Frequently Asked Questions

Whether you're preparing data for predictive analytics, customer segmentation models, or generative AI applications, this guide gives you the framework to move from fragmented data sources to AI-ready infrastructure.

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 models are only as good as the data pipelines feeding them. This guide shows enterprise IT teams how to build AI-ready data pipelines with the quality, governance, and infrastructure control that machine learning requires.

If your team is exploring how to bring AI into your enterprise workflows, you’ve probably hit a familiar challenge: the data isn’t ready. It’s trapped in siloed systems, inconsistent across platforms, or missing altogether. And while plenty of vendors will offer to “start fresh,” building a new data foundation from scratch is time-consuming, expensive, and often unnecessary. Here’s the good news: you may already have what you need if you can access, move, and prepare your dat