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Testing Agility Itself

25-minute Talk

How do we know Agile adoption is actually improving quality, flow, and customer outcomes, especially in real-world environments with fixed contracts, fixed budgets, and AI products?

Virtual Pass session

Timetable

11:45 a.m. – 12:30 p.m. Tuesday 17th

Room

Room D7 - Track 9: Talks

Agile Methodologies Artificial Intelligence (AI) Collaboration & Communication

Audience

QA, Engineers in AI Product Development, Scrum Masters, Agile Coaches, Project Managers

Required

mobile phone

Key-Learnings

  • Apply testing and quality practices to enable innovation in non-ideal conditions
  • Recognize how testers can validate Agile adoption, not just test features
  • Design lightweight, learning-oriented metrics that support quality and flow

Metrics, Fixed Contracts & AI Products

How do we know Agile adoption is actually improving quality, flow, and customer outcomes, especially in real-world environments with fixed contracts, fixed budgets, and fixed dates?

In this experience report, Maria shares industrial stories from large global organizations where testing and quality practices were used to test agility itself, not just the product.

The talk connects two real delivery contexts:

  • Gamified measurement experiments where testers and teams treated metrics as learning tools and not KPIs to explore quality, customer focus, and organizational constraints.
  • A fixed-budget, fixed-date project to deliver AI product delivered for a large enterprise client, where Scrum was used for delivery and Kanban for discovery to surface risks early and enable innovation without breaking the contract.

Instead of heavy dashboards or theoretical frameworks, the teams relied on visual work management, test-driven feedback loops, and transparent goals to support decision-making and stakeholder alignment. These practices helped teams expose quality risks early, adapt responsibly, and deliver measurable outcomes ending the project ahead of schedule.

This session is a practical, honest reflection on what worked, what failed, and what testers can realistically influence when Agile meets industrial constraints.

 

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