Unlocking AI’s full potential in procurement with smarter data management

Supply chain operations are often stymied by inefficiencies that result in higher costs, longer lead times, and dissatisfied customers.

These inefficiencies stem from manual processes that slow operations down, data silos that worsen decision making and a lack of visibility into supply chain activities, including procurement.

By implementing cutting-edge AI solutions into supply chain operations, procurement teams can overcome these challenges. Since data is the fuel for AI, unlocking its full potential is only possible when organizations have mastered data management.

“Over the next decade, AI and data management will redefine procurement by automating complex decision-making, enhancing supplier collaboration, and improving risk management,” says Santosh Nair, chief product officer at GEP.

However, according to Foundry research conducted for GEP, weak internal data management capabilities were the most common challenge organizations face when preparing data for AI initiatives (45%). Moreover, internal resistance and caution over sharing data (43%) and complexity in managing unstructured data (4o%) were the second and third ranked answers.

With a robust data management strategy, organizations can overcome these challenges, avoiding financial pain and outpacing their peers.

“AI in procurement thrives on high-quality, structured, well-governed data,” Nair says. “Smarter data management ensures that AI models can generate accurate insights, automate decision-making, and drive efficiency.”

Procurement challenges & how AI can help

Entering the age of AI, procurement teams face several profound challenges. A lack of visibility into supply chain operations makes it difficult to anticipate disruptions, optimize inventory, or identify reliable suppliers.

Manual tasks — like processing purchase orders and managing invoices — limit scalability and waste valuable time that could be spent on strategic initiatives. On top of this, data silos hold teams back.

“Despite AI’s potential, many procurement teams struggle with data-related obstacles that hinder adoption,” Nair explains. “For example, procurement data is often scattered across ERP systems, suppliers’ portals, and spreadsheets — making it difficult for AI models to access comprehensive insights.”

AI solves these challenges by offering real-time visibility, automating manual tasks, and eliminating data silos through integration. This enables teams to better predict risks, streamline workflows, and otherwise operate more efficiently.

Data management: The key to AI success

AI success starts with strong data management — a capability most organizations aspire to possess.

“To maximize AI’s impact in procurement, organizations must adopt proactive data management strategies,” Nair says.

To do this, Nair believes companies should implement centralized procurement data strategies, standardize and cleanse data regularly, strengthen data governance and security, and leverage AI for data enrichment.

Almost all businesses in the study are thinking about or enacting training programs to address data management challenges.

The study found that 34% of organizations are investing in data management training already, with 65% planning to follow suit. Meanwhile, 56% are conducting regular data audits while 39% plan to do so.

Another way to accelerate time to value is by partnering with third-party experts; 55% intend to collaborate with AI vendors to maximize their investments in the technology.

Learn more about how AI can optimize your supply chain, end to end.