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Background AI for Relentless Improvement

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

In our last post, we discussed the importance of positioning and readiness in the age of AI. We saw how a clear understanding of the landscape and a portfolio of prepared plays can create a decisive advantage. But how do we ensure that the organisation is always ready to execute, without drowning in technical debt and operational friction?

The sharpest organisations let AI work in the background, continually raising internal quality while humans focus on intent and imagination. Background agents monitor maps, refactor components, and tune processes so that Wardley plays fire from a better baseline every week. Rather than heroic transformation programmes, leaders deploy ambient intelligence that nudges the system toward higher maturity as a matter of routine. It is the maintenance layer that keeps autonomous strategy execution trustworthy and ensures the diffused agency described in anti-fragile chaos engineering drills does not descend into entropy.

Quietly compounding architectural quality​

Background AI is most effective when it starts with composable building blocks. Agent swarms can review dependency graphs, cost telemetry, and code health, and then propose or implement refactors that improve cohesion and reduce coupling. They can rewrite integration boundaries to enforce contracts, spin up scaffolded services when custom logic becomes a liability, and retire shadow interfaces before they become critical dependencies. Leaders can turn "clean architecture" into a standing order, using agents as tireless gardeners who keep platforms ready for new gameplay.

The payoff is strategic options. When a play calls for shifting a component from custom-built to product, the groundwork is already in place. Internal platforms have fewer hidden problems, and the organisation can redeploy talent from maintenance to pioneering new value.

Automating maturity climbs across CMMI layers​

Wardley doctrine emphasizes situational awareness, and maturity models like CMMI provide a vocabulary for process evolution. Background AI can translate telemetry into maturity checkpoints, nudging teams upward without waiting for quarterly audits. Agents can ingest delivery metrics, incident data, and compliance evidence, and then compare them against CMMI practice statements.

  • Managed to defined – Agents can detect when teams are repeatedly solving the same problem and auto-generate standard operating procedures, inviting humans to ratify them.
  • Defined to quantitatively managed – Background services can instrument workflows, track variance, and recommend statistical controls where volatility still exceeds guardrails.
  • Quantitatively managed to optimising – Sense-making agents can surface improvement experiments, run A/B tests on process tweaks, and retire rituals that no longer move the needle.

Leaders can oversee the learning loops without micromanaging every checklist. Instead, they can curate the intent—what "good" looks like—and let agents keep teams honest about living up to it.

Accelerating the pioneers, settlers, town planners cycle​

Pioneers invent the new, Settlers scale it, and Town Planners industrialise it. Background AI can shorten the handoffs between these groups. As Pioneers discover new user needs, background agents can capture their decisions, map the emergent value chain, and annotate friction points. When Settlers take over, AI assistants can auto-generate runbooks, integrate telemetry, and flag where patterns diverge from doctrine. By the time the Town Planners get involved, agents have already standardised APIs, provisioned infrastructure-as-code templates, and benchmarked costs against market utilities.

The result is a faster cycle with less drag. Each group inherits artefacts that are already refactored, measured, and ready for the next level of control. The leadership focus shifts to sequencing plays and refreshing doctrine, rather than cleaning up the past.

Leadership playbook for ambient improvement​

  1. Instrument the landscape – Make sure that telemetry covers architecture health, process performance, and user outcomes so that background agents have trustworthy signals.
  2. Codify north-star qualities – Translate desired traits, such as modularity, reliability, and compliance, into machine-actionable policies.
  3. Assign guardianship – Give agents clear domains and escalation paths, making sure that humans know when to intervene and when to let automation run.
  4. Review drift dashboards – Leaders should inspect variance reports that highlight where the system is sliding backward or where agents lack the authority to act.
  5. Invest in explainability – Require agents to explain the rationale behind their refactors or process changes so that teams can keep learning and trust can be maintained.

Watchpoints and guardrails​

  • Over-optimised sameness – Background AI can stifle creativity if it standardises too aggressively. It's important to pair it with a doctrine that protects exploratory zones.
  • Data blind spots – Improvement agents will inherit the bias of the data they are watching. It's important to periodically audit the signals to ensure that they cover the full value chain.
  • Authority gaps – If agents don't have permission to implement their recommendations, work will pile up and humans will lose faith in the system. It's important to close the loop or reduce the scope.
  • Ethical creep – Automating process audits can drift into surveillance. It's important to be explicit about boundaries and consent.

Signals that background improvement is working​

  • The cycle time for map updates and follow-on gameplay is shrinking.
  • The age of the refactor backlog is trending down without the need for weekend heroics.
  • Process capability indices are moving steadily toward the target band.
  • Handoffs between Pioneers, Settlers, and Town Planners require fewer manual cleanups.
  • Teams are reporting higher confidence in the quality of shared platforms and practices.

Ambient AI will not make strategy effortless, but it removes friction that used to consume entire quarters. When the background hum is tuned correctly, leaders earn the space to explore new plays while trusting that the operating system of the organisation is getting stronger on its own. That creates the slack required to run sharper OODA loops (explored further in OODA-driven leadership cycles) and to hold more positional options from the AI-era readiness playbook.

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