Wardley Mapping Automation with LLM Prompts and NetworkX

Objective and methodological context

Video tutorial demonstrating a practical workflow using AI (LLM) to bootstrap the creation of a Wardley Map. The author, a product manager in the ERP/Business Intelligence domain, seeks to explore a product space and quickly obtain a solid starting point rather than starting from a blank page. The approach acknowledges that manual Wardley Mapping is lengthy and complex, and proposes partial automation to accelerate the initial phase of strategic exploration.

Technical architecture: stack and tools

The workflow relies on four components: (1) the OpenAI API to generate capabilities and relationships via structured prompts; (2) Obsidian as a knowledge management tool leveraging its native relationship graph; (3) Python with the NetworkX library for social-graph-style clustering analysis; (4) a custom frontend (optional) to facilitate entering prompts and capabilities. The seamless integration allows passing the LLM's JSON outputs directly into Obsidian, then exporting them to Python for advanced analysis, then re-importing the enriched data into the Obsidian canvas.

Three structured sequential prompts

Prompt 1 - Capability decomposition: strict format: "I'm product manager for [product] in [space]. Frame capabilities as 'the ability to [blank]'. Break down capabilities using '... is a function of the ability to...' Return results in JSON." Concrete example: "buy lunch for team" (top-level capability) automatically broken down into sub-capabilities: planning balanced meals, sourcing quality ingredients, efficiently preparing meals, accommodating preferences/allergies. The decomposition creates hierarchical parent → child relationships automatically linked in the Obsidian graph.

Prompt 2 - Y-axis positioning: Wardley Maps use a Y-axis representing customer proximity (top = visible to the customer, bottom = abstract/invisible infrastructure). The prompt categorizes capabilities according to their proximity to different roles in an operational-excellence-oriented value chain: (level 1) operational excellence leaders, COO, strategic program managers; (level 2) coaches, designers; (level 3) operations/IT engineers; (level 4) platform and data engineers; (level 5) infrastructure/utility layers. Crucial point: always request the justification along with the level assignment. This makes it possible to "get into the LLM's reasoning" and facilitates iterative tuning. The author notes that this prompt required behind-the-scenes tuning for their specific domain.

Prompt 3 - Relationships between capabilities: "Given a list of capabilities (each with ID, name, description), identify meaningful relationships. Either functionally similar OR enabling. Be very precise. Return JSON with: pair (two related capability IDs), type (similar/enables), reason (clear explanation)." Strategy: insert capabilities at random, strict analysis, like scanning a table and drawing lines between similar elements. Example output: "analyze data insights" ↔ "trend analysis" = similar (both centered on data analysis); "analyze data insights" enables "actionable intelligence" (derives intelligence from data patterns). This enriches relationships beyond simple parent-child hierarchy.

NetworkX clustering and final canvas

After creating the capabilities, hierarchical relationships, similarity/enabling relationships, and Y-axis levels, the workflow uses the Python NetworkX library (a standard for social graph analysis) to identify clusters within each level. The analysis of connection density, as in a social network, assigns cluster IDs. Result: each capability has (1) a Y-axis level (customer proximity), (2) a cluster ID (logical grouping within the level), (3) parent-child links, (4) similarity/enabling links with justifications.

The enriched data is imported into the Obsidian canvas where the capabilities are visualized. The author uses Obsidian's grouping function for readability. NetworkX clustering sometimes produces sensible groupings (example: "timestamped entries, audit trails of key actions, preservation of historical data" grouped together).

Value chain navigation and bootstrap philosophy

The canvas enables value chain navigation: example "a leader wants prioritization" (top of the map) → move down the stack level by level → identify the different elements involved in prioritization. A concrete demonstration of how a high-level need breaks down into progressively more abstract/infrastructural capabilities.

Key lesson: the author emphasizes: "this is only the beginning, just to bootstrap it". The AI output is not the final map but an accelerated starting point. The intent: "then spend a lot of time learning the domain in depth". AI reduces the initial friction of the blank page and allows the product manager to immediately begin iteration and refinement with a solid base structure, rather than weeks of manual mapping.

Methodological implications

The workflow demonstrates a pragmatic AI augmentation: neither fully automated strategy (impossible given the nuance and context involved), nor fully manual (too slow). The hybrid approach leverages the LLM's strengths (pattern recognition, logical decomposition, relationship identification) while recognizing that human expertise remains indispensable for validation, prompt tuning (via justifications), and in-depth domain learning after the bootstrap. The systematic justifications create a feedback loop allowing the practitioner to understand the LLM's reasoning, iteratively adjust prompts, and improve output quality.

Transferability beyond Wardley Mapping

Although focused on Wardley Maps, the techniques are generalizable: capability decomposition, proximity categorization, relationship identification, and clustering analysis apply to other strategic frameworks requiring structured thinking about value chains, dependencies, and abstraction layers. The Obsidian + NetworkX + LLM API stack is particularly powerful for knowledge workers exploring complex domains.