Making AI work for people: a practical guide to people-centered AI adoption for IC teams

Lead AI adoption as organizational change — not a tech rollout. Learn how to build employee confidence, capability, and trust through structured, people-centered implementation.

Structured adoption of AI for internal communications

Download the ebook to get:

  • Why AI adoption fails when treated as a technical upgrade (pp. 3-4)
  • How to balance leadership enthusiasm with employee concerns (pp. 6-9)
  • How to establish responsible AI foundations before scaling (pp. 10-13)
  • A four-step framework for building confidence and capability (pp. 14-18)
  • Metrics that measure trust and readiness beyond just usage (pp. 19-21)

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SNEAK PEEK

Internal comms is uniquely positioned to lead AI adoption

Most organizations roll out AI tools without redesigning workflows, building confidence, or addressing employee fears. The difference between success and failure in implementation is people-centered change management.

Shape narratives that build trust

When AI is introduced without clear context, employees fill communication gaps with assumptions about job security and relevance. Set the narrative early by explaining what AI does, why it matters, and how it supports work rather than replaces it.
Employees connecting in a collaborative workspace, representing thoughtful AI adoption for IC teams at work.

Follow a framework designed for IC practitioners

AI adoption often stalls when teams move straight from announcement to rollout without a shared path forward. Use a proven four-stage framework to guide teams from understanding to implementation to sustained adoption, in coordination with HR, IT, and Risk.
Team members working on a four-stage cross-functional AI adoption framework for IC, HR & IT

Codesign guardrails before tools spread unchecked

Deploying AI tools before establishing clear guidance and policies increases risk for the company and uncertainty for employees. Bring stakeholders together to implement responsible AI guardrails that reflect real work and address genuine concerns.
Employee working on her laptop towards AI adoption guided by stakeholder collaboration to establish clear, responsible guardrails.

Measure what truly matters in AI adoption

Usage data alone doesn’t show whether people feel equipped to use AI effectively or whether the tools are genuinely improving work. Measure readiness, capability growth, and operational impact to show whether adoption is improving work and building trust.
Employees reviewing AI adoption in IC metrics to track skills, readiness, and impact.
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