Ontology: Fix Your AI Quality by Fixing Your Data

<p>Your AI agents aren&#39;t failing because the models are bad. They&#39;re failing because your data was built for humans, not machines. </p><p><br></p><p>Jochem has spent years working on the structural problem underneath failing enterprise AI initiatives. In this episode, he breaks down why CX AI pilots stall after six to eight weeks, what an experience ontology actually is and how it differs from a taxonomy or tag list, and why the architectural decision of where your ontology lives - data layer versus AI layer - determines whether your AI investment compounds or drifts. </p><p><br></p><p>KEY TAKEAWAYS</p><ul><li><p>AI pilots fail because the data environment was designed for human interpretation, not machine reasoning.</p></li><li><p>Tagging the same word across systems is co-location, not integration — AI can&#39;t bridge that gap reliably.</p></li><li><p>An ontology defines not just what things are, but how they relate and what an agent can do with them.</p></li><li><p>Location primitives (journey, phase, step) give disparate data a shared address so it can finally connect.</p></li><li><p>The architectural choice of where the ontology lives — data layer vs. tool layer — determines whether it scales or drifts.</p></li></ul><p>CHAPTERS</p><p>00:00 Introduction — Why AI pilots hit a wall after six to eight weeks </p><p>01:45 The three lenses enterprises use to understand customers </p><p>03:10 Why co-location isn&#39;t integration and what breaks when humans leave the loop </p><p>04:47 The core problem: confident AI output with no traceable foundation </p><p>06:20 What an ontology actually is — and how it differs from a taxonomy </p><p>08:00 Location primitives: journey, phase, and step as shared address </p><p>09:30 Connecting VOC evidence and BI metrics to the same structural coordinate </p><p>10:30 Pattern primitives: spotting recurring opportunities across journeys </p><p>12:00 The KYC banking example — one named object, many product teams </p><p>13:30 The third job of an ontology: structural rules for what agents can do </p><p>15:00 Why data binding is where most CX data efforts actually break down </p><p>16:30 How a working ontology creates a self-reinforcing context layer </p><p>17:54 The critical architectural decision: data layer vs. AI layer </p><p>19:30 Why CIOs and CX leaders need to make this call together </p><p>21:00 Bringing it back: the real fix isn&#39;t a better model, it&#39;s a better foundation </p><p>22:00 How TheyDo is built as the data layer AI agents run on</p><p><br></p><p>LINKEDIN</p><p>Jochem van der Veer —<a href="https://www.linkedin.com/in/jochemvanderveer/"> <u>https://www.linkedin.com/in/jochemvanderveer/</u></a></p><p><br></p><p>THEYDO</p><p>Learn more about Journey Management with TheyDo:<a href="https://www.theydo.com"> <u>https://www.theydo.com</u></a></p><p><br></p><p>Subscribe to The Experience Edge for weekly conversations on customer experience, journey management, and the future of enterprise CX. Share this episode with someone who&#39;s thinking about how their organisation connects customer insight to real decisions.</p><p><br></p><p>#TheExperienceEdge #vanderVeer #ExperienceOntology #CXAI #EnterpriseAI #CustomerExperience #JourneyManagement #CX #TheyDo #podcast</p>