AI adoption works when governance leads - not prohibition
AI rollouts don't fail because of models or tools. They stall because people don't trust the process. PR and communications teams can fix that by translating how AI fits the work, setting clear guardrails, and giving employees a safe way to try it.
Think of adoption as a trust-building exercise, not an edict. Your job is to reduce risk and fear while keeping momentum intact.
Lead with curiosity - paired with empathy
At a recent AI conference, Lisa Low of Texas Tech urged communicators to start with curiosity and respect for different comfort levels across the org. Some teammates are eager; others are skeptical. Both groups need to feel heard.
Your stance: explore openly, acknowledge risks, and show how AI supports existing craft instead of replacing it. That tone creates space for real progress.
Make experimentation safe and visible
Faith McGrain of NiSource shared how a controlled sandbox changed the conversation. Leaders tried tools, shared what worked and what didn't - up to the CEO. That visibility normalizes learning and reduces quiet resistance.
Frame governance as permission, not policing. Strong guidance plus clear pathways for exceptions signals, "Here's how to do this right - and here's how to request a different approach when there's a solid business case."
Curiosity needs guardrails
Dan Hebert at the Canada Revenue Agency tested AI on a basic task: drafting a tax season news release. The draft looked clean - but referenced the U.S. season. A quick fix, and a useful lesson.
In highly regulated environments, curiosity without structure creates risk and shadow IT. Pair experiments with rules for data, validation steps, and approval flows. That keeps usage above board and reassures teams who worry their work might be changed overnight.
A practical playbook for PR and comms teams
- Clarify use cases by risk level: low (drafting, summaries), medium (analysis), high (personal data, public statements).
- Publish a one-page policy: approved tools, data rules (no PII in public tools), human review steps, and who to call for help.
- Stand up a safe sandbox with logging. Require fact checks and citations for anything external-facing.
- Create prompt libraries and checklists: tone, brand, claims, bias, and legal flags. Build "red team" prompts for stress tests.
- Run leader-led show-and-tells. Short demos beat long memos. Share misses and fixes to normalize learning.
- Add an exception path: how to pilot a new tool, criteria for approval, and sunset or scale decisions.
- Measure what matters: adoption rate, time saved, rework avoided, quality scores, and risk incidents.
- Set up office hours and a champions network across business units. Keep a living FAQ with real examples.
- Align with established frameworks where useful, like the NIST AI Risk Management Framework or the Government of Canada's Directive on Automated Decision-Making.
Governance beats bans - every time
Bans push usage underground. Governance brings it into the light. When people know what's allowed, how to test ideas, and how to escalate exceptions, they participate instead of hide.
That's the communications edge: make AI safe to try, easy to discuss, and accountable to clear standards. Do that, and adoption stops being a fight - it becomes routine.
Want a faster path to team readiness?
If your comms team needs structured upskilling, explore role-based options here: AI courses by job. Build skills, then plug them into the governance model above.
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