From Hallucinations to Confabulations: What AI Errors Really Mean for MarTech Leaders
“Hallucinations” has become the catch-all explanation for why GenAI sometimes makes things up. Lately, the industry has started using a more precise term: confabulation, the act of filling in missing information with something plausible, but wrong.
The label switch is useful, but it shouldn’t become a way to quietly blame customers for the failings of LLMs.
Yes, GenAI systems fill gaps when context is missing. But they also shouldn’t be so eager to do so. Confidently inventing product claims, metrics, or insights isn’t just a data hygiene issue, it’s also a technical and design shortcoming in how today’s models are built and deployed.
For MarTech leaders, the real takeaway isn’t linguistic. It’s economic.
What this shift is telling us is that enterprise AI takes real work to do well:
- Iterative tuning
- Strong governance
- Better and more (often much more) context and constraints
- Ongoing investment
And that reality inevitably erodes the early ROI narrative of “cheaper, faster, better - out of the box.”
It’s fair and necessary for enterprises to ask more of LLM vendors: safer defaults, clearer confidence signals, better refusal behavior, and less reliance on customers endlessly feeding constraints just to prevent fabrication.
At the same time, AI is exposing long-standing weaknesses in MarTech stacks: fragmented content, inconsistent definitions, unclear sources of truth. That’s not new work, but current AI tools make the cost of ignoring it much higher.
The practical conclusion isn’t that AI is broken or that marketers are. It’s that AI in MarTech is a system-level capability, not a plug-in.
The shift from “hallucination” to “confabulation” helps clarify why things go wrong. But success will come from shared accountability: vendors building more auditable, and ultimately more responsible systems, and MarTech leaders making deliberate, well-funded choices about where AI actually belongs.
That may be less magical than the early hype, but it’s far more realistic.
Real Story Group’s next Council Meeting will focus on how taxonomies, ontologies, and knowledge graphs actually support context for AI systems in production. If you’re an enterprise MarTech stack leader who values thoughtful, vendor-free conversation, we encourage you to apply:
