Brownbag | Your guide towards an AI-ready test strategy

Description

Artificial Intelligence is rapidly becoming embedded in all sorts of testing tools. While many organizations focus on individual AI capabilities within (test) tooling, far fewer are prepared for the impact AI has on their testing strategy.

AI will increasingly be adopted to support and accelerate testing activities through AI-powered tools and AI-agents. From generating test scenarios, test data, automated test scripts, to supporting decision-making, AI is changing how testing is performed.

We will not be diving into specific tools, but focus on how AI changes how we test and why testing becomes a strategic enabler for AI adoption. We explore how organizations can responsibly introduce AI into their QA strategy and what must change in your processes, skills, and governance to make this effective.

Rather than positioning AI as a replacement for testers, we frame AI as a collaborative colleague that augments human expertise, increases coverage, accelerates, and enables teams to focus on higher-value QA activities.

This session is a foundational introduction for anyone involved in digital transformation, quality assurance, or solution delivery, and sets the stage for deeper follow-up sessions or demo’s on AI and testing.

During this brownbag session we will

• Clarify what testing with AI means, and how it differs from traditional testing
• Explain the shift from “AI in tools” to AI agents as active QA collaborators
• Show where AI can realistically add value today (scenario generation, data creation, analysis, automation)
• Highlight common pitfalls, false expectations, and organizational blockers when adopting AI in testing
• Explain how test strategies and workflows must evolve to work effectively with AI
• Describe the changing role of test engineers from execution to orchestration and validation (Human-in-the-Loop) when AI becomes part of the toolchain
• Identify practical first steps to move from experimentation to a structured, AI-ready testing approach