The “To AI or not to AI Canvas” is a one-page decision canvas that helps teams quickly assess whether it actually makes sense to apply AI to a specific problem. It walks you from clarifying the problem, through quantifying value and risk, to scoping the data and training effort, so you can decide if an AI solution is justified or if a simpler approach is better.
The canvas is structured into eight sections:
Problem we are trying to solve with AI – Clearly describe the real-world problem before mentioning AI at all.
Action – Capture what the AI is expected to do (the decision or action it should take) and whether this could be handled in a non‑AI way.
Value of Success – Describe what success looks like, how outcomes improve, and how much value that creates (savings, revenue, risk reduction).
Cost of Failure – Map out what happens if the AI is wrong or underperforms, and how that impacts people, operations, and cost.
Organization Gains – Identify broader benefits for the organization, such as differentiation, learning, or strategic capabilities.
Inputs – List the data and signals the AI would need, their quality and availability, and the expected cost to access and integrate them.
Training – Outline what it would take to train the model (data volume, labeling, expertise, infrastructure) and estimate that effort and cost.
Feedback & Outcome – Define how feedback from real usage will be collected, how the model will be improved over time, what success metrics you’ll track, and the ongoing cost of maintaining and refining the system.
Teams can use this canvas in workshops or discovery sessions to align stakeholders, compare multiple AI ideas, and make transparent, evidence-based decisions about where to invest in AI and where not to.
