CFOs Discover AI Is Only as Strong as Their Data

For years, artificial intelligence (AI) has been pitched to finance leaders as a faster way to do what they already do: close the books, reconcile accounts, forecast cash, and manage risk.

But as CFOs look to move AI from pilots to production across their department workflows, many are running into a new reality. AI doesn’t fail in finance because the models aren’t smart enough. It fails because the underlying data architecture was never designed for machines that reason, predict and act in real time.

Findings in the December 2025 edition of “The CAIO Report” from PYMNTS Intelligence highlight the pragmatic posture CFOs are taking as they deploy AI across finance functions, particularly in areas like cash flow visibility, anomaly detection, and compliance monitoring.

The report found that CFOs are deliberately retaining human oversight for judgment-intensive decisions. This is not hesitation. It reflects a clear understanding that finance performance depends on trust, explainability and accuracy; and that those qualities are rooted in data foundations.

Traditional finance systems were built to deliver precision at defined intervals. Month-end close, quarterly guidance and annual plans established a cadence that balanced accuracy with operational feasibility. These systems have served enterprises well, enabling finance teams to produce forecasts that are both reliable and auditable.

AI introduces a complementary capability: continuous insight. Rather than replacing established forecasting processes, AI extends them by ingesting data more frequently, detecting emerging patterns earlier, and updating scenarios dynamically as conditions change. For CFOs, the opportunity is not to abandon periodic forecasting discipline, but to strengthen it with real-time context.

The report found that nearly half of CFOs surveyed are using AI for tasks like continuously monitoring working capital and cash flows, standardizing account charts and intercompany transactions, improving audit readiness and compliance monitoring, and detecting anomalies while strengthening data governance.

These are the “low-hanging fruit” of AI adoption: tasks that are labor-intensive, highly regimented and governed by clear decision frameworks.

And despite broad acceptance of basic AI tools, the report found that surveyed CFOs remain cautious about deploying the technology in areas that involve multiple systems, require contextual judgment, or carry heightened operational risk.

Still, to support AI-enhanced forecasting, finance organizations are evolving their data architectures in measured but meaningful ways. First, many are moving toward near-real-time ingestion of financial events. This does not eliminate traditional close cycles, but it allows forecasts to be updated continuously between them.

Second, CFOs are prioritizing semantic consistency. Establishing shared definitions for key metrics across business units ensures that AI models and human analysts are working from the same conceptual foundation.

Third, governance is becoming more proactive. Rather than validating results after the fact, finance teams are embedding controls into data pipelines and AI workflows. This ensures that forecasts remain explainable, auditable, and aligned with established financial principles.

Read the report: CFOs Push AI Forward but Keep a Hand on the Wheel

This pattern highlights a trust gradient: CFOs are comfortable letting AI handle clear, structured problems and provide recommendations, but less so when the technology is asked to coordinate across systems or make real-time decisions with strategic consequences.

AI shines where decisions are data rich and rule based; leaders are cautious where context, nuance and integration are paramount. This sequencing makes sense for a function whose core mission is to provide accurate, defensible insight for leaders and stakeholders.

If there is a common thread in the surveyed CFOs’ attitudes toward AI it is this: They acknowledge that the technology’s role will only expand over time, but they are intent on shaping that expansion in ways that preserve confidence, control and context.

CFOs are therefore sequencing adoption deliberately. Advisory and predictive use cases come first, followed by limited automation in controlled environments. As data architectures mature and governance frameworks solidify, autonomy can expand responsibly. This phased approach preserves forecasting integrity while allowing innovation to scale.

In this sense, AI acts as a forcing function. It does not diminish forecasting discipline; it exposes where finance infrastructure can be strengthened to support it. CFOs who embrace this dynamic are treating AI as a quality accelerator, not a shortcut.

At PYMNTS Intelligence, we work with businesses to uncover insights that fuel intelligent, data-driven discussions on changing customer expectations, a more connected economy and the strategic shifts necessary to achieve outcomes. With rigorous research methodologies and unwavering commitment to objective quality, we offer trusted data to grow your business. As our partner, you’ll have access to our diverse team of PhDs, researchers, data analysts, number crunchers, subject matter veterans and editorial experts.

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