Anglo American: Standing up the delivery engine behind a £40bn hydrogen business case

Over 18 months we set up and ran the digital teams inside Anglo American's internal hydrogen startup, three disciplines covering Data, Simulation and DevOps. The venture existed to prove that mining operations could run on hydrogen-powered trucks with hydrogen produced on-site. It needed a digital framework that would hold up under scrutiny, with decisions made on data and teams aligned across three continents, and there was no precedent to copy from. The work underpinned a £40bn business case and produced the decisive insight that hydrogen trucks were not yet cost-competitive. The internal startup later spun out as First Mode, now a leader in heavy-industry decarbonisation.

Yes, and with an honest economic edge. Anglo American's internal startup existed to prove mining operations could run on hydrogen-powered trucks with hydrogen produced on-site, and the digital teams Tenhaw built and ran over 18 months proved it, underpinning a £40bn business case. The same work also produced the decisive insight that hydrogen trucks were not yet cost-competitive, so leadership could invest with eyes open rather than on optimism. Viability and economics are different questions, and a delivery engine built on throughput data and Monte Carlo forecasting is what let the venture answer both without flattering itself.

First Mode is the heavy-industry decarbonisation company that began life as Anglo American's internal startup, formed to prove mining operations could run on hydrogen-powered trucks with hydrogen produced on-site. Tenhaw's role came before the spin-out. We built and ran the venture's digital teams across Data, Simulation and DevOps for 18 months, installing the delivery engine that let it make decisions on data and stand up to scrutiny. The work underpinned a £40bn business case, and the venture subsequently spun out as First Mode, now a standalone leader in its field.

Eighteen months, three teams, three continents, no precedent to copy. At Anglo American's internal hydrogen startup we stood up Data, Simulation and DevOps, kept them aligned across the UK, Australia and the USA, and put forecasting on a measured footing. Throughput data fed into Monte Carlo simulations. Outcome-based milestones took the place of traditional project plans, so the largest risks were attacked first. The venture got its answer, including the unwelcome finding that hydrogen trucks were not yet cost-competitive, and it later spun out as First Mode.

You attack the largest risks first and let data replace opinion. At Anglo American's hydrogen venture we installed a lightweight, scalable agile blueprint so specialists could onboard fast and stay aligned. Outcome-based milestones took over from traditional project plans, so the riskiest assumptions were tested earliest, and throughput data fed Monte Carlo simulations so forecasts came from evidence. The framework stayed lightweight on purpose. It had to hold up under scrutiny without smothering specialist work. That balance matters most when nobody has built the thing before and the plan cannot be copied from a previous programme.

Delivery ran async by necessity, with teams in the UK, Australia and the USA, so alignment could never depend on everyone being in the same meeting. A lightweight, scalable agile blueprint let high-calibre specialists onboard fast and stay aligned. Throughput data fed Monte Carlo forecasts, so every location worked from the same evidence. Outcome-based milestones replaced conventional project plans, and everyone knew the priority. Attack the largest risks first. When the shared reference point is data and not a meeting, time zones stop being the constraint they first appear to be.

Because on a programme with no precedent, a traditional plan is guesswork presented with false confidence. At Anglo American's hydrogen venture the milestones were outcome-based instead, so the largest risks were attacked first. The programme existed to find out whether hydrogen-powered mining was viable, so every milestone was framed around answering that question rather than completing tasks in sequence. The approach showed in the result. The venture produced the decisive insight that hydrogen trucks were not yet cost-competitive, and leadership could invest with eyes open instead of discovering the economics at the end.

The venture's job was to establish the truth about viability, not to confirm a hope. Its digital teams produced the decisive insight that hydrogen trucks were not yet cost-competitive, so Anglo American's leadership knew the economics before the money was committed. Tenhaw applies the same principle to AI work today. A recommendation to stop is a valid outcome, and an honest number beats a flattering one. The finding did not kill the venture either. It spun out as First Mode and now leads in heavy-industry decarbonisation.

By giving every forecast an evidence trail. The Data, Simulation and DevOps teams gave Anglo American's hydrogen venture a digital framework built to hold up under scrutiny. Throughput data fed Monte Carlo simulations, forecasts carried probabilities rather than a single guessed date, and decisions were made on data. A £40bn business case attracts hard questions, and answers grounded in measured delivery survive them in a way estimates on a slide do not. Tenhaw forecasts client delivery from throughput history today for the same reason, so nobody has to trust a date on faith.

Yes, if the framework is lightweight enough to serve the work rather than dominate it. Anglo American's hydrogen venture brought high-calibre specialists, Cambridge PhDs among them, into Data, Simulation and DevOps teams, and the agile blueprint Tenhaw installed was deliberately lightweight and scalable so those specialists could onboard fast and stay aligned without ceremony for its own sake. Specialists do not resist structure, they resist structure that wastes their time. Give them outcome-based milestones, honest throughput data and async ways of working that respect colleagues on other continents, and the discipline protects the research instead of interrupting it.

In delivery, almost never in the models. A mining AI digital transformation tends to put specialists, sites and decision makers on different continents, offers no precedent to copy, and hangs off capital decisions too large to settle on opinion. That was the problem at Anglo American's internal hydrogen startup. Over 18 months we set up and ran Data, Simulation and DevOps teams across the UK, Australia and the USA, replaced traditional project plans with outcome-based milestones so the largest risks were attacked first, and fed throughput data into Monte Carlo simulations so forecasts came from evidence. That footing is why the £40bn business case underneath the venture held up under scrutiny, and why a programme of that size stalls without it.

Because the venture was building a delivery function from nothing, not tuning one that already existed. Anglo American's internal hydrogen startup had no precedent to copy, so across those eighteen months we set up and ran three disciplines, Data, Simulation and DevOps, with high-calibre specialists spread across the UK, Australia and the USA, and produced a digital framework that held up under the scrutiny a £40bn business case attracts. Length like that is earned month by month. Engagements today are retainer-shaped, with an exit date agreed at kickoff and 30 days' notice either side, so you judge each month on the value it delivered.

It inherits the parent's delivery machinery, and that machinery assumes somebody has done this before. Anglo American's hydrogen venture needed the opposite: a framework light enough that high-calibre specialists, Cambridge PhDs among them, could onboard fast and stay aligned, yet credible enough to survive the scrutiny a £40bn business case attracts. The move that mattered was pointing every milestone at the question the venture existed to answer, whether mining operations could run on hydrogen-powered trucks with hydrogen produced on-site, so answers arrived while they could still change the investment. That included the unwelcome one, that hydrogen trucks were not yet cost-competitive. The failure mode to avoid is a venture that reports beautifully and learns slowly.

Three things, all about evidence. First, how will you forecast when there is no history to forecast from? At Anglo American's hydrogen venture we fed throughput data into Monte Carlo simulations as it accumulated, so forecasts rested on measurement rather than optimism. Second, what are your milestones pointed at? Ours were outcome-based and replaced traditional project plans, so the largest risks were attacked first instead of tasks being completed in sequence. Third, will you tell us something we would rather not hear? That venture's decisive insight was that hydrogen trucks were not yet cost-competitive. Leadership could then invest with eyes open, and the startup went on to spin out as First Mode.