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AutomationSTAR

Test Automation Conference Europe

Deep Dive DD1

From AI Mandate to AI Strategy

Katja Obring

15:55-17:25 CET, Wednesday 4th November

Your leadership has spoken: “We need to adopt AI for testing.” But what does that actually mean? Generate thousands of test cases? Trust AI-written test code? Replace your existing workflow with whatever the latest tool promises?

Without a structured approach, teams either drown in AI-generated noise, waste months on experiments that don’t address real problems, or avoid AI entirely while competitors move ahead.

This tutorial teaches the QED (Question, Evidence, Develop) framework: a systematic method for turning “we should probably use AI” into concrete experiments that prove (or disprove) value in your specific context. You’ll learn to identify which quality problems AI might actually solve, design lightweight metrics that reveal truth rather than hype, and run time-boxed experiments that produce actionable decisions.

Through strategic planning exercises, you’ll design experiments addressing common AI testing scenarios: prompt quality versus test case value, coverage metrics versus actual defect detection, AI-assisted versus manual approaches, false positive costs, and signal-to-noise ratios in AI-generated test suites.

You’ll leave with experiment designs ready to run in your first sprint back, decision criteria for scaling or abandoning AI approaches, and a framework for making evidence-based choices about AI adoption rather than reacting to vendor promises or executive pressure.

This tutorial gives you the capability to answer whether AI helps your testing scientifically, in your environment, with your constraints, for your actual quality challenges.

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What you will Learn

  1. Transform vague AI mandates into testable experiments with clear success criteria, time-boxed to 1-2 weeks, and scoped to a single feature or team
  2. Design 2-3 lightweight metrics that measure AI impact on actual quality outcomes (defect detection, false positive rates, investigation time) using existing tooling
  3. Make evidence-based scale/pivot/abandon decisions about AI testing approaches by running small experiments that prove (or disprove) value in their specific context

Session Details

  • Intermediate
  • 90 minutes
  • Hands-on workshop
  • AI-Augmented Automation (Gen AI)
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Session Speaker

Katja Obring

Kato Coaching Ltd., United Kingdom

Kat Obring is the founder of Kato Coaching and creator of the Q.E.D. (Quality-focused Experimentation and Development) framework. With over 20 years in the software industry, Kat now focuses on what matters most: teaching teams and individuals how to measurably improve the quality of their work. Her practical frameworks combine insights from her diverse experience as a DevOps QA engineer, Head of Delivery, and, surprisingly, her early career as a chef. She specialises in helping QA and test professionals transform from reactive testers to strategic quality advisors through evidence-based decision-making. She's learned that evidence always beats guesswork, and a well-designed experiment will reveal more truth than months of planning ever could.

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