The Embodied Enterprise

When business nerds tried to drive F1 cars

Jun 16, 2025

I. The Dawn of Data-Driven Dreams: A View from the Analytics Front Line (c. 2010)

A. The "Sexiest Job" and the Corporate Scramble

I finished studying at MIT in 2013 and was hired by a global corporation as the Advanced Analytics Team Lead. That time, more extensively, the early 2010s heralded a significant transformation in the corporate landscape. Leaders like me of newly formed "Advanced Analytics" teams within large corporations experienced firsthand an explosion of interest in data. The 2012 Harvard Business Review article, "Data Scientist: The Sexiest Job of the 21st Century," co-authored by Thomas Davenport and D.J. Patil, who would later become the U.S. Chief Data Scientist, was more than a catchy phrase; it was a cultural marker that resonated in boardrooms.¹ This article defined the data scientist as a novel "hybrid of data hacker, analyst, communicator, and trusted adviser," a figure suddenly in intense demand.²

The corporate appetite for data scientists surged. Job postings on platforms like Indeed saw a dramatic 256% rise by 2019, a trend that had its roots earlier in the decade.² McKinsey Global Institute's 2011 report, "Big data: The next frontier for innovation, competition, and productivity," became essential reading for executives.³ This report projected a significant talent deficit in the United States alone, forecasting a need for an additional 140,000 to 190,000 individuals with "deep analytical talent" and 1.5 million "data-savvy managers" by 2018.³

Universities moved quickly to address the burgeoning demand, with hundreds of new degree programs in data science, analytics, and AI appearing where few had existed prior to 2012.² Recruitment from these new programs became highly competitive. However, the rapid expansion of academic offerings sometimes resulted in a "competency lag"; many programs, being new, struggled to keep curricula aligned with the fast-evolving needs and tools of the industry.⁷ Graduates often possessed strong theoretical knowledge but lacked the practical experience of applying it to complex, real-world business problems. This gap spurred the creation of internal corporate programs focused on experiential learning and upskilling the existing workforce. The emphasis in the field, as HBR noted, began to transition from pure coding skills towards predictive modeling and the crucial ability to translate business challenges into effective analytical frameworks.² Conferences on the topic became vital hubs, bringing together practitioners, business leaders, and academics to share knowledge and shape the future of this rapidly expanding field.⁸

B. The Formula 1 Catalyst: Speeding Up Business Imagination

For executives seeking tangible proof of analytics delivering spectacular results, Formula 1 racing often served as a powerful and easily understood example. F1 teams were visibly leveraging real-time data from myriad sensors on their cars to make critical, split-second decisions, optimizing everything from engine performance and aerodynamics to complex race strategies.¹⁰ The narrative was undeniably attractive: if sophisticated mathematical models and continuous data streams could make a physical object like a racing car demonstrably faster and lead to victories, surely the same principles could accelerate business operations, provide a competitive edge, and improve the bottom line.¹² The direct and visible translation of data into competitive advantage in F1—a faster lap, a strategic pit stop, a championship—cut through the complexities often associated with corporate analytics projects, making it a persuasive tool for advocating data-driven initiatives.

The gains were concrete. Amazon Web Services (AWS), for instance, detailed how F1 utilized its high-performance computing (HPC) platform for Computational Fluid Dynamics (CFD) simulations, drastically reducing car redesign simulation times by 80% (from 60 to 12 hours) and cutting associated costs by 30%.¹¹ This acceleration in the design cycle allowed for more iterations, leading to innovations like car designs that minimized downforce loss in close racing scenarios, thereby enhancing the spectator experience.¹¹ This clear return on investment was precisely the kind of success story that business leaders aimed to replicate.

However, the F1 analogy, while compelling, sometimes inadvertently fostered a view of analytics as an external "booster" or a specialized technological add-on, rather than a deeply integrated organizational capability. This perspective risked overlooking the holistic integration of data into every facet of an F1 team's operations, from initial design and manufacturing to driver coaching and race-day execution, which is underpinned by a pervasive data-centric culture.

C. Early Victories: Analytics Delivering Tangible Results

The period from the early to mid-2010s saw widespread adoption of big data strategies across various sectors, including retail, finance, healthcare, and manufacturing.⁵ Retailers like Target gained notoriety for using customer purchase histories to predict life events such as pregnancy, enabling highly targeted marketing.¹⁵ Walmart, a retail giant, leveraged big data to optimize customer experiences both in-store and online, as well as to enhance logistics, reportedly achieving a 10-15% increase in online sales.⁶ Netflix, in its evolution from a DVD rental service, adeptly used data analytics to inform its content acquisition and original programming strategy, alongside personalizing recommendations, which contributed to its growth to 20 million subscribers by the end of 2010 and helped popularize the "binge-watching" phenomenon.¹⁶ These early successes often centered on customer-facing analytics, partly because customer data was becoming increasingly abundant through digital interactions and loyalty programs, and the return on investment—such as increased sales or higher engagement—was often more direct and measurable.

McKinsey identified several key pathways through which big data created value: enhancing transparency, enabling systematic experimentation, allowing for sophisticated population segmentation for customized offerings, replacing or augmenting human decision-making with automated algorithms, and fostering innovation in business models, products, and services.³ The report suggested, for example, that retailers fully embracing big data could potentially increase their operating margins by more than 60%.³ Business media, including publications like Forbes and the McKinsey Quarterly, actively chronicled this "age of analytics," publishing numerous articles and reports on how data was revolutionizing business, further stoking executive interest.¹⁸ A critical enabler during this period was the "democratization of data" facilitated by the rise of self-service Business Intelligence (BI) tools around the mid-2010s.²⁰ These tools empowered non-IT professionals to engage directly with data, fostering a more data-driven culture. However, this accessibility also introduced new challenges related to data governance, quality, and the potential for misinterpretation if users lacked adequate analytical literacy—issues that newly formed analytics teams frequently had to address through training and the establishment of robust data governance frameworks.

II. Beyond the Algorithm: The Unseen Physicality of Peak Performance

A. The F1 Driver's Crucible: The Human Element

The sleek Formula 1 car, a testament to data-driven engineering, is piloted by a human being enduring extraordinary physical and mental duress. This human element is often overshadowed in discussions focusing solely on the technological aspects of the sport. Drivers are subjected to extreme G-forces, reaching up to 6G during braking and cornering—a force equivalent to having a 600-pound weight pressing upon them.²¹ Their heart rates can average 170 beats per minute, peaking at 200 bpm, levels comparable to those of elite marathon runners.²² They perform in cockpit temperatures that can soar to 50°C (122°F), leading to significant fluid loss, sometimes up to 3 kg of body weight in sweat during a single race.²² Even the act of breathing becomes a conscious, strenuous effort against the crushing G-forces.²² Beyond these physical extremes, the mental concentration required is immense; a momentary lapse at such speeds can have catastrophic consequences.²² This intense, intertwined mental and physical challenge is far removed from a purely analytical problem to be solved by algorithms alone.

The story of Niki Lauda serves as a powerful anchor illustrating this human dimension. During the 1976 German Grand Prix at the notoriously dangerous Nürburgring, Lauda, then a dominant figure in F1, was involved in a horrific, fiery crash.²³ Trapped within the burning wreckage of his Ferrari, he suffered severe burns to his head and hands and inhaled toxic fumes that critically damaged his lungs.²⁴ His condition was so grave that last rites were performed at the hospital.²³ Yet, in an almost unimaginable display of resilience, physical endurance, and sheer willpower, Lauda, aided by the expertise of fitness specialist Willi Dungl ²⁶, made a courageous return to racing just six weeks later at the Italian Grand Prix, his injuries still painfully evident.²³ While he did not win the championship that year, his comeback became an enduring legend, epitomizing the raw courage and indomitable spirit that define the human drama of F1. It is this human struggle and triumph over profound physical adversity that truly captivates global audiences, arguably more so than the telemetry data streaming from the cars' sensors.²⁶ The driver's physicality is not an imperfection to be engineered away, but a defining feature of the sport's appeal and the ultimate determinant of performance. While analytics optimize the machine, the human driver remains the critical, variable, and often heroic, component. The intense focus by businesses on F1's analytical prowess often overlooked the comprehensive human performance programs F1 teams developed around their drivers, encompassing rigorous fitness regimes, specialized nutrition, physiotherapy, and mental conditioning.²² This holistic optimization of the human element is a crucial lesson frequently missed by organizations fixated solely on data and algorithms.

B. The Corporate Organism: The "Flesh and Bones" of Business

Just as an F1 car requires a driver, a business enterprise is far more than its data streams and analytical models. It possesses a tangible "physical body": its workforce, with their collective skills, tacit knowledge, and well-being; its intricate supply chains, vulnerable to disruption; its operational systems and physical infrastructure, which can degrade or fail; and even the data centers that house its analytical "mind".⁵ These are not abstract variables but concrete realities that profoundly influence performance and resilience. Neglecting this corporate body while focusing excessively on its analytical "mind" can lead to significant vulnerabilities. An overemphasis on dashboards and predictive models might detach leadership from the physical realities of operations, potentially resulting in underinvestment in critical infrastructure, overlooking employee burnout, or failing to build robust supply chains.

The work of David G. White, Jr., particularly in "Rethinking Culture," offers valuable perspectives here. He argues that organizational culture is not merely a set of espoused values but emerges from the "schemas" or mental models that employees develop to make sense of their work and the organization—an understanding that is deeply embodied and enacted through daily practices and interactions.²⁸ This implies that culture is lived and experienced, shaped by the physical work environment and the quality of interactions within it. Practical applications of embodied cognition in the workplace include thoughtful design of physical spaces, encouragement of movement, engagement of multiple senses, and leadership that is physically present and attuned to nonverbal cues.³⁰ Furthermore, applying autopoietic theory (even when Matura would have hated this analogy) to organizations suggests that they are, or should strive to be, self-making and self-maintaining systems, continuously regenerating their structures, processes, and identity to adapt and survive in dynamic environments.³¹ The health and functionality of this corporate "body" directly influence the quality and relevance of its "mind." If supply chains are chaotic, data quality suffers. If employees are disengaged or poorly trained, their inputs into, and interpretations of, data will be compromised. Robust physical infrastructure, including reliable data centers, is a prerequisite for analytics to function effectively. Thus, a well-functioning corporate body is not just a passive recipient of analytical outputs but an active and essential contributor to their efficacy.

C. Cognition Embodied, Reality Enacted: The Insights of Maturana and Varela

The pioneering work of biologists Humberto Maturana and Francisco Varela provides a profound theoretical foundation for understanding the limitations of a purely disembodied view of intelligence. They challenged the traditional notion of cognition as an abstract, brain-centric process, akin to a computer processing information. Their theory of "embodied cognition" posits that the mind is not confined to the brain but is distributed throughout the body and is fundamentally shaped by the body's interactions with its environment.³³ We do not simply think with our brains; we think through and with our entire physical being.

Two central concepts in their framework are "autopoiesis" and "enaction." Autopoiesis describes the capacity of living systems to continuously self-produce and self-maintain their own organization and components, defining their own boundaries in relation to their environment.³³ Enaction refers to the idea that cognition arises from the active, dynamic engagement of an organism with its world. As Varela put it, organisms "do not passively receive information from their environments... they enact a world".³⁶ Our perception and understanding are not passive reflections of an objective external reality, but are actively constructed through our sensorimotor experiences and goal-directed actions.³³ This means that thinking, learning, and understanding are inextricably linked to our physical form and our active participation in the world. Our bodies are not mere instruments for our brains; they are integral to how we know and what we know.

The traditional business emphasis on "information processing" often mirrors the pre-embodiment view of cognition, treating organizations as machines where data is inputted, processed centrally, and outputs decisions. Maturana and Varela's work suggests this is an incomplete model. True organizational intelligence, much like individual cognition, is enacted and embodied, emerging from the complex interactions of all its parts within its specific environmental context. Consequently, "tacit knowledge"—the intuitive understanding, practical skills, and experiential wisdom held by experienced employees, which is often difficult to articulate or codify—can be understood as a manifestation of embodied cognition in the workplace.³⁷ An over-reliance on purely explicit, quantifiable data risks undervaluing and discarding this crucial, embodied wisdom, which is often essential for navigating complex, ambiguous situations where algorithms fall short.

III. The Siren Song of AI: The Allure of Pure Intellect and the Transhumanist Horizon

A. Analytics Supercharged: The Shift to AI Dominance

The groundswell of enthusiasm for advanced analytics and data science naturally prepared the terrain for the subsequent boom in Artificial Intelligence. Machine learning was already a foundational element of data science practice.³⁸ Around 2015, advancements in deep learning gained significant momentum, propelling AI from the realm of speculative fiction to the forefront of technological innovation.³⁸ Illustrating this shift, Google reportedly scaled its AI-driven software projects from "sporadic usage" to over 2,700 distinct projects within a single year.³⁹ AI offered an enticing narrative: systems that could not only analyze vast datasets but could also "learn," "reason," and potentially even "create," seemingly replicating or surpassing core human cognitive functions. This promise of "intelligent" automation presented a powerful allure for businesses relentlessly seeking the next breakthrough in efficiency, productivity, and competitive differentiation.

B. The Disembodiment Delusion: Forgetting the Corporate Body, Again

The intense and often singular focus on AI's cognitive capabilities carries the risk of further devaluing the physical, tangible aspects of business operations. If "intelligence," particularly as embodied in algorithms, is perceived as the primary, or even sole, driver of value, then the "body" of the organization—its factories, logistical networks, and, crucially, its human workforce performing physical and service tasks—can be increasingly viewed as mere cost centers to be minimized or as constraints to be engineered away or entirely replaced. This mindset can lead to a form of "solutionism," where AI is reflexively applied to every problem, even those where embodied human skills, simpler solutions, or a blend of human and machine capabilities might be more appropriate or effective.

This trend is manifesting in companies across various sectors actively considering or implementing AI technologies to replace human workers.⁴⁰ Reports mention firms like Klarna, UPS, Duolingo, Intuit, and Cisco as examples of organizations that have replaced laid-off workers with AI and automation technologies.⁴⁰ While such moves are often framed in terms of boosting productivity and reducing operational costs, they raise profound questions about the future of work, the social contract between employers and employees, and the very definition of a company. Deloitte has noted that AI often automates the more routine, predictable tasks, leaving human workers to grapple with the most complex, ambiguous, and often stressful, aspects of their roles. This can paradoxically increase workload and cognitive load for humans, potentially reducing person-to-person interaction and contributing to feelings of isolation.⁴¹ If organizations, in their pursuit of AI-driven efficiency, indiscriminately remove human components without considering their role in the "autopoietic" or self-maintaining nature of the organization ³¹, they risk inadvertently dismantling the very systems of learning, adaptation, and cultural transmission that enable long-term resilience and innovation. Current AI, for the most part, functions as an allopoietic system—one that produces something other than itself—rather than contributing to the self-regeneration of the organizational system as a whole.

C. Echoes of Transhumanism: Silicon Valley's Intellectual Undercurrents

The discourse surrounding AI, particularly within influential circles in Silicon Valley, is frequently interwoven with transhumanist ideologies. Transhumanism posits that human beings can, and should, transcend their current biological limitations through the sophisticated application of technology.⁴² This vision encompasses ambitions such as radical life extension, significant cognitive enhancement, and a perspective that views the human body itself as "just another piece of hardware to be hacked, optimized and upgraded".⁴²

Prominent tech billionaires, including figures like Peter Thiel, Sam Altman, and Elon Musk, are reportedly investing substantial sums in technologies aimed at redefining human potential, such as advanced gene therapies and brain-computer interfaces.⁴² This alignment of technological development with transhumanist goals has drawn criticism. For instance, Christopher Wylie, the whistleblower associated with the Cambridge Analytica scandal, has characterized some of these tendencies as an "anti-human ideology," suggesting that certain "tech bros" are "convinced that machines should replace humanity".⁴³ While not all businesses or AI developers explicitly subscribe to transhumanist tenets, this underlying philosophical current within parts of the tech world can subtly influence the trajectory of AI development and shape the broader narrative around its ultimate potential. This, in turn, can further reinforce the notion of disembodied intelligence as a paramount objective, providing a philosophical justification for prioritizing "mind" (data, algorithms, AI) over "body" (physicality, traditional human labor, and ecological constraints). If the perceived evolutionary path leads towards transcending physical limitations, then business strategies that de-emphasize or seek to replace physical components, including human workers, can be rationalized as steps along this trajectory. This "replacement" narrative, fueled by AI enthusiasm and transhumanist thought, also risks eroding employee trust and engagement, potentially damaging the existing "corporate body" by creating fear and disincentivizing the contribution of uniquely human physico-chemical aspects.

D. The "Smart Garden" Paradox: Analytics vs. Ecological Reality

The burgeoning field of "smart gardens" and "smart farming"—which employs sensors, data analytics, and mobile applications to optimize plant growth and agricultural processes—serves as a compelling microcosm of the broader tension between data-driven optimization and embodied, ecological understanding.⁴⁴ The promise is one of perfectly calibrated inputs, maximized yields, and a seemingly scientific mastery over natural systems. This approach often prioritizes quantifiable data over holistic, embodied understanding, mirroring how businesses might focus on easily measurable KPIs from analytical dashboards while missing deeper, qualitative insights about their organizational health or market dynamics.

However, this technologically mediated approach frequently overlooks the profound physico-chemical and ecological complexity inherent in natural systems. Traditional gardening and farming involve a significant degree of tacit, embodied knowledge—an intuitive understanding of soil texture, microclimates, pest behaviors, plant-specific needs, and the subtle interplay of myriad environmental factors that sensors may not capture accurately or that algorithms may misinterpret or oversimplify.³⁷ Critiques and limitations of precision agriculture and smart farming technologies are increasingly recognized. These include high upfront costs, which can be prohibitive for smaller operations, unresolved issues around farm data ownership and privacy, a lack of interoperability standards between different technological platforms, and a shortage of effective analytical tools to translate raw sensor data into genuinely actionable, context-specific decisions.⁴⁷ There is a tangible risk of creating a disconnect from the hands-on, experiential learning and intuitive understanding that have defined successful agriculture for millennia. Some farmers express a lack of confidence in Smart Farming Technology (SFT) or face practical limitations such as inadequate storage for increased yields. Moreover, there's a concern that some adopters may possess only a theoretical rather than a deep practical understanding of these technologies, sometimes overemphasizing certain aspects like synthetic fertilizers as the entirety of "new technology".⁴⁸ The failure to effectively integrate this invaluable tacit knowledge with data-driven approaches in "smart" systems can lead not only to suboptimal outcomes but also to a potential erosion of crucial human skills over time, fostering greater dependence on inherently imperfect technologies.

IV. The Path to Harmony: Embodiment as the Next Competitive Frontier

Painting by Isabelle Scurry Chapman

A. Rebalancing the Equation: Beyond Hype to Holistic Integration

The journey from the initial fervor surrounding data science to the current pervasive enthusiasm for AI has undeniably equipped businesses with immense new capabilities. However, as the nuanced lessons from Formula 1 and the foundational principles of embodied cognition illustrate, sustainable success and genuine progress require more than just powerful analytical "minds." A conscious rebalancing is essential, one that involves integrating these advanced technological tools with a profound respect and understanding for the "body" of the organization—its people, its operational processes, its physical assets, and its dynamic interaction with the real world. The most insightful lesson from F1 is not merely that data can make cars faster, but rather the demonstration of a synergistic fusion: cutting-edge technology working in concert with peak human physical and mental performance, all orchestrated within a highly coordinated and adaptive organizational system.¹¹

B. The Embodied Advantage: Resilience, Innovation, and Human Connection

Companies that actively cultivate this mind-body harmony are poised to unlock a distinct and sustainable competitive advantage. Such organizations will likely exhibit greater resilience, being better equipped to adapt to unforeseen disruptions. Their "sensing" capabilities will not be limited to data streams and algorithms but will also encompass the rich experiential knowledge of their workforce and the inherent robustness of their well-maintained physical operations. Systems optimized solely on historical data and predictive models can be highly efficient in stable, predictable environments but may prove brittle and fail catastrophically when confronted with novel, unpredicted events—the so-called "black swans." An embodied organization, with its distributed intelligence, readily available tacit knowledge, and capacity for improvisation rooted in enacted experience, is better positioned to adapt and even strengthen when faced with such shocks, aligning with the principles of autopoietic systems that maintain themselves through dynamic interaction and self-renewal.³²

True innovation often emerges from the fertile interplay of analytical insight and embodied experience—the engineer who tinkers with physical prototypes, the designer who intuitively understands user needs through observation and empathy, the frontline worker who, through direct engagement with a process, intuits a more effective way of working.³² An embodied organization actively fosters environments where such creative interactions can flourish, valuing diverse forms of knowledge and encouraging experimentation.²⁸ Furthermore, valuing embodiment inherently means valuing the whole person, not just their capacity to process information or execute tasks. This holistic approach can lead to greater employee well-being, deeper engagement, and the unleashing of invaluable tacit knowledge and creativity.³⁰ This stands in stark contrast to a purely instrumental view of employees as "human resources" to be merely augmented or, ultimately, replaced by AI.⁴¹ A focus on embodiment can also guide more ethical and sustainable AI development and deployment. By grounding AI initiatives in the context of human experience, physical realities, and societal values, businesses are more likely to design and implement AI systems that genuinely augment human capabilities and support human well-being, rather than pursuing disembodied "superintelligence" at any potential human or ecological cost. This involves a careful consideration of the "silent impacts" of AI on workers and a commitment to designing for human-machine collaboration, not just automated replacement.⁴¹

C. Concluding Thought: Cultivating the Whole Enterprise for a Thriving Future

The future does not belong to organizations that chase the latest technological hype at the expense of their fundamental, embodied nature. Instead, it will be shaped by those enterprises that wisely and thoughtfully integrate the undeniable power of analytics and AI with a deep understanding of themselves as living, enacted systems, inextricably intertwined with their people, their communities, and the physical world. This is not a call to choose between the "mind" of data and the "body" of tangible operations and human experience, but rather an invitation to cultivate the whole enterprise. By fostering this balance and harmony, businesses can build a future that is not only more technologically advanced but also more resilient, more innovative, and, ultimately, more human and sustainably successful.

We at Holon aim to do and be exactly that by embodying and enacting the present and with that the future.

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