Half of companies don't trust their own AI agents. Bad data is why.
A survey of 300 executives finds that most organisations are feeding AI agents less than half their company data, and it's costing them speed, accuracy, and confidence.
Key points
- AI agents currently have access to an average of just 45% of company data across surveyed organisations, falling to 30% or less among the weakest performers.
- Only around half of the 300 surveyed executives trust the decisions their AI agents make, according to the report.
- 100% of so-called "data leaders" trust their agents' decisions, compared with widespread doubt among peers still running older data systems.
- Two-thirds of data laggards say older systems prevent their agents from scaling or making fast decisions; only 8% of data leaders report the same problem.
- Every respondent plans to use AI agents within two years, with 69% expecting to use them widely across their business.
AI agents are software programs that can carry out multi-step tasks on their own, things like processing invoices, flagging supply-chain problems, or screening job applications, without a human clicking through each step. Businesses are adopting them quickly. The trouble is that many agents are running half-blind.
A new report, first surfaced by MIT Technology Review's custom research arm, surveyed 300 data and technology executives and found a striking gap between what agents are promised and what they actually get to work with.
So what's going wrong?
The core problem is old data infrastructure. Legacy systems, even ones updated a few years ago, were built to answer questions, not take actions. An agent that needs to check stock levels, pull a customer's order history, and update a shipping record all at once is asking something those systems were never designed to handle.
On average, AI agents only have access to 45% of company data. At organisations the report labels "data laggards", that figure drops below 30%. That is like hiring a new employee and locking half the filing cabinets before they start.
The consequences are practical. Around 66% of data laggards say their legacy systems stop agents from scaling up across the business. About 68% say those same systems prevent agents from making decisions fast enough to be useful.
Who's getting it right?
A smaller group, the report's "data leaders", have cleared most of those hurdles. These organisations give agents access to more than 70% of their data, structured figures like sales numbers alongside unstructured material like emails and documents. Every single data leader in the survey trusts the decisions their agents make. Among everyone else, only about half do.
That trust gap matters. An agent your team doesn't trust is an agent your team ignores.
Data leaders are also focusing heavily on automating data management itself, so that keeping information clean, labelled, and accessible doesn't require a small army of analysts.
What does this mean for ordinary businesses?
Gartner, the research firm, predicts that AI agents will handle or assist with 50% of business decisions by 2027. Every executive surveyed plans to be using agents within two years. Getting the data foundation right before then is not a back-office IT concern: it's the difference between an agent that helps and one that quietly makes bad calls.
The report's top recommendation is straightforward: improve what data agents can reach, and make sure that data carries enough business context (such as which products are seasonal or which customers have special terms) for the agent to act sensibly.
The gap between data leaders and everyone else is large, but it is not mysterious. It comes down to whether an organisation has done the unglamorous work of connecting its data systems before asking software to run on top of them.
