Gartner ThinkCast No APIs, No AI: Organizing Software Engineering for Today's AI Reality
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Mar 10, 2026 Shameen Pillai, API strategy and governance expert, Akis Sklavounakis, platform engineering and developer productivity specialist, and Manjunath Bhat, software engineering and AI adoption strategist, discuss reorganizing engineering for AI. They cover team topologies to scale GenAI. They explain how platform engineering reduces cognitive load. They stress APIs as essential infrastructure and outline API maturity priorities.
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Team Topologies Are Essential To Scale GenAI
- Scaling generative AI fails without coordinated team topologies that separate product, enabling, complicated subsystem, and platform teams.
- Manjunath Bhat cites Verizon's Generative AI center of excellence combining IT, risk, legal, and finance as a repeatable recipe for scaling.
Inventory Use Cases Then Standardize Practices
- Do inventory existing applications and use cases, spot scaling gaps, and institutionalize best practices to move prototypes into production.
- Manjunath Bhat recommends a three‑pronged approach: inventory, identify competency gaps, and standardize practices across teams.
Cognitive Load Is The Main Barrier To Developer Velocity
- Developer productivity is limited by cognitive load from complex environments, now worsened by AI integration.
- Akis Sklavounakis argues platform engineering — self-service internal platforms — reduces that load by becoming the path of least resistance.