Before pursuing moonshots, assess execution fundamentals. A key indicator of readiness is the ability to reliably forecast a launch's impact and then see that impact materialize. If predictions are consistently wrong, the underlying measurement capabilities are not mature enough for bigger risks.

Surprising your manager with a major failure is one of the worst mistakes you can make. You must proactively communicate risks as soon as they arise. This gives your leader time to manage expectations up the chain and prevents them from being blindsided.

Projects fail not from lack of tools, but from a lack of executive sponsorship. Success hinges on leaders dedicating significant weekly time (e.g., half a day) to a few key projects, rather than passively sponsoring many with monthly check-ins. This deep engagement is a primary driver of success.

The 'fake press release' is a useful vision-setting tool, but a 'pre-mortem' is more tactical. It involves writing out two scenarios before a project starts: one detailing exactly *why* it succeeded (e.g., team structure, metrics alignment) and another detailing *why* it failed. This forces a proactive discussion of process and risks, not just the desired outcome.

Instead of complex prioritization frameworks like RICE, designers can use a more intuitive model based on Value, Cost, and Risk. This mirrors the mental calculation humans use for everyday decisions, allowing for a more holistic and natural conversation about project trade-offs.

The primary cause of failure in engineering projects is not technical incompetence but a lack of visibility into budget, schedule, scope, and risk. Successful project execution hinges on addressing these core management areas before they derail the work.

Executives and investors care about lagging business indicators like ARR and churn, not leading product indicators like user engagement. It is the PM's job to connect the dots and clearly articulate how improvements in product metrics will directly result in moving the high-level business needles.

While Key Performance Indicators (KPIs) measure past results, Cultural Performance Indicators (CPIs) like 'trust flow' or 'decision latency' quantify the human conditions that predict future outcomes. Paired together, they provide a complete view of systemic health.

Don't rely on traditional project milestones to gauge AI progress. Instead, measure success through granular unit economics and operational metrics. Metrics like 'cost per release' or 'cycle time per feature' provide immediate feedback on whether your strategic hypothesis is valid, enabling rapid iteration.