The other Napoleon

Hill was not a trustworthy individual, but sometimes bad people have good ideas. I have always liked the board of advisors concept. It encourages you to step outside your natural thinking style and orientation, and consider situations as if you were someone else. I have used the concept for everything from career decisions to band management. (Incidentally, my band management board of advisors includes Miles Davis — he dislikes all of my ideas and regularly tells me they’re all terrible.)

This week I asked myself: if I could form my own AI board of advisors, who would I recruit for it? A board to help me understand AI’s impact on my career, on my family, on society?

While I have sometimes used Hill’s technique of conversing with the dead, in this case I decided to eschew the Ouija board and limit myself strictly to the living. I came up with a list of six:

1. Ed Zitron - British, skeptical, cranky, angry at AI, but a dogged investigative journalist with absolutely no tolerance for hype, publicity stunts, fudged numbers, or anything else you need to watch out for during a technology boom.

2. Mo Bitar - A developer, a believer, a skeptic, a comedian, a philosopher ... to act as a bridge between Ed and the rest of the board.

3. Nate Jones - A developer, an enthusiast, a content machine trying out anything and everything from the major AI players every single day.

4. Allie K. Miller - An investor, a hands-on experimenter, a business mind, a technical mind.

5. Scott Galloway - Business professor, investor, podcaster, writer, interviewer and interviewee. Not an AI expert in the least. Rather, the voice of the market on this board of mostly-techies.

6. Ted Gioia - A writer, a musician, a historian of jazz, a proponent of the great books, a critic of AI’s and the platform economy’s effects on the arts.

All of these people are complex polymaths and difficult to summarize. I’m concerned that, despite Miller and Galloway’s best efforts, the group would get regularly sidetracked and end up discussing topics like the nature of consciousness and free will. All the same, that’s OK … I’m not in this purely for the money.

Who would be on your AI board of advisors?

We are about to see a mass wave of agentic AI adoption. All we need to do is get data governance sorted out.

Right? Right?

I’d love to believe this conceit, but unfortunately I’ve already lived through two waves of enterprise data governance enthusiasm — the first in support of BI, the second during the era of “big data” and analytics enthusiasm.

In the film Rocky III (nothing but highbrow references from me), Rocky discovers that although he has won the heavyweight title and defended it ten times, he’s not actually quite the fighter he believed. His manager has been arranging easy fights against inferior opponents. The new challenger Clubber Lang (played by Mr. T, one of my childhood heroes) is smarter, tougher, stronger, and faster. In the first fight, he takes on Rocky and beats the snot out of him.

Data Governance III is going to be a lot like Rocky III.

Like the original Rocky film, the original Data Governance picture ended with a loss. Gartner estimated that 70-80% of BI-motivated data governance initiatives failed, mostly due to data quality challenges and squabbles over data ownership. Everyone remembers it as a win, but go back and rewatch the film — both Rocky and the governors of data took a beating, and simply managed to hang on through the final round.

Then Data Governance II. Just like Rocky, the data people trained harder this time. They bought superior tools, set up better governance boards, learned some lessons from their original loss. And they started winning. We started seeing self-service BI, user-built dashboards, sometimes even the elusive “single pane of glass” and “single version of the truth,” all to much acclaim and applause.

But as Mickey told Rocky about his opponents: “Those were handpicked. Set-ups. You never had real competition.”

In Data Governance III, the role of Clubber will be played by AI agents.

Clubber was smarter, tougher, stronger and faster than Rocky’s previous ten opponents. AI agents are smarter, tougher, stronger and faster than the human users who were consuming data during the last big governance wave. They don’t just read data — they act on it. They send emails, update records, trigger workflows, make decisions, all much faster than any human being.

And so the governed data is going to have to be better than ever before. What’s more, governing the data won’t be enough. The agents themselves will also have to be governed. Companies will need to define what an agent is allowed to see, touch, and do, on whose behalf, in which contexts.

Rocky won the second fight in Rocky III, of course. And I believe organizations will win Data Governance III as well. But they’re quite likely going to take a beating first, followed by some soul-searching, some staring out at the ocean, and some very hard training that’s tougher than anything they’ve every done.

Then Agile happened.

The original illustration of a “Feature Factory” posted with John Cutler’s 2016 article.

Agile was a factory-line revolt. Developers were tired of PMs, especially non-technical PMs, telling them what to do, demanding status reports, tracking their activity, and so on. All evidence from the software industry indicated that Agile delivery was more productive than the PM-directed waterfall method. And so developers made their case, and mostly won. Enterprise shops converted to Agile, to working against backlogs, to pushing code regularly and to continuous updates.

Agile is a more productive way to write software. And so we might assume the story ends here. PMOs shrank or disappeared, development shops got more productive, hooray the end.

There’s a problem though, and it’s this: Agile made shops better at What and How. It made them worse at Why and When. John Cutler named the symptoms a decade ago in 12 Signs You’re Working in a Feature Factory — no measurement of impact, success theater around shipping, roadmaps that list features instead of outcomes. In the last decade, none of that has changed.

I will now repeat the first two sentences of the post.

Agentic AI is the first enterprise technology wave in twenty years that will demand project portfolio management (PPM) discipline. But most companies do not have PPM discipline anymore.

AI agents cross system boundaries, touch multiple business processes, create dependencies between teams that don’t talk to each other, and produce outcomes that sprint metrics cannot measure. You cannot run an agentic AI program out of a backlog. You need management intelligence that can see across initiatives, kill the ones that won’t work, reallocate budget mid-flight, and hold the line on outcomes.

That capability used to be standard. It isn’t anymore.

Project Portfolio Management died after Agile and nothing replaced it. Project management itself got handed to functional leads who never trained for it — PMI’s own data shows fewer than half of projects today are led by formally trained PMs. Deloitte has been blunt about the mechanism: when organizations moved to continuous backlog reprioritization, the link between portfolio decisions and team delivery broke, and most enterprises never rebuilt it.

Walk into a large enterprise and ask who is working on what, and why. You will not get a clean answer. You will get a Jira export, a slide from last quarter’s OKR review, and a functional lead who can describe their team’s sprint but not how it ladders to anything.

Now layer agents on top of that. Cross-functional. Cross-system. Outcome-dependent. The mismatch between what agentic AI requires and what most enterprises can actually execute is the real adoption problem — bigger than model selection, bigger than vendor choice, bigger than the infrastructure debates that dominate the conversation.

The companies that get agentic AI done will be the ones that rebuild portfolio and program management before the budget overruns force them to. Gartner is already projecting 40% of agentic AI projects will be cancelled by 2027. That number is not about the technology. It is about execution capacity that was dismantled fifteen years ago and has not been reinvented.

The work ahead is unglamorous: real PMs, real portfolio governance, real outcome accountability. The firms that do it quietly, now, will be running production agents while their competitors are still reorganizing. Agile didn’t kill PPM. We did. The good news is we can rebuild it — and the ones who do first will win the race to agentic success.