4 Operating Model Upgrades for Success with AI

MS

Matthew Skelton

CEO; Co-author, Team Topologies, Conflux

Many organizations struggle to achieve genuine ROI from AI because they typically attempt to apply AI onto existing legacy workflows and structures. To see real returns from AI, it’s vital to upgrade the operating model by reshaping teams, workflows, and system boundaries to match value flows. It is now clear that organizations that organize for bounded agency in humans are well-suited to adopting AI effectively and humanely. The core principles from Team Topologies - organizing and empowering teams around independently-viable services, making cognitive load a key design principle, and making capabilities available via clear "vending machine" interfaces - translate superbly into the AI space by providing guardrails and heuristics for effective AI agency. However, flow architecture via Team Topologies is not enough. Organizations must also operate an intentional approach to curating context, finding and removing duplication, defining value flows, identifying conflicting incentives, and empowering teams to steward value delivery: core practices from Adapt Together(TM). In this talk, Matthew Skelton - co-author of the groundbreaking book Team Topologies and co-author of the forthcoming book Adapt Together - shares practical insights into the operating model changes needed to achieve ROI from AI by using the patterns and principles from both Team Topologies and Adapt Together, based on experience with hundreds of organizations worldwide.