Evaluating AI Tools – A Practical Framework for Testers and Leaders
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The AI ecosystem is exploding with tools that promise to accelerate delivery, improve quality, and transform the way we work. Yet for many teams, evaluating these tools is overwhelming - flashy demos and marketing claims rarely answer the real questions:
Will this work in our context? Can it scale? Is it sustainable?
This talk presents a structured framework to cut through the hype and make confident, informed decisions. The framework covers six key dimensions:
- capabilities,
- inputs
- outputs
- LLM considerations,
- control
- cost transparency
The framework gives testers and leaders a holistic lens to evaluate any AI solution. Rather than prescribing which tools to use, it provides a mindset and practical checklist to guide your own assessments.
We will look at how these dimensions uncover strengths, risks, and trade-offs: from integration and extensibility, to handling data securely, to balancing automation with human oversight. The framework also highlights how to engage stakeholders, avoid vendor lock-in, and measure long-term value instead of short-term gains.
Attendees will leave with clarity, structure, and confidence - equipped to evaluate AI tools objectively and ensure that the ones they adopt truly deliver meaningful impact.