Enterprises urged to build custom benchmarks for selecting AI models

Public AI model leaderboards often fail to predict which model works best for specific enterprise needs. This is because they measure general performance on standardized tasks, not specialized, real-world business functions. To select the right model, a company must create its own internal benchmark using its actual data and workflows. This internal test evaluates models on criteria critical to the business, such as accuracy on proprietary tasks, cost, and latency.