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Chief AI Officer For Global Steel Producer

How MSH Tech placed the Chief AI Officer who took a global steel producer off a nine-figure consulting dependency and built an AI organization that owns its own code.

$40M → $11M

Annual external AI consulting spend, over 24 months

4 → 60+

Models running in production

9 mo → 6 wks

New model to production

140

In-house AI organization, built from zero

The Challenge

The client makes steel. Blast furnaces, electric arc furnaces, casters, hot strip mills, mines, ports, and rail, across sixty countries, in a business where a single percentage point of yield is worth more than most software companies earn in a year.

For six years it had bought its artificial intelligence the way it bought most transformation, from two tier-one strategy consultancies and their embedded AI units. Roughly $40 million a year. More than sixty consultants on site at peak. The output was real, roughly thirty models built, some of them genuinely good. Four were running in production. The other twenty-six had been handed over as notebooks, decks, and a slide that said "operationalize." Nobody internally could maintain them. Models drifted. When a model broke, the consultancy was re-engaged to fix the model it had built. When an engagement ended, the people who understood the work got on a plane, and the understanding went with them.

The problem was never the consultants' competence. It was that the client had outsourced a capability rather than buying a project, and had no mechanism to ever take it back. Every year the dependency deepened, the internal skill base thinned, and the cost of insourcing rose. The board had begun asking why a company that builds its own blast furnaces could not build its own software.

The Solution

MSH Tech also had to solve a compensation and geography problem. The role was a newly created C-suite seat at a European industrial headquarters, competing for a profile that the technology sector pays aggressively for. The search moved when MSH reframed the role to the board as an insourcing mandate with a hard cost-avoidance number attached to it. That changed the band, and it changed who would take the call.

Candidates had to have run AI inside a physical operation, process, energy, mining, chemicals, automotive manufacturing. Org-building evidence. The Chief AI Officer had to hire roughly 140 people in two years, in a market where the client was not the obvious employer of choice. Every finalist was asked how they would wind down a nine-figure consulting relationship without losing the four things that actually worked. A candidate who had only ever shipped in a single-regulator market was a hiring risk. If the candidate could not hold that room, nothing else mattered.

The placed candidate came out of automotive manufacturing, where they had spent five years building an internal machine learning organization from a standing start inside a company that had also been consulting-dependent. That answer, unprompted, specific, unflattering, was the reason MSH advanced them over a more decorated slate.

“We will never again pay someone to build something we cannot maintain. That sentence is the entire strategy. Everything else is implementation.”

— Chief AI Officer

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