Enterprises must ensure that AI applications align with their business values and goals, rather than solely focusing on performance metrics.

Enterprises are now more focused on the value AI can deliver rather than the specific models or tools being used, seeking stable and reliable solutions.

Enterprises prioritize outcomes over the underlying AI models; they want effective solutions for customer support and workflow automation.

Business users are increasingly taking ownership of AI tools, monitoring their performance and ensuring they align with business processes, rather than relying solely on IT departments.

We need to shift our benchmarks for evaluating AI models beyond just parameters and coding tests to include practical applications and cost-effectiveness.

Meta AI's benchmark for success is user value, focusing on what users report as valuable rather than solely on traditional benchmarks, which can be skewed and not representative of actual user needs.

The challenge lies in defining the right measures of progress in AI evaluation, moving beyond traditional benchmarks.

The challenge lies in defining the right measures of progress in AI evaluation, moving beyond traditional benchmarks.

Enterprise clients often have established metrics and performance indicators, allowing us to track how much time is spent on tasks and the impact of AI adoption.