Topic
Formal models of enterprise meaning, and the graphs that make them operational.
Bojan Ciric
Technology Fellow (CTO), Deloitte
25+ years building AI and data systems for global banks and Fortune 100 enterprises, published in Deloitte Insights and WSJ CIO Journal. Defined the Knowledge Spine concept and architecture.
AboutIn Part 5 I gave agents a rule: they propose, a human approves. Here is where that rule stops scaling, what the evidence says about it, and what has to replace it.
Essay16 min read
The series covered the why. This is the how, at keyboard level: the artifacts, the snippets, the checklists, and the order of operations for shipping your first vertebra and hardening it into a spine.
Essay10 min read
Agents ask constantly, at machine volume. The spine stops being a reference resource and becomes a runtime dependency — the operating system your machines run on.
Essay5 min read
Semantic projects rarely die of bad modeling. They die at budget season, because the value lands in everyone else's numbers. How to count meaning as infrastructure.
Essay6 min read
An ontology is a photograph of the business. The business is a movie. How to keep meaning true while everything it describes keeps moving.
Essay7 min read
The multi-year semantic program is obsolete. How to go from nothing to a working knowledge spine in six months, vertebra by vertebra.
Essay6 min read
Why enterprise ontologies need to stop being documentation and become operational infrastructure for data, applications and AI.
Essay6 min read
Three conflations I keep hearing in every AI conversation, and why the tooling is getting commoditized while the modeling is where the durable advantage lives.
Idea1 min read
Everyone is telling you to build a knowledge graph for AI. Before launching another data movement project, ask a harder question: what does AI actually need from your data?
Idea1 min read