AI Agents Earn Autonomy Process By Process As Companies Verify The Data Behind Them
As more AI systems act without real-time oversight, leaders are weighing what a wrong decision would cost before handing over more work.
Many of the decisions AI makes are based on that data, so what is your cross-check mechanism?
The opinions expressed in this article are those of Sanjay Choubey alone and do not necessarily reflect those of any organization.
AI agents at many large companies already act without a person approving each step, running code, placing inventory orders, and detecting security threats on their own. The pressing question for data and technology leaders is whether they can trust the data behind the decisions an agent makes, since that trust sets how much autonomy the agent should get. A system that routes customer emails and one that adjusts a production line both make decisions from data, but the cost of a wrong decision differs between them, as does how easily someone can verify the inputs.
In one survey of senior AI executives at large US companies, 85% of those using agentic AI said at least a handful of their systems act without real-time human involvement, and 49% said their governance frameworks haven't been updated to cover agentic AI. Leaders who set limits on these systems often start with the data. They grant autonomy one process at a time, based on how far they can trust the quality and lineage of the inputs and what's at stake if the output is wrong.
The data behind the decision
Many executives worry they can't accurately trace or audit the data lineage and inputs feeding critical AI decision models. Sanjay Choubey, Global Chief Digital and Information Officer at Sylvamo, oversees AI programs that draw on both customer records and production data. "Can you trust the underlying data?" he asks. "Many of the decisions AI makes are based on that data, so what is your cross-check mechanism?"
A system that acts on years of records can inherit whatever errors those records carry, and those errors then affect every decision the system makes from them. The first step in data intelligence is knowing where those records came from and how they were collected. Enterprises are beginning to recognize that data provenance can help determine how much authority they can safely give an AI system. "When you use AI, the real deal is how good or bad your data is," Choubey explains. "Your historical data will tell you mostly how you behave. That's why we look at people's experience when we hire: what have you done in the past?"
Autonomy by what's at stake
Deciding how much autonomy to grant starts with the workflow itself. An agent added to one step of a process affects the steps before and after it, including where its input data comes from, what context the agent needs to interpret that data, and who checks its output. For each process, Choubey's teams weigh how far they trust the data at each step, then decide which steps the system can complete alone and which need a person to review the output before the work passes to the next step.
Lower-stakes back-office work is often where companies start. Tasks that run on well-documented data, follow set rules, and leave a record a person can check afterward let a company test an AI system on real work, since someone can catch and correct a mistake before it causes much harm. Some companies now onboard agents much as they would a new hire, starting them on well-defined work before trusting them with more.
Agents working on live operational data need credentials and controls that limit what they can access and let a team stop them quickly. Some decisions can't be reversed once an agent has acted on them, such as a shipment released or a production run started. "Can you let AI manage your banking for you?" Choubey asks. "Can you trust AI with your life?"
Comfort levels differ
Some companies have already seen what a poorly governed AI deployment can cost, including lost data, financial damage, and disrupted operations. After a problem like that, executives often want more proof that the underlying data can be trusted before they approve another system that acts on its own. "Every organization has a different level of comfort," notes Choubey. "Some organizations that are almost entirely white collar may be more comfortable letting AI make some decisions on its own."
Formal AI assurance reviews, the audits companies run on their AI systems, often lead companies to change systems they've already deployed. In the same survey of senior AI executives, 64% of those whose organizations ran one significantly modified a quarter or more of their AI systems, and 29% paused a quarter or more. Companies that decide how much autonomy each process gets before a system goes live, based first on the quality and provenance of its data and then on what a mistake would cost, can avoid some of those corrections later. "Even if you have the smartest and best AI, you may want a human in the loop who says, 'You do this much, but I need to review it,'" Choubey concludes.
