If your team cannot articulate the specific business outcome of their AI usage in a single sentence, you don't have an AI strategy. You simply have 'token maxing'—usage for the sake of usage. This framework forces a direct link between AI spend and business results.

The "Outcomes" feature requires a markdown "rubric" to define success. This forces developers to codify what "done" looks like, allowing the AI agent to self-grade and iterate up to 20 times. This introduces a structured, testable approach to achieving reliable results from agentic systems.

Instead of setting rigid goals, the OHL framework defines objectives as puzzles. Teams then form hypotheses on how to solve them and are measured on their learnings through a cycle of three questions: "How well did it work?", "What did you learn?", and "What will you try next?"

Avoid vague, company-wide AI mandates. Instead, apply a maturity framework to individual processes (e.g., account research). This approach builds a practical roadmap, moving specific use cases up the maturity ladder as needed and preventing costly over-engineering.

The main obstacle to deploying enterprise AI isn't just technical; it's achieving organizational alignment on a quantifiable definition of success. Creating a comprehensive evaluation suite is crucial before building, as no single person typically knows all the right answers.

Evals transform product specs from ambiguous documents into testable, measurable criteria. This gives product managers more leverage and provides clear targets for engineers, improving alignment and the quality of the final product.

Instead of traditional product requirements documents, AI PMs should define success through a set of specific evaluation metrics. Engineers then work to improve the system's performance against these evals in a "hill climbing" process, making the evals the functional specification for the product.

Don't view AI tools as just software; treat them like junior team members. Apply management principles: 'hire' the right model for the job (People), define how it should work through structured prompts (Process), and give it a clear, narrow goal (Purpose). This mental model maximizes their effectiveness.

To prevent engineers from going down a rabbit hole of endless improvements, teams must pre-define success criteria. When there's a clear, shared definition of the goal, it becomes easy to recognize when the objective is met and it's time to move on.