I agents are pitched as digital coworkers that can work alongside teams. But new research suggests companies are struggling to make that vision a reality.
A new Deloitte survey of more than 500 U.S. business and IT leaders found a wide gap between companies’ ambitions for AI agents and their ability to actually use them at scale. Nearly three-quarters of leaders said they expect half of their businesses to be rebuilt or designed around AI agents within the next four years. However, even though 42% said their organizations have tested or deployed agents, just 15% have scaled multi-agent systems across the business.
The bottlenecks boil down to the architecture surrounding the agents:
- Just 21% of leaders said their business processes are prepared for agentic AI, and only one in five said their organizations are ready to redesign processes for agents.
- Instead, many companies are slapping agents onto existing ways of working in hopes of getting faster returns.
- Leaders also pointed to inaccessible data, difficulty governing agents, and costly integrations as major barriers to scaling.
“Limited, layered-on approaches may create the sense of getting ahead with quick wins, but in reality, they may not be enough,” Laura Shact, Deloitte’s US Technology, Media and Telecommunications AI growth leader, said in the study.
A separate study from Google and MIT Technology Review drills into another one of the biggest obstacles to using agents: data. More than half of the 300 global data and technology executives surveyed said legacy data systems are preventing them from scaling AI agents. On average, companies currently give AI access to just 45% of their enterprise data, which could limit its potential for efficiency gains.
The challenge goes beyond simply opening up more databases. Companies said their data is scattered across disconnected systems, locked away in unstructured formats like emails and PDFs, unavailable in real time, or missing the business context agents need to understand what the information actually means. Those shortcomings become more consequential when agents are expected to make decisions and take actions instead of simply answering questions.
Different levels of data access can compromise the quality of agentic workflows. Among organizations that give AI access to more than 70% of their enterprise data, every respondent characterized their agents’ outputs and decisions as either mostly or consistently accurate. And of organizations giving AI access to 30% or less of their data, just 22% said they trust the accuracy and relevance of their agents’ decisions.
"The so-called 'modern' data stacks that are glued together with disjointed parts were not built for the agentic era," Andi Gutmans, Google Cloud’s vice president and general manager of Data Cloud, said in the study.
Our Deeper View #
The bottlenecks point to a bigger problem: Most companies weren't built for autonomous software. Getting agents to work reliably at scale will require more than deploying better models. Companies will need to break down data silos, redesign workflows around what agents can actually do, put guardrails in place for when they act out of line, and train workers to use and oversee them. The technology may be moving fast, but the harder work will be getting the rest of the business ready for it.