To move beyond static playbooks, treat your team's ways of working (e.g., meetings, frameworks) as a product. Define the problem they solve, for whom, and what success looks like. This approach allows for public reflection and iterative improvement based on whether the process is achieving its goal.
Instead of a universal definition, "real progress" is achieved by first defining what change you want to see in your organization. You then adapt your ways of working—strategy, discovery, OKRs—to support that specific goal, rather than just following a generic playbook.
To avoid stifling teams with bureaucracy, leaders should provide slightly less structure than seems necessary. This approach, described as "give ground grudgingly," forces teams to think actively and prevents the feeling of "walking in the muck" that comes from excessive process. It's a sign of a healthy system when people feel they need a bit more structure, not less.
To create a cohesive product across multiple teams, GitHub uses a framework that forces alignment upfront. By ensuring all teams first deeply understand the problem and collectively identify solutions, the final execution is naturally integrated, preventing a disjointed experience that mirrors the org structure.
The CDO argues that one-size-fits-all structures are ineffective. He believes management's true job is to thoughtfully and dynamically create the right rituals, structures, and processes for each unique combination of problem, people, and timeline, rather than forcing teams into a pre-defined box.
In an AI-driven world, product teams should operate like a busy shipyard: seemingly chaotic but underpinned by high skill and careful communication. This cross-functional pod (PM, Eng, Design, Research, Data, Marketing) collaborates constantly, breaking down traditional processes like standups.
Instead of over-analyzing and philosophizing about process improvements, simply force the team to increase its cadence and ship faster. This discomfort forces quicker, more natural problem-solving, causing many underlying inefficiencies to self-correct without needing a formal change initiative.
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.
Teams often focus on perfectly implementing frameworks like OKRs or Discovery, creating a false sense of achievement. This "alibi progress" prioritizes methodology correctness over creating value in a specific context, leading to lots of outputs but no outcomes.
Teams can cultivate a shared sense of taste by encouraging constant and rigorous critique of both internal and external work. This process allows the team to self-regulate, learn from each other, and elevate their collective craft without top-down mandates.
Instead of static org charts, AI can monitor team performance and sentiment to propose small, ongoing adjustments—like rotating a member for fresh eyes or changing meeting formats. This turns organizational design into a dynamic, data-driven process of continuous improvement, overcoming human inertia.