Years ago, while commanding one of the Navy’s newest warships, I hosted an admiral aboard for a visit.

At the time, the ship represented the cutting edge of naval technology. Information from our own sensors, aircraft, satellites, intelligence sources, and other ships was fused together and displayed across a wall of tactical displays. Compared to the systems I had learned as a young officer, it was a remarkable leap forward.

The admiral studied the displays for a moment before reaching into his pocket and pulling out what, by today’s standards, would be considered a fairly ordinary smartphone.

He smiled and said something I’ve never forgotten.

“All this technology is just giving people more information and more work.”

I thought he had a point even if I disagreed with it. The systems certainly generated exponentially more information than previous generations could have imagined.

Looking back, however, I think we were witnessing something much more significant. The breakthrough wasn’t simply more information. It was the ability to assemble, curate, and interpret the right information quickly enough to make better decisions. Technology wasn’t replacing judgment; it was increasing the value of judgment.

That conversation came back to me this summer while updating my online classroom at Florida State University, where I teach federal budgeting and federal lobbying to sixty-five undergraduate public policy students.

Over the past three semesters, the quality of student writing has improved dramatically. Arguments are more organized, conclusions are more persuasive, and discussion posts read as though every class has suddenly become filled with stronger writers.

Of course, we all know what changed.

AI has become part of nearly every student’s workflow. Most universities—including mine—have wisely accepted that reality. We encourage responsible use, establish expectations for disclosure, and remind students to verify the original source material rather than rely solely on generated summaries.

The difficult part isn’t grading the paper. It’s determining whether the student actually wrestled with the underlying material or simply polished the output.

So I’ve changed what I evaluate. Rather than rewarding polished writing alone, I place greater emphasis on interpretation and judgment. Given an ambiguous situation, can the student identify what actually matters? Can they explain why? Can they defend their reasoning when challenged? Those are much harder skills to outsource.

From the Classroom to the Boardroom

The same evolution is underway across the defense market.

For years, competitive advantage often belonged to companies that possessed more information than everyone else. Today, AI has dramatically lowered the cost of research, proposal development, market analysis, legislative summaries, customer profiles, and executive briefings. Information has become abundant.

Judgment has not.

The Center of Gravity Has Moved

The strongest companies won’t necessarily possess more information than their competitors. They’ll recognize sooner when the environment around an opportunity has changed.

• From acquisition execution to funding strategy.
• From program offices to congressional and industrial base priorities.
• From gathering information to interpreting signals.
• From measuring activity to developing judgment.

AI accelerates information.

Leaders still must recognize when the game itself has changed.

Increasingly, the hardest questions facing leadership teams aren’t answered by producing another white paper or another PowerPoint presentation. They’re answered by understanding how government decisions are actually being made.

A program that appears stalled may not have an acquisition problem at all. It may have become a funding issue, an industrial base priority, a congressional challenge, or the result of changing executive guidance.

A customer saying “not now” may really mean “wrong funding source.”

A Commercial Solutions Opening, APFIT award, SBIR transition, or congressional direction may suddenly represent a better path than the acquisition strategy the company has pursued for the past two years.

These decisions don’t come with obvious answers because they require context. They require experience. They require connecting signals that rarely arrive neatly packaged together. AI can organize the information, but leaders still have to decide which signals matter and which assumptions should be challenged.

As I’ve written before, government deals are funded before they’re sold. Access is not influence. Activity is not progress. Process is not strategy. Those principles haven’t changed. If anything, AI has increased the premium on leaders who know how to apply them.

The Time Dividend

Reid Hoffman has suggested that the nature of work itself may look dramatically different by 2030. I suspect we’re already living through that transition.

Most executives I know can now accomplish in minutes what routinely took hours just a short time ago. Research is faster. Drafting is faster. Analysis is faster. Meeting preparation is faster. The productivity dividend is real.

The more interesting question isn’t how much faster AI allows us to work.

It’s how we choose to use the time we’ve recovered. I’ve previously asked rhetorically what you’ll do with this dividend. Too often, we refill the calendar with more proposals, more meetings, more reporting, and more activity. We compress execution only to create more execution.

I wonder if we’re investing the dividend in the wrong place.

The highest return may come from something executives have traditionally viewed as inefficient: wrestling with optionality.

What if the opportunity isn’t really an acquisition problem?

What if Congress is the center of gravity?

What if an industrial base initiative changes the competitive landscape?

What assumptions are we carrying forward simply because they’ve always been true?

What if there are three viable paths instead of one?

Those conversations rarely produce an immediate deliverable. They often feel like time spent instead of time saved.

I suspect they’re becoming the highest-value work executives can do—iterating on options and possibilities.

My students are learning that AI can produce a polished answer almost instantly. My hope is that they also learn something more important—that the real value isn’t generating the first answer. It’s having the curiosity, discipline, and confidence to explore better ones.

Leadership teams face exactly the same challenge.

AI is giving us something executives have always claimed they needed more of: time. The companies that separate themselves won’t simply use that time to produce more output. They’ll reinvest it in developing judgment, challenging assumptions, and exploring strategic options before committing to a course of action.

That may prove to be AI’s greatest contribution—not that it helps us work faster, but that it gives us more opportunities to think better.