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Winning AI Leadership Cycles with the OODA Loop

· 5 min read
Dave Hulbert
Dave Hulbert
Builder and maintainer of Wardley Leadership Strategies

In our last post, we examined the collapse of differentiation in an AI-driven world. When the competitive landscape is constantly shifting and advantages are fleeting, the ability to observe, orient, decide, and act faster than the competition becomes paramount.

AI leaders who treat the OODA loop—John Boyd’s cycle of Observe, Orient, Decide, Act—as a living command system gain an advantage over rivals that only iterate plans. Too many teams equate Observe:Orient:Decide:Act with Plan.Do.Check.Act, yet the OODA loop is richer: orientation rewrites perception, decisions adjust doctrine, and actions feed new signals back into maps. The question that matters: how can leaders apply the full OODA loop to stay ahead of autonomous competitors while keeping human values in charge?

Observe: instrument the landscape faster than the opposition​

Observation is more than just dashboards. Leaders should combine Wardley Maps, telemetry from AI agents, and user research into a single sensing stack. The map layers should expose:

  • Market moves: Shifts in commodity pricing, new entrants, or regulations that alter the landscape.
  • Operational signals: Latency, error rates, drift metrics, and escalation logs from AI services.
  • Human feedback: Frontline insights about where automation is causing friction or providing relief.

Treat observation as competitive counter-intelligence. If your rivals are compressing their loops, you need to instrument your own to spot their probes and rehearse rapid response scenarios. When the telemetry starts to lag, it's time to revisit the practices in continuous map governance to ensure that the loop is always pulling from a living landscape, not a quarterly slide deck.

Orient: rewrite mental models, not just reports​

Orientation is the hidden lever in the OODA loop because it shapes what data is considered important. Leaders need to:

  1. Update doctrine: Refresh principles like Use a Common Language or Focus on User Needs when AI agents change how work is described.
  2. Reframe maps: Redraw Wardley Maps when components evolve or when data utilities change ownership. Orientation should clarify whether a capability is becoming a commodity or a differentiator.
  3. Stress-test assumptions: Run red-team exercises where synthetic agents challenge governance rules or ethics policies. This can reveal blind spots before adversaries have a chance to exploit them.

Orientation is where culture can either accelerate learning or stifle it. It's important to make the orientation ritual explicit so that teams know how to challenge their own biases rather than just following a checklist.

Decide: encode intent at the speed of software​

In an AI-first organisation, decisions need to be encoded as policies, APIs, and playbooks. Leaders can accelerate decision-making by:

  • Binding decisions to the evolutionary stage: Assign responsibility to Pioneers, Settlers, or Town Planners based on where the component is on the map.
  • Automating doctrine checks: Use policy-as-code to prevent agents from promoting a complex-domain experiment into production without human approval.
  • Pre-authorising manoeuvres: Create libraries of sanctioned plays that agents can trigger when specific signals are detected, such as spinning up redundancy when a supply chain is disrupted.

Decisions should leave an auditable trail that links back to the orientation artefacts. This traceability keeps the loop tight without sacrificing accountability.

Act: choreograph humans and agents together​

Action is where loops can collapse under stress. To maintain the tempo:

  • Synchronise cadences: Align deployment pipelines, incident response, and strategic reviews so that the organisation can act without waiting for calendar-driven meetings.
  • Leverage mixed teams: Pair human judgement with AI execution, especially when the legal or ethical stakes are high. Humans can shape the intent, and agents can carry the workload.
  • Close the loop deliberately: After each action, feed the outcomes straight into observation tools and map updates. Waiting for quarterly post-mortems will slow the loop to a crawl.

Leading the meta-loop: stay ahead ethically​

Applying OODA in AI leadership means running a meta-loop that inspects the loop itself:

  • Govern the cadence: Monitor how long each phase takes, and remove bottlenecks. Measure the ratio of automated to human decisions.
  • Audit for alignment: Ensure that outcomes still align with user value and ethical commitments. Use independent review boards to inspect high-impact loops.
  • Adapt doctrine: When a practice repeatedly causes rework or harms trust, revise the doctrine and push the change through the orientation artefacts.

Leaders who run OODA in this way will outpace rivals who confuse movement with learning. Observation will provide richer intelligence, orientation will reframe meaning, decisions will travel as code, and actions will reinforce trust. Tie the loop into autonomous strategy execution when the doctrine is mature, and let background AI for continual improvement keep the baseline sharp so that every loop spins a little faster than the last. This is how AI-era leadership can stay faster than the autonomous competition while keeping human purpose in command.