AI Implementation Stakeholder Register
This is a practical guide to building a stakeholder register for an ai implementation project — a record of stakeholders, their influence and how to engage them, adapted to the realities of delivering an AI/ML capability from data to production value.
What a Stakeholder Register is
A stakeholder register is a record of stakeholders, their influence and how to engage them. For the full concept and how it works in general, see Stakeholder Register. On an ai implementation project it plays the same role, tuned to this kind of work.
Why it matters for an AI Implementation project
AI Implementation projects live or die on delivering an AI/ML capability from data to production value. A well-built stakeholder register gives the team a shared, explicit reference for exactly that — reducing ambiguity, aligning stakeholders, and making problems visible early enough to act. Skipping it, or doing it generically, is how ai implementation projects drift into avoidable delay and cost.
What to include
- Stakeholder name and role
- Power and interest
- Attitude
- Engagement approach
AI Implementation-specific considerations
Tailor the stakeholder register to the risks that most often derail ai implementation projects:
- Poor or insufficient data
- Models that don’t generalise
- Unclear ROI and adoption
Example
On a real ai implementation project, the stakeholder register would be shaped by delivering an AI/ML capability from data to production value. In particular, it should explicitly account for the project’s biggest risks — poor or insufficient data, models that don’t generalise, unclear ROI and adoption — rather than treating them as afterthoughts.