The Teams Layer

Build, operate and improve AI-enabled products and workflows through one integrated backlog and explicit human responsibilities.

At the Teams layer, bring AI transformation into delivery and daily operations. Connect practical work, evaluation, human capability and feedback to enterprise priorities.

Why it matters

Production behavior emerges in daily delivery and operation. Teams need evidence, support and clear boundaries alongside engineering capability.

Start with a Pilot Agentic Product Team

A pilot cross-functional team can carry several documented role hats. The Big Picture adds no new named enterprise structure at this layer. Responsibilities specialize as scale, consequence, workload or independence needs increase.

Build and run inside the mandate

The team covers business and product context, workflow design, engineering, data, evaluation, assurance and operation. Its backlog brings product change, reliability, safety, evaluation, cost, experience, control work and technical debt together.

Keep recommendation distinct from formal disposition

Delivery & Runtime, Quality & Experience, and Guardrail Application & Exception conversations connect local evidence to improvement. A team can prepare release-readiness evidence and route authority-change requests. It does not self-approve strategic intent, material delegated authority, residual risk or value claims.

• Return delivery, runtime, evaluation and intervention evidence.
• Use shared platform, intelligence, experience and policy capabilities.
• Preserve formal release decisions with the named Release Authority.

Evidence to make visible

• One integrated team backlog
• Documented role hats and operating responsibilities
• Release-readiness, runtime and quality evidence

Chapter map

• 2.6.1 Operating Purpose and Scope
• 2.6.2 Inherited Intent, Inputs and Constraints
• 2.6.3 AIDIVE-Defined Structures
• 2.6.4 Pilot Agentic Product Team and Role Hats
• 2.6.5 Roles and Operating Responsibilities
• 2.6.6 Application of Approved Decision Rights and Escalation
• 2.6.7 Operating Conversations, Cadences and Decision Gates
• 2.6.8 Operational Artifacts and Outputs
• 2.6.9 Interfaces, Evidence and Feedback

Framework source

Editorial adaptation from the supplied AIDIVE Version 1 master documents.

• AIDIVE - Architecture & Rhythm - Agentic AI Operating Model Version-1.docx — §2.6

Go a little deeper.

Put AI transformation
into practice.

Build capability through training, contribute your expertise or explore a connected workspace for applying the holistic AIDIVE framework.

Build the capability.

Develop the skills to connect AI transformation strategy, business outcomes, people and governance to your own decisions and responsibilities.

Grow the practice.

Bring your learning, delivery or research expertise to the holistic AIDIVE approach to enterprise AI transformation.

Bring AIDIVE into your work.

See how the proposed workspace could connect AI transformation learning, maturity evidence and practical artifacts.

There is more to explore.