To fix a failing process like cash collections, the CEO should hold a daily 8 a.m. meeting with the team. By repeatedly asking a direct question like, "Where's my money?", you force the rapid resolution of small, overlooked blockers and create an unscalable but effective communication channel.

For data-heavy queries like financial projections, AI responses should transcend static text. The ideal output is an interactive visualization, such as a chart or graph, that the user can directly manipulate. This empowers them to explore scenarios and gain a deeper understanding of the data.

A one-time meeting with finance is "surface level" advice. To truly build financial acumen, PMs must integrate hard financial targets and business levers directly into their squad's goals. This creates an enduring, operational fluency that informs daily product decisions.

Founders are consistently and universally wrong about their financial projections, particularly cash runway. AI tools can provide an objective, data-driven forecast based on trailing growth, correcting for inherent founder optimism and preventing critical miscalculations.

When a critical process like cash collection fails, use a tactic from Intel's Andy Grove: a daily 8 a.m. meeting where the CEO directly asks, "Where's my money?" This intense, unscalable focus rapidly uncovers and resolves the small, systemic blockers that are derailing the entire process.

To prevent constant interruptions from automated tasks, schedule recurring AI agents to align with your work week. For example, receive competitive research on Fridays before planning and support summaries on Mondays before the team meeting. This integrates agent output into your natural workflow.

Before engaging expensive experts like lawyers or accountants, use AI to do preliminary work. You can draft initial documents, analyze data, or formulate questions. This prepares you for a more productive conversation, saving time and money while ensuring you still rely on the human expert for final verification and strategy.

A killer app for AI in IT is automating tedious but critical tasks. For example, investigating why daily cloud spend deviates by more than 5%. This simple-sounding query requires complex data analysis across multiple services—a perfect, high-value problem for an AI agent to solve.

Before diving into SQL, analysts can use enterprise AI search (like Notion AI) to query internal documents, PRDs, and Slack messages. This rapidly generates context and hypotheses about metric changes, replacing hours of manual digging and leading to better, faster analysis.