AI Adoption & Transformation

My Approach

Most organizations do not have an AI problem. They have a scoping problem, an evaluation problem, or a deployment problem. I help teams identify where AI will actually move the needle, build the systems, and set up the practices that keep things improving over time.

I have done this as a founder (Grownomic AI, Adaptive Gradients), as a tech lead scaling AI platforms (Axon), and as a data scientist bringing ML into heavy industry (Rio Tinto). The pattern is always the same: start with the business problem, not the technology.

What This Looks Like in Practice

Use Case Identification

Working with stakeholders to separate high-impact AI opportunities from noise. At Rio Tinto, this meant contributing to the Intelligent Mine proposal. At ServiceNow, it meant identifying the right use cases for federated learning across the enterprise.

End-to-End Delivery

Owning the full lifecycle from prototype to production. At Grownomic, I built and shipped multiple AI products end-to-end, from product design and system architecture to production infrastructure.

Evaluation & MLOps

Establishing the evaluation frameworks and operational practices that keep AI systems reliable after launch. This is where most AI projects fail, and where I spend a lot of my time.