AI programs often fail for social reasons, not technical ones. Users are surprised by behaviour changes. Leaders do not understand what is controlled vs probabilistic. Incidents are handled quietly until trust is lost.
Stakeholder communications is a delivery capability. It reduces confusion, protects trust, and improves adoption.
Communicate in moments that matter
Most AI communication needs fall into four moments:
- Launch. What the AI does, what it does not do, and how to use it safely.
- Change. Model/prompt/policy changes that affect behaviour (see AI release notes).
- Deprecation. A capability is being retired or migrated (see model deprecation).
- Incident. Quality, safety, tooling or cost incidents that impact users (see incident response).
Use a consistent message structure
Regardless of audience, consistent structure reduces confusion:
- What changed (or what happened).
- Who is impacted and how to tell.
- What users should expect now.
- What you are doing next and when you will update again.
- How to report issues or escalate.
Be transparent about limitations and controls
Trust improves when limitations are explicit. Use user-facing transparency patterns: citations, disclaimers for uncertain cases, and clear human escalation paths (see user transparency).
Align comms to operational controls
Communications should reflect what the team can actually do:
- Feature flags and staged rollouts for change control (see feature flags).
- Change freezes when stability is required (see change freeze).
- Governance forums where exceptions and risks are decided (see governance councils).
Use evidence, not reassurance
When reporting progress, use measurable signals: escalation rate, groundedness checks, tool error rates, and incident trends. Decision logs and telemetry make this credible (see decision logging and telemetry schema).
Clear communication is not PR. It is part of trustworthy AI delivery.