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Enterprise AI in production.
This is the centre of gravity for my current research and public positioning: what it takes to move AI past proof of concept theatre and into systems that senior leaders can trust, fund, govern, and measure.
The production checklist
Governance
Ownership, risk boundaries, model policies, approvals, human oversight, and accountability.
MLOps and monitoring
Deployment, drift, observability, quality checks, incident response, and continuous improvement.
FinOps
Understanding the cost curve of inference, experimentation, model choice, and operational scale.
Adoption
Workflow fit, change management, training, incentives, and the painful gap between demo usage and real usage.
Planned assets
- Why AI proofs of concept die before production.
- The enterprise AI production checklist.
- AI governance as an operating model.
- FinOps for AI product leaders.
- MLOps for executives.
Recommended next
Keep reading from this thread
From 500 indexed pages and articles.
- Wiki concept MLOps Coding Discipline The set of software-engineering habits that turn notebooks, models, and experiments into reproducible, maintainable, observable machine-learning systems [src-078]. Related by production
- Wiki concept Proof-of-Concept First (PoC First) The principle of building the lightest-weight version of a system — such as a Claude artifact dashboard — to confirm you will Related by pocs
- Wiki concept ML Project Production Failure The gap between a model that works in a notebook or demo and a system that creates Related by production