Context: The Real Reason Agentic AI Fails In The Enterprise

Michel Tricot is Cofounder and CEO of Airbyte.

AI agents have quickly moved from novelty to priority, with McKinsey finding that “88 percent of organizations are now experimenting with AI.” Agents are automating workflows, assisting employees and promising faster, more efficient operations.

But there is a growing disconnect between what AI agents can do in demos and what they actually deliver in production, with the same McKinsey research finding that 81% of companies “do not report any meaningful bottom-line gains” from AI. ​

When asked to operate on real business problems, agents often fall short with incomplete answers and failure to take meaningful action.

I’ve worked in data infrastructure technology for a long time. Many of the traditional problems I have worked on for customers, such as fragmented data and unreliable integrations, are compounding in the era of agents. We see our customers increasingly adding AI to their workflows, but the root problems remain the same: fragmented data and unreliable integrations.​​

Most AI strategies today focus on model selection: which LLM to use, how to fine-tune it, how to prompt it and optimize each model for the task at hand.

But in practice, model performance is rarely the limiting factor. The real issue is that AI agents don’t have reliable, contextualized access to internal, constantly changing business data.

Even Anthropic acknowledges this in their

report on AI agents

. “The biggest barriers to enterprise AI value aren’t model capabilities or costs—they’re organizational readiness, especially around data accessibility and context aggregation.”​

From ‘Knowing’ To ‘Doing’

​There is a big shift happening in how companies use data that has been brought about by AI.

In the past and still today, data systems were built for people to access to make informed decisions. Dashboards and reports provided people with the raw information that they needed, and people took action and made decisions based on that data.​