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Navigating AI Leadership with Cynefin

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

In the previous post, we explored how AI is making it possible for everyone to be a CEO, by lowering the barriers to execution and putting powerful tools in the hands of individuals. This newfound agency creates a more dynamic and unpredictable landscape. To navigate this, leaders need new sense-making tools.

AI leadership needs Cynefin's sensemaking discipline to decide when to experiment, when to codify, and when to get out of the way. Wardley Mapping explains how components evolve along the value chain, but leaders still have to choose the right play for the terrain in front of them. Cynefin complements Wardley Mapping by framing how decision-making should adapt when the landscape is obvious, complicated, complex, or chaotic—exactly the challenge AI agents introduce.

How this post fits the series​

  • Anchors the series in a shared sensemaking language after the opening essay on AI-enabled agency.
  • Sets up the need for continuous map governance so Cynefin decisions rest on live data instead of static diagrams.
  • Prepares the ground for autonomous strategy execution, where that governance becomes executable doctrine.

Why Cynefin matters for AI-era leadership​

AI collapses the time it takes for capabilities to evolve, pushing organisations through multiple Wardley evolutionary stages in a single planning cycle. Cynefin prevents leaders from defaulting to a single management style. It reminds them that automation doesn't erase complexity; it often creates more by connecting systems and actors in unpredictable ways. Using Cynefin in AI strategy gives leadership teams the language and guardrails to switch between doctrine, experimentation, and containment as the map shifts.

Linking Cynefin domains to Wardley evolution​

Clear domain – codify and scale​

When a capability has matured into a commodity on the map, it usually lands in Cynefin's clear domain. Here, leaders should codify best practices, embed doctrine in automation, and push the work into utilities or platform services. Metrics should focus on reliability and cost. AI can run almost unsupervised, provided ethical guardrails and monitoring are in place.

Complicated domain – engineer for assurance​

Capabilities in the product or transitional stages often belong in the complicated domain. They are knowable with enough expertise. Leaders can use Wardley Maps and Cynefin to assign these components to Town Planners and specialist agents. Governance should focus on peer review, simulation, and scenario planning to ensure that AI-driven decisions are auditable.

Complex domain – probe with guardrails​

Genesis and custom-built components typically live in the complex domain, where cause and effect are only clear in hindsight. Here, leaders should deploy Pioneers, establish safe-to-fail probes, and treat AI agents as hypothesis engines rather than production services. Success metrics should focus on the speed of learning, not the volume of output. The insights gained should be linked back to the map so that Settlers know when to stabilise the discoveries.

Chaotic domain – stabilise then sense​

Crises caused by unexpected model behaviour, security incidents, or runaway automation can drag components into chaos. The first task is to contain the situation: freeze automated actions, assemble a cross-functional response team, and re-establish a minimum viable map of the affected value chain. Only when stability returns should leaders decide whether the component belongs in the complex or complicated domain.

Practical leadership moves​

  • Map and sense simultaneously. Run mapping sessions alongside Cynefin workshops so teams identify where each component sits and which leadership posture it requires.
  • Design playbooks per domain. Document escalation paths, decision rights, and oversight cadence for each domain. This prevents teams from applying complicated-domain controls to complex-domain experiments.
  • Instrument transitions. Use telemetry to trigger alerts when components shift domains—such as an AI service moving from complex experimentation into clear automation—so governance and doctrine can adjust.
  • Train for domain switching. Leaders and agents should rehearse moving between sensemaking modes, especially when AI outputs nudge a system from complicated to chaotic faster than humans can react.

Strategic implications​

Using Cynefin with Wardley Mapping reframes AI leadership as a dynamic practice. It's about sensing where a component sits, deciding which domain playbook to apply, and evolving doctrine as the system learns. Leaders who embrace both models can build organisations that scale automation in the clear domain, while still being able to explore the complex domain and respond to chaos. The result is an adaptive command structure that keeps human judgement ahead of machine speed. This sense-making posture should be paired with continuous map governance to ensure that data reflects reality, and with OODA-driven leadership cycles to translate orientation into decisive action.

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