Nordic Testing Days 2026

Artificial Intelligence is increasingly used to write test code. Today it is estimated that 60% of new test code is written by AI tools and this number will only continue to rise. Surprisingly, however, the testing is now the critical component needed is to ‘test’ the 40% that AI is unable to test itself, that is where the future of testers.

That 40% is not busy work. This is not your typical motivational speech dressed up as another AI testing keynote. What I will talk about in the keynote is the three critical capabilities that are becoming more valuable not less, and the underlying reason for this is that all of them depend on something that current models lack: the human intent behind engineering quality software for other humans to use.

• Engineer the Context Understanding: While AI can greatly amplify the effectiveness of testing, the quality of results is only as good as the input you give it. In 2026, the testers who will rise to the top will not be the ones who came up with the cutest prompt for the AI to answer. Rather, they will be those who can successfully engineer the context of the problem for the AI to solve. We will look at real-world best practices for testing with AI, and I will share an AI Assurance playbook for context engineering that will immediately raise the quality of your AI-infused testing.
• Review with Heuristic Judgment: AI generated test suites look good - until they fail to test what really matters silently. Green pipelines are the most dangerous artefact in organisations today. No one asks: what are we not testing? This capability helps organisations audit test suites generated by AI with a sense of heuristic judgement. I can explain how to design world class AI augmented tests like an experienced navigator reading a map to identify the blank spaces where organisation specific risks reside.
• Orchestrate Trust: Humans trust humans they still need decide when the machine is wrong. That is not a technical skill - it is a quality leadership act. This capability describes the shifting role of quality engineers to trust AI Assurance oracles. This capability explores the new role of orchestrating AI Assurance that trust across teams, tools, and stakeholders, and answers the question: “Who is really responsible for quality?”

By the end of this keynote, you will have an assessment of your skills in planning, architecting, designing and leading AI governance, a practical plan of action to implement in the real world, and the assurance that you will be “ready” to become part of the “Irreplaceable 40%”.

Key Takeaways:

• Context Engineering is our New Superpower: AI-augmented tests are only as valuable as the context a human provides. Attendees will leave with a repeatable framework for curating system, user, and business context that transforms AI output from "technically correct" into "intent driven tests" - a skill that compounds in value as the important of testing of AI infused systems improve.

• Heuristic Auditing Catches the Real Truth: A passing automated test regression suite is not proof of quality; it is proof that the tests you wrote passed. Attendees will gain practical heuristic-led auditing patterns to interrogate value-driven testing, identify dangerous blind spots, and ask the questions that thinking machines are structurally incapable of asking themselves.

• Trust Orchestration is the Quality Engineer's Next Career: The highest-value skill in an AI-augmented team is not technical testing - it is the ability to calibrate, communicate, and own confidence decisions across people, tools, and stakeholders. Attendees will understand how to position themselves as the trust architect oracles in their organisation increasingly needs, turning a perceived threat into their most durable career advantage.

Intro:Artificial Intelligence challenges almost every assumption the testing discipline is built on. Traditional testing depends on fixed inputs and predictable logic, but AI systems are adaptive, probabilistic, and context-dependent. That means our classical test cases are no longer stable reference points.


In this 20-minute talk, Nicole van Gijn explores what testing looks like when your system learns, reasons, and occasionally hallucinates. She introduces the AI Quality Grid, a structured framework co-developed with John Kronenberg, that helps define quality attributes, risks, and validation strategies for AI applications. The session bridges theory and practice through concrete examples from a real AI test project, showing how LLM-evals and risk-based thinking can be combined to test prompt robustness, output consistency, and bias control within modern CI/CD pipelines.


Attendees will walk away with a lightweight but actionable structure for AI quality assessment and a new mindset: understanding quality not as a checklist, but as an intelligent, adaptive discipline. Why this topic is relevant:

1. AI systems are rapidly entering production pipelines, yet testing methods lag behind.
2. Testers and QA leads urgently need practical models to evaluate non-deterministic outputs.
3. The AI Quality Grid offers a bridge between AI model evaluation (LLM-evals) and classical test strategy, providing testers with new tools and thinking patterns to stay relevant in the AI era.

Deploying to production shouldn't require a meeting, three approvals, and a prayer. Yet most teams treat every release like launching a rocket - mission control on standby, everyone watching the countdown, but no one wanting to press the red button.

At Sokos Hotels, our web booking system handles thousands of reservations daily, processing millions monthly across 50 hotels all over Finland and Estonia. One critical bug means lost revenue; one outage means thousands of unhappy guests. We were trapped in weekly releases, manual verification, and the kind of Thursday tension that put everyone in the mission control room. We asked ourselves if Continuous Deployment is just a myth.

In less than a year, we broke the cycle. We went from weekly manual releases to deploying seven times per day with higher confidence than ever. The results? 4.5/5.0 customer effort score, 40% higher conversion rates and 4.3+/5.0 overall team happiness. The secret? A testing strategy that made deployment boring for the last 2 years.

Join my talk to find out how we did it!

What you’ll get from this talk:

1. The three signs your team is not ready for Continuous Deployment, and the technical enabler that breaks the testing tension. 
2. How we shifted from “QA signs off on releases” to "Quality is built-in", and why the cultural change was harder than the technical one. 
3. The valuable failure lessons we learned along the way and what it taught us about green pipeline.

Your takeaway: A practical, battle-tested roadmap for testing-enabled Continuous Deployment. You'll leave knowing which tests to automate first, how to build confidence without sacrificing speed, and how to prove to skeptics that this isn't just another risky experiment. Real patterns, real failures, real results - ready to implement Monday morning.

Who should attend: QA engineers, test automation engineers, developers, engineering managers, and DevOps practitioners who believe testing should accelerate delivery, not slow it down.