7 posts tagged with "strategy"

Wardley Maps already give us a vocabulary for evolutionary pressure, but they rarely tell us where to invite productive disorder. Building on the NK model from Rugged Landscapes and Wardley Maps, this post introduces entropy budgets—intentional allowances for coupling, variation, and option generation. Instead of letting ruggedness emerge accidentally, leaders decide where high-K experimentation is welcome and where governance should clamp down to protect reliability and cost.

An entropy budget is a bounded zone of controlled disorder on the map. It declares which components may tolerate extra coupling, slack interfaces, or duplicate paths, and it pairs that freedom with governance levers that cap coordination cost and rework. The goal is to open competitive windows deliberately while preventing the rest of the system from drowning in churn.

In our last post, we explored how Soft Systems Methodology can help us make sense of messy, contested situations. We saw how SSM allows us to negotiate a shared understanding of a problem space, creating a foundation for purposeful action. But how do we move from understanding the present to designing a better future?

Russell Ackoff's interactive planning and Wardley Mapping both ask leaders to design the future, not just forecast it. Together, they give teams the narrative, visual, and strategic tools to make that future a reality. Idealised design sketches the destination, while maps expose the terrain we must cross and the moves that will get us there.

In the previous post, we explored how the Viable System Model provides a blueprint for designing adaptive organisations. We saw how the VSM helps us balance autonomy and control, creating a system that can sense and respond to a complex environment. But what makes an environment complex in the first place?

Kauffman's NK model explains why the left side of a Wardley Map feels chaotic, and it shows how leaders can deliberately smooth that landscape without losing strategic edge. When N components are tightly coupled (high K), every move can collapse into a local optimum; modularity, doctrine, and adaptive gameplay are the tools for reshaping the terrain.

In our last post, we explored how the NK model helps us understand the rugged landscapes of innovation and the challenges of navigating complex, interconnected systems. But how do these landscapes change over time, and how can we anticipate and adapt to the inevitable cycles of growth, collapse, and renewal?

Wardley Mapping tells us which way the river of evolution flows; Panarchy—the ecological model of nested adaptive cycles—shows how each boat gains, loses, and renews its resilience along the way. Pairing the two reveals why some organisations ride climatic currents toward new value while others sink under their own rigidity.

In our previous post, we explored how to use LLM-driven competitor simulations to anticipate and prepare for the moves of our rivals. But a purely external focus is not enough. To create lasting value, we must also have a deep and evolving understanding of our users.

Leaders default to visible requirements, yet competitive advantage emerges when you stretch beyond the backlog to hypothesise the needs users can’t articulate. Wardley Mapping, and its user needs-focused cousin, remind us that "what people ask for" is only the top layer. AI now gives us leverage to work the deeper layers without guesswork.

How this post fits the series​

• Grounds the flashy simulations and autonomy work in user reality, ensuring the playbook remains anchored on needs.
• Complements continuous map governance by keeping the inputs to the map fresh and evidence-based.
• Sets up double-loop learning by emphasising the need to revisit assumptions as needs change.

In the last post, we explored how background AI can drive relentless improvement, ensuring that the organisation is always operating from a position of strength. But a strong internal foundation is only half the battle. How do we anticipate and prepare for the moves of our competitors in a rapidly evolving, AI-driven landscape?

Competitors rarely share their Wardley Maps, but language models can synthesize likely alternatives so you can prepare without guessing blindly. Treating large language models as hypothesis engines lets leaders surface combinations of doctrine, climatic patterns, and intent that rival teams could pursue. The trick is to design the prompts like Monte Carlo simulations—generate many maps, prune bias, and focus your attention on the handful of plays that would genuinely disrupt your landscape.

How this post fits the series​

• Extends positioning readiness with rehearsal tools rather than relying on intuition.
• Feeds back into continuous map governance by generating scenarios the governance stack must monitor.
• Connects to NK-model rugged landscapes for readers who want deeper complexity science behind the simulations.

In our previous post, we explored how anti-fragile chaos engineering can help organisations build resilience in the face of uncertainty. We saw how injecting controlled volatility can strengthen systems and prepare them for real-world shocks. But what is it about the current environment that makes this so crucial?

Leadership is moving beyond the jagged frontier of what machines can do. Artificial Intelligence is not yet Artificial General Intelligence, yet the boundary of machine capability continues to advance. The space of tasks that require human-only intervention shrinks each quarter as new language models pair with agentic tooling to run longer chains of execution with less supervision. Competence that once demanded firms, teams, or specialist expertise now sits within reach of motivated individuals, sometimes on a single high-end consumer GPU.

Agency is diffusing. Execution power is no longer a privilege reserved for large organisations because it is being unbundled and placed directly in individual hands. With the right orchestration, anyone can behave like a chief executive who directs an army of digital staff. This is not the singularity, yet it is already a strategic revolution.

How this post fits the series​

• Resets the cultural context after discussing stress-tested autonomy, showing why agency abundance changes power dynamics.
• Provides the rationale for introducing autonomy gradients so leaders can choreograph decision rights as capability diffuses.
• Reinforces the need for double-loop learning to keep assumptions honest when individuals can act without waiting for hierarchy.