Why Context Matters When Working with Generative AI in Product Development
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About this event
ZOOM event. ZOOM LINK would be distributed on the date of the event
If you have started experimenting with generative AI, you have probably seen this already: better context usually leads to better output. If you have not started yet, that is okay too.
This Bay Area Agile Leadership Network session is for people on the “business side” of product development who want a practical, low-hype way to think about AI. We will explore:
- - How context shapes results
- - Why being a beginner is still acceptable
- - How to use small experiments to improve collaboration between business and build
- - Access The AI-fluent Product Team Development Playbook to guide achieving the outcome
- - Learn about context from two practical examples: one with and one without
Join us for an open conversation around the influence of generative AI on product development.
SPEAKER: Tim Dickey
BIO: Tim Dickey is a product development and business agility coach who helps teams close the gap between strategy, delivery, and real-world product work. A LinkedIn Top Voice in 2024 and 2025, he is known for practical, human-centered insights on product development and for helping people grow into new ways of working without pretending expertise arrives fully formed.
Before his consulting career, Tim served nearly 25 years in the United States Navy (active duty and reserve) across the submarine force and in support of deployed special operations units, where he learned servant leadership under pressure and the realities of high-performing teams. He has worked with organizations including Carnival Cruise Lines, Verizon, IBM, and Improving, bringing together coaching, product thinking, and delivery experience across complex environments.
Today, Tim focuses on helping product teams become more AI-fluent by encouraging business-side and product-side leaders to experiment, learn in public, and collaborate more directly with builders. His work blends coaching, systems thinking, and hands-on exploration, including rapid MVP experiments inspired by frameworks such as Cynefin and emerging AI-native development practices.
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