The first step in ADKAR is Awareness—the understanding that change is necessary. Not compliance. Not passive acceptance. Genuine understanding of why the current state is unsustainable and why the proposed direction matters.

For AI adoption, that awareness is often shallow. Teams hear “we’re adding AI” and fill in the blanks with whatever they’ve seen in demos or read in headlines. The real picture—what actually shifts, what stays the same, what gets harder—rarely lands before the rollout begins.

What Teams Need to See

1. The nature of the shift.
AI doesn’t just speed things up. It changes what gets built and who is accountable. When agents generate most of the code, the developer’s job shifts from author to editor and orchestrator. When agents draft specs and user stories, the product manager’s job shifts from writer to validator and strategist. Awareness means seeing that shift clearly—not as augmentation of the old role, but as redefinition.

2. The dependencies that matter.
AI systems depend on prompts, context, tool access, and human judgment at key moments. Teams need to understand that adoption isn’t “turn it on and go”—it’s a system with failure modes, feedback loops, and places where humans must stay in the loop. Awareness includes knowing where those touchpoints are and why they exist.

3. The downstream effects.
What happens to code reviews when agents write 90% of the code? What happens to sprint planning when “story points” no longer map to human effort? What happens to career paths when the premium skill is judgment rather than implementation? Awareness means anticipating these second-order effects before they become crises.

4. The upside.
Awareness isn’t doom. The teams that lean into this shift get to focus on the work that only humans can do: strategy, coherence, learning, and craft. Less queue management, more meaning. That narrative needs to be as visible as the challenges.

Building Awareness That Sticks

Awareness isn’t a one-time presentation. It’s ongoing exposure to the reality of the change—through pilots, demos, honest post-mortems, and candid conversation about what’s working and what isn’t. Teams that see the full picture—risks, dependencies, and upside—are better equipped to move to the next phase: Desire.