AI-Ready Data

AI initiatives don’t fail because of bad models. They fail because objectives, data, and business processes aren’t clearly understood first. In this thought-leadership piece, Sesame Software CEO Rick Banister explains why AI should be treated as a tool, not a goal, and how examining data quality and workflows during the discovery phase often delivers more value than deploying AI itself.

AI is now part of every conversation. CIOs are being asked where AI fits, how fast it can be deployed, and what needs to happen first. But as with any new technology wave, not every problem is an AI problem.

It’s been three years since ChatGPT’s public debut—a milestone that seemed to bring the Turing Test within reach. Yet, rather than fearing a Skynet-style future, most of us are grappling with a more mundane problem: what exactly to do with this new toolset.

Business leaders want results fast. Composable data pipelines let you spin up proof of concepts and production flows without rewriting large jobs.