Contrary to fears of job displacement, AI agents are voracious data consumers needing far more context than humans. Salesforce's CDO finds this dramatically increases the workload and hiring needs for data teams, as they must produce a much higher volume of trusted, agent-ready data to fuel the new automated workforce.

To get high-quality, autonomous work from an AI agent, you must treat it like a new hire, not just give it a simple prompt. You must provide a clear goal, specific skills (pre-defined knowledge), the right tools (APIs, etc.), and rich context (company data).

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.

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.

AI models lack access to the rich, contextual signals from physical, real-world interactions. Humans will remain essential because their job is to participate in this world, gather unique context from experiences like customer conversations, and feed it into AI systems, which cannot glean it on their own.

While data cleanliness is a challenge, AI models will become proficient at structuring data themselves. The true bottleneck for enterprise AI is codifying the vast amount of tacit knowledge that exists only in employees' heads. The new job of employees will be to translate this context for AI agents to perform effectively.

A critical learning at LinkedIn was that pointing an AI at an entire company drive for context results in poor performance and hallucinations. The team had to manually curate "golden examples" and specific knowledge bases to train agents effectively, as the AI couldn't discern quality on its own.

The primary barrier for useful AI agents is not the underlying model but the complex task of 'data wiring'—connecting to a user's real-world context like emails, local files, and support tickets. Products that solve this difficult integration challenge, where most agents currently fail, will gain a significant competitive advantage.

AI adoption in large companies is slow because models can't access unwritten institutional knowledge. A new human hire learns by talking to colleagues and observing culture—an onboarding process current AIs are blind to, making it hard for them to perform complex, context-dependent jobs.