Most current AI tools for sales are general large language models with a thin layer of data on top. The real productivity leap will come from future tools where deep, domain-specific knowledge—like complex enterprise sales methodologies—is embedded from the ground up.

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.

Instead of simply adding AI features, treat your AI as the product's most important user. Your unique data, content, and existing functionalities are "superpowers" that differentiate your AI from generic models, creating a durable competitive advantage. This leverages proprietary assets.

The LLM itself only creates the opportunity for agentic behavior. The actual business value is unlocked when an agent is given runtime access to high-value data and tools, allowing it to perform actions and complete tasks. Without this runtime context, agents are merely sophisticated Q&A bots querying old data.

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."

Instead of a generalist AI, LinkedIn built a suite of specialized internal agents for tasks like trust reviews, growth analysis, and user research. These agents are trained on LinkedIn's unique historical data and playbooks, providing critiques and insights impossible for external tools.

For entrepreneurs building on top of large language models, the key differentiator is not creating general platforms but achieving deep domain specialization. The call to arms is to know a vertical better than anyone and imbue that unique knowledge into AI agents, creating a defensible moat against more generalized tools.

While the "bitter lesson" suggests powerful general models will dominate, vertical AI solutions can thrive by deeply integrating with a company's specific data, workflows, and project context. The model can't know this proprietary information; value is created by the application that bridges this gap.

AI agents are simply 'context and actions.' To prevent hallucination and failure, they must be grounded in rich context. This is best provided by a knowledge graph built from the unique data and metadata collected across a platform, creating a powerful, defensible moat.