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Quality & Engineering Leadership

Organisational maturity

Quality Maturity
Assessment

Nine dimensions, five levels. It measures the capabilities and practices that produce quality outcomes, not the outcomes themselves, which arrive too late to steer by.

The scale

  1. 01Ad hoc
  2. 02Repeatable
  3. 03Defined
  4. 04Measured
  5. 05Continuously improving
Illustrative profile: the current and target level of each dimension
AxisDimensionCurrentTarget
01Quality Strategy & Governance34
02People & Quality Culture34
03Development & Delivery Practices45
04Testing & Automation34
05Environments & Test Data23
06Measurement & Continuous Improvement24
07Customer Experience & Operational Reliability45
08Security, Privacy & Compliance34
09AI & Agent Quality13

Reading the profile

  • Currentwhere the organisation is today
  • Targetwhere it needs to be in twelve months

The shape matters more than the area. A balanced profile at level 3 ships more reliably than a spiked one averaging 4. The weakest dimension sets the ceiling, because that is where the failures come from.

Widest gaps in this profile

  1. AI & Agent Quality1 to 3
  2. Measurement & Continuous Improvement2 to 4
  3. Environments & Test Data2 to 3

Illustrative profile. Scores come from evidence and interviews, never self-assessment alone. Self-scores cluster at 3.

Dimensions

The nine dimensions.

Each scored 1–5 against written criteria, with the evidence recorded.

  1. Quality Strategy & Governance

    Priorities, risk management and accountability.

    The real question

    Who decides what “good enough to ship” means, and who carries it when it is wrong.

  2. People & Quality Culture

    Skills, shared ownership and collaboration.

    The real question

    Whether quality is a team’s property or a department’s job.

  3. Development & Delivery Practices

    Prevention, reviews and release discipline.

    The real question

    How much is caught before it is written, not after it is merged.

  4. Testing & Automation

    Coverage, effectiveness and trustworthy automation.

    The real question

    Whether a red build is information or noise.

  5. Environments & Test Data

    Availability, consistency and realism.

    The real question

    How far the thing you tested is from the thing you shipped.

  6. Measurement & Continuous Improvement

    Useful metrics, feedback and learning.

    The real question

    Whether anyone could tell you if quality improved last quarter.

  7. Customer Experience & Operational Reliability

    Customer outcomes, monitoring and recovery.

    The real question

    Who finds out first when it breaks: you or the customer.

  8. Security, Privacy & Compliance

    Safeguards embedded throughout delivery.

    The real question

    Whether controls are part of the work or a gate at the end of it.

  9. AI & Agent Quality

    Accuracy, safety and reliability of AI systems and multi-agent workflows.

    The real question

    How you know a non-deterministic system got better rather than different.

Levels

The five levels, in full.

  1. Ad hoc

    It happens, or it doesn’t. Outcome depends who is on the team.

  2. Repeatable

    The same thing usually happens twice. Still person-dependent.

  3. Defined

    Written down, agreed, and followed across teams.

  4. Measured

    Instrumented, so you can tell whether it is working.

  5. Continuously improving

    Measured, then acted on: the practice improves from its own evidence.

Run it

Find out which dimension is setting
your ceiling.

The criteria are open. Score yourself against them, or have me run it across your teams and hand back the evidence.

Let’s run it

In practice

The dimensions,
in practice.

Case studies from leadership roles, each with its starting point, my contribution and the limits of the figures.

  1. Case study 01

    Sustaining quality across two mobile apps and their content delivery platform.

    The Economist app, the Espresso app and the platform behind them: regression for the flagship app went from 4–5 days to 1–2 hours of automated test execution, while collaboration, layered automation, platform integration testing and physical-device coverage helped sustain ratings of 4.9 on the App Store and 4.8 on Google Play.

    Context
    The Economist · 2021–2025 · Senior QA/QE Manager, mobile and content delivery
    Result
    Regression before a release of The Economist app went from 4–5 days to 1–2 hours of automated test execution, while ratings of 4.9 on the App Store and 4.8 on Google Play were sustained.

    Quality & scale, Quality engineering, Test automation, Integration testing, Device testing

  2. Case study 02

    Clearing the way for a release train at an AI-native language-technology platform.

    Code changes lost much of their time to manual handovers and constant rebasing. I measured where the time went and planned a release train. In QA’s part, which I lead, the share of Playwright test runs passing without a retry roughly doubled; the train itself has not run yet.

    Context
    An AI-native language-technology platform · 2026 · Head of Customer Trust
    Result
    Between August and late September, the share of Playwright test runs that passed without a retry roughly doubled, while more tests checked each build. By early October the Playwright test stage took less than half as long as in August and September.

    Quality & scale, Test reliability, CI/CD, Delivery flow, Engineering leadership

  3. Case study 03

    Modernising quality engineering at a UK property-technology company.

    Leading QA across the UK and EMEA, I drove an automation transformation spanning desktop and API testing. We replaced legacy TestArchitect automation with FlaUI, reduced regression execution from 15 hours to 1–2 hours, and introduced a Playwright API testing framework covering 100% of endpoints.

    Context
    Reapit · 2025–2026 · Head of QA, UK & EMEA
    Result
    The clearest outcome was faster regression feedback, supported by comprehensive API endpoint coverage and a more consistent approach to Quality Engineering across distributed teams.

    Quality engineering leadership, Automation modernisation, Desktop and API testing, Quality strategy and governance, Distributed team leadership

  4. Case study 04

    Making AI evaluations trustworthy at an AI-native language-technology platform.

    I reviewed the platform’s AI evaluations to make sure they tested the feature, not just the judges’ criteria. Improving unstable tests and assertions raised the pass rate from just over half to three in four within a month, and new dashboards made the evals’ effectiveness, time and cost, and the success rates of live user journeys, visible.

    Context
    An AI-native language-technology platform · 2026 · Head of Customer Trust, AI workflow quality and release confidence
    Result
    Within a month, the evals’ pass rate rose from just over half to three in four, and the failures that remained were known product issues rather than faults in the tests.

    AI evaluation, Test design, Test reliability, Observability, Customer trust

More in this area

The other topics.

  • Quality strategy

    What quality means for your product, who owns it, and how “good enough to ship” gets decided.

  • Test architecture

    Testing designed around your product’s risks and system boundaries, so a green build says what is safe to release.

  • Release confidence

    A release decision that can point to what was verified and what was not.

  • CI/CD

    Pipelines and feedback loops that tell the team quickly whether a change is safe to ship.

  • Fractional leadership

    Sustained direction across quality and delivery, without a full-time hire.

Where is
your ceiling?

Describe the team, the release process and where quality is decided today, and we can work out which dimension to move first.

Discuss consulting