#ai-measurement

#ai-measurement

Artificial intelligence (AI) has moved beyond proof-of-concept experiments, but many organizations still find it difficult to prove it delivers tangible value rather than hype. While model accuracy and innovation often capture the spotlight, executives want to see financial outcomes, and data scientists need clear technical benchmarks that validate success. This gap demands rigorous measurement frameworks that tie advanced metrics to real-world results.

If your organization is still relying on ad hoc employee experimentation with gen AI, it's time to shift gears. While experiments like using Claude to draft emails or ChatGPT to brainstorm can yield learning and productivity benefits on an individual or unit level, they are typically unstructured and unmeasured and rarely yield large-scale results. (We believe that this is part of why companies aren't yet seeing bottom-line impact from AI investments.)