Your team is using AI in testing. Can you show it's paying off?

Most teams started with ChatGPT, Claude or Copilot one person at a time, usually just to write test cases. We help your team use AI well across the whole testing lifecycle, from requirements review to regression analysis to defect triage, and give leadership real evidence of what it's worth, with review controls your risk and audit teams can check.

Talk to us about your team See how it works
BEFORE EXECUTION BUILD AND RUN DECIDE AND CLOSE 1Requirements 2Test scope 3Risk & plan 4Test design 5Test data 6Automation 7Regression 8Execution 9Root cause 10Severity 11Reporting 12Completion
TitleAI across the testing lifecycle
ForQA, test & V&V teams
FormatPrivate, fixed fee
ByNOPMARK

AI arrived in your testing before a plan did

The four things we hear most from teams starting out.

  1. Everyone does it differently

    Some testers paste requirements into ChatGPT, others use Claude or Copilot, some avoid AI entirely. Results depend on who wrote the prompt.

  2. No baseline, so no ROI

    Nobody measured how testing worked before AI, so nobody can show what changed. "It feels faster" won't survive a budget review.

  3. Only one corner of the lifecycle

    AI is used to churn out test cases, while requirements review, regression analysis and defect triage, often the bigger wins, go untouched.

  4. No rules, no review record

    Which requirements, code or data can go into which tool? When risk, compliance or audit asks how AI output is checked, there's no record to show.

You're not behind. Most organizations are still piloting AI in testing, and few can yet show what it's delivering.

Your team's path to AI that pays off

Four steps from ad hoc to proven. Each one builds on the last and is useful on its own.

CAPABILITY TIME 1 2 3 4 STEP 1 · OPTIONAL · HALF DAYAI Fundamentals→ Shared basics STEP 2 · 2 DAYSLifecycle workshop→ Consistent practice STEP 3 · ABOUT 2 WEEKSAI Testing Baseline→ Proven value STEP 4 · 4–6 WEEKSEvaluation Sprint→ Rolled out and owned TODAYAd hoc AI use HOW YOUR TEAM'S AI CAPABILITY GROWS, STEP BY STEP SUCCESSFUL OUTCOME AI that pays off Better testing acrossthe lifecycle, a teamthat runs it, and proofleadership trusts WHEN YOU'RE READYSAME OR NEXT WEEK2–4 WEEKS OF PRACTICERIGHT AFTER BASELINEONGOING≈ 3 MONTHS END TO END
  1. TodayAd hoc AI useIndividual prompts, no shared picture
  2. When you're ready
  3. Step 1 · optional · half dayShared basicsAI Fundamentals: one AI vocabulary for the whole team
  4. Same or next week
  5. Step 2 · 2 daysConsistent practiceLifecycle workshop: shared prompts, rubrics and safe-use rules
  6. 2–4 weeks of practice
  7. Step 3 · about 2 weeksProven valueAI Testing Baseline: a scorecard leaders trust
  8. Right after Baseline
  9. Step 4 · 4–6 weeksRolled out and ownedEvaluation Sprint: piloted, governed and run by your team
  10. Ongoing support
  11. Successful outcomeAI that pays offBetter testing across the lifecycle, a team that runs it, and proof leadership trusts

≈ 3 months end to end

Not sure where your team is today? Talk to us

We cover the whole testing lifecycle, not just test generation

Twelve activities. For each, your team learns where AI helps, what it gets wrong, and how to review it. Start with low-risk uses, then add higher-consequence ones as review controls mature.

Before execution

  1. Requirements review
  2. Test scope
  3. Risk analysis & planning
  4. Test design

Build and run

  1. Test data
  2. Test automation
  3. Regression analysis
  4. Execution & results

Decide and close

  1. Defect root cause
  2. Severity & priority
  3. Monitoring & reporting
  4. Test completion
Show where AI helps and what to watch for, activity by activity
No. Activity Where AI helps Watch for
Before execution
1 Requirements review Flags ambiguous, untestable or conflicting requirements before development starts. Generic "looks clear" reviews that miss what matters in your domain.
2 Test scope Proposes what's in and out of scope for a release from the change set and requirements. Scope that follows the diff but misses what the change affects.
3 Risk analysis & test planning Drafts risk lists, test approach, priorities and effort estimates. Plausible plans that ignore your real hazards, history and constraints.
4 Test design Derives test conditions, cases and expected results from requirements and user stories. Happy-path volume, vague expected results and broken traceability.
Build and run
5 Test data Generates realistic, privacy-safe data sets and boundary values. Synthetic data that quietly removes the edge cases.
6 Test automation Writes and maintains test scripts and unit tests alongside developers. Tests that pass without really checking anything; brittle scripts.
7 Regression analysis Maps a change to affected features and selects the regression tests to rerun. Missed indirect impacts, and suites trimmed too aggressively.
8 Execution & results analysis Clusters failures, separates flaky from real, summarizes runs. Real failures dismissed as "flaky".
Decide and close
9 Defect root cause Reads logs and traces, proposes and ranks root-cause hypotheses to verify. Confident guesses presented as findings.
10 Severity & priority Suggests severity against your own definitions and spots duplicate defects. Under-rating safety, compliance or customer impact.
11 Monitoring & reporting Drafts status, coverage-versus-risk and release-readiness summaries. Reassuring summaries that bury open risks.
12 Test completion Compiles evidence, traceability and lessons learned at closeout. Gaps in the record that only surface at audit.

The difference is the method, not the tool

Same AI, same input. What changes is the context, the review rubric and the team habits we teach.

A

Defect severity, root cause and regression

Medical device · activities 7, 9, 10

Infusion pump occlusion alarm sounds 4 seconds late when the flow rate is below 1 mL/h.

Ad hoc promptRisky triage

"Severity: Minor. Likely root cause: UI refresh delay. Recommend fixing in a future release."

A confident guess, stated as fact, that under-rates a safety-related defect and would push it out of the release.

Team rubric + contextTriage you can defend
  1. Severity: Critical under your own definitions, because a delayed alarm is linked to a hazard in your risk file.
  2. Root cause: three ranked hypotheses (sampling interval at low flow, averaging window, UI refresh), each marked unverified.
  3. Next step: reproduce at 0.5 mL/h with pressure logging to confirm or rule out each hypothesis.
  4. Regression: rerun occlusion detection, low-flow modes and alarm-silence tests; flag battery mode as untested.
B

Requirements review

Banking · activity 1

The system shall calculate interest on the daily balance and round appropriately.

An ad hoc prompt called this "clear and testable." With the team's rubric and context, the same AI found five problems: no rounding rule or precision, no day-count convention, no cutoff time that defines the "daily" balance, no case for zero or negative balances, and no link to the disclosure or control it supports.

We apply the same approach to scope decisions, test plans, test cases, automation and test reports.

Leadership gets a one-page answer

Every baseline ends with a scorecard like this, built from your team's real work.

TitleAI Testing Scorecard: Sample
TeamDevice Software QA
Scope5 lifecycle activities
Period6 weeks
Measure Result Status
Requirements review: AI-flagged issues confirmed by reviewers 71% Keep
Test design: AI-drafted test cases accepted after review 62% Improve
Regression analysis: known impacts missed by AI-selected tests 3 Improve
Defects: AI severity ratings changed at triage 23% Improve
Automation: AI-written scripts needing major rework 38% Gap
Review and rework time per accepted artifact, vs. manual −31% Keep

Continue

AI-assisted requirements review and bug report drafting.

Fix before scaling

Regression selection: give the AI your dependency map and risk file as context. Severity: add your definitions to the prompt. Re-measure in 4 weeks.

Stop for now

Generating UI scripts from screenshots: high rework, little time saved.

Your scorecard uses your own workflows, measures and data.

Want a scorecard like this for your team? Ask about a Baseline

Four fixed-fee steps, private to your company

Each is scoped in writing before you commit.

AI Fundamentals for Testing TeamsOptional starting point

PurposeA shared foundation for the whole team: how AI tools work, why they get things wrong, and how to use them safely in testing work.
FormatHalf day · up to 30 people · remote or on-site · on the AI tools you already have
You leave with
  • A common vocabulary across testers, analysts, developers and managers
  • Why AI output fails: confident errors, run-to-run variation, missing context
  • How to give AI tools good context
  • Safe-use rules mapped to your company's AI policy
  • A hands-on drill in reviewing AI output
From $4,000Private team session

AI Across the Testing LifecycleRecommended first step

PurposeA private, hands-on workshop that turns individual experiments into a shared, reliable practice at every lifecycle activity.
Format2 days or 4 half-days · up to 12 people · remote or on-site
You leave with
  • Prompts and context for requirements review, scope, risk, design, regression and defect triage
  • Review rubrics for AI output
  • Safe-use rules for AI tools
  • Your AI uses ranked by risk and value, low-risk first
  • A measurement plan to run next
From $12,000Private team workshop

AI Testing BaselineEvidence for leadership

PurposeMeasures how AI is being used at one or two lifecycle activities on your real work, and tells you honestly whether it's paying off.
FormatAbout 2 weeks · 1–2 lifecycle activities
You leave with
  • AI-assisted vs. non-AI comparison of effort and quality
  • What AI output is accepted, reworked or missed
  • Your AI Testing Scorecard
  • A continue, fix or stop recommendation
  • A one-page AI adoption roadmap
From $5,000Fixed fee

Applied Evaluation SprintWhen you're ready to scale

PurposePilot the highest-value AI use against agreed success criteria, roll it out, and hand it over to your team.
FormatTypically 4–6 weeks · one team
You leave with
  • A pilot with agreed success criteria, then rollout
  • Reusable prompts, context packs and checklists
  • AI governance your quality, risk or compliance team can review: work instruction, risk-based review levels, review records
  • Automated checks in your pipeline where they fit
  • A before/after report and coached handover
From $20,000Fixed fee, after a baseline

Optional ongoing support, from $2,500 a month, keeps prompts, rubrics and measurements current as AI tools and models change. Only when it's useful; many teams prefer a periodic re-check.

For budget holders: the fundamentals session, workshop and baseline together cost a small fraction of a typical annual AI tool budget, and tell you whether that spend is working. Final pricing depends on team size, scope and format.

Questions teams ask first

Is this only about using AI to generate tests?

No. We cover the whole lifecycle, from requirements review and test scope through regression analysis, defect root cause and severity, to reporting and test completion. We start with low-risk uses such as test summaries and bug-report drafting, then move to higher-consequence ones like requirements review and severity triage once review controls are in place. Most of it needs no coding.

We work in a regulated or audited environment. How do you handle AI governance and risk?

We help you put AI use in testing under control: a work instruction, review levels matched to risk, and records of human review. Your quality, compliance or audit team can then assess them against your own requirements, whether that's a medical-device quality system, financial controls and model-risk policy, or ISO-based processes. It's the testing side of responsible AI. We don't give regulatory advice or certify compliance.

Does the whole team need an AI fundamentals class first?

No, it's optional. The half-day AI Fundamentals session helps when experience varies a lot across the team, or when you want managers, analysts and developers to share the same basics. The lifecycle workshop includes a condensed version. Individuals who want a certificate can take ISTQB CT-GenAI, which NOPMARK also teaches.

We're only just getting started with AI. Is it too early?

No. It's the best time. A baseline taken now is what lets you prove improvement later, and shared habits are easier to build before everyone settles into their own.

Can you prove ROI for us?

We can't promise a productivity number in advance, and we'll tell you if a use isn't paying off. What we give you is a measured baseline, a few metrics that connect AI use to quality and effort, and a scorecard your leadership can act on.

Do you need access to our code or data?

Not for the workshop, which runs on NOPMARK's own sample product. Baselines and sprints use your real work, inside your environment, under your NDA and supplier requirements.

Which AI tools do you work with?

Your own. We work with whatever your team already uses, such as ChatGPT, Microsoft Copilot, GitHub Copilot, Claude, Gemini or AI built into your test management and issue-tracking tools. We don't sell or install tools.

How does purchasing work, and what isn't included?

Every engagement is fixed-fee and described in a written proposal or statement of work, delivered on-site or remotely. We invoice your company directly and work through your procurement and supplier onboarding. Not included: choosing or installing AI tools, guaranteed productivity percentages, compliance certification or regulatory advice.

Get an honest answer before your next budget review

Tell us how your team uses AI in testing today. We'll suggest the smallest step that gets you real evidence.

Talk to us about your team
EngagementPrivate to your company
TermsFixed fee, scoped in writing
ToolsThe AI tools you already have
ByNOPMARK