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By Martin Bergmann | AI Project Lab | October 2026 Why a business case should end with a stop rule, and how an AI pre-mortem can write it. Before you approve your next project, ask yourself one question: what would need to happen in 90 days for us to stop it? I think most business cases cannot answer it. The numbers are rarely the weak part. The weak part is that nobody decided in advance what evidence would change the decision, so once the money is approved, stopping becomes

By Martin Bergmann | AI Project Lab | October 2026 Look at the sidebar of your AI chat history. Every conversation has a title the AI wrote for you, a one-line summary of what you asked, something like “options for the delayed vendor contract.” That line describes your problem better than most email subject lines do. A new paper asks where it goes. What the researchers found. Researchers at IMDEA Networks in Spain tested nine widely used AI chat services in May 2026 on both t

By Martin Bergmann | AI Project Lab | October 2026 Picture the last document you wrote with AI’s help. Now, picture adding one line at the bottom: drafted with AI. Most people hesitate at that line. The hesitation is not paranoia. It is a forecast. It is tempting to explain that hesitation with policy: unclear rules, security worries, doubt about what is allowed. Researchers at Duke found a simpler reason. In a 2025 PNAS paper, Jessica Reif, Richard Larrick, and Jack Soll ran

By Martin Bergmann | AI Project Lab | September 2026 Ask most people what they actually use AI for when a real decision is on the line, and the honest answer is a gut check: here is my plan, does this make sense? What comes back is almost always encouraging, well-organized, and confident. It rarely tells them anything they did not already believe. Agreement is not the same as insight. AI is very good at making a plan sound more finished than it is. Ask it to review a proposal

By Martin Bergmann | AI Project Lab | September 2026 Every shared drive already has a folder like this: a file, a “final” version of that file, and a “final, actually final” version after that, and nobody agrees which one is current. That habit did not start with AI. What has changed is how quickly the next version shows up and how little it costs to make a new one rather than to fix the one that already exists. The cost AI didn’t remove. AI made producing things nearly free:

By Martin Bergmann | AI Project Lab | September 2026 Every portfolio review has one of these. A project that hasn’t produced a real update or any real value in months, and everyone already knows it. Nobody has canceled it, and nobody has escalated it either. It just gets renewed every month or quarter, because renewing it is easier than being the person who kills it. I have watched this exact scenario play out more than once, in more than one organization. I am not naming the

By Martin Bergmann | AI Project Lab | September 2026 Three people on this team used AI this month: one to synthesize customer feedback into themes, one to draft a first pass at a design option, and one to catch a gap in a requirements doc that a manual review missed. Nobody counted any of it. The uncounted head start. It’s easy to write this off as not real progress. Scattered, unofficial, nobody coordinating it, it doesn’t feel like the same thing as a team actually being re

By Martin Bergmann | AI Project Lab | August 2026 A line in the Decisions section of a RAID log said “Target: Friday.” That was three Fridays ago. No one took the date out, and no one changed it either. It just remains there, in a technical sense past its due date, and in practical terms exactly the same as if no date had been given at all. The date was never the hard part. Setting a target date is simple, and most project managers already do so. That isn't where the problem

By Martin Bergmann | AI Project Lab | August 2026 Captain Alexandre Gandini, who flies a Boeing 737 for GOL Airlines, explained in a Lean Enterprise Institute interview what happens when something goes wrong during a flight. Only one amber light, called Master Caution, lights up right in front of him. A panel then shows which system needs attention. For example, if the fuel pump has an issue, a low-pressure light appears in the fuel section, and that’s where he focuses. Nothi

By Martin Bergmann | AI Project Lab | August 2026 Back in 2000, I worked on a financial analysis platform for the insurance market. It was a strong product. Our analytics were more advanced than anything else out there, we delivered updates quickly, and our technology was ahead of its time. But hardly anyone used it. The reason was simple. Many of our users still had dial-up internet. Those with better connections didn’t want to put financial data online at all. We designed f

By Martin Bergmann | AI Project Lab | August 2026 PMI’s new AI Standard makes something explicit that most organizations have been quietly avoiding: the project manager is the accountability checkpoint for AI on their projects. Not IT. Not governance in the abstract. You. That raises a question most self-assessments don’t actually answer: are you using AI well, or are you just using AI? The Question Everyone Gets Wrong Almost every “AI maturity” check asks the same thing: ho

Post 2 — What the Course Actually Taught Me By Martin Bergmann | AI Project Lab | July 2026 Six modules and one completed course later, three details changed how I think about generative AI projects. First: operationalization and data preparation took up more of the course than I expected. The volume of work in data preparation alone, cleaning, labeling, verifying quality, structuring for training and testing, is closer to what a data engineer does daily than what most PM tra

By Martin Bergmann | AI Project Lab | July 2026 A honeybee swarm has roughly 72 hours to find a new home before exposure becomes fatal. There is no queen directing the search. No single bee makes the call. Instead, scout bees fan out independently and explore multiple candidate sites in parallel. When they return to the swarm, they perform a waggle dance to advertise what they found, with the vigor of the dance roughly proportional to the quality of the site. Better options g

By Martin Bergmann | AI Project Lab | July 2026 I was born in Germany. One word I carry into every AI adoption program I run is Einstellung, which roughly translates as mental set or attitude. It does not have a clean English equivalent, which is fitting, because the phenomenon it describes tends to go unnamed in most organizations, too. In cognitive science, Einstellung refers to a specific failure mode: prior success with a solution blocks you from seeing a better one, even

By Martin Bergmann | AI Project Lab | July 2026 Most AI adoption programs built by project managers start the same way: gather the best prompts, package them into a library, and hand them out on day one. It feels like good program design. Lower the barrier, give people a head start, make it easy to say yes. Cognitive science says this is exactly backward. In 1978, psychologists Slamecka and Graf ran a deceptively simple experiment. Participants who generated a word by complet

Every PMO has a lessons learned process. Almost none of them work effectively. Not because project managers do not try. But because the process was designed for documentation, not retrieval. Knowledge gets captured at project close, filed in a shared drive or PPM tool, and never surfaces again. The next project manager starts a similar project without realizing that three teams before them hit the same wall in month four. This is not a template problem. It is an architectural

By Martin Bergmann | AI Project Lab | June 2026 Those numbers have not moved in years. The meeting format has not changed either. Project team meetings have one job: surface the honest thinking that makes projects succeed. Most of them are not designed for it. Ro Fernandez published a compelling argument recently, worth building on. Her case is grounded in neuroscience, not productivity folklore. Your brain processes conscious thought at just 10 bits per second. Put that brai

By Martin Bergmann | AI Project Lab | June 2026 PMI published its first Standard for Artificial Intelligence in Portfolio, Program, and Project Management in early 2026. At 297 pages, it is substantive, technology-agnostic by design, and more honest about risk than most AI guidance I have read recently. Here is my practitioner-level read. The accountability shift is real: The standard does not treat AI as a tool that sits outside the PM's responsibility boundary. It places Hu

By Martin Bergmann | AI Project Lab | May 2026 Understanding the Legal Landscape of AI AI is moving fast. Organizations are deploying it faster than they understand it. The legal system is starting to catch up. In 2022, a passenger asked Air Canada's chatbot about bereavement fares after losing his grandmother. The chatbot gave him the wrong information. He relied on it, and the airline denied his claim. A Canadian tribunal ruled against Air Canada in 2024. However, here is w

What Is the PMI-CPMAI Certification, Why Am I Doing It, and What to Expect By Martin Bergmann | AI Project Lab | April 2026 PMI's own pitch for this certification includes a quiet admission: you can "navigate fast-changing technologies without needing tool-specific training." Read between the lines: the tools are not the problem. The management layer is. That is the belief behind the AI Project Lab and the reason I am pursuing this certification. What Is the PMI-CPMAI Certifi