Instead of accepting a single answer, prompt the AI to generate multiple options and then argue the pros and cons of each. This "debating partner" technique forces the model to stress-test its own logic, leading to more robust and nuanced outputs for strategic decision-making.
For an AI optimizing physical infrastructure like buildings, customer adoption hinges on explainability. Product leader John Boothroyd's team had to create visual representations showing how the AI made decisions to gain trust. This proves transparency is essential for automated systems with real-world consequences.
Move beyond simple research and use AI to create complex, interconnected business artifacts like a 20-part security policy architecture or multi-tab financial models. This advanced application can reduce multi-day tasks to minutes, dramatically boosting productivity for core business functions.
True AI agents take autonomous action. However, connecting a tool like Microsoft Copilot to internal data (e.g., SharePoint) provides "agentic" capabilities. It can independently scan, select, and synthesize relevant resources to create finished deliverables, blurring the line between tool and autonomous assistant.
When a free AI tool repeatedly fails a complex, multi-step task, it's likely hitting an invisible resource limit or "thinking budget." Upgrading to paid tiers or using developer platforms like Google AI Studio unlocks greater computational power, enabling the model to handle complexity and deliver complete, elegant results.
An AI tool's inability to perform a task a month ago doesn't mean it can't today. The guest notes Copilot went from producing useless spreadsheet templates to fully functional models in months. Users should periodically re-test tools on previously failed tasks to leverage rapid, often unannounced, improvements.