Starting an AI adoption programme without assessing your readiness is one of the most common reasons programmes fail. This checklist covers the six areas that consistently determine whether an AI adoption programme succeeds or fails.
1. Workflow documentation
Do you have documented, current descriptions of the workflows you want to improve with AI? Not high-level process maps — detailed workflow documentation that captures every step, every handoff, and every decision point. If not, this is the first thing to fix. You cannot redesign a workflow you haven't documented.
2. Data quality and accessibility
AI tools are only as good as the data they work with. Do you have clean, accessible data for the workflows you want to improve? Are the relevant data sources integrated, or are they siloed across multiple systems? Data quality issues are the most common technical blocker for AI adoption.
3. Change management capacity
Do you have the internal capacity to manage the change that AI adoption requires? This means someone who can own adoption outcomes, run training, collect feedback, and make adjustments. If this capacity doesn't exist internally, it needs to come from somewhere.
4. Leadership alignment
Are the relevant business leaders aligned on the priority workflows, the success metrics, and the investment required? Misalignment at the leadership level is a programme-killer. It's better to surface and resolve misalignment before starting than to discover it mid-programme.
5. Team readiness
How does your team currently feel about AI? Resistance is normal and manageable if it's acknowledged and addressed. Ignored resistance becomes the adoption blocker that derails programmes in their third or fourth week.
6. Measurement infrastructure
Do you have the ability to measure the outcomes you're targeting? Time savings, error rate reduction, volume handled — these need to be measurable before you start, so you can demonstrate ROI after you finish.
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