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.

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.

Marc Benioff asserts that the true value in enterprise AI comes from grounding LLMs in a company's specific data. The success of tools like Slackbot isn't from a clever prompt, but from its access to the user's private context (messages, files, history), which commodity models on the public web lack, creating a defensible moat.

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.

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."

The primary competitive vector for consumer AI is shifting from raw model intelligence to accessing a user's unique data (emails, photos, desktop files). Recent product launches from Google, Anthropic, and OpenAI are all strategic moves to capture this valuable personal context, which acts as a powerful moat.

AI models fail in business applications because they lack the specific context of an organization's operations. Siloed data from sales, marketing, and service leads to disconnected and irrelevant AI-driven actions, making agents seem ineffective despite their power. Unified data provides the necessary 'corporate intelligence'.

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.