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Future of Data Management

AI Agents Are Changing How Procurement Teams Work and Think About Enterprise Data

Marc Behring, Director of Procurement Excellence at Messer Americas, explains how AI agents are reshaping procurement’s relationship with enterprise data and raising new questions about data quality, governance, and human oversight.

ByThe Data Wire - News Team

Oct 25, 2026

The views and opinions expressed are those of Marc Behring and do not represent the official policy or position of any organization.

An AI procurement agent can research suppliers, compare prices, and prepare a request for quotation using information from multiple sources. But those sources serve different purposes, and their reliability varies. As agents move beyond routine transactions into more complex sourcing decisions, enterprises need to establish which information they can rely on, how it should be governed, and when human judgment is required.

Marc Behring, Director of Procurement Excellence at Messer Americas, has spent more than 20 years working in procurement and operational transformation, including eight years in consulting. He now leads the industrial gases company's procurement digital transformation across SAP, Coupa, and emerging technologies. Since September 2025, Messer has been implementing agentic AI with procurement technology provider Lio. The initiative is bringing questions about data quality, process definitions, and the information agents need to make reliable decisions.

"It's not black magic. You need to know your process, and you need to work on your data," Behring says.

Reliable procurement AI starts with the right data sources

Behring believes the information an agent should rely on depends heavily on what it's purchasing. Comparing laptop prices is relatively straightforward because specifications and prices are publicly available. Specialized chemicals are more complicated, with valuable pricing intelligence often available only through commercial data providers. "You want to bring in a data feed that you trust, that you know is market-leading, and has great quality," he says.

For procurement teams, that means identifying appropriate sources for each purchasing category before agents begin making recommendations. A sourcing agent may need internal purchasing records alongside subscription-based market intelligence from providers such as S&P Global. "That's something you need to define as you set up the agent," Behring says. Those configuration decisions also extend to the purchasing rules agents must follow.

Agentic AI is testing the foundations of procurement automation

Behring sees transactional procurement as a natural starting point for agentic AI because much of the work follows established procedures. Agents can populate purchase requisitions, verify information, and flag incomplete requests for employee review. Still, reliable automation depends on clearly defined purchasing policies, approval thresholds, and exception-handling procedures.

During Messer's implementation in Brazil, the team discovered that several purchasing workflows hadn't been formally documented. Employees had to reconstruct existing procedures and clarify rules before automating them. "You have to go back and map your process first," Behring says. The additional work extended the timeline but also prompted employees to reassess established practices.

Accurate master data is equally important. ERP systems maintain the supplier, material, and service records agents rely on to execute transactions. "That data is still core," Behring says. He's exploring how AI might help enrich those records over time, even as questions remain about the role of traditional data cleanup.

AI agents are pushing procurement data beyond traditional ERP systems

Behring expects ERP platforms to remain the foundation for core business records. Supplier identities, material definitions, and transactional information need authoritative systems of record. But sourcing decisions draw on considerably more context, including previous bids, supplier interactions, negotiation history, and changing market conditions. "Where do I layer all that additional data that I collect on the way?" he asks.

Consider a company that purchases the same material every year. An agent preparing the next sourcing event could draw on previous research and supplier interactions, incorporating lessons from earlier negotiations while adjusting for current market conditions. As Behring puts it, "How do I make sure I build on what I learned the previous year while adjusting for changes in the market?"

He expects some of that contextual information to live within specialized AI platforms, although the approach remains unresolved. Traditional ERP systems weren't designed to preserve every interaction or insight generated throughout a sourcing process. For companies working with third-party agent providers, this also creates a dependency on how those platforms retain and reuse information.

Greater autonomy puts enterprise data control under scrutiny

“I think the biggest question everyone has is where their property and intellectual property lives and who controls it,” Behring says. Supplier agreements, negotiated prices, and purchasing histories contain sensitive commercial information. Giving agents access to those records creates dependencies on external platforms and the security controls their providers maintain.

At Messer, employees are directed toward approved AI tools, including Microsoft Copilot, while access to unapproved services is restricted to reduce the risk of exposing sensitive information. "We need to trust Microsoft that the data stays with us and doesn't go anywhere," he says.

Specialized AI vendors introduce similar considerations. Enterprises can examine security certifications and independent assessments, but Behring points to the difficulty of independently verifying the protections provided throughout the technology stack. “How much can you really rely on third parties to conduct the assessments and audits they claim to perform effectively?” he says.

His concern extends to the decisions agents make using enterprise information. Behring expects human oversight to remain part of procurement workflows, particularly as agents begin supporting supplier negotiations and more complex sourcing decisions. "So the human stays in control," he says. "But at different defined points of the process, you can basically punch out to the human, get their input, and then you put it back in the automation."

The adoption is changing the skills procurement professionals need

As agents take on repetitive purchasing activities, procurement professionals can focus more on supplier relationships, strategic sourcing, and decisions requiring business judgment. Maintaining those workflows, however, will demand new technical skills. Purchasing policies change, supplier information evolves, and automation rules need ongoing adjustments.

Behring anticipates a growing need for employees comfortable with low-code and no-code configurations and is considering introducing a dedicated role within procurement. "I now most likely need to have a new role in my team that takes care of these things," he says. As companies adopt more sophisticated AI agents, maintaining their workflows and the information behind their decisions could become a permanent part of procurement's responsibilities.