Contracts live in SharePoint. Case files in a CRM no one fully integrated. Institutional knowledge in someone's inbox. Your AI gets wired up to one or two sources, produces answers that reflect maybe 20% of what the company actually knows, and a promising pilot quietly dies before it ever reaches production.
In this session, we'll show you why the knowledge gap is the real ceiling on enterprise AI, and what a unified Agentic Knowledge Layer looks like in practice - one that connects every AI experience your organization builds to the full depth of your enterprise content, with the source attribution and governance controls that let compliance and legal finally say yes.
We'll also take on the economics question that comes up in many CFO conversations: as agentic workflows multiply model calls and frontier-model spend climbs, how do organizations control cost without trading away quality?
You'll leave with:
A clear diagnosis of why enterprise AI hits a knowledge ceiling that has nothing to do with the model
A framework for building AI that shows its work — grounded, cited, and auditable enough for regulated and customer-facing workflows
A practical architecture for compounding AI investments across use cases, instead of rebuilding from scratch every time
A lens for evaluating AI vendor decisions in a market that reshuffles every quarter
If you've watched a promising AI pilot stall because the answer was only as good as the data you managed to wire up in time, this session is for you.
