Companies struggle with AI not because of the models, but because their data is siloed. Adopting an 'integration-first' mindset is crucial for creating the unified data foundation AI requires.

The primary barrier for enterprise AI is the 'context gap.' Models trained on public data have no understanding of your specific business—its metrics, language, or history. The key is building infrastructure to feed this proprietary context to the AI, not waiting for smarter models.

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'.

For complex enterprise tasks, the latest AI models are often intelligent enough. The true challenge is the 'context gap'—engineering systems that can absorb, clean, and understand the vast, messy, domain-specific context of a single client, like 25 years of financial documents, to apply that intelligence effectively.

Companies struggle to get value from AI because their data is fragmented across different systems (ERP, CRM, finance) with poor integrity. The primary challenge isn't the AI models themselves, but integrating these disparate data sets into a unified platform that agents can act upon.

The biggest AI opportunity for large companies is breaking down data silos. By building a 'context graph,' you give AI agents access to information from different departments and systems. This enables agents to perform cross-functional tasks and surface insights that were previously impossible.

The main driver for centralizing data is shifting from business intelligence to providing essential context for AI agents. Without a unified data source, agents are as limited as pre-internet ChatGPT, unable to understand current business realities.

The primary barrier to enterprise AI agent adoption isn't the AI's intelligence, but the company's messy data infrastructure. An agent is like a new employee with no tribal knowledge; if it can't find the authoritative source of truth across siloed systems, it will be ineffective and unreliable.

The key to valuable enterprise AI is solving the underlying data problem first. Knowledge is fragmented across systems and employee heads. Build a platform to unify this data before applying AI, which becomes the final, easier step.