The biggest barrier to getting value from AI isn't the technology itself, but a lack of internal clarity. Teams that haven't defined their goals, customers, and core work processes will get poor AI outcomes, as the technology exposes pre-existing strategic weaknesses.

A lack of written documentation for strategic initiatives is often a deliberate tactic, not an oversight. By keeping big bets as verbal directives, executives can later pivot, reframe failure, or deny the original premise, effectively gaslighting their teams. This prevents creating a clear record for accountability.

When boards pressure CEOs for AI, the result is often a centralized, consultant-led project disconnected from operations. These initiatives fail because they lack alignment and nobody understands how they work, creating skepticism for future efforts.

When AI-driven development produces poor results, leaders must diagnose the root cause. It's critical to differentiate between failures caused by unclear product requirements and those caused by limitations in the AI tooling or underlying systems. Misattributing blame demoralizes teams and hinders the adoption of new, faster processes.

Employees produce low-quality AI work not because they are lazy, but as a symptom of a leadership problem. The combination of generalized mandates to use AI and increased workload expectations creates a perfect storm for 'work slop' as a survival mechanism, rather than a productivity tool.

Leadership often imposes AI automation on processes without understanding the nuances. The employees executing daily tasks are best positioned to identify high-impact opportunities. A bottom-up approach ensures AI solves real problems and delivers meaningful impact, avoiding top-down miscalculations.

AI tools, likened to "1,000 interns," require explicit instructions to be effective. This new reality of one-day sprints quickly reveals which product managers have a clear vision and which do not, as ambiguity leads directly to poor development results and exposes a core skill gap.

The most successful AI automation projects are identified by employees who perform the manual workflows day-to-day, not by executives. A top-down approach often fails to account for practical data and implementation challenges that front-line workers and technical teams understand best.

When facing top-down pressure to "do AI," leaders can regain control by framing the decision as a choice between distinct "games": 1) building foundational models, 2) being first-to-market with features, or 3) an internal efficiency play. This forces alignment on a North Star metric and provides a clear filter for random ideas.