Engineering & Industrial

Industries

Engineering & Industrial

Agentic AI and Machine Learning for engineering-led businesses — from the shop floor to the boardroom.

My Experience

I understand engineering businesses from the inside, having spent over a decade as a board director in heavy machinery, managing finance, analytics and IT for a £35m-revenue operation. That first-hand experience — dealing with asset valuations, credit risk, regulatory compliance and the daily pressure to do more with less — shapes everything I deliver.

Today I bring that same operational empathy to agentic AI strategy for industrial clients, combining board-level credibility with hands-on technical delivery.

Case · 01 of 02
Heavy machinery · UK
ML & Analytics 12 years in production

Machine learning for heavy machinery

Over twelve years on the board of a heavy machinery business, I designed and deployed ML systems that became part of day-to-day operations:

  • Fraud detection using autoencoder neural networks (developed with PwC methodology), identifying anomalous transactions across the business
  • Equipment valuation forecasting to improve pricing accuracy on a fleet worth millions
  • Debtor classification using random forest models and embeddings, enabling a smarter credit control operation
  • Revenue and demand forecasting with ARIMA time-series models, supporting better financial planning
  • Inventory optimisation through data-driven demand analysis, reducing capital tied up in stock

These weren't proof-of-concept experiments. They ran in production, informed board decisions, and collectively saved the firm over £100k annually.

Models in production 05
01 Fraud detection
Autoencoder neural net, PwC methodology
02 Equipment valuation forecasting
Fleet pricing across a multi-million asset base
03 Debtor classification
Random forest + embeddings for credit control
04 Revenue & demand forecasting
ARIMA time-series, financial planning
05 Inventory optimisation
Demand-driven stock reduction
01 / 02
Case · 02 of 02
Waste & recycling · UK
Strategy & Architecture In progress

Agentic AI strategy for a leading UK waste services provider

Delivered a comprehensive agentic AI strategy for a fast-growing waste and recycling business serving customers across the UK. The engagement covered:

  • Discovery and stakeholder interviews across operations, sales, customer services, finance and regulatory teams — understanding real pain points before proposing solutions
  • Two lighthouse programmes: Operational Intelligence (smarter scheduling, route optimisation, regulatory compliance) and Augmented Workforce (empowering frontline staff with AI-assisted decision-making)
  • Architecture on Microsoft Azure, with solutions designed around CoPilot Studio and the Microsoft Agent Framework
  • Board-level strategy deliverables including investment cases, risk assessments, and a phased roadmap from pilot to production

This project exemplifies my approach: listen first, map the business reality, then design AI that fits.

Scope & scale
UK-wide
Recycling operations served
2
Lighthouse programmes
Azure
CoPilot Studio · Agent Framework
In progress
Discovery → roadmap
02 / 02

Why Engineering & Industrial?

Engineering businesses generate rich operational data but often lack the internal AI capability to exploit it. Legacy ERP systems, paper-based processes, and dispersed workforces create both challenges and enormous opportunity.

I bridge the gap — translating board-level objectives into deployable AI systems, with governance frameworks that satisfy compliance teams and regulators alike.

Ready to explore what agentic AI can do for your organisation?

Let's start with a conversation about where you are today and where you want to be.

Book a Discovery Session Read Our Thesis

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