# Operate like a Formula 1 team: The new AI operating model > Source: > Published: 2026-07-10 09:00:00+00:00 It is lap 47 of 57. Before the race began, the team had already processed gigabytes of race data, simulations, tire models, weather forecasts, competitor tendencies and scenario plans. But on the pit wall, there is tension. The race leader’s tires are degrading faster than predicted. A rival has just pitted for fresh tires and is closing the gap by three-tenths of a second per lap. The lead may not hold. In short, the race is not going to plan. A strategist now has only seconds to synthesize live telemetry, competitor data, weather projections, tire inventory, track position and race simulations into one call that could determine the outcome. They do not have those seconds because they are simply fast. They have them because the entire system behind the decision was designed that way: the data architecture, simulation models, communication protocols, decision rights, scenario playbooks and feedback loops all work together to compress complexity into a clear decision window. What if this is not just a racing story? What if it is also a blueprint for how the best enterprises will operate in the AI era? This builds on a broader shift I’ve described as the [intent-driven future of work](https://url.usb.m.mimecastprotect.com/s/d_0XCXYGMGtpp756C6fncW3mhs?domain=cio.com), where enterprise work begins less with navigating systems and more with expressing outcomes, context and intent. The AI advantage will not belong to companies with the most tools. It will belong to companies that redesign how work senses, decides, acts and learns. [The popular story about Formula 1 is usually about speed or the quality of the driver](https://url.usb.m.mimecastprotect.com/s/cf3ZCYVJMJcGGo10tGh5cxi2wD?domain=cio.com). The fastest car with the most powerful engine with the driver with the quickest reflexes will win. But anyone who follows the sport closely knows that raw speed is only the starting point. Every car on the track is fast. Speed gets you into the race. It does not guarantee you a win. The teams that win consistently do so because of the quality of the system surrounding the car. They connect telemetry, simulations, strategy, engineering, pit operations, driver judgment and real-time learning into one high-performance operating model. Every part of that operating model matters. But the best individual part alone does not win the race. Enterprise AI strategy is at risk of making the same mistake that would keep an F1 team stuck in the middle of the pack: investing heavily in the engine while underinvesting in the entire race system. I see enterprising investing in more copilots, more agents, more dashboards, more tools and ultimately more automation. The AI systems perform their tasks at unprecedented speed. But the business outcomes do not change. In many ways, [ AI is becoming a new operating system of work](https://url.usb.m.mimecastprotect.com/s/Om9vCZZKWKuOOn4mfKiwcBwunD?domain=deloitte.wsj.com) not because it replaces every application, but because it changes how intent, context, workflow and execution come together. That is the gap many organizations are now facing. They have access to powerful AI capabilities, but they have not yet redesigned the operating model around those capabilities. The result is faster individual task execution inside disconnected systems, fragmented workflows and unclear accountability. In fact, a recent McKinsey report found that [88% use AI but two-thirds haven’t scaled it](https://url.usb.m.mimecastprotect.com/s/q5DRC1Vo9ocvvwzjFXsKcVUXck?domain=mckinsey.com). The next phase of AI value will not come from simply adding more AI tools. It will come from redesigning how the enterprise senses, decides, acts and learns. Most enterprises do not suffer from a lack of signals. In fact, they are everywhere across the business. Customer intent signals, campaign performance data, product usage patterns, sales activity, support interactions, contract information, financial indicators, employee sentiment, security events and operational metrics already exist throughout an organization. The problem is signal fragmentation. The average knowledge worker has become the integration layer of the enterprise. They move between CRM, marketing automation, analytics dashboards, spreadsheets, collaboration tools, support systems, workflow platforms and financial reports. Then they manually assemble context that no single system provides. They do this to answer questions that should take seconds, not hours. In Formula 1 terms, this would be like a pit crew strategist having to call five different team members to gather tire degradation data, track conditions, competitor lap times, fuel load, weather forecasts and pit stop windows before making a race-defining call. The data exists. But the latency in accessing, interpreting and acting on it makes it less valuable at the moment of decision. That is the signal-to-action gap. And closing that gap is one of the most important opportunities in enterprise AI. The AI-native enterprise needs to operate more like a Formula 1 team: continuously sensing, deciding, acting and learning. In Formula 1, every lap creates learning. Tire wear, track temperature, driver feedback, competitor movement and weather changes continuously reshape strategy. The enterprise needs the same kind of learning loop. To close the signal-to-action gap, enterprises need more than data integration. They need semantic intelligence. Semantic intelligence is what helps AI understand enterprise meaning. It connects business language, customer context, workflow relationships, policies, roles, systems and outcomes so AI can reason across the business, not just retrieve information from systems. A customer health score is not just a number. Its meaning depends on product usage, renewal timing, support history, stakeholder engagement, commercial value, sentiment, implementation milestones and prior interventions. A delayed workflow is not just a status update. It may signal unclear ownership, missing approvals, poor handoffs, missing context, poor data quality or a decision that needs escalation. A sales opportunity at risk is not just a CRM field. It may reflect adoption gaps, customer sentiment, usage decline, executive sponsor changes, pricing friction, support issues or service delivery risk. Without semantic intelligence, AI can summarize what happened. With semantic intelligence, AI can understand what matters, why it matters, who needs to act and what action is most likely to improve the outcome. This is where enterprise AI value compounds. Foundation models will become broadly available. The model itself will not be the moat. The moat will be enterprise context, semantic intelligence, workflow intelligence, governance and learning loops. There is a warning in the Formula 1 analogy that deserves attention: adding more power to a poorly designed system does not make it high performing. The same is true for enterprise AI. Adding AI to a broken workflow does not fix the workflow. It just compounds the dysfunction. If the data is fragmented, AI will produce incomplete recommendations confidently. If governance is disconnected from execution, AI can scale risk as quickly as it scales productivity. The question teams ask shouldn’t be, “Where can we insert AI into this existing process?” The better question is, “If we were designing this work from scratch, knowing what AI now makes possible, how should it operate?” This pushes leaders to clarify where work starts, what signals matter, which decisions should be automated, where human judgment is required, what controls must be embedded, how outcomes should be measured and how the system should learn. This is where CIOs, CTOs and technology leaders have an expanded role. AI transformation is no longer only about deploying technology. It is about redesigning how the enterprise works. In a world where every enterprise can access powerful models, context becomes the differentiator. The winning organizations will not be the ones with the most AI tools. They will be the ones with the strongest enterprise context and the clearest path from signal to action. That context includes customer history, product usage, workflow patterns, decision history, business rules, governance standards, risk boundaries, organizational knowledge and outcome feedback. It also includes knowing what happened after a decision was made. Did the action improve retention? Did it accelerate a deal? Did it reduce cycle time? Did it improve customer experience? Did it create risk? Did it scale? Without that feedback, AI remains a recommendation layer. With it, AI becomes part of a learning operating model. This is why the most important AI investments are not always the most visible ones. Data quality, identity, access, governance, workflow integration, observability, semantic models, feedback loops and change management may not sound as exciting as the latest AI agent. But they are what allow AI to create durable enterprise value. The CIO’s role is evolving from technology operator to architect of the enterprise race system. That means connecting strategy, workflows, data, platforms, governance, security, talent and execution into an operating model that can move faster without losing control. The CIO’s job is no longer just to provide platforms. It is to design the conditions where intelligence can move safely and effectively through the enterprise with the right context, controls, accountability and feedback loops. Business teams need the ability to experiment and innovate. But they need to do so within clear standards for data access, identity, security, privacy, model usage, auditability, human oversight and business accountability. This is the balance every enterprise needs to strike: speed with control. The future is federated innovation with centralized guardrails. It is an enterprise operating model where more people can create value with AI, but within a trusted architecture that protects the company, the customer and the quality of decisions. The companies that pull ahead in the next decade will not be the ones that deployed AI first or assembled the largest portfolio of tools. They will be the ones who built the enterprise equivalent of a winning Formula 1 race system: a connected operating model. In Formula 1, the gap between the team that wins the championship and the team that finishes fourth is often measured in tenths of a second per lap. Compounded over a race distance, those tenths become decisive. The same dynamic is emerging in enterprise AI. **This article is published as part of the Foundry Expert Contributor Network.****Want to join?**