From Hierarchies to hyperloops
The emergence of a new breed of corporates
Introduction
Artificial Intelligence isn’t just accelerating existing workflows – it may also rebuild the very architecture of corporate organisations. For decades, businesses have used new tech primarily to streamline tasks without questioning the underlying corporate structure. AI, however, offers a more radical promise: it could collapse long-standing hierarchies and silos, ushering in agile, “hyperloop” organisations that move information and decisions at blistering speed. In this article, we argue that AI won’t merely make companies faster at doing the same old things – it could fundamentally reshape how they are organised. Some experts already foresee a “fundamental shift towards flatter, more agile organisational structures” as AI challenges traditional models. Our journey will take us from historical examples of incremental tech adoption to a future where AI-native firms might outmanoeuvre legacy corporates, much like ironclad warships outclassed wooden fleets.
1. New Wine in Old Bottles
New technologies often arrive with a whiff of revolution, yet businesses initially use them to carry on familiar practices, just slightly enhanced. History is filled with this “new wine in old bottles” pattern. For instance, early in the Industrial Revolution, steam engines weren’t immediately adopted to fundamentally redesign factories; instead, mill owners simply bolted steam power onto existing water-powered mills to sustain operations during droughts. Centuries later, when law firms first went digital, their initial step was typically converting piles of paper documents into PDFs—merely transferring the same filing cabinets onto a computer hard drive without changing any underlying processes. Similarly, in the 1990s, British retailer Littlewoods embraced digital innovation by placing its bulky mail-order catalogues onto CD-ROMs. Though innovative at the time, this approach merely replicated traditional paper catalogues in electronic form, without rethinking the shopping experience itself.
These examples may induce a smile, but they illustrate a serious point. Time and again, businesses introduce novel tech as an add-on rather than a disrupter. The forms change (steam instead of water, PDF instead of paper, CD instead of print) but the functionsand structures remain remarkably constant. Today we see a similar pattern with AI. Companies are eagerly adopting AI tools to enhance existing processes – automating routine customer inquiries, assisting with coding or document analysis, speeding up data crunching – all of which boosts efficiency. Yet, look at an org chart and its business as usual. The fundamental corporate hierarchy and departmental silos are still firmly in place. It’s as if we’ve poured the powerful new wine of AI into the same old corporate bottles.
But what if the bottle itself can’t contain this new wine? Perhaps AI is more than an efficiency tool – perhaps it’s an organisational game-changer. As we’ll explore, the advent of AI is exposing cracks in the old corporate design logic and hinting at a bold new architecture. In other words, the way we structure companies might be up for its first radical rethink in over a century.
2. Why Corporates Are Designed as They Are
To understand why AI might trigger a corporate overhaul, we must first appreciate why traditional corporations evolved into silos and hierarchies. Historically, companies organised into familiar specialist departments—finance, human resources, sales, operations, and IT—not out of whim, but as a rational response to the realities of 20th-century enterprises. Specialist knowledge was scarce and expensive, making it impractical to embed experts throughout every team or project. Instead, organisations grouped these scarce specialists into departments, enabling the efficient reuse of talent across the firm, avoiding costly duplication, and maximising the use of limited expertise.
Silos provided additional advantages beyond efficiency. Specialists grouped together could aggregate their knowledge, fostering innovation and deeper expertise within focused domains. Marketing teams, for example, could collectively brainstorm new campaigns, while manufacturing teams refined processes within their dedicated spheres. Simultaneously, hierarchical structures provided clear lines of command and coordination, vital at a time when information moved slowly and needed careful management. This layered chain of reporting—from junior staff up to senior executives—maintained organisational control and facilitated communication.
Yet, despite their rational foundation, these structures carried inherent limitations. While duplication of roles was minimised, it was never fully eliminated, and coordination between silos became a perennial challenge—so much so that “organisational silos” became synonymous with poor inter-departmental communication. Fundamentally, this structure made sense only when knowledge remained scarce, expertise costly, and the environment relatively stable. AI, by shifting knowledge from scarce to abundant and accelerating information flow, now challenges the very logic that shaped these traditional structures, prompting a critical question: can corporate design continue unchanged when its foundational assumptions are overturned?
3. How AI Changes the Logic of Corporate Organisational Design
AI fundamentally disrupts traditional, scarcity-based organisation design, ushering in an era of abundant, distributed intelligence. Now, front-line staff armed with AI can instantly access specialist knowledge previously locked away in departments. Need legal input? AI drafts contracts or summarises case law. Crunching financial data? AI replaces entire analytical teams. This shift weakens the rationale for rigid departmental silos, transforming isolated islands of expertise into free-flowing knowledge networks, as one tech strategist aptly summarises: “AI breaks down traditional silos by making expertise available on demand.” Companies no longer organise around who knows what but instead focus on workflows defined by business objectives—what needs doing—embedding specialist AI at each step.
To illustrate this concretely, consider organisations structured around Local AIs and Global AIs working in tandem. Local AIs function as digital first-responders embedded directly in daily operations, handling rapid tactical tasks. A customer-service AI instantly assesses customer history and sentiment, swiftly proposing tailored solutions, while a marketing AI continuously fine-tunes ad campaigns based on live analytics. These nimble AI agents operate on short horizons, optimising immediate outcomes and reacting in real time to unfolding scenarios.
Meanwhile, Global AIs serve as strategic overseers, continuously scanning vast streams of data—market trends, competitor moves, supply-chain disruptions—and updating strategic direction accordingly. Think of this global AI as a vigilant, central brain steering the entire organisation, promptly disseminating strategic insights to the network of local AIs. Spotting a competitive shift, it might swiftly recalibrate pricing strategies; detecting consumer-preference changes, it realigns product development priorities. The resulting corporate structure moves beyond classic departmental hierarchies into a flexible, objective-oriented network—less rigid pyramid, more hyperloop, with data and decisions flowing seamlessly across all parts of the organisation.
4. Automated Competition
This internal corporate restructuring is only one side of the coin; the other is surviving in a competitive market that’s about to speed up dramatically. Business competition has always been dynamic – companies constantly adapt to changing conditions or get left behind. But historically, adapting took time. Strategies were set in annual meetings, product roadmaps spanned quarters or years, and turning a big organisation was often like steering a ship – sluggish and deliberate. AI promises to change the tempo of this competition by compressing the cycle of observe, decide, and act into hyper-speed. To grasp the significance, let’s borrow a concept from military strategy: the OODA loop.
The OODA loop – which stands for Observe, Orient, Decide, Act – is a decision-making cycle developed by U.S. Air Force Colonel John Boyd, originally to train fighter pilots in air combat during the Korean war. Boyd taught that a pilot who could go through these four steps faster than an opponent would gain the upper hand. In essence, it’s about rapid cognition and action: observe the situation, orient (interpret and analyse the info), decide on a course, and act before the opponent does, then repeat continuously. Boyd’s insight was that agility can beat sheer size or strength. An entity (whether a pilot or an entire organisation) that can cycle through OODA faster “can thereby get inside the opponent’s decision cycle and gain the advantage”. In dogfights, Boyd’s fast-thinking pilots could outmanoeuvre technically superior foes by being quicker and more adaptable – agility overcoming raw power.
In business, companies have their own OODA loops. But let’s face it: today’s corporate decision cycle is often glacial. Many firms effectively operate on an annual OODA loop for strategy (observe market trends, orient with analysis, decide at yearly planning, act via that year’s initiatives). Tactical decisions might be faster but truly reorienting a company can take quarters if not years. This slow cycle is a vulnerability when facing a faster-moving competitor. And this is exactly where AI can tilt the scales.
An AI-empowered organisation can potentially run its OODA loop at a dizzying pace compared to a traditional firm. Remember our Global AI from the previous section – the always-on strategist. This AI can be observing the environment in real time: reading news and social media for market sentiment, monitoring competitor pricing changes, tracking supply chain data, you name it. It can orient by instantly analysing how those inputs affect the company’s situation, using up-to-the-minute dashboards and predictive models (far beyond the capacity of human analysts working on monthly reports). It can then decide on strategic adjustments – not in a yearly retreat, but perhaps daily or continuously – and act by disseminating those decisions to the local AIs and teams. In effect, the strategic planning cycle becomes a live feed rather than a scheduled meeting.
Consider a concrete scenario: Company A and Company B are rivals. Company A is an AI-driven “hyperloop” org; Company B is sticking to traditional structure and pace. One morning, a new trend emerges on TikTok that suddenly boosts demand for a certain product feature. Company A’s systems observe the spike in social media mentions that very day. Its Global AI orients by checking inventory and noticing a gap in that feature in its lineup. By afternoon it has decided to prioritise developing that feature and maybe even shifting some marketing budget toward it. By evening, it acts: local AIs in product development alert teams to the new priority and spin up design ideas (perhaps using generative AI), while local marketing AIs adjust the ad targeting to highlight whatever related capability the company already has. Company B, meanwhile, might only find out about this trend in a report next week, discuss it in a meeting a week later, decide to respond by next quarter, and act much later – by which time Company A has locked in the market. This is the compressed OODA loop in action. The company whose AI allows it to observe and respond in near real-time will “get inside” the slower competitor’s decision cycle, just as Boyd predicted in combat. This is a radical change from today’s norm, and it would give an immense competitive advantage to the companies that master it. The companies that embrace this in the era of AI will effectively be playing a different game – Algorithmic Warfare in the marketplace – leaving slow movers scrambling in an outdated playbook.
5. Of ‘Ships-of-the-Line’ and ‘Ironclads’
How will this all play out? History offers a vivid lesson. In 1862, at the Battle of Hampton Roads, naval warfare changed forever when traditional wooden ships faced their first ironclad opponent—armoured, steam-powered vessels impervious to cannonballs. In a single clash, centuries of naval tactics were upended. The wooden ships, once symbols of pride and power, were suddenly outdated, unable to match the speed, resilience, and strength of their iron rivals.
Today’s businesses stand on a similar precipice. Many firms remain anchored in tradition, incrementally bolting AI onto existing hierarchical structures—much like reinforcing wooden hulls with scraps of iron. These piecemeal enhancements might prolong their relevance but won’t overcome inherent vulnerabilities. Meanwhile, forward-thinking competitors are building entirely new organisational models around AI: flexible networks free from rigid departmental silos, rapidly adaptive, and strategically superior—corporate ironclads built for the new age.
When these two corporate visions inevitably collide, the outcome will echo that fateful naval battle. Companies that merely upgrade the old model will quickly discover they cannot match competitors who embraced AI’s transformative potential from the outset. The choice for corporate leaders is stark yet clear: either fundamentally reinvent your organisation or risk becoming an enduring example of obsolescence—an admiral proudly sailing the last wooden ship into an unwinnable fight.