To avoid pursuing low-value AI initiatives, use the RICE scoring method (Reach, Impact, Confidence, Effort). This product management framework helps teams quantify and rank potential projects, ensuring resources are allocated to initiatives with the highest potential return on investment.

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.

To control spiraling AI costs, teams should first determine if a task can be solved with deterministic, rules-based logic. Using AI for problems that have a straightforward, non-AI solution is an inefficient use of resources and introduces unnecessary variability and expense.

All early AI systems produce "slop" (imperfect output). Instead of dismissing them, analyze the ratio of value delivered versus the slop produced. The key metric is the slope of improvement; if this ratio is rapidly getting better, the technology is on the right track.

Don't wait for AI to be perfect. The correct strategy is to apply current AI models—which are roughly 60-80% accurate—to business processes where that level of performance is sufficient for a human to then review and bring to 100%. Chasing perfection in-house is a waste of resources given the pace of model improvement.

While content generation is impressive, the highest value for financial professionals lies in using AI as a verification layer. A tool that can audit a complex model and catch a single, costly mistake provides more immediate ROI than one that simply builds the model from scratch.

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.

AI can generate vast amounts of content, but its value is limited by our ability to verify its accuracy. This is fast for visual outputs (images, UI) where our eyes instantly spot flaws, but slow and difficult for abstract domains like back-end code, math, or financial data, which require deep expertise to validate.

To decide where to start with AI, use a framework that maps Possibilities to their Payoff and Probability of success to find the expected value. Then, divide this by the required Perspiration (effort) to get a final Priority score. This structured approach helps focus resources on high-impact, achievable projects.