Move fast. Release confidently. Know the difference.
Your teams are experimenting faster than ever. Vibe coding, shadow AI, rapid iteration. That energy is an asset and our Graded Test Approach makes sure it stays that way.
AI tools are being embedded into retail technology at every level, from individual productivity hacks to business-critical automations.
It doesn’t all carry the same risk. So why apply the same level of testing to everything?
The Graded Test Approach gives retail technology teams – from CTOs to delivery leads – a clear, proportional path from early experimentation to production-grade confidence.
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A practical reference for retail technology teams adopting AI who want to test smarter, not just more. It's free. No sales call attached.
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Five stages. One shared language for risk.
| Stage | Stage name | Signal |
|---|---|---|
|
G0
|
Personal Utility |
"If it breaks, I shrug" |
|
G1
|
Shared Team Tool |
"If it breaks, someone pings me"
|
|
G2
|
Internal Dependency
|
"If it breaks, work stops"
|
|
G3
|
Customer-Facing
|
"If it breaks, customers notice"
|
|
G4
|
Mission Critical
|
"If it breaks, we lose money or trust" |
The Graded Test Approach defines five stages of maturity – from personal experimentation to mission-critical systems – with testing expectations that grow proportionally at each one.
The guide covers minimum test activities, release posture and sign-off expectations for every stage – including the critical distinction between G3, where rollback is still an option, and G4, where it largely isn't.
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Receive a two-page version of this case study, featuring more detail on what's made the project a success.
A practical reference your teams will actually use
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A clear explanation of all five stages, with minimum test activities for each
-
The go/no-go and sign-off expectations at G3 and G4 – and why they're different
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How to govern AI adoption at every stage, from personal tools to enterprise-wide deployment
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A plain-English guide to staged rollouts and when to use them
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Promotion checklists for moving work between stages with evidence, not assumption
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How to apply the approach alongside our FLOW Method.