What we transform
Choose how AI should change products and engineering: build AI into the product, redesign product and engineering work with AI, or build the product with AI.
Recognizable problems
Intended outcomes
Decision in front of you
Start with the decision and the evidence it needs. The journey, capabilities, and mechanisms can then be composed around that context.
Three modes
The product, the work used to create it, and the engineering system are related but distinct transformation questions.
TriggerA user or business outcome may improve through AI-enabled product behavior.
Intended changeShape and govern an AI capability around an explicit outcome, user need, and accountable operating boundary.
TriggerProduct and engineering teams adopt AI tools while flow, decisions, review, and accountability remain largely unchanged.
Intended changeRedesign discovery, delivery, review, and Human/AI responsibilities around improved end-to-end work.
TriggerAI can materially change how software is designed, built, tested, reviewed, and operated.
Intended changeReconfigure the engineering system while preserving architecture, verification, security, reliability, and accountable release decisions.
FlowBuilder can support these modes when useful. It is an optional execution method, not a prerequisite.
Relevant Playbook movements
A shaped transformation intent with explicit choices, tradeoffs, and decision confidence appropriate to consequence.
Earlier evidence about what to scale, adapt, stop, or reframe before overcommitting time and cost.
Changed systems and work that carry transformation intent into practical execution.
People and teams prepared to perform changed work with appropriate judgment, skills, and support.
Transformation outcomes managed through operational evidence rather than implementation completion alone.
An enterprise transformation system that becomes more informed through evidence and action.
Ways of working
Context-led advisory and transformation support that connects enterprise intent to choices, work, governance, execution, and evidence.
Structured support that helps leaders and teams make deliberate choices, redesign work, coordinate action, and learn from evidence.
Workshops, labs, simulations, and learning experiences shaped around the decisions, roles, and practices required by changed work.
Capabilities and selective mechanisms
The capability mix is shaped by the transformation question. Established practices remain available and evolve as work, technology, and accountability change.
Primary capabilities
Create valuable products and evolve product operating models as AI changes product behavior and opportunity.
Connected practices: Product management, Product development, Continuous discovery, AI product behavior and evaluation
Build and operate products through disciplined engineering systems that support reliable Human + AI delivery.
Connected practices: DevOps, Agile delivery, Architecture, Verification, Coding agents
Supporting capabilities
Understand enterprise reality and identify, evaluate, and prioritize valuable change in context.
Improve how value, work, investments, and decisions flow across teams and portfolios.
Lead adoption and transformation while keeping governance, assurance, decisions, and consequences accountable.
Build the role- and work-specific capability people need as technology, work, and accountability change.
These mechanisms may strengthen the work selectively. They are not mandatory products or required engagement steps.
Next step
Use Start Here for an orientation, or explore how the Playbook connects this transformation question to context, action, capability, and evidence.
