Customers want clearer guidance on what they can and cannot build using SAP data and interfaces. At first glance, the issue may appear procedural. But beneath that request lies a deeper concern about risk. Enterprises operating at scale cannot afford to invest in artificial intelligence (AI)-driven workflows or integrations that may later run afoul of compliance rules. In an environment where regulatory scrutiny is already high, this degree of ambiguity can function as a deterrent.

The complaint itself is not unusual; large enterprise customers often push back on licensing or technical constraints. What makes this moment different is the context of accelerating AI adoption and intensifying integration demands.

Strategic issues, not purely technological ones, are at the center of the debate, which reflects a broader shift across enterprise software as vendors are tighten control over data and interfaces just as customers start to seek greater flexibility to deploy AI tools and cross-platform workflows.

More here: The Classic ERP Model Is Dying. What Comes Next? 

When Policy Friction Turns to Innovation Risk

For years, enterprise software vendors have walked a careful line between openness and control. Too much openness risks security gaps and lost revenue; too much control can risk suffocating the very ecosystem that makes a platform valuable.

While policies like SAP’s are not new, what has changed is the level of scrutiny being applied to them. Customers and user groups argue that the latest iteration of API rules introduces uncertainty around what constitutes permissible use, particularly when APIs are leveraged for AI training, automation or cross-platform data orchestration.

In the pre-AI era, ambiguity-driven delays might have been absorbed into normal enterprise timelines. Today, they can carry competitive consequences.

The PYMNTS Intelligence report “Smart Spending: How AI Is Transforming Financial Decision Making” found that more than 8 in 10 CFOs at large companies are either already using AI or considering adopting it.

And as FIS Head of Product Management, Payment Networks Mladen Vladic wrote in a new PYMNTS eBook, “AI Runs Payments. Governance Decides What Happens Next,” integration is key to ensuring effective AI governance.

Unlike traditional enterprise applications, AI systems are inherently integrative. They require access to multiple data sources, often in real time, and they derive value from connecting previously siloed systems. In this context, APIs are not optional but foundational.

SAP did not immediately reply to PYMNTS request for comment.

See also: CFOs Turn to AI Harnesses as Agentic Capabilities Scale 

CFOs See ERP Data as Key Unlock For AI

Enterprise resource planning (ERP) systems sit at the core of corporate data infrastructure. They contain financial records, supply chain data, procurement workflows and operational metrics. These are the very datasets that AI systems rely on to generate insights and automate decisions.

“There’s a continuous evolution and … dynamic disruption in finance that requires CFOs to harness data and AI to make finance more efficient, more effective and substantially more strategic,” Raj Seshadri, chief commercial payments officer at Mastercard, told PYMNTS in an earlier interview.

As companies race to embed AI into everything from forecasting to customer service, ERP data has become a strategic asset. But unlocking that asset requires seamless integration across platforms, including cloud data warehouses, third-party analytics tools, and increasingly, generative AI models.

See also: Agentic B2B Is Here. Are Your Contracts and Invoices Ready?

“We see challenges around legacy ERP systems with limited AR API capabilities,” Michael Younkie, vice president of product management at Billtrust, told PYMNTS in an earlier interview. “We like to tie clear measurable KPIs to upfront things like DSO reduction, straight-through processing, digital invoice adoption.”

The technology stack for AI is advancing rapidly, with new models, tools and platforms emerging at a relentless pace. If access to data cannot keep up, the entire ecosystem may slow down.

The irony is that ERP vendors themselves are heavily investing in AI capabilities. SAP, like its peers, is embedding AI into its own products, positioning itself as a central platform for intelligent enterprise operations. PYMNTS covered earlier how SAP is pushing agentic AI deeper into its ERP suite.

Ultimately, just as reliable systems enable business operations, reliable rules can enable innovation. When customers understand the boundaries, they can push up against them confidently. When they do not, they stay well within them or even step away entirely.