A practical framework for AI decisions

Make better AI investment decisions before you build.

The AI Value Canvas helps teams test the user need, AI fit, risks, and economics behind an initiative—before more time, budget, or confidence is committed.

Explore the guide Download the Canvas

Work from the customer outward, not from the model backward.

Customer
UnderstandingDesirability

AI Opportunity
& SolutionFeasibility

Data, Model
& RiskSustainability

Value & Cost Viability

AI VALUE CANVAS · FOUR CONNECTED DOMAINS

Before commitment

Move from a promising idea to a defensible next decision.

The Canvas is not designed to make every idea look viable. It makes assumptions visible early enough to improve, test, rescope, defer, or stop before they become expensive commitments.

Is there a meaningful user problem?Start with real work, not a preferred technology.

Is AI the right response?Compare it with rules, process change, search, or human support.

Can the organisation operate it responsibly?Make data, safeguards, ownership, and escalation practical.

Is the value case credible?Set a baseline, a target, realistic costs, and a decision gate.

Four connected domains

Ask the questions in the order that matters.

See the framework

Desirability

Customer Understanding

Do users want this?

Customer/User Segments, Jobs to Be Done, and Pains & Gains.

Feasibility

AI Opportunity & Solution

Can this be solved this way with AI — and should it be?

AI Opportunity Fit and Solution Concept / Interaction Flow.

Sustainability

Data, Model & Risk

Can we build and maintain it responsibly?

Data & Model Considerations and Risks & Guardrails.

Viability

Value & Cost

Does it make economic sense?

Value & Metrics of Success, Cost Drivers, and Build / Buy / Partner.

The complete public guide

Everything's linked below — dive in wherever you like.

Follow the full reading path, go directly to the domain that matters now, or use the working template when you are ready to assess an initiative.

Open the guide hub

01 · Start here

Why the AI Value Canvas Exists

Why an AI idea needs more than technical possibility before commitment.

Read this section → 02 · Framework

The AI Value Canvas at a Glance

See the ten elements, four domains, and decision context that make up the Canvas.

Read this section → 03 · Desirability

Customer Understanding: Start with the Work, Not the AI

Start with the people, work, pains, gains, and adoption conditions that matter.

Read this section → 04 · Feasibility

AI Opportunity & Solution

Test whether AI is proportionate to the task and design the interaction responsibly.

Read this section → 05 · Sustainability

Data, Model & Risk: Build and Maintain Responsibly

Establish the data, model, safeguards, and operating conditions needed to proceed.

Read this section → 06 · Viability

Value & Cost: Make the Viability Case

Define credible outcomes, costs, unit economics, and build-buy-partner choices.

Read this section → 07 · Portfolio

Portfolio Decisions and Scaling AI Initiatives

Choose, sequence, and scale initiatives with evidence and accountable ownership.

Read this section → 08 · Examples

Worked Scenarios and Evidence Vignettes: Applying the AI Value Canvas

Apply the Canvas through worked scenarios and bounded evidence vignettes.

Read this section → 09 · Working template

AI Value Canvas Working Template

Use the detailed prompts, thresholds, and decision records for one initiative.

Read this section → 10 · References

References and Further Reading

Review the sources and further reading that support the public guide.

Read this section → 11 · Next decision

Closing: Use the AI Value Canvas on Your Next Decision

Close the loop with practical next steps, human support, and accountability.

Read this section → 12 · About

About the Author

Meet the creator of the AI Value Canvas and the intent behind the resource.

Read this section →

A practical starting point

Download the AI Value Canvas

Get the current AI Value Canvas template as a Microsoft Word (.docx) file and use the guide alongside it to work through an initiative.

The web guide contains the latest v3 guidance. The downloadable template will be updated separately to incorporate every v3 element.

AI with
human support

Design for accountability

Use AI where it helps—without losing human judgment, responsibility, or care.

For consequential outputs, make the accountable owner, correction path, human handoff, and escalation route clear before launch. The guide includes practical ways to turn that principle into an operating decision.

Read the closing guidance

Created by Eric Wong

A resource for clearer AI decisions.

Eric Wong created the AI Value Canvas to bring customer, business, technical, risk, and operating perspectives into one conversation before code is written.

About the author