Effective use of AI for value delivery needs clear organizational architecture: bounded scopes, traceability, ongoing stewardship from teams, trust via bounded agency, and platform abstractions that enable humans and AI to operate effectively.

At the same time, the speed of innovation and sheer variety of approaches available means that large and growing organizations must deliberately curate (find, select, and restrict) implementation choices to nudge and empower teams in sensible directions: this is ‘choice architecture’.

Engineers seeking to increase their influence and outcomes are ideally placed to define, guide, and amplify both the organizational architecture and the choice architecture needed for success with AI-native value delivery.

In this talk, Matthew Skelton - co-author of the groundbreaking book Team Topologies and lead author of the forthcoming book Adapt Together - explores the skills upgrades that engineers  should invest in to shape AI-native outcomes at scale: techniques and language from Team Topologies to drive effective organizational architecture, and ways of working and behaving that drive effective choice architecture across the organization from Adapt Together, based on experience with hundreds of organizations worldwide.

Key takeaways

• Organizations need clear organizational architecture and ‘choice architecture’ for effective AI-native operation
• Language, patterns, and techniques for shaping effective value-centric organizational architecture are provided by Team Topologies
• Techniques, metrics, and language for shaping choice architecture and curated context are provided by Adapt Together
• Engineers can expand their influence and outcome success AI-native by upskilling with language, techniques, and ways of working for both organizational architecture and  choice architecture