AI, Automation, and the Real Value of Testers - Daniel Knott

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"I truly believe that we have like in five to 10 years we see a huge demand in people who are able to understand system architectures." - Daniel Knott

In this episode, I talk with Daniel Knott about the real pains in testing and what comes next. Why do managers cut quality when money gets tight. We look at AI and low code that spit out apps fast, often without clear architecture. We warn about skipping performance and security. We also reflect on how testers can sell value in business terms. Speak revenue, KPIs, and user happiness, not code coverage. Daniel says domain knowledge may beat deep coding as AI writes more code. We explore prompt reviews as a new shift left habit.

Daniel Knott loves digital products with high quality being web or native mobile applications. He has been working in the IT industry for almost 20 years with experience in hands-on software testing for desktop, web and mobile applications. He also worked as product manager for mobile and web products. At the moment, Daniel is working as an IT manager as Head of Engineering, helping software development teams ship great products with high quality.

Daniel wrote two books - Hands-On Mobile App Testing and Smartwatch App Testing and is a frequent blogger and conference speaker. In 2022 he also created his YouTube Channel about Software Testing which has grown to more than 145k subscribers.

Highlights:

• Testing's core problem is not AI disruption but a decades-long failure to communicate testing value in business terms, such as revenue impact and user satisfaction, rather than technical metrics like code coverage.
• Prompt reviews are an emerging shift-left practice: because small changes in wording produce significantly different AI outputs, reviewing the prompts used to generate code becomes a quality gate in its own right.
• Domain knowledge is becoming more valuable than deep technical skills, because testers increasingly need to judge AI-generated outcomes rather than write or maintain code themselves.
• Non-functional requirements, including load, performance, and accessibility, are routinely overlooked, and ignoring architecture early makes them expensive to fix once a product ships.
• Cutting testers in a downturn is a short-term saving that creates a long-term shortage of people who can understand and verify system architectures, especially as AI-generated codebases grow.