmly believe that their organization is already woefully behind the eight ball and must fervently launch into using AI.

Rushing into AI might seem expedient, but if the overarching elements of AI governance haven’t been suitably planned and realized, the odds are that a one-off standalone AI project is going to create more problems than it solves. I immensely appreciate it when they are open to learning about AI governance and understanding how it not only applies to the private sector but equally so to the public sector.

I make use of my strawman definition that gets the AI governance discussion underway:

• My handy definition of AI governance: “Public-sector AI governance encompasses the principles, structures, practices, and stewardship through which a public-sector entity ensures that its development or procurement of AI systems, and the fielding, use, and eventual retirement of those AI systems, are undertaken in a properly documented and effective way while safeguarding democratic values, individual rights, public trust, and the rule of law.”

I emphasize the now classic consideration that it is vital to consider the full life cycle of AI, historically known as the SDLC or system development life cycle, along with the life cycle of the AI portfolio that a public sector entity aims to embark upon.

AI Is Likely Already In Their Midst

Another aspect that I gently bring up is that there is a solid chance that AI has already seeped into their entity. This often brings forth an adamant assertion that this is impossible because they would certainly know if AI were being utilized. No approval for AI has crossed the desks of the entity managers and overseers. They are absolutely scot-free of any AI.

I point out that AI can arise in many guises.

For example, suppose they recently procured a system to keep track of requests that come to the entity. The tracking system collects data, provides helpful reporting, and expedites the processing of requests. What they might not know is that under the hood, AI is often infused into these modern-day systems, enabling those systems to generate written summaries and automatically analyze metrics associated with the tracked requests. This is not likely to be surfaced during the procurement, as a vendor might not think it is worth pointing out that a tinge of AI was deeply buried inside their request-processing software.

Another strong possibility is that some of the members of the entity are turning to AI to get assistance while on-the-job, doing so without realizing they are accessing a system outside the official purview of the entity. To them, it seems completely innocuous. Imagine they are trying to write a memo and have a hard time doing so; thus, they just access a conveniently free version of AI such as ChatGPT, Claude, Grok, Gemini, Copilot, etc., to help, possibly via their personal smartphone. This type of seemingly simple or tangential use of AI needs to be given due consideration when viewing the organization from a holistic AI governance perspective.

There are prudent ways to surface whether AI is already being used here or there inside the entity – doing so must not be a witch hunt. That won’t be productive; it will be abysmally counter-productive. The idea is to get an understanding of the AI landscape that already exists and then ascertain how to transform the ad hoc under-the-table usage, some or much of which is taking place improperly, and structure this into a fitting approach that adheres to AI governance principles and practices.

Public Sector AI Gets A Good Book

I recently read a very helpful and insightful book on governing AI in the public sector, entitled “Governing With AI: How the Public Sector Can Use Artificial Intelligence to Improve Performance,” co-authored by Mark Fagan and Ben Gillies, and published in February 2026 (available on Amazon, Barnes & Noble, and from many other purveyors).

Please know that practical and well-written books of this sort are quite rare, and I applaud the authors for writing one that will be of tremendous value to the public sector. Coming in at around 200 pages in length, it is breezy, provides crucial fundamentals, and serves as a valuable guide or playbook for public sector leaders about the crux of AI governance.

Instructors would relish the fact that the book could also serve as a potential textbook, containing food-for-thought questions that could be assigned to students, short essay areas that a student could use to write out their thoughts, and other such accoutrements. In Appendix D, there is a list of around forty or so overarching questions about AI governance that serves as both a pre-assessment and a post-assessment tool that a student could undertake. They would rate themselves on each question before the class and then do a rating again at the end of the class.

One small note is that the book recommends a 3-point scale for those self-assessments, which is certainly fine, but I usually advocate a 5-point or possibly 10-point scale in my books, a personal preference based on using similar tools throughout my many years of having been a professor. In any case, an instructor can naturally choose whichever appeals best to their tastes.

Sharp-eyed readers will probably remember that I co-authored with Mark Fagan a trailblazing paper on specialized AI policies in the public sector; see coverage at the link here. He also recently co-authored a report on doing AI risk management, entitled “AI For The People: Including Risk In Your AI Calculus” by Mark Fagan, Selina Gong, Fabian Ulmer, Taubman Center for State and Local Government, Harvard Kennedy School, January 2026, which was covered at the link here. His work at the Harvard Kennedy School provides an ongoing stream of robust and watch-worthy publications.

Public Sector Distinctiveness

A recurring theme in this engaging book is that there are six essential challenges associated with AI in a public sector entity:

• (1) Rule-based environment.
• (2) Limited resources.
• (3) Shifting priorities.
• (4) Lack of expertise.
• (5) Politics.
• (6) Complexity of government.

Those are very tough challenges. This again illuminates the difference between the public sector and the private sector. For example, though not all private companies necessarily have ample resources, the odds are that commercial enterprises have more opportunities to spend set-aside pockets of money on innovations such as adopting AI. The same is not often the case for the public sector.

Another of those six challenges is the lack of expertise. Companies can quickly hire AI-savvy people. It is easy and fast. Internal teams can be sent to AI bootcamps. Getting AI expertise is not much of a barrier. Meanwhile, regrettably, the public sector often tends to be saddled with older tech and not have readily available AI expertise in-house, nor can it turn on a dime and hire AI specialists.

Anyone who is seasoned in the public sector knows that those challenges make doing just about anything new or innovative inside a public entity a frustrating and daunting task. The authors know this too, and they carry in the book a variety of approaches to aid in dealing with and overcoming those challenges. The eleven chapters showcase how to build the case for AI governance, laying out strategies, tactics and practical steps, including case studies to illustrate the points made.

Risk/Rewards Of AI In The Public Sector

Governmental entities tend to make decisions affecting nearly every citizen. AI used by public agencies carries extraordinarily high stakes. For a commercial firm, having AI flop when giving a shopping recommendation to a potential customer is inconvenient and might result in losing a prospective sale. An AI error in a public sector sphere determining disability benefits, parole eligibility, or disaster response may have life-changing consequences. In that sense, public-sector AI governance deserves an even higher standard than commercial AI governance, yet rapt attention to AI governance in the public sector has been disappointingly low.

At the same time, AI governance should not become so burdensome that it prevents public entities from realizing AI’s potential to improve public services. AI can reduce administrative backlogs, detect fraud, optimize infrastructure, assist in emergency response, and improve service delivery. An overly restrictive framework could deprive people of these benefits. AI governance in the public sector must up its game, strive for a balance, and stand out as a mission-critical element that comes part and parcel with any interest in adopting AI.

I’d say we are starting to turn a corner in that direction, and I herald the start of a new chapter toward that revered goal. As per the wise words of the famous statesman and scientist, Johann Wolfgang von Goethe: “What is not started today is never finished tomorrow.”