To efficiently assess new AI models, develop a personal portfolio of your most critical tasks. This 'reusable evaluation set,' complete with prompts and success criteria, allows you to quickly and consistently benchmark new models against your specific needs, rather than relying on general capabilities.
Knowledge workers waste over two hours a week organizing context for AI. Combat this by proactively building portable context assets—either broad personal portfolios or per-project packs. This one-time effort creates a reusable foundation that makes every AI tool you use instantly more effective.
Review your organization's incentive structure for AI. Are employees only rewarded for executing known use cases faster, or are they encouraged to experiment and share lessons? Without explicit rewards for exploration, companies risk stifling innovation and missing out on transformative AI applications that come from experimentation.
While tracking ROI is important, a heavy-handed focus can create a bias toward 'efficiency AI'—using AI for existing work. This overlooks 'opportunity AI,' which unlocks entirely new products and capabilities. The former is a foundation, but the latter should be the ultimate strategic goal for true growth.
Evolve your interaction with AI from a manual, iterative prompting process to one of system design. The advanced approach is to architect 'agent loops' where you set a high-level goal and clear evaluation criteria, then allow the AI to iterate on its own. This reframes your role from active manager to systems architect.
To scale your use of AI agents, move beyond single-use builds. Identify recurring capabilities and package them as reusable 'skills.' This modular approach makes your work transportable, allowing you to easily apply successful processes across different projects and agents, which compounds your efficiency over time.