EPISODE · Jun 24, 2026 · 21 MIN
Ontology: Fix Your AI Quality by Fixing Your Data
from The Experience Edge · host Jochem van der Veer
Your AI agents aren't failing because the models are bad. They're failing because your data was built for humans, not machines. 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. KEY TAKEAWAYSAI pilots fail because the data environment was designed for human interpretation, not machine reasoning.Tagging the same word across systems is co-location, not integration — AI can't bridge that gap reliably.An ontology defines not just what things are, but how they relate and what an agent can do with them.Location primitives (journey, phase, step) give disparate data a shared address so it can finally connect.The architectural choice of where the ontology lives — data layer vs. tool layer — determines whether it scales or drifts.CHAPTERS00:00 Introduction — Why AI pilots hit a wall after six to eight weeks 01:45 The three lenses enterprises use to understand customers 03:10 Why co-location isn't integration and what breaks when humans leave the loop 04:47 The core problem: confident AI output with no traceable foundation 06:20 What an ontology actually is — and how it differs from a taxonomy 08:00 Location primitives: journey, phase, and step as shared address 09:30 Connecting VOC evidence and BI metrics to the same structural coordinate 10:30 Pattern primitives: spotting recurring opportunities across journeys 12:00 The KYC banking example — one named object, many product teams 13:30 The third job of an ontology: structural rules for what agents can do 15:00 Why data binding is where most CX data efforts actually break down 16:30 How a working ontology creates a self-reinforcing context layer 17:54 The critical architectural decision: data layer vs. AI layer 19:30 Why CIOs and CX leaders need to make this call together 21:00 Bringing it back: the real fix isn't a better model, it's a better foundation 22:00 How TheyDo is built as the data layer AI agents run onLINKEDINJochem van der Veer — https://www.linkedin.com/in/jochemvanderveer/THEYDOLearn more about Journey Management with TheyDo: https://www.theydo.comSubscribe to The Experience Edge for weekly conversations on customer experience, journey management, and the future of enterprise CX. Share this episode with someone who's thinking about how their organisation connects customer insight to real decisions.#TheExperienceEdge #vanderVeer #ExperienceOntology #CXAI #EnterpriseAI #CustomerExperience #JourneyManagement #CX #TheyDo #podcast
What this episode covers
Your AI agents aren't failing because the models are bad. They're failing because your data was built for humans, not machines. 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. KEY TAKEAWAYSAI pilots fail because the data environment was designed for human interpretation, not machine reasoning.Tagging the same word across systems is co-location, not integration — AI can't bridge that gap reliably.An ontology defines not just what things are, but how they relate and what an agent can do with them.Location primitives (journey, phase, step) give disparate data a shared address so it can finally connect.The architectural choice of where the ontology lives — data layer vs. tool layer — determines whether it scales or drifts.CHAPTERS00:00 Introduction — Why AI pilots hit a wall after six to eight weeks 01:45 The three lenses enterprises use to understand customers 03:10 Why co-location isn't integration and what breaks when humans leave the loop 04:47 The core problem: confident AI output with no traceable foundation 06:20 What an ontology actually is — and how it differs from a taxonomy 08:00 Location primitives: journey, phase, and step as shared address 09:30 Connecting VOC evidence and BI metrics to the same structural coordinate 10:30 Pattern primitives: spotting recurring opportunities across journeys 12:00 The KYC banking example — one named object, many product teams 13:30 The third job of an ontology: structural rules for what agents can do 15:00 Why data binding is where most CX data efforts actually break down 16:30 How a working ontology creates a self-reinforcing context layer 17:54 The critical architectural decision: data layer vs. AI layer 19:30 Why CIOs and CX leaders need to make this call together 21:00 Bringing it back: the real fix isn't a better model, it's a better foundation 22:00 How TheyDo is built as the data layer AI agents run onLINKEDINJochem van der Veer — https://www.linkedin.com/in/jochemvanderveer/THEYDOLearn more about Journey Management with TheyDo: https://www.theydo.comSubscribe to The Experience Edge for weekly conversations on customer experience, journey management, and the future of enterprise CX. Share this episode with someone who's thinking about how their organisation connects customer insight to real decisions.#TheExperienceEdge #vanderVeer #ExperienceOntology #CXAI #EnterpriseAI #CustomerExperience #JourneyManagement #CX #TheyDo #podcast
NOW PLAYING
Ontology: Fix Your AI Quality by Fixing Your Data
No transcript for this episode yet
Similar Episodes
Mar 26, 2026 ·1m
Mar 19, 2026 ·34m
Feb 18, 2026 ·11m
Feb 11, 2026 ·45m