Can Your Company’s Data Keep Up With Enterprise AI?

For all the urgency surrounding artificial intelligence, many companies are still in Automation 1.0.

While AI as a buzzword has become shorthand for progress, inside most organizations, what is labeled “AI” is more accurately described as an extension of earlier automation technologies.

Thes firms are focused on using technology to move faster, not think smarter. The distinction matters. Because while automation can deliver incremental efficiency, it rarely produces durable competitive advantage.

The average mid-market CFO, after all, is still a ways away from developing, much less relying on, the type of executive AI agent that tech giants like Meta are experimenting with. And until firms fix their data foundations, even the most ambitious AI initiatives may risk becoming expensive dead ends.

True AI transformation begins when systems move beyond executing tasks to informing or even making decisions. The difference for corporate functions is not a semantic one; it is increasingly structural.

Read more: How AI Is Supercharging the Tools CFOs Already Trust

Why Real AI Transformation Takes Data, Not Just Algorithms

In an automated system, the rules are known in advance. The system follows instructions. In an AI-driven system, outcomes are probabilistic. The system identifies patterns, evaluates scenarios and produces recommendations that may not be explicitly programmed.

This shift changes the role of technology within the organization. Instead of supporting workflows, AI begins to shape strategy. Forecasting becomes dynamic rather than static. Pricing adjusts in response to real-time signals. Supply chains are optimized continuously rather than periodically.

“Folks are just starting to understand that AI isn’t just automation with kind of sexier marketing,” Finexio CEO and founder Ernest Rolfson told PYMNTS in December. “Embracing it as infrastructure lets you use your data as a strategic asset.”

But most companies are still optimizing how work gets done rather than questioning what work should be done differently.

The reason for this gap is not a lack of access to advanced models. Today’s AI capabilities are widely available, increasingly affordable and rapidly improving. The constraint is data.

Enterprise data environments are typically fragmented across systems: finance, sales, operations and customer platforms each maintain their own records, often with inconsistent definitions and formats. Metrics that appear straightforward such as revenue, margin, or customer lifetime value, can vary subtly but significantly across departments.

In this context, introducing AI does not create clarity; it amplifies confusion. Models trained on inconsistent or incomplete data produce outputs that are difficult to trust. And without trust, adoption stalls.

“In financial services, when workflows involve capital decisions, 95% correct is 100% wrong,” Apoorv Saxena, CEO and co-founder of Obin AI, told PYMNTS in an interview this month.

See also: Vibe Coding Comes to Finance as CFOs Embrace Conversational AI

Rethinking the Sequence of Transformation

One of the most persistent misconceptions about AI is that it represents a starting point for transformation. In practice, it is closer to an endpoint.

Before AI can deliver value, organizations must establish a foundation of clean, integrated, and accessible data. That requires a different set of priorities: auditing data quality, aligning definitions across teams, integrating core systems and building reliable pipelines that update in real time.

In this sense, artificial intelligence is less a discrete investment than a layer that sits atop a broader transformation. Companies that skip the foundational steps may still deploy AI tools, but they will struggle to extract meaningful value from them.

This can make AI adoption an uncomfortable but revealing exercise. It may expose inefficiencies that might otherwise remain hidden. And it could force organizations to confront structural issues that predate the technology.

The Time to Cash report from PYMNTS Intelligence found that 83.3% of surveyed chief financial officers are planning to use at least one AI tool to help with cash flow cycle improvements. The most advanced, those using agentic AI, capable of autonomous decision-making, have automated up to 95% their accounts receivable processes, compared to just 38% among firms without AI integration.

For companies seeking to move beyond Automation 1.0, the path is not mysterious, but it is demanding. It requires shifting attention from tools to infrastructure, from experimentation to integration, and from short-term wins to long-term capability. The most valuable investments may not be in new models or applications, but in the systems that enable those models to function effectively.

“It’s no longer a nice-to-have,” Steve Wiley, vice president of product management at FIS, told PYMNTS in May. “Artificial intelligence is a must-have, and that’s happened very, very quickly.”

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