Paved Road AI
A paved road for AI in regulated organizations.
A paved road is the supported route most teams take by default because it is the easiest one. I build that route for AI: an operating model that covers people, process, and technology, and three boundaries that govern every agent.
The operating model
People
A small central team owns the platform, the standards, and the intake of new use cases. Each business function names an owner for its AI work. Developers, architects, and leaders get training matched to their role.
Process
One intake path for new use cases, ranked by value and risk. Clear review steps before anything reaches users. A regular cadence for continuing, fixing, or stopping each pilot.
Technology
A shared platform every team builds on: approved models, a gateway in front of them, evaluation harnesses, and audit logs. Teams reuse it instead of rebuilding it.
Three boundaries for every agent
Before an agent goes near production, I want three answers written down and enforced in code, not only in a policy document.
- What it may see
- The data, systems, and documents the agent can read. Sensitive data is masked or kept out unless there is a stated reason and an approval.
- What it may do
- The tools and actions it can take, and which of them need a person to confirm first. Anything outside the list is refused.
- What it may ship
- The output that can reach a customer, a record, or a production system, and the evaluation checks it has to pass before it does.
What the platform looks like.
Every AI system I build routes through the same five stages, so nothing reaches a user without being checked, and nothing has to be rebuilt by the next team that needs it.
Regulated first. Vendor neutral.
I work with organizations that answer to regulators, such as financial services, insurance, healthcare and life sciences, energy and utilities, and the public sector. I work with organizations of any size, from growing firms to large enterprises. Governance is part of the road, not a gate at the end of it.
I recommend the models and platforms that fit your controls and your existing stack, whichever vendor provides them.
- Policy as code and evaluation harnesses built into your CI/CD pipeline
- Provenance and telemetry for every model driven decision
- Verification gates in your release pipeline before anything reaches users
- An open maturity model so you can measure progress over time
Start with a conversation.
Tell me where AI stands in your organization today. I will tell you honestly what I would do first.