From IT support to AI strategy: Lessons from an F1 CIO for 2026 and beyond

Driver or car? It’s a perennial question in F1: which one really makes the difference?

Celonis’ Patrick Thompson, a CIO Hall of Famer, interviewed Gary Foote, CIO of the MoneyGram Haas F1 Team, to find out. Foote’s heard the question countless times over the years, and he says the real question, and its answers, are more complex.

“A great driver in a mediocre car can only go so far,” says Foote.

The fastest car on the grid still needs flawless execution. What separates teams at the front isn’t just talent or machinery. It’s everything that happens behind the scenes: systems, decisions, and the ability to turn data into action faster than everyone else.

That’s where F1 starts to look a lot like the modern enterprise.

Keep reading for a handful of takeaways. Want all the details? Watch the free playback on demand.

• Top gear transformation: What an F1 CIO can teach IT pros

The CIO is out of the pit

The CIO role has, in the past, primarily focused on support. The business looked to the CIO to keep infrastructure running, fix anything broken, and ensure tools are available.

But in 2026, that model doesn’t do enough for F1 or business.

Today’s F1 cars generate around 1.5 million data points per second. If the technology stack fails, the car doesn’t race.

As Foote puts it, “Technology has gone from being a support function to being a real performance differentiator. If it’s not working, the car doesn’t go out.”

The same shift is happening in the enterprise. CIOs are no longer measured by uptime alone. They’re expected to drive outcomes.

“Boards expect CIOs to go beyond providing the technology platform. They look to them as the authority on technology – to enable the business, create efficiency, and support growth,” Foote notes.

That only works when the CIO is trusted to operate as a business leader, not a service desk. When IT is pulled into strategy instead of handed requirements, transformation stops being theoretical and becomes operational.

Data volume isn’t the challenge, relevance is

With so much data coming from the car, it’s easy to assume the goal is to see everything in real time. But, Foote explained, that’s not always the case.

Haas deliberately splits its data into tiers. Some information is mission-critical in the moment. Engineers on the pit wall need it instantly, and strategists need specific signals to make calls lap by lap. The rest still matters — just not right now.

There’s a physical limit at play: Data sent from a racetrack to engineering teams across the world will always have latency, and after a second or two, real-time insights are out of date. So the real work happens upstream, in deciding what data goes to whom and making sure the right person gets the right insight at the right moment.

This is where many enterprises struggle: data volumes overwhelm, dashboards multiply, and everyone has access to everything.

Foote’s lesson is clear: more data doesn’t make you faster. Better data distribution does.

He explains, “We spend a lot of time making sure the right bit of data gets to the right person. If someone sees everything, they end up seeing nothing.”

That’s really what data distribution comes down to. Not collecting more data, but being intentional about how it flows. Engineers need one view, and leaders need another. When data is routed with purpose, decisions get faster, and teams stay focused on what actually matters.

Digital twins fix the “great disconnect”

In F1, physical testing is constrained. Every experiment costs money and time the team may not readily have. For a smaller, newer team like Haas, which operates with a lean "startup" mentality against older, more-well-funded giants, outspending rivals isn’t an option.

That’s why digital twins are so critical.

As Foote explains, “A digital twin in our world is where we can recreate the car in mathematics.”

This lets Haas “play with the aerodynamics of the car by making lots of iterative changes” without ever manufacturing a single part. They can simulate how a tweak to the front wing affects cooling and downforce across the entire car.

“That digital twin concept allows us to do all of that in mathematics,” says Foote, avoiding the massive cost of physical testing.

Digital twins can be similarly useful for enterprises, which often tend towards fragmentation. Finance sees one reality. Operations sees another. IT sees systems. Leaders spend more time reconciling views than improving performance. All of this reflects a great disconnect at the heart of many enterprises.

A digital twin of operations helps close that gap, creating a shared, objective view of how work actually flows across departments and systems. Instead of reacting to symptoms, leaders can test scenarios.

• Read more: What is a process digital twin?

Performance isn’t just about the drivers

For decades, F1 optimization focused almost entirely on the car and the two team drivers. And, because they’re the visible athletes, that made sense.

But Foote made a subtle point that applies directly to business. “We haven't just got these two athletes in the car... We've now got all these guys and girls back in the garage, and each one of them is having a performance impact.”

There are dozens of engineers on the track, and hundreds more team members supporting remotely. Each one has a measurable impact on performance.

Now layer in reality: back-to-back race weekends, long-haul travel, jet lag, and fatigue.

This is where Haas is starting to push digital twin thinking even further – beyond machines and into human performance.If you can understand how fatigue impacts reaction time, or how travel affects decision quality, you can adapt and improve outcomes without burning people out.

Enterprises face the same challenges. Burnout doesn’t show up in dashboards. But it does show up in mistakes, delays, and missed opportunities.

• Discover how Process Intelligence makes those invisible costs visible and manageable.

AI only works when it understands the business

AI is the topic everyone expects to talk about. And for good reason.

F1 teams are budget-capped, and headcount at the track is regulated. You can’t just throw people at the problem. So, for Foote, AI becomes a force multiplier.

“I look at it as an opportunity for digital workers,” says Foote. “If I can create two digital workers in AI that are doing strategy… then guess what? I just [got four] strategists. That's where I see the opportunity, is really being able to use the technology to overcome some of the constraints that are in place.”

But there’s a catch.

AI in F1 is safety-critical. A bad recommendation doesn’t just cost lap time. It can endanger drivers, pit crews, marshals, and fans.

That’s why context matters. AI can only be effective when it understands how the team (or, for most IT folks, the business) and all its parts work, and what everyone is working toward.

• Read more: Why AI needs process context to work to its potential

Trust as a transformation lever

Technology alone doesn’t change outcomes. As Foote emphasizes, “You have to instill trust within the company.”

It starts small. You solve a problem on a “micro scale,” and suddenly, “you start to get internal champions.” Word spreads through what Foote calls “osmosis,” transforming the CIO from a service provider into a true business enabler.

This is how transformation scales.

Boards don’t just need dashboards. They need confidence. When it comes to managing relationships with CIOs, Foote’s advice to fellow C-suite leaders is simple: “Trust them.” If that trust exists, IT leaders can “come out from that shell, sit around the board table,” and go from just enabling the business to driving it forward.