To maximize ROI from AI, evaluate potential use cases on two axes: the value they provide (time saved, revenue generated) and the amount of ongoing "babysitting" they require (maintenance, monitoring, support). Prioritize high-value, low-babysitting tasks first.

The "AI ROI flywheel" is a strategy where an organization starts with AI projects that deliver massive, measurable returns (e.g., 10:1 to 30:1). These initial wins create credibility and buy-in, making it progressively easier to secure resources for future AI initiatives.

A core part of a real AI strategy is creating repeatable actions, not just completing one-off tasks. Before starting an AI project, apply a simple filter: 'Will I use this more than once?' If the output is completely disposable and takes significant time, it's likely not a strategic use of resources.

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.

When using prioritization frameworks like RICE for AI-generated ideas, human oversight is crucial. The 'Confidence' score for a feature ideated by AI should be intentionally set low. This forces the team to conduct real user testing before gaining confidence, preventing unverified AI suggestions from being fast-tracked.

To find valuable AI use cases, start with projects that save time (efficiency gains). Next, focus on improving the quality of existing outputs. Finally, pursue entirely new capabilities that were previously impossible, creating a roadmap from immediate to transformative value.

Aim for "good enough" financial estimates to differentiate multi-million dollar opportunities from thousand-dollar ones. This high-level sorting is more valuable and efficient than creating detailed, yet still speculative, forecasts for every idea.

While frameworks like RICE appear scientific, their inputs are highly subjective. Their primary value isn't for making decisions, but for providing a seemingly objective, data-driven justification to decline stakeholder or management feature requests that don't align with the current strategy.

A great source for high-impact AI projects is your company's 'graveyard' of past initiatives. Revisit projects that were strategically sound but failed because they were too time-consuming or administratively burdensome. The manual effort that made them unfeasible is often what AI is best suited to automate now.