A mid-size insurance company spent $45,000 implementing an AI system to process claims documents. Within six weeks, the system was handling 70% of routine claims processing that previously required a team of eight. Annual savings: $1.2 million in labor costs alone, plus faster processing times that improved customer satisfaction scores by 23%.

A boutique law firm invested $8,000 in AI contract review tools. Their associates could now review contracts three times faster, allowing the firm to take on 40% more clients without hiring. Revenue increased by $600,000 in the first year.

But not every AI investment pays off. A retail chain spent $200,000 on an AI inventory prediction system that performed worse than their existing spreadsheet-based approach because the training data was too sparse. The lesson: ROI isn't guaranteed. You need a framework to evaluate opportunities before investing, track results rigorously, and know when to cut losses. That framework is what separates successful AI adoption from expensive experiments.