Better Standards Outshine Flashier Tech as Winning GenAI Recipe for Procurement

Few innovations throughout history have moved beyond the theoretical into tangible, real-world applications faster than generative AI (GenAI).

It’s even happening across enterprise operations. No matter their industry, organizations are exploring how artificial intelligence (AI) can streamline decision-making, automate complex workflows and enhance efficiency. Nowhere is this more apparent than in the procure-to-pay (P2P) cycle, where AI-driven automation holds immense potential to revolutionize procurement, invoicing and payments.

Yet, despite this promise, many companies face a critical barrier to scaling GenAI adoption: the startling lack of operating, safety, and governance standards.

This concern is particularly pronounced among organizations deploying GenAI for high-impact tasks and complex applications that directly influence business outcomes. In this group, the latest numbers from the PYMNTS Intelligence March 2025 CAIO Report show that 91% express apprehensions regarding side effects of GenAI on sensitive company-wide data.

AI adoption in enterprise procurement has been incremental, often confined to specific functions such as contract analytics or fraud detection. However, GenAI introduces a more dynamic paradigm. Its ability to generate insights, automate negotiations, and optimize supplier management necessitates a holistic framework that extends beyond traditional AI governance models.

Read more: The Investment Impact of GenAI Operating Standards on Enterprise Adoption

Within the enterprise back office, GenAI has the potential to revolutionize procurement by automating routine tasks, analyzing vast datasets for supplier selection and predicting market trends.

Such capabilities can lead to cost reductions, improved efficiency and enhanced decision-making. The PYMNTS Intelligence data reveals that approximately 73% of enterprises are either utilizing or considering the integration of GenAI into their procure-to-pay cycles, underscoring its perceived value.​

Still, as organizations integrate GenAI into P2P, they can frequently encounter three primary concerns:

1. Data Integrity and Model Reliability: AI-generated recommendations are only as reliable as the data they are trained on. Given the sensitive nature of procurement transactions, vendors must ensure that GenAI models operate within stringent data governance guidelines to prevent biases, inaccuracies or security vulnerabilities.
2. Regulatory Compliance and Auditability: As regulatory scrutiny on AI intensifies, enterprises need clear visibility into how GenAI models make decisions. Vendors must embed compliance safeguards into AI frameworks, ensuring that outputs can be audited and traced to specific data sources and logic paths.
3. Operational and Ethical Safety: AI-driven automation must align with corporate risk policies. Establishing standardized AI risk management protocols will be essential to prevent unintended consequences, such as unauthorized contract commitments or non-compliant payment executions.

At the same time, 38% of chief financial officers (CFOs) cite a lack of clear operating standards around GenAI use as a significant barrier to their investment in the technology.

Read also: 3 Ways Embedded Finance Solutions Are Remaking B2B Procurement

To unlock GenAI’s full potential, the establishment of comprehensive governance frameworks is imperative. Such frameworks should address data privacy, accountability and traceability. Rather than leaving AI deployment solely to IT departments, organizations should establish AI governance committees comprising procurement, compliance, finance and legal stakeholders.

The absence of these standards not only stalls investment but also fosters skepticism among potential adopters. For instance, 42% of CFOs already using AI tools have no plans to adopt GenAI, primarily due to governance concerns.

Ultimately, the coming years will define how enterprises operationalize GenAI within core business functions. In procurement, success will not be dictated by the sophistication of AI models alone but by the frameworks that govern their use.

For more reading on the role of GenAI governance models in unlocking a competitive edge in an increasingly AI-powered business landscape, check out the full PYMNTS Intelligence’s March 2025 CAIO Report, “The Investment Impact of GenAI Operating Standards on Enterprise Adoption.”

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