AI in Projects: More Data Is Not More Knowledge

Antonio Bassi puts it succinctly in the current GPM blog: more data does not mean more knowledge. In projects, AI acts as an amplifier of the knowledge processes already in place — good ones get better, poor ones become visible faster. The post from 28 August condenses what Bassi describes in depth in his original paper on “Human-AI Hybrid Knowledge Ecosystems” for project-based organisations — and it matches what I observe daily in software and IT projects.

I use AI in every one of my mandates — for analyses, evaluations, documentation, and increasingly for building my own tools. Precisely because I am convinced of its potential, I can see where the real leverage lies: not in producing even more information, but in the architecture that turns information into reliable project knowledge.

Where the paradox strikes in day-to-day project work

Four situations from everyday software delivery where more AI output does not automatically mean better decisions:

• AI status reports in the steering committee: The summary reads print-ready — but who vouches for the number if nobody has interpreted it? A report without an accountable sender is a statement by the model, not a basis for decisions.
• Test and defect triage: AI classifies hundreds of defect reports in minutes. Without evaluation rules defined up front, this produces false precision — the numbers look exact, but nobody can say by which criteria they were prioritised.
• Automatic summaries: Meeting minutes and document digests save time but lose context. The decision log — who decided what, when, and on what basis — remains the project’s knowledge anchor that no summary can replace.
• Accountability: AI can analyse, but it cannot take responsibility. Every AI output that feeds into a project decision needs a human owner who has checked it and stands behind it.

Knowledge architecture instead of a tool list

Bassi calls this a knowledge architecture: clear rules for who validates which AI outputs, how decisions remain traceable, and where human judgement is mandatory. This aligns with the finding of PMI’s Pulse of the Profession 2026, which explicitly describes complexity as a system and leadership problem — not a tooling problem. Bring AI into a project without defining these rules, and you get faster reports about the same fog.

What project leaders should do now

Three things before the next AI analysis lands in a steering meeting: first, name an owner for every AI output who validates and takes responsibility. Second, define evaluation and validation rules before automating — not afterwards. Third, keep a decision log that records decisions together with their basis; it is the project’s memory, and it keeps carrying after go-live. The metric that matters is not how many analyses the AI delivers — but how many decisions have become traceably better because of them.