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This article addresses the critical challenge of structuring enterprise AI by converting opaque business processes into queryable graph data, enabling agents to navigate complex workflows without relying on static diagrams or human interpretation. By modeling tasks, conditions, and roles as factual data points rather than visual representations, organizations can ensure that autonomous systems have a reliable foundation for executing specific operational sequences like loan approvals.
Sources coveringNeo4j, Latent Space, Slack Blog, SiliconANGLE
What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

The knowledge layer for enterprise: Processes

Neo4j9 min read

The knowledge layer for enterprise AI: ProcessesThis series is the practical build companion to Jesús Barrasa’s knowledge layer manifesto. This chapter is built on the previous chapter 1: Operating StructureRosa Delgado runs Risk & Compliance at AcmeBank. In the first… Read more →

This series is the practical build companion to Jesús Barrasa’s knowledge layer manifesto. This chapter is built on the previous chapter 1: Operating Structure Rosa Delgado runs Risk & Compliance at AcmeBank. In the first review meeting after the org graph went live, she asked a question nobody in the room expected to be hard: “Who owns the loan approval process?” Chapter 01 gave AcmeBank a graph that answers who’s accountable for a team, right now and at any point in time. It’s already better than the org chart but Rosa didn’t ask about the team. She asked about the process: the sequence of tasks, who performs each step, where the handoffs are, and where it stalls. That knowledge isn’t in the org structure. It lives in BPMN tools if your company has one, in procedure documents nobody has opened since the last audit, in ticket histories, and most often than it should, in people’s heads. And if the people in the room can’t get a straight answer, an agent has no chance. You can’t ask an agent to do the work if nobody…

Excerpt shown under the publisher's RSS terms · 184 words · full text and figures are on the publisher's site

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