AI Implementation Communication Plan
This is a practical guide to building a communication plan for an ai implementation project — who gets what information, when, how and from whom, adapted to the realities of delivering an AI/ML capability from data to production value.
What a Communication Plan is
A communication plan is who gets what information, when, how and from whom. For the full concept and how it works in general, see Communication Plan. 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 communication plan 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
- Audiences
- Messages / information needs
- Frequency and channel
- Owner
AI Implementation-specific considerations
Tailor the communication plan 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 communication plan 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.