OSS Networks

Jeremy Taylor

Writing about the gap that matters most in enterprise AI right now, the one between experimentation and operational value, as a six-pillar operating model.

Connect on LinkedIn jeremy.taylor@ossnetworks.com

Publications

Library

One model, published in pieces. The operating model is the spine. What follows adds to it: more depth where depth is needed, and angles that cut across several pillars at once.

More pieces are coming: detail the model leaves out, and angles that don't belong to any single pillar. They get listed here when they are published.

The framework

The Enterprise AI Operating Model

Every organization differs, but these six pillars are universal requirements for moving from experimentation to the value phase.

  1. Strategy & Value Realization Which use cases deserve our scarce resources?
  2. AI FinOps What is this costing, and against what value?
  3. Data Management & Intelligence Can the agent be trusted with our context?
  4. Technology & Operations What do we build, what do we buy, and how do we know it still works?
  5. People & Enablement Will anyone actually use what we deploy?
  6. Risk, Ethics & Policy What are the boundaries, and who is accountable?

About

Who is writing this

Jeremy Taylor

I lead global data and AI organizations with twenty years in technology. I've spent the past eleven years building and transforming cloud data platforms. Most recently, I've focused on adding an AI platform layer and agents in production.

I completed the Carnegie Mellon University Chief Data and AI Officer executive certificate in 2026. This model came out of that work.

Contact

Continue the conversation

If you're working through any of this or think part of the model is wrong, I'd like to hear about it: what's working, what isn't, and where the model breaks down against a real environment.

jeremy.taylor@ossnetworks.com

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