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In this episode of Definitely Maybe Agile, Peter Maddison and David Sharrock explore the critical question: "How do we know when work is ready to start developing?" They discuss the challenges of translating business requirements into technical implementation, the importance of having the right people in collaborative discussions, and practical approaches to defining "ready" work. Peter shares recent experiences with organizations struggling with this exact problem, while Dave highlights how trust between business and technology teams impacts the handoff process. They explore visual collaboration techniques, the concept of "full kit," and practical ways to determine if work is truly ready to begin.

This week´s takeaways:

1. Revisit and reinforce your work definition process regularly, as changing roles and organizational shifts can erode even the most robust systems over time.
2. Use the "full kit" concept as part of your definition of ready, and be willing to say no to work that doesn't meet these criteria.
3. Work is ready to start when it's the team's top priority, has a clearly defined problem to solve, and the team can confidently estimate it within their typical delivery range.

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In this insightful conversation with Suzanne El-Moursi, co-founder and CEO of BrightHive, Peter and Dave explore how organizations are addressing the growing gap between data volume and analytical capacity. Suzanne reveals that while 90% of the world's data was created in just the last two years, only about 3% of enterprise employees are data professionals, creating a massive bottleneck where business teams must wait in line for insights from central data teams.

BrightHive's solution is an "agentic data team in a box" – seven AI agents that work in unison to handle the entire data lifecycle from ingestion to governance to analytics. Unlike typical AI solutions, these agents operate at the metadata layer to ensure quality, compliance, and meaningful insights without replacing human expertise.

The conversation covers compelling use cases across industries – from helping resource-constrained organizations extend their analytical capacity to unifying fragmented data landscapes resulting from mergers and acquisitions. Perhaps most striking is Suzanne's vision for measuring AI's impact through what she calls the "delight KPI" – are employees finding their work more fulfilling when augmented by these tools?

Key Takeaways:

• Data fragmentation persists - Organizations struggle with siloed data across systems, especially after mergers, blocking comprehensive analysis.
• AI augments human intelligence - "A doctor with AI will displace a doctor without AI" - the goal is removing grunt work so humans tackle higher-value analysis.
• Measure the "delight KPI" - Track how AI improves job satisfaction by enabling more data-informed work without technical bottlenecks.
• Cultural shift needs technical solutions AND organizational buy-in to overcome skepticism about AI in the workplace.

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In this episode of Definitely Maybe Agile, Peter Maddison and David Sharrock explore how increasing technological capabilities—particularly AI and modern development tools—are changing the landscape of organizational change management. They discuss the implications of newly created capacity, the value of team autonomy, and the importance of balancing efficiency with innovation.

This week´s takeaways:

• Creating capacity through new technologies doesn't mean downsizing teams—it means enabling organizations to address previously neglected but valuable work while maintaining knowledge pipelines.
• Team autonomy is crucial for effective change management—when teams have both direction and freedom to make decisions about their workspace, they can respond more effectively to urgent needs in the system.
• Organizations must recognize and protect "slack time" as a valuable resource rather than inefficiency—this time for maintenance, innovation, and thinking is essential for sustainable systems.