To make their AI models truly effective, Personio enriched them with specific, internal go-to-market knowledge. They uploaded ICP definitions, pitch decks, and onboarding processes. This proprietary context, layered on top of customer data, is critical for training LLMs to be relevant for a specific business.

Go beyond viewing prompts as mere instructions. The detailed system prompts your team develops to automate work constitute a new form of valuable IP. A well-developed library of internal prompts can increase a company's acquisition value, as it represents a codified, efficient operating system.

Simply offering the latest model is no longer a competitive advantage. True value is created in the system built around the model—the system prompts, tools, and overall scaffolding. This 'harness' is what optimizes a model's performance for specific tasks and delivers a superior user experience.

The notion of building a business as a 'thin wrapper' around a foundational model like GPT is flawed. Truly defensible AI products, like Cursor, build numerous specific, fine-tuned models to deeply understand a user's domain. This creates a data and performance moat that a generic model cannot easily replicate, much like Salesforce was more than just a 'thin wrapper' on a database.

A custom AI tool offers more value than a generic one like ChatGPT because it can be trained on a brand's unique, paywalled intellectual property. This creates a curated experience that aligns perfectly with your teachings and provides answers that cannot be found for free on the web, solidifying your expertise.

The key for enterprises isn't integrating general AI like ChatGPT but creating "proprietary intelligence." This involves fine-tuning smaller, custom models on their unique internal data and workflows, creating a competitive moat that off-the-shelf solutions cannot replicate.

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.

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."

If a company and its competitor both ask a generic LLM for strategy, they'll get the same answer, erasing any edge. The only way to generate unique, defensible strategies is by building evolving models trained on a company's own private data.