The authors argue that successful builders of enterprise healthcare AI solutions will need to understand both AI advancements and how to commercialize a product with a durable go-to-market strategy. They believe that the healthcare industry needs entrepreneurs to build AI solutions to address scalability and cost structure problems. The upcoming Part B of the article will discuss defensibility, pricing, and packaging of AI solutions based on interviews with key decision makers at leading payor and provider enterprises.
Key takeaways:
- AI has a significant potential in enterprise healthcare due to the complexity of tasks and the large amount of data that needs to be synthesized. The areas of high spend on highly trained labor, potential for 10x performance with AI, low adoption of software, well understood regulatory risk, and established revenue rails and financial incentives are key criteria for AI adoption.
- Healthcare tasks can be subdivided into clinical vs. non-clinical, and consumer- vs. professional-facing. Each category represents a viable opportunity for a company to build an AI-native approach that will confer a competitive edge and where a credible business model exists.
- Building specialist AIs to perform healthcare tasks offers the most challenging technical problems in the field today, as well as the greatest opportunity for impact. The healthcare industry needs entrepreneurs building solutions to the scalability and cost structure problems that can uniquely be addressed by AI.
- The winning companies in the healthcare AI space will likely have multiple integrated products that perform a wide breadth of tasks, as healthcare enterprises are consolidating their vendor relationships and relying on each partner to cover a large surface area of use cases.