Service 05
AI Enablement
Most "AI in testing" efforts start with a tool and stall at a pilot. This is the engagement that starts with your test process — what's manual, what's brittle, what's actually worth AI's help — and builds the practice and the guardrails your team needs to trust it enough to keep using it.
- Identify
- Pilot
- Embed
Who it's for
Your team is experimenting with AI testing tools ad hoc, with no shared standard or review process
You want AI to speed up testing without losing coverage, judgement, or trust in the results
Leadership is asking "what's our AI strategy for quality" and nobody owns the answer
What happens
Mapping
I look at where your testing effort actually goes — what's repetitive, what's judgement-heavy, what's already breaking under manual load — and where AI is genuinely suited to help versus where it would just add risk.
Piloting
We run AI-assisted approaches against real test work, not a demo environment — test generation, coverage analysis, defect triage, whatever fits your context — and measure what it changes against what you already track.
Embedding
The practices that hold get written up, taught to your team, and given the guardrails — review points, escalation, what AI doesn't get to decide — that let them survive after I leave.
What you get
Concretely, in your hands.
- A written assessment of where AI can and can't safely help in your testing process
- A piloted approach with evidence of what it actually changed — coverage, cycle time, defect escape rate
- A set of working guardrails: where AI output gets reviewed, and what stays human-owned
- A team that knows how to extend the practice into new areas, not just run the pilot
The other four