Most AI use today is open-loop. People prompt, get an output, use it, and move on, meaning that each interaction is consumed the moment it’s produced. Last week I wrote about AI as compounding capability in the context of agencies and operating models, but it’s a principle that has much broader application. Getting value from AI and compounding value from AI are not the same things, and very little of what’s currently in production is set up for the latter.
The OODA loop, originated by Colonel John Boyd, is a neat way to think about adaptive but compounding value creation with AI because it is closed and reinforcing by design. Boyd was a US Air Force fighter pilot turned military strategist whose theories on manoeuvre warfare, decision-making under uncertainty, and the dynamics of competition shaped modern strategic thinking well beyond the military. He was something of a maverick who clashed repeatedly with the Pentagon establishment, challenging entrenched assumptions about air combat, strategy, and the design of fighter planes. When I was writing my first book I read Robert Coram’s biography of Boyd and it gave me a lot of useful nuance around the man and his thinking. His OODA model (Observe, Orient, Decide, Act) describes how individuals and organisations adapt under uncertainty. Observation gathers information from the environment. Orientation synthesises that information against experience, models, and culture to make sense of it. Decision selects a course of action and Action commits it, generating new observations that feed the next cycle.
OODA captures the potential of compounding value because, if it is architected well, each Decide and Act feeds the next Observe, and understanding accumulates over time.
For value to compound with AI you need structured knowledge and retained context so that every interaction, decision and outcome improves the next. You need a closed, reinforcing loop. Much of the current focus for AI application is on the Observe stage (e.g. market and competitive intelligence, customer insight, trend analysis, meeting capture and synthesis, monitoring, briefing) and Act phase (execution, production, automation, optimisation, reporting). These are legitimate, valuable and increasingly well-funded use cases. They are also visible, measurable, and easy to justify and to accelerate with AI. But neither of them, working in isolation, improve and reinforce understanding over time. For that to happen you need to structure and feed context and knowledge from each decision and action into your next observation and orientation.
Boyd insisted that orientation is the most important stage of the loop. It’s also both the constraint and the prize – the part where AI is slowest to help, and the part where durable advantage lives. The work of orientation (the work of ‘meaning-making’ if you like) still sits more naturally with human cognition and institutional culture and it’s slower to develop, so many organisations defer it. But good orientation is where value truly compounds because you are using your latest known context to inform your next decision. Faster observation without better orientation can simply result in faster error generation. Faster action without better orientation can result in hasty commitment to badly informed decisions. Without good orientation the loop risks producing motion without meaning.
The orientation phase is also likely to be where the distinctive frames, models, ways of working, and tacit knowledge that have grown up within the organisation sit. As the tools commoditise, orientation is the durable, defensible, distinctive thing left in the system. You might call it ‘asymmetric orientation’. It’s the moat. The practical question is whether you’re capturing it deliberately or leaving it to chance. This means architecting your institutional knowledge in ways that codify your operational ethos and values, but it also means continually updating this with new context from new decisions, new actions and new outcomes.
Good orientation will always rely on human qualities, notably adaptability, judgement, imagination, abstraction, tacit experience, combinatorial creativity. But AI can contribute in a couple of key ways, by codifying existing frames of reference, and by challenging us with new perspectives that break open our assumptions. In 1976 Boyd wrote an essay called ‘Destruction and Creation’ (PDF) arguing that staying oriented in a changing world requires both pulling existing frames apart and building new ones. The two halves are inseparable. AI arguably makes creation much easier, but destruction remains less obvious, and much harder. AI is helping organisations produce more without necessarily helping them to think differently, and the risk is that AI ends up encoding our current assumptions faster than we can question them.
So far this has assumed a single loop. In practice an organisation runs many OODA loops, and Boyd recognised that tactical loops sit within operational ones which in turn sit within strategic ones. But he also made a good point about tempo. Relative tempo is important in situations where manoeuvrability becomes essential. If you can adapt faster than your opponent you reduce ambiguity quicker and gain advantage. But not every loop benefits from speed. Tactical execution may reward faster cycles, but strategic orientation often benefits from slower, more deliberate ones.
The risk with AI is that it tends to accelerate the inner, tactical loops while leaving the outer ones untouched, producing tactical agility that lacks strategic coherence. Boyd recognised that advantage was not always about maximum speed but instead about controlled pace, and sometimes the best strategic move is to slow down. So the question here is what kind of orientation is required? Is this a responsive, rapid loop that requires specific, well-defined information to maintain agility or is it a more strategic question that needs deeper, and more nuanced thought and orientation? Put simply, an awareness of tempo in orientation helps us to make better decisions with the right level of context. With AI, we’re at risk of making everything about speed and potentially moving very fast but in the wrong direction.
Compounding value lives in reinforcing loops, and reinforcing loops live on good orientation. Pulling old frames apart and building new ones is slow work, hard to measure, and easy to defer. But it’s also the work that decides whether everything else accumulates or evaporates.
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