What does Albers Advisory do?

Michael Albers works with companies three ways, by how much they hand over. He steps into the product role, interim, fractional, or full-time. He rebuilds how a team operates around AI and installs it as a working pilot the team runs. Or he comes in for a bounded, fixed-fee read that gets a stalled decision made. Same operator discipline in all three.

We bought AI tools but the team never got faster. Why?

Because the tools changed and the operating model did not. Teams that move faster inside the same workflow get faster meetings and the same product. The gain comes from restructuring the workflow itself, which is the individual-to-team AI gap Albers Advisory closes.

What is the individual-to-team AI gap?

AI can make an individual dramatically faster without making their team any faster. Tools get bought, individual habits change, but the way the team builds, validates, and decides does not. Closing that gap means redesigning the team operating model, not adopting more tools.

We keep circling the same decision and never actually make it. Can you help?

Yes, and it is the smallest way in: a bounded engagement with a fixed fee and a fixed end date. Pre-work with the people who actually know, then a working session where the decision gets made live, in the room. Michael arrives with a real position to react to rather than a deck of options, because most of the progress comes from people reacting to something in front of them instead of a blank page. Your team leaves owning the call.

Is Michael Albers open to a full-time product leadership role?

Yes, for the right company. Michael takes the product seat full-time, and also works on interim and fractional terms. He spent 20+ years leading product at consumer scale, most recently as CPO and SVP at Yahoo! and AOL, where he ran multi-billion-dollar portfolios and organizations of 5 to 750+ people across product and engineering. He builds the AI systems an org runs on rather than only directing the roadmap.

Does Michael Albers take fractional or interim product leadership roles?

Yes. Alongside the AI-native operating-model work, Michael steps into product leadership gaps on a fractional or interim basis, and for the right company full-time, to stabilize direction, reset execution, and hand off cleanly.

What do you leave behind when the engagement ends?

Two or three of your own people, trained through the engagement to own the new operating model so it keeps running after Michael leaves. He calls them Human Systems Architects. They are existing team members, not new hires.

Writing

Recent Thinking

Written as I work through the shift. Increasingly how new clients and founders find me.

FEATURED →

A Working Guide to Product Discovery in the AI Era

Building got cheap. Judgment didn't. AI removed the governor, not the constraint.

All essays

01 Everyone Is Learning to Grade AI. Who Owns the Answer Key? Evals are a skill. The standard behind them is a job, and it needs a name next to it. → 02 From Running Product at Yahoo! to Building My Own AI System One conversation, on the Meta @Scale podcast with Francois Richard. Video, 41 minutes. → 03 AI Is Splitting Your Teams and Merging Your Executives Two executives who no longer have to negotiate are one executive. → 04 AI Cannot Get Six People in a Room to Align Building compressed. Then analysis compressed. Then everyone showed up with their own prototype, and the room got harder. → 05 You Cannot Price a Standing Refusal Until You Remove It His most valuable output was work that did not happen. Now we are training models on a record that cannot see it. → 06 Your AI Rollout Is Missing a Kill Rate Faster building without faster deciding doesn't compound. It just hands you a bigger pile. → 07 From Individual AI to Team AI: The Next Problem Personal AI wins are real. Team AI wins are a different problem. → 08 AI Isn't a Tool. It's a Team You Have to Design. Six months ago I called this 'using AI.' I don't anymore. → 09 When AI Adoption Stalls Inside an Organization People blame the tools. Or the training. → 10 I Built an AI System. It Had to Earn Its Access. In my quest to continue to learn about all things AI, I started with OpenClaw. → 11 Betting on AI as THE Intelligence. Here's What That Misses. Two CEOs are betting AI is the work. The labs that sold them the AI just spent $11.5B betting against them. → 12 AI Made the Build Faster. It Didn't Make Users Faster. When I managed Yahoo Mail, 225+ million people used it every month. → 13 Most PMs Quit AI Too Soon. Reach the 4-Hour Mark. Marty Cagan ('PM guru') said two important things over the last 10 days. → 14 AI Doesn't Need More Builders. It Needs Thinkers. I gave a talk at Pie Fi (a builder community) in Santa Cruz a couple weeks ago. → 15 AI Didn't Unlock Everyone. It Unlocked the Connectors. My thinking on who AI actually unlocks has evolved. Here's what changed it. → 16 AI Won't Replace Your Kid. The Kid Next to Them Might. Every week someone asks me some version of the same question. → 17 AI Didn't Unlock Everyone. It Specifically Unlocked Product Managers. There's a specific profile of person AI unlocks. Not the programmer. Not the non-technical person. The one in between. → 18 Your AI Doesn't Know What Your Team Wouldn't Ship Every company runs on judgment nobody wrote down: what your team wouldn't ship, what good means here. Your moat, and your blind spot. →

Also on Medium ↗

Landed here from a note? The writing is the thinking. The rest of it is on the home page: what I've built, what I run it on, and how I work with a team. Or hand the whole picture to your AI and ask it anything you'd ask me.

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