The discipline of writing down your thought process is crucial for decision analysis. AI now amplifies this by creating a searchable, analyzable record of your thinking over time, helping you identify blind spots and get objective feedback on your reasoning.

The defensibility of AI-native software will shift from systems of record (what happened) to 'context graphs' that capture the institutional memory of *why* a decision was made. This reasoning, currently lost in human heads or Slack, will become the key competitive advantage for AI agents.

The effectiveness of enterprise AI agents is limited not by data access, but by the absence of context for *why* decisions were made. 'Context graphs' aim to solve this by capturing 'decision traces'—exceptions, precedents, and overrides that currently live in Slack threads and employee's heads, creating a true source of truth for automation.

WCM avoids generic AI use cases. Instead, they've built a "research partner" AI model specifically tuned to codify and diagnose their core concepts of "moat trajectory" and "culture." This allows them to amplify their unique edge by systematically flagging changes across a vast universe of data, rather than just automating simple tasks.

A key competitive advantage for AI companies lies in capturing proprietary outcomes data by owning a customer's end-to-end workflow. This data, such as which legal cases are won or lost, is not publicly available. It creates a powerful feedback loop where the AI gets smarter at predicting valuable outcomes, a moat that general models cannot replicate.

Since LLMs are commodities, sustainable competitive advantage in AI comes from leveraging proprietary data and unique business processes that competitors cannot replicate. Companies must focus on building AI that understands their specific "secret sauce."

Capturing the critical 'why' behind decisions for a context graph cannot be done after the fact by analyzing data. Companies must be directly in the flow of work where decisions are made to build this defensible data layer, giving workflow-native tools a structural advantage over external data aggregators.

The real competitive advantage from AI comes from encoding your organization's unique intellectual property—its frameworks, theses, and internal voice—directly into prompts. This 'Savile Row' level of tailoring transforms a generic tool into a bespoke, high-value asset that competitors cannot replicate.

The ultimate value of AI will be its ability to act as a long-term corporate memory. By feeding it historical data—ICPs, past experiments, key decisions, and customer feedback—companies can create a queryable "brain" that dramatically accelerates onboarding and institutional knowledge transfer.