Anglo American: Standing up the delivery engine behind a £40bn hydrogen business case
The challenge
Anglo American formed an ambitious internal startup to prove mining operations could run on hydrogen-powered trucks with hydrogen produced on-site. It needed a digital framework that would hold up under scrutiny, decisions made on data, and aligned teams across three continents, with no precedent to copy from.
What we did
Tenhaw set up and ran digital teams specialising in Data, Simulation, and DevOps. We installed a lightweight, scalable agile blueprint so high-calibre specialists (Cambridge PhDs among them) could onboard fast and stay aligned across the UK, Australia, and the USA. Delivery ran async by necessity, throughput data fed Monte Carlo simulations, and outcome-based milestones replaced traditional project plans so the largest risks were attacked first.
// run against The Tenhaw Way, published in full and free to adopt without engaging us
The outcome
The work underpinned a £40bn business case and produced the decisive insight that hydrogen trucks were not yet cost-competitive, letting leadership invest with eyes open. The internal startup spun out as First Mode, now a leader in heavy-industry decarbonisation.
The most valuable thing this programme produced was a no, delivered early enough to act on.
Limits, and what is withheld
What transfers is standing up a delivery function with no precedent and forecasting from throughput rather than opinion. What does not transfer is anything about agents. There were none on this engagement.
Why a buyer usually lands on this one
Written for the person arriving mid-programme with a question.
Who lands on this one
Two kinds of buyer read this engagement. The first is a programme manager or transformation director holding a venture with no precedent, three time zones and a business case large enough that being wrong is expensive. The second is a head of AI trying to get an unproven capability in front of an investment committee without either overselling it or killing it.
The mechanics are the same problem. You are being asked to commit real money to something nobody has done, and the only defensible route is to attack the largest uncertainty first and report what you find, including when what you find is a no.
Why a negative result was the valuable one
The decisive output was that hydrogen trucks were not yet cost-competitive. Leadership invested with that in front of them rather than discovering it three years later.
AI programmes rarely get that. The reason so many end up described as stuck in pilot is not that the pilots fail, it is that they were never designed to produce a decision. A pilot with no pre-agreed threshold cannot return a no, so it returns another pilot. Outcome-based milestones, throughput data feeding Monte Carlo forecasts and a standing commitment to report the largest risk first are what make a programme capable of stopping, which is the same discipline that later lets you scale AI agents past the first team: you can only widen something whose throughput and failure modes you already measure.
What this engagement does not claim
The same caveats the case studies hub carries, narrowed to this engagement so nothing here is a surprise to your analyst.
- 01
This was delivery leadership, not AI work.
There were no agents and no models on this engagement. It is on this site because the operating discipline transfers.
- 02
The £40bn is the business case we supported, not value we created.
Our work built and ran the teams whose evidence underpinned it. Read the figure as the scale of the decision, not as a return attributable to Tenhaw.
- 03
First Mode's later trajectory is not ours to claim.
The venture spun out and has its own history since. We were there for the eighteen months described above, and nothing after that is our work.
Other engagements
Sector first, because that is the next question. All twelve are on the hub, grouped into the two we would call AI work and the ten we would not.
Turning erratic global delivery into something the business could plan around
3, regions: UK, USA, Singapore
Roughly a year of stalled work, rebuilt as a working proof of concept in two weeks
12 months → 2 weeks, prior build effort rebuilt as a working proof of concept
Landing the Discovery+ launch on a CEO-set deadline
6, development teams on the launch, one of them the visual rebrand team James ran
See how we did it
A real engagement walked through by the person who led it, then the same method applied to yours.
- The ways of working, published in full and free to adopt without hiring us.
- The target operating model James co-led at HSBC: designed and piloted for 500 teams, with global rollout due in 2026 and not yet rolled out.
- The AI build inside a live London specialty insurer: a working proof of concept, month by month, with the client anonymised to a market.
Everything the call covers about our work is already published on this site. What it adds is the person who did that work, and your own situation put through the same method.
The 30-minute discovery call starts with your problem. This one starts with our work.
Pick a time on cal.comWant the same thing, in your organisation?
A 30-minute call with James Rooney. We will tell you which parts of this we have done before and which we would be doing for the first time, and you will leave with a rough scope either way.
most start with a fixed-price AI Readiness Audit · £30k–£90k · 6–8 weeks
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