Buy the Engine, Own the Playbook

I. Dirt Court in Greece: The Nine-Foot Rim Problem

One summer, I was on a dirt basketball court over 5,000 miles from home, in a small village in Greece, living there as a foreign exchange student. I was making the most of my first trip outside the United States: dunking. One-handed dunks. Two-handed dunks. Reverse dunks. The local kids were gathered around the court and chanting, “Jordan, Jordan!” Michael Jordan (MJ) wasn’t just a legendary athlete; he turned that same brand into a global business empire.

Being half a foot shorter than MJ didn’t stop me from trying to “be like Mike” and glide like he did in the 1988 slam dunk contest. With the basketball hoops resting at nine feet, a foot lower than NBA regulation height, and everybody already cheering, I wasn’t thinking about what could go wrong. Dunking at that height felt like real mastery, so I never stopped to consider whether the uneven dirt beneath me, or a move I’d never actually practiced, could bring me down. So I jumped, tongue wagging like Jordan, and dunked, then fell flat on my back in the dirt. The cheering stopped and there was a moment of dead silence. Then, we all burst into laughter. Nobody expected me to be MJ, and I didn’t expect to fall flat on my back either. That same dynamic, mistaking an easy environment for real mastery, plays out inside organizations every day.

Leaders face a parallel challenge navigating today’s Artificial Intelligence (AI) revolution. With boards, employees, and competitors chanting, “Innovation! Modernization! ROI!” the pressure to deliver on the promise of emerging technology is immense. Building momentum through a carefully controlled Proof-of-Concept can protect the rest of the business while something new gets tested. Those early outcomes are real, but they can be the enterprise equivalent of dunking on a nine-foot rim. When leaders try to scale them into production, the real issue is rarely a lack of talent or ambition; it is a systemic gap between the certainty a controlled pilot builds and the operational readiness required to govern the technology at scale.

II. Abdicating the Playbook: A Cautionary Tale in Digital Transformation

Before the mainstream AI boom that began in late 2022, nearly 84% of digital transformations failed or stalled¹. The primary reasons were behavioral, sociological, and managerial factors. Today, Generative AI is one of the fastest adopted products in history, with 88% of organizations regularly using AI in at least one business function². However, only 5% of organizations successfully scaled their AI initiatives to yield sustained business value^3,4, with AI projects failing at roughly twice the rate of traditional IT projects⁵.

One of the clearest managerial factors is abdicated ownership: leadership handing off a digital initiative to an outside partner without retaining the internal capability to govern it. That governance gap matters more now than it did even two years ago. Agentic AI, systems that take autonomous, multi-step action rather than simply responding to a prompt, doesn’t just answer questions inside a sandbox. It acts, accesses, and executes.

Without clearly defined guardrails, that same system will keep working exactly as instructed, even when the instructions themselves were never fully governed. To understand how that plays out in practice, consider the following case:

In 2016, a leading car rental brand launched a modernization initiative to completely overhaul their customer-facing web presence and mobile applications. They engaged a prominent global technology integration partner to manage and execute the $32 million project. By 2019, the client/vendor relationship had completely dissolved into a highly publicized breach-of-contract lawsuit in federal court⁶.

The filings reveal that the customer lacked the internal digital product and engineering expertise to design, build, test, and deploy the platform, which is precisely why they turned to an external partner. That same gap in internal capability left the systems integrator without a counterpart to monitor progress, verify architecture decisions, or make timely governance calls, forcing them to make judgment calls that should have involved the customer all along.

The project stalled, but the true cost was not the $32 million spent without the internal oversight to guide it, nor was it the brand’s eventual bankruptcy in 2020. The real cost was the forfeiture of a three-year strategic market window. Without anyone internally positioned to govern the engagement, the customer permanently ceded market share to agile, digitally native competitors right before COVID-19 upended the travel industry. They had bought the engine, but never built the playbook to run it.

III. Buying the Engine: The NASCAR Cup Series

For an example of how an enterprise can successfully partner with external specialists without abdicating strategic control, we won’t look to a corporate boardroom. We’ll look to an unlikely place: the modern NASCAR garage.

The NASCAR Cup Series generates approximately $1.7 billion in annual revenue with an estimated market valuation of $5 billion, which includes a massive $7.7 billion, seven-year media rights agreement⁷. The ecosystem relies on a precise, three-tiered operating model, one that will look familiar by the end of this piece:

1. Original Equipment Manufacturers (OEMs): Toyota (TRD), Ford, and Chevrolet are the only three manufacturers competing in the sport. They define the baseline performance and structural blueprints every team builds from.
2. Engine Shops: These specialized systems integrators act as the Consultants and Tuners. They take the OEM’s standardized blueprint and apply the precise, high-performance tuning necessary to win.
3. Race Teams: These operate as the Master Systems Integrators. They accept the standardized components from the OEMs and Engine Shops, but they maintain absolute, sovereign control over the ultimate operational playbook: race strategy, real-time performance data, and trackside decision-making.

For decades, racing teams ran on manufacturing muscle. To compete, they built their own chassis from raw steel and rolled custom sheet metal in-house, requiring tens of millions of dollars in physical foundry infrastructure.

In 2022, the Next Gen car debut shattered this legacy model. By standardizing the car through single-source spec suppliers, NASCAR systematically commoditized the physical hardware, leaving the competition to be fought in the virtual simulation space. Cars were delivered as modular kits and NASCAR legally restricted physical modifications to single-source spec parts, instantly shifting the sport’s focal point from manufacturing muscle to analytical insight⁸.

Teams now run Driver-in-the-Loop (DIL) physics simulators, testing millions of virtual setup combinations before ever arriving at the track for a brief 20-minute practice session. On race day, the advantage belongs to whoever acts as the better data custodian, backed by pit crews trained like Olympic athletes to shave tenths of a second off tire-and-fuel stops⁹.

A single NASCAR Cup Series race generates roughly 1.3 terabytes of high-frequency telemetry data¹⁰. To grasp the scale of that volume:

1. That data could fill roughly 65 million pages of paper. Stacked up, that’s four miles into the sky.
2. It’s what a hospital system generates in a full year of electronic health record activity.
3. The system updates 120 times per second, fast enough to catch a tire-pressure fluctuation and adjust before the driver even feels it.

Hardware became commoditized, which is what made competition within the sport software-defined: when every competitor runs the same physical machine, the only place left to compete is the data center. That’s why analytical insight, not manufacturing muscle, now decides who wins.

IV. Owning the Playbook: Michael Jordan’s Second Act

NASCAR’s shift to Next Gen did not necessarily lower the overall cost of competing, but it fundamentally redirected the target of executive investment, and the shape of the workforce required to compete. Capital and headcount that once funded physical fabrication moved directly toward vendor-supplied parts and standardized-platform optimization:

This shift shows the difference between a job and a career, an idea echoed by Drew Silverstein, founder of SourceAudio, in a recent conversation about AI’s impact on musicians¹³. A job is how you create value in a given moment. A career is sustaining that value over time, sometimes by evolving how you deliver it. The fabricators in the modern garage are a case in point: their job changed, but their career didn’t end. Welders became calibration specialists. Panel builders became fitting and repair technicians. They evolved their responsibilities to meet the moment, and kept delivering value.

That same shift also redrew the competitive map. Traditional powerhouses possessed massive physical facilities, millions of dollars in specialized tooling, and large headcounts of dedicated fabricators, sunk costs that no longer bought an advantage. A new owner, by contrast, could bypass the physical fabrication phase entirely, entering the sport with an asset-light footprint and directing capital straight toward data infrastructure, simulation, and software-driven talent. This is the exact competitive landscape Michael Jordan entered when he launched 23XI Racing in 2020.

Unlike my reckless leap on that court in Greece, the real Michael Jordan understood that sustainable victory requires preparation. To execute this blueprint, he partnered with veteran championship driver Denny Hamlin, combining Hamlin’s decades of deep trackside operational experience with Jordan’s relentless business standards. They built ‘Airspeed,’ a 114,000-square-foot Huntersville, North Carolina headquarters explicitly modeled on the open, high-collaboration workspace of the Mercedes-AMG Petronas Formula 1 team.

Hamlin wanted it to ‘feel like the Google of race shops’¹⁴, and the design shows it: engineers and crew chiefs work from open desk clusters rather than closed offices, built for direct, real-time communication. Each race team runs with a dedicated vehicle dynamics engineer and analytics engineer, coordinating in real time from a theater-style war room¹⁵.

V. Yours to Govern

This is directly relevant to your own AI strategy. Your OEMs are like hyperscale cloud providers, Microsoft Azure, AWS, and Google Cloud, supplying the raw compute engine. Your Engine Shops are the specialized consultancies and systems integrators licensed to tune that engine for your specific use case. And your Race Team is your own organization: the only party fully responsible for integrating everything the others provide into your culture, your workflows, and your strategy. Winning requires everyone in this chain to do their job well. The hyperscaler’s job is to build the engine. The integrator’s job is to tune it. Our job, as leaders, is to own what happens next.

To win in the era of AI, it’s important to remember that your long-term competitive value depends heavily on how your internal team governs and executes your proprietary playbook³. Treat that governance as optional, and the outcome looks like the car rental brand from Section II: a fully funded initiative that still forfeits market position, not because the technology failed, but because no one was minding it. By establishing robust internal governance and drawing on your experienced professionals, you can turn that engine into compounding, durable value. Whether you are a legacy giant optimizing decades of experience or a lean disruptor entering a new market, the playbook remains the same: buy the engine, own the telemetry, and master the race.