Ivalua: AI Readiness Remains Procurement’s Biggest Challenge

Enterprise AI organisation Ivalua and supply management research firm Ardent Partners have released a new report titled, The Path to AI-First Procurement: Closing the Gap between AI Ambition and Execution.

The report examines how prepared procurement functions are to adopt AI, highlighting the system readiness, data challenges and governance gaps preventing organisations from scaling AI effectively.

While 61% of procurement leaders view AI as essential for improving productivity and scaling operations, and 90% of organisations are either exploring or implementing the technology, 67% continue to face barriers. 

According to the report, fragmented data foundations and underdeveloped governance frameworks remain the primary obstacles.

Data quality slows AI adoption

Data readiness remains one of procurement's biggest challenges.

According to the report, 59% of respondents identified data quality, availability or structure as a significant barrier. Meanwhile, integrating data into existing systems was the leading challenge for 51% of organisations, while just 11% maintain a unified procurement data model across the source-to-pay process.

Among those surveyed, 41% said their data is integrated but still requires manual system reconciliation. A further 40% reported fragmented data across multiple systems, while 8% said they continue to rely on unstructured "dark data".

The findings suggest procurement teams must strengthen data foundations before AI can be deployed effectively in source-to-pay activities.

Governance remains central to AI deployment

The report also highlights growing concerns around AI governance.

According to the findings, 96% of respondents consider the risks associated with black box AI to be either moderate or high, while 34% of organisations currently have no formal AI governance framework in place.

The University of Michigan-Dearborn defines black box AI as the inability to explain how an AI model reaches a particular decision.

Data hallucinations remain the leading concern for more than half of respondents, while 20% expressed concerns over organisational data being used to train public large language models.

As a result, 63% of procurement leaders believe human verification should remain mandatory for critical procurement decisions involving AI.

Organisations prioritise strategic AI adoption

Despite these challenges, 23% of respondents have successfully implemented AI-first procurement by establishing strong data foundations, embedded governance and integrated workflows.

Alex Saric, CMO at Ivalua, says: “The organisations who are already scaling AI reliably are unlocking massive strategic capacity, giving them a significant competitive advantage.

"There is immense organisational pressure to move fast with AI in procurement, but procurement leaders must take a strategic approach, collaborating closely with IT.

“Chasing AI-wins on infrastructure that cannot support it delays business impact and limits scale. The playbook for success requires choosing the right unified architecture, instating human-in-the-loop controls and only then accelerating broad deployment to make sure that AI in procurement can add real value to everyday operations.”

Rather than replacing procurement professionals, respondents favour AI models that augment decision-making and free teams to focus on higher-value strategic activities.

Among those surveyed, 47% preferred a human-agent operating model with cognitive augmentation, while 36% said supplier relationships should remain procurement's primary focus.

AI adoption is currently strongest in procurement functions with easily accessible data, including spend analytics (22%), supplier discovery and onboarding (20%) and accounts payable automation (20%). More than 80% of organisations also expect to introduce agentic AI capabilities within the next three years.

Andrew Bartolini, Founder & CRO at Ardent Partners, adds: “Those making tangible progress with AI in procurement are not necessarily moving faster but are rather doing so with strategic intent.

“They are learning where intelligent systems create the greatest operational leverage, where governance matters most, and how AI can augment execution without creating new operational risks.”