Why are billion-dollar enterprise AI initiatives stalling out in production?

In this episode of The AI Table, former Adobe Senior Information Architect and taxonomy veteran Jessica Talismanbreaks down why raw LLMs fail inside large enterprises—and why the secret to reliable, non-hallucinating agentic AI isn’t bigger models, but the Context Layer.

Most enterprise AI pipelines fail because they treat data as a technical storage problem rather than a knowledge management challenge. Without a formal ontology pipeline, enterprise knowledge graphs, and semantic data models, AI agents lack the business logic, data provenance, and procedural context required to deliver accurate outcomes.

Jessica walks us through how Fortune 500 leaders can leverage library science principles to turn messy unstructured data into deterministic, decision-ready intelligence.

Key Takeaways

AI bias stems from flawed training data, so businesses must prioritize context and knowledge management to address it. Responsible AI demands a human-centered approach, investment in knowledge systems, and a focus on augmenting—not replacing—human capabilities.

• The Context Gap: AI bias and hallucinations stem from unvetted, context-free data pipelines. Fixing AI output requires fixing the semantic architecture underlying your models.
• Ontologies as Defensive Assets: A formal ontology model provides the rules, relationships, and lineage necessary for autonomous agents to execute complex workflows safely.
• Human-Centered Augmentation: Responsible enterprise AI execution focuses on structuring tacit human knowledge to augment—not replace—high-value workforce decisions.

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