Intentional Discovery
In December 2020, I was one year into my time at Microsoft and four months into intentional discovery for my mission, creating the Data Center of the Future. Between September and that point we had interviewed 200 people — 100 inside the company, 100 outside. We had built a catalog of parts. We had drawn the current journey of creating a data center “from dirt to data,” and a hypothesis about how that journey needed to change. We had personas, guiding principles, and a growing coalition of experts from seven companies (Microsoft plus six more) running as one team. We had identified the people on the team who were constitutively suited for discovery — the ones who could keep asking questions when the room wanted to move on.
That December, the head of data center design heard through the grapevine that I had been asking questions (apparently forgetting that he was one of the first people we interviewed). Specifically, he heard a misremembered account that I had suggested Microsoft’s design was no different than Amazon’s. I hadn’t. But the question itself was a big problem to him.
He spent the next two weeks setting up meetings to discuss my behavior. Two weeks, during the first Christmas of Covid. He brought one-line diagrams to set me straight. He shut me down in meetings. And he made it clear that he did not believe I was qualified to question the design. His team’s design. He didn’t see asking questions as part of my job. He saw it as a problem to be managed. He saw it as my arrogance, hubris, and insubordination.
Like I said. People really hate intentional discovery.
While that was going down, the team pressed on. By February we had the vision, the guiding principles, the first video in a Why–What–How series, and a roadmap to 2025. We had CVP approval for a demo project that solved an urgent, current-state problem — permits being blocked in the Netherlands. That demo project got funded, went into action, and is still highlighted in annual reports today. The Why video eventually became the org’s public sustainability video.
The February timing was not an accident. The next fiscal year’s budgeting started in March. I had built the rhythm of the program around it. Every February, we had tangible results to put in front of the people deciding what to invest in next. Discovery never stopped. It just did not have to look like discovery to the people we needed something from. That was my job as the orchestrator: to keep the discovery open while feeding the organization on its own terms.
With the Netherlands win under our belts, we went back to that same DC design leader with the Why video.
He teared up. He said, “This is the future. We’ll get you the money.”
The same person who had spent December trying to censure me for asking questions teared up in February at the result of those questions.
He did’t hate the work, exactly. He hated the visibility of the work — the part where it looked like nothing was getting built, where the team was talking to too many people, where the answer was not yet defensible in one-line-diagram form. Where it looked like his team’s work was being second-guessed.
By the time our work converged, he loved it. He just didn’t want to see how it was made.
The dominant pattern in this AI moment is the opposite of what we did. Give AI your question, AI will give you an answer. You receive the output without building the understanding. You cannot reliably build on top of it, because there is nothing underneath. You have a result. But discovery’s most valuable outputs aren’t results. They are often better questions. Shared vocabulary. Reduced scope. Architectural clarity. Trust between collaborators who have looked at the same hard thing together.
None of those show up on a status report. All of them determine whether what gets built will hold. The data center work — five months of intentional discovery in 2020 — produced a solution that is more viable in 2026, more feasible and more desirable, even through the disruption of AI’s emergence.
That is why we “say it ugly” — so that we can “build it better.” It’s the discipline the lab is built on. Intentional discovery is not a phase we move through. It is a mode we hold open — in parallel with design and development, narrowing as the work hardens, never fully closing while something genuinely new is being built.
Innovation is not only loopy. It’s layered.
The people who have worked with me inside that mode have told me, more than once, that it was the hardest and most rewarding work of their careers. Four of my DOTF teammates are now my partners in the Lab. Many of our coalition colleagues are frequent collaborators with the Lab.
Discovery is where dormancy meets expansion.
Most organizations have engineered out both. When you intentionally make space for both, the work and the people change together. Roots form. They hold up everything built on top of them, and compound over time.
The Regenerative Datacenter of the Future work my team and I did between 2020 and 2023 is some of the most exciting, interesting, and important work I’ve done so far. But then, so was the work I did before that. I don’t know where intentional discovery will take us this time, but I’ve been through enough loops to know it’ll be amazing.
Onward!
Don’t miss this week’s live Innovating Out Loud webcast with special guest Jayshree Seth — Corporate Scientist and Chief Science Advocate of 3M. She’ll be sharing her experience and frameworks bridging the gaps between research and practice, business and technology, and data and wisdom.
Thursday, May 28th at 9 AM PT / 12 PM ET.
Connections to The Insider's Guide to Innovation at Microsoft
Intentional Discovery: purposefully exploring with the goal of ultimately having an impact on the world, resides in Pasteur’s quadrant (of the Stokes framework by the same name). The 200 interviews, the catalog of parts, the journey map from dirt to data — that is intentional discovery in practice.
Because intentional discovery is a foundational practice of repeat innovation, it connects to many other frameworks in the book. Here’s a short list:
Pasteur’s Quadrant is the motivational state operating underneath the work. The DOTF team was not doing pure research (Bohr’s quadrant — no practical application in mind) and not doing applied research (Edison’s quadrant — scaling a pre-specified problem with a known solution). They were holding both simultaneously: genuine curiosity about what the data center of the future could be, driven by clear awareness that it had to work in the real world at scale. That specific orientation — use-inspired basic research — is what made the work generative rather than just exploratory or just incremental.
The Pasteur-Pisano Innovation Configuration — my framework combining the work of Stokes and Pisano — explains why the solution is still more viable in 2026 than it was in 2020. The DOTF team explored both the technical dimension (the physical and systems design of a regenerative data center) and the business model dimension (the stakeholders told them early that the existing business structure would not support the future). Exploring both dimensions places the work in the architectural quadrant of the Innovation Configuration — the highest-complexity, highest-value space. Most teams explore only one dimension, which is why they produce incremental results even when their technical work is strong.
Discover-Design-Develop — the three-phase innovation process — is the structural spine of the DOTF program. But the piece makes explicit something the book describes and the DOTF proved: the phases are not sequential. Discovery did not end when Design began. A subset of the team continued exploring the business model dimension while the physical design was being developed. The work was loopy and layered — both qualities named in the book, and the second word extended by the piece’s new aphorism: Innovation is not only loopy. It’s layered.
Diverge-Converge-Synthesize is the internal discipline of each phase, and the December 2020 story is a case study in what happens when someone outside the process mistakes divergence for incompetence. The head of data center design could not see the value of the asking phase because he was waiting for the convergence phase. The book is direct on this: “Often, though, out of a conditioned need for order, control, simplicity, or a sense of security, we converge far too early.” His two weeks of meetings to discuss JoAnn’s behavior were, in effect, an institutional demand to converge before the work was ready.
Pattern #1: Innovating Every Day — Step back and complete Discovery before moving to Design and Develop — is the operating instruction the DOTF team followed and the DC design leader tried to prevent. The February rhythm — tangible results timed to the budget cycle — is how the orchestrator of discovery makes Pattern #1 survivable inside a large organization. Discovery needs a strategy for persisting. The February rhythm was that strategy.
Fit People with Phase appears in one sentence of the piece: “We had identified the people on the team who were constitutively suited for discovery — the ones who could keep asking questions when the room wanted to move on.” That is the Pioneers category in the Pioneers-Settlers-Town Planners-Boundary Crossers taxonomy. Knowing which people belong in which phase, and making those assignments deliberately, is one of the practices the book identifies as common to the most successful innovation teams.
BXT / Lead with X is the invisible architecture of the coalition. The head of data center design was leading with T — his team had the technical design and defended it. The DOTF team led with X — they started with the people (200 interviews, personas, journey maps) and built the technical and business case inward from that understanding. By February, the Why video was X-led communication: it did not argue the technical case first. It made the case for why the future mattered to people. The tears came after the X, not after the diagrams.
Say it Ugly is named in the piece and is the lab’s direct inheritance from the book. In the DOTF context, it meant running the Why-What-How video series even before the answers were fully formed — committing ideas to video while the design was still in progress. The book frames it as prioritizing speed of communication and iteration over perfection, requiring psychological safety to do so. The December 2020 story is a reminder of what happens when the environment does not provide that safety, and the team commits to the work anyway.
AI Disclosure: as usual, AI helped me research, write, and produce this piece. All ideas, memories, observations, and conclusions are my own. Mistakes too. Say it Ugly, Build it Better. Onward!
