For mature companies struggling with AI inference costs, the solution isn't feature parity. They must develop an AI agent so valuable—one that replaces multiple employees and shows ROI in weeks—that customers will pay a significant premium, thereby financing the high operational costs of AI.

For companies with jaw-dropping technology, it's easy to chase 'wow moments' and PR instead of solving real problems. Synthesia instills a core value of 'utility over novelty,' obsessing over delivering value for enterprise customers rather than getting lost in the novelty of their own tech.

Successful AI strategy development begins by asking executives about their primary business challenges, such as R&D costs or time-to-market. Only after identifying these core problems should AI solutions be mapped to them. This ensures AI initiatives are directly tied to tangible value creation.

Don't let the novelty of GenAI distract you from product management fundamentals. Before exploring any solution, start with the core questions: What is the customer's problem, and is solving it a viable business opportunity? The technology is a means to an end, not the end itself.

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.

Instead of chasing futuristic 'shiny objects,' the most impactful digital initiatives solve tangible, existing problems. For example, using an AI model to predict when pharmacists will run out of medication directly prevents lost sales and improves the patient experience.

The most durable AI applications are those that directly amplify their customers' revenue streams rather than merely offering efficiency gains. For businesses with non-hourly billing models, like contingency-based law firms, AI that helps them win more cases is infinitely more valuable and defensible than AI that just saves time.

AI companies are pivoting from simply building more powerful models to creating downstream applications. This shift is driven by the fact that enterprises, despite investing heavily in AI promises, have largely failed to see financial returns. The focus is now on customized, problem-first solutions to deliver tangible value.

Instead of asking for general feedback, Decagon's founder systematized ideation by pressing potential customers on exactly how much they would pay, who approves the budget, and how they would justify ROI. This filters out weak ideas and provides strong commercial signals.