A one-day private review of your QA team, process, automation, dashboards, and constraints. Through a leverage lens.
Engagement
Audit
Delivery
Pre-work intake → 1-day private review → post-day takeaway packet
Code
AUD-001
What you buy
A curated one-day diagnosis built around your actual team.
The promise
By the end of one day, you'll know where your QA team is losing leverage, which five changes matter most, and exactly what to take to leadership as a 90-day plan they can sign off on.
Who it's for
QA leaders who want a clear, outside diagnosis of their current setup before they buy more tools, reorganize the team, or expand automation. Best for teams that have read the methodology, taken the public course, or already believe the leverage frame and now want personalized judgment.
Buyer state. “I need a clear, outside diagnosis of our current QA setup before we buy more tools, reorganize the team, or expand automation.”
Before the day
We review your intake before the live day and curate the agenda around your specific leverage gaps. The day is yours. We don't waste it gathering context.
Structured intake
~30 minutes. We send the intake when you book; you fill it out before the day.
Optional artifacts
Drop in 1–3 of these if you want the day to discuss your specific examples. Optional, not required.
Strip confidential customer data, credentials, and proprietary architecture details before sending.
The day, hour by hour
By the end of the day you will know where your team is losing leverage, which five changes matter most, and exactly what to take to leadership as a 90-day plan they can sign off on.
9:00 – 9:30
Opening: What Are We Optimizing For?
Define the business outcome for the day. What prompted the review? What's the biggest pressure on the QA team? What does leadership expect? What's the main constraint. People, process, automation, access, quality, or output? You leave the opening with a primary success metric, named economic and technical buyers, the current leadership narrative, and a desired 90-day outcome.
9:30 – 10:30
Working session
Understand how the QA team actually works today. And where it's absorbing work engineering should own.
Team structure, roles, manual vs. automation split, embedded vs. centralized, QA-to-engineer ratio, release sign-off, where QA enters the SDLC, where it's creating leverage versus absorbing work. Is QA acting as a quality strategy function or a late-stage testing function? Where is it compensating for weak engineering ownership?
Live exercise · High-Leverage / Low-Leverage Work Map
Sort current QA activities into: creates durable leverage · necessary but non-compounding · should be automated · should move to engineering · should stop or shrink.
What you walk out with
10:30 – 10:45
Break
10:45 – 11:45
Working session
Get more leverage from the team you already have, not redesign the org.
Who's doing manual execution, who can work closer to engineering, who can read code, who can write or review automation, who can debug across the stack, who can influence developers, who's blocked by access or training. Mapped honestly across product knowledge, technical ability, automation, systems thinking, debugging, AI usage, and communication with engineering.
Live exercise · QA Capability Matrix
Each role or person mapped across 8 dimensions, with concrete next moves for each.
What you walk out with
11:45 – 12:30
Working session
Identify the access required for QA to do high-leverage work. And how to win it.
Whether QA can read front-end code, add or request test IDs, open PRs, inspect API contracts, access logs, view CI failures, create or modify test data, run the app locally, inspect DB state, review unit/integration/API tests, and influence acceptance criteria before development starts. A QA team without engineering access uses AI for surface-level work. A QA team with access uses AI to change how quality gets produced.
Live exercise · Access and Permissions Gap Audit
Identify what QA has, what QA needs, what unlock would create the most leverage, what engineering may resist, and how to frame the request so it lands.
What you walk out with
12:30 – 1:15
Lunch
1:15 – 2:15
Working session
Clarify what QA owns, what engineering owns, and what needs to be codified.
When QA gets involved, how requirements are reviewed, how test planning happens, how bugs are triaged, how regression is triggered, how release readiness is decided, who owns automation maintenance, test data, flaky tests, and unit/integration/API/E2E coverage. Where is QA acting as the safety net? Where should engineering own more quality? Where does product need to provide clearer acceptance criteria?
Live exercise · QA vs. Engineering Ownership Charter
For each major area (unit, API, E2E, test IDs, regression, release confidence): name the current owner, the recommended owner, and the change needed.
What you walk out with
2:15 – 3:15
Working session
What should be automated, what's blocking it, and how AI changes the process.
Current tools, UI vs. API vs. integration coverage, test reliability, flaky patterns, maintenance burden, CI/CD integration, manual flows that should already be automated, AI test-generation opportunities, AI failure-analysis opportunities, and where AI will create more maintenance instead of leverage.
Live exercise · Automation Constraint Diagnosis
For each major automation gap: desired automation, current blocker (access / skill / architecture / process / ownership / time), AI opportunity, required access, correct owner, quality standard, next action.
What you walk out with
3:15 – 3:30
Break
3:30 – 4:15
Working session
AI can increase output volume without improving quality. Define metrics that catch both.
Current QA dashboards, what leadership sees, activity vs. business-value metrics, regression hours, automation coverage, API coverage, E2E quality standards, flaky cost, escaped defects, release delays, and the weekly habit managers need to spot fake AI productivity before it ships.
Live exercise · Output and Quality Metrics Design
Define one output metric, one quality metric, one risk metric, one weekly review habit, and one executive-facing narrative.
What you walk out with
4:15 – 5:00
Working session
Turn the review into decisions you can actually move on.
We identify the five highest-leverage changes available within your current constraints. Access unlocks, test-layer moves, regression flow conversions, ownership shifts, AI pilots, acceptance-criteria changes, dashboard publishing, role reassignments. And score each by impact, effort, required buy-in, time to value, risk reduction, capacity freed, and political difficulty.
Live exercise · Leverage Opportunity Prioritization
Each candidate change scored on a single matrix so you walk out with a ranked list, not a wish list.
What you walk out with
5:00 – 5:30
Working session
Leave with a practical plan that leadership signs off on.
30 days: what to stop, what to measure, what access to request, what process to codify, what automation blocker to remove. 31–60 days: regression work to reduce, AI workflow to pilot, test-layer changes to make, manager review cadence to start, ownership changes to enforce. 61–90 days: dashboard to publish, quality metrics to report, team role changes, leadership narrative, what to escalate for broader buy-in.
Live exercise · Leadership Narrative Builder
Turn the day into a single message: where QA is losing leverage today, the constraints, the five changes recommended first, and how output, quality, and capacity freed will be measured over the next 90 days.
What you walk out with
After the day
Takeaway packet · within a few business days
QA Leverage Review Report · Current-State QA Operating Model Map · Team Capability Readout · Access and Permissions Scorecard · QA vs. Engineering Ownership Map · Automation Strategy Assessment · AI Leverage Opportunities List · Dashboard and Metrics Recommendations · Top 5 Optimization Opportunities · 90-Day QA Optimization Plan · Boss-Ready Summary Memo.
What's inside
01
How the team actually works today: structure, manual vs automation split, embedded vs centralized, QA-to-engineer ratio, release sign-off, where QA enters the SDLC, where it's creating leverage versus absorbing work that engineering should own.
02
A practical review of the team you have. Who can work closer to engineering, who can read code, who can write or review automation, who can debug across the stack, who's blocked by access or process. Mapped to where they could create more leverage.
03
Whether QA can read code, add or request test IDs, open PRs, inspect API contracts, access logs, view CI failures, modify test data, run the app locally, inspect DB state, and influence acceptance criteria. The single biggest determinant of how high-leverage your team can be.
04
What QA owns, what engineering owns, what needs to be codified. We name where QA is acting as a safety net, where engineering should own more quality, where product needs clearer acceptance criteria, and which decisions are implicit but need to be made explicit.
05
What should already be automated, why it isn't, whether the blocker is access, skill, architecture, ownership, or time. Where AI can generate the starting point, who should review and maintain it, and the quality bar automation must clear before it counts.
06
How QA is measured today versus how it should be measured. We design a metrics set that captures both output and quality. Including the manager's habit of inspecting AI-assisted work so you don't reward fake productivity.
07
We turn the review into decisions: the five highest-leverage changes available within your current constraints, scored by impact, effort, required buy-in, time to value, and political difficulty.
08
A 30/60/90-day plan with what to stop, what to measure, what access to request, what process to codify, what automation blocker to remove, what AI workflow to pilot, and what leadership narrative to use. Boss-ready by the end of the day.
Deliverables
The boss-approval frame
“Before we spend more on AI tools, automation work, or headcount changes, I want an outside review of where our QA model is losing leverage and what to fix first. One curated day, with all the diagnosis, prioritization, and a 90-day plan I can present to leadership the next week.”
ROI logic. Pays for itself if it helps you avoid one bad AI-tool purchase, stop expanding brittle E2E automation, reallocate one team member to higher-value work, identify a major source of flakiness, or get an access unlock from engineering you've been chasing for months.
Up the ladder · Sideways · Related
Quality Strategy & Leadership Alignment
A 3–6 week leadership engagement to align QA, engineering, product, and executives around how quality should work in the AI era.
See it →Sprint · SPR-001Quality Transformation Sprint
A 6–10 week implementation sprint where we work alongside your team to move QA from test execution to a higher-leverage quality operating model.
See it →People · PPL-001Embedded QA
A senior SDET on your team, beachheading AI-native quality.
See it →The offer ladder
Pick by what you're trying to answer this quarter. Not by what tier looks "best." The depth of change escalates with the question. The entry course is publicly priced; higher tiers are scoped per engagement.
“Teach me the model.”
You leave with the framework, the worksheets, and a 90-day plan you can hand to your boss on Monday.
See this tier →“Apply the model to my current QA team.”
An outside diagnosis built around your team. You leave with the top-5 leverage opportunities, scored, and a 90-day plan ready for leadership.
This page“Redesign our company-wide quality strategy.”
Cross-functional alignment, ownership clarity, and a 90-day implementation roadmap. Backed by 3 follow-up reviews so the strategy actually ships.
See this tier →“Lead the transformation.”
Quality intelligence dashboard, AI/automation pilot, manager operating cadence, and 180-day roadmap. Built into how the team actually works, not delivered as a deck.
See this tier →Most clients move up the ladder one tier at a time. Skipping tiers works only when the depth of change you need is obvious from the start.
90-Day QA Leverage Plan
Use it on Monday · Editable doc + Notion template