Before you diagnose readiness or plan around risks — you need to know what you're actually trying to achieve. Not "get the team using AI" — something specific enough that you'll know when you've got there. A goal that could mean anything drives a plan that could go anywhere.
There's a real difference between a vague goal and a working vision:
- "I want my team to adopt AI."
- Could mean training, usage, or just awareness
- No way to know when you've actually got there
- A named behaviour — drafting weekly reports with AI
- A deadline — by end of Q3
- A measurable result — 80% of the team, unprompted, two hours saved each
Specificity does something practical. It makes the next two sections easier, because you're diagnosing readiness and mapping risks against something concrete, not a mood. It makes success measurable, because you've defined it before you start — not after, when it's tempting to reframe whatever happened as what you were always aiming at. And it helps you notice drift early, before a stall sets in.
Before you move to Section 2, work through the worksheet question below. Complete this sentence: "In six months, I'll know this has worked if..." Keep it specific enough that you could read it to your team and they'd know exactly what you meant. That's the bar.
Most AI rollouts fail before they start — not because of the tool, but because of the opening question. "How do we roll this out?" takes implementation as the starting point. "Where is our team right now, and what do they actually need?" is where the real plan begins. Different questions, very different outcomes.
This is where the AI Readiness Scorecard from Change Made Simple comes in. It assesses your team across five areas: Governance, Transparency, Manager Readiness, Training, and Measurement — where they actually are, rather than where you assumed they'd be.
Run your team through it, then come back and work through the reflection below with the results in front of you. What the Scorecard tends to reveal surprises most managers — there's a persistent gap between how ready leadership believes the team is and how ready they actually are:
Your plan is only as good as your diagnosis. If you haven't completed the AI Readiness Scorecard yet, do that first — the reflection below will be a lot more useful with real results in front of you.
You have a working vision and a readiness diagnosis. The next step is mapping the human landscape — who are the people who will make or break this rollout? Not in the abstract, but by name and by role relative to the change.
The Project Risks & Actions Report from Change Made Simple is built for exactly this step. You enter your vision, and it helps you map stakeholder risks, concerns by person and by role, and the actions needed to address each one.
What you get out is a structured view of where the human risk in your rollout actually lives — and what to do about it.
However, good stakeholder mapping goes beyond a list of names — it means understanding each person's position relative to this change, their role in the change, not just their job title:
Most managers know who's resistant. Fewer know who's quietly influential — and that's usually where the real influence sits.
What the report gives you is team-based insight — the patterns and themes across your rollout, not a name-by-name printout. From there, it's on you to map those patterns onto specific people: who's an Influencer, who's a Blocker, who's a Bystander. Make a mental note as you go — this isn't something you need to write down, especially anywhere someone else might see it.
Your AI Change Plan — First Draft
Your three answers, gathered from each section above. Edit them up there — this is a read-only copy for review, saving and printing.