AI rarely fails because the model is not good enough. It fails because ownership is unclear, work is not connected to real workflows, and pilots never become an operable product.

This step sets the baseline for how AI work is prioritized, built, and governed so it can move from experimentation into execution.

Key points

• Foundations: align on the minimum baseline across strategy, MLOps, generative AI, and governance.
• Strategy → products: translate goals into a focused portfolio of AI products tied to real workflows and outcomes.
• Adoption approach (80–15–5): decide what to use from existing platforms, what to buy, and what to build in-house.
• Operating model: define decision rights, core roles, and a simple delivery cadence so pilots do not stall.
• Readiness check: pressure-test strategy, operational capability, and guardrails before scaling.
• FAQs: resolve common questions on safety, accuracy, and practical use cases.

Clarity on ownership, priorities, and guardrails is the difference between pilots and production.

When the operating assumptions are explicit, teams can ship faster, reuse assets, and scale with fewer surprises.