Managing evolving stakeholder expectations in AI Projects

Most of the "expectation drift" on AI projects isn't scope creep — it's that nobody actually knows what the thing can do until it's built. You can't spec a capability you haven't demoed yet.

What works: skip the charter language ("the system will accurately summarize X"). Show a rough version against real data in week one, ugly and all. Expectations anchor to what they see, not to the word "AI."

Then keep two lines visible every sprint: what it does today, what we're confident it'll do by ship. Say the gap out loud, in the room, every time — not buried in the backlog. Left alone, the stakeholder's mental model drifts back to the pitch deck between meetings.

Hybrid handles the cadence fine. The PM's job is re-anchoring expectations to the demo, repeatedly, because they don't stay anchored on their own.

A very relevant question.
In AI projects, I think an important distinction is that changing stakeholder expectations are not necessarily a sign of poor expectation management.
They can be a natural consequence of learning as technical feasibility, data limitations, risks and potential value become clearer.

The real challenge is ensuring that every new expectation does not automatically become a new project commitment.
A newly discovered AI capability may be technically possible, but that does not necessarily mean it is valuable, viable or strategically justified.

A hybrid approach can support this by combining clear governance and decision boundaries with adaptability in solution assumptions, priorities and scope details as evidence evolves.

So perhaps the key is not simply managing evolving expectations, but creating a clear decision process for translating new evidence into revised commitments: what may change, why the change is justified, who has the authority to decide, and what the implications are for value, risk, scope and timeline.

This allows stakeholder expectations to evolve with learning while preserving the strategic coherence of the project.

Set expectations early that AI outputs improve iteratively, not linearly; show progress in short sprints so stakeholders see real capability instead of assuming it. Use a change log tied to business goals, so every scope shift gets weighed against timeline impact before it's approved.