True excellence lies in the intimate process of caring deeply and giving your all toward a goal that aligns with your values. This pursuit shapes you as much as you shape the outcome, a more sustainable and democratic view than fixating on external metrics.

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?"

Instead of aiming for vague outcomes like "empowerment," start by defining the specific, observable behaviors you want to see. For example, what does "being data-driven" actually look like day-to-day? This focus allows you to diagnose and remove concrete barriers related to competency, accessibility, or social reinforcement.

When handed a specific solution to build, don't just execute. Reverse-engineer the intended customer behavior and outcome. This creates an opportunity to define better success metrics, pressure-test the underlying problem, and potentially propose more effective solutions in the future.

To encourage participation from everyone, leaders should focus on the 'why' behind an idea (intention) and ask curious questions rather than judging the final output. This levels the playing field by rewarding effort and thoughtfulness over innate talent, making it safe for people to share imperfect ideas.

Many people struggle to define what 'good' looks like. Building an evaluation (eval) for an AI system requires you to codify your quality standards, forcing a level of clarity and commitment that improves your own process and the AI's output.

Solely measuring a team's output fails to capture the health of their collaboration. A more robust assessment includes tracking goal achievement, team psychological safety, role clarity, and the speed of execution. This provides a holistic view of team effectiveness.

To avoid bias and misalignment, collaboratively create a weighted decision-making rubric with stakeholders *before* evaluating options. This ensures everyone agrees on the evaluation criteria, making the final decision easier to accept and implement.

Don't fall into the trap of believing a scored rubric provides an objective, mathematical truth. Its primary value is forcing alignment on what criteria matter and ensuring a consistent data-gathering process, not spitting out an infallible answer.