The Great AI Trust Deficit: Why Finance Leaders Are Captivated by Artificial Intelligence—But Reluctant to Hand Over the Reins

NEW YORK — Artificial intelligence promises to revolutionize the back office, turning tedious administrative burdens into streamlined, automated workflows. For corporate finance departments, the allure is undeniable. Yet, a stark disconnect persists between enthusiasm and execution. According to a comprehensive new benchmark report by spend management platform PEX, while finance teams are deeply intrigued by the potential efficiencies of AI, they are fundamentally unprepared to relinquish control of the financial steering wheel.

The findings illuminate a complex transition period for corporate finance. Even as digitalization sweeps through global industries, the financial sector—built on precision, regulatory compliance, and risk mitigation—remains fiercely protective of its traditional oversight mechanisms. The evolution of AI in finance is not blocked by a lack of imagination or interest, but by a profound, foundational deficit of trust.

Main Facts: The "Want-Versus-Have" Gap in Corporate Finance

The PEX State of Finance benchmark report, which surveyed 687 finance and operations leaders, reveals that corporate interest in artificial intelligence is remarkably high. Roughly 66% of respondents expressed a clear interest in integrating AI into their operations, and 31% reported utilizing some form of AI technology today.

However, this enthusiasm hits a wall when it comes to autonomous decision-making. Only 28% of surveyed leaders stated they were comfortable "letting AI decide" routine financial choices.

This hesitation creates a wide "want-versus-have" gap across multiple high-value financial capabilities. The disparity is particularly glaring in advanced reporting and forecasting tools:

• AI-Generated Financial Reports: While 61% of respondents expressed interest in using AI to compile financial reports, only 14% currently utilize the technology for this task—a massive 47-percentage-point gap.
• Cash Flow Forecasting & Audit Documentation: Similar double-digit divides separate the number of finance professionals desiring automated forecasting and audit trails from those actually deploying them.

At the heart of this divide is the issue of accuracy and accountability. When asked to identify the primary barrier to AI implementation, 36% of finance and operations leaders pointed directly to trust in the accuracy of AI’s output. Furthermore, 20% cited a lack of interoperability with existing enterprise legacy systems as a major hurdle.

For an industry where a single misplaced decimal point or erroneous transaction can result in massive financial penalties, damaged vendor relationships, or regulatory scrutiny, the margin for error with AI is virtually nonexistent.

Chronology: From Manual Ledgers to the Promise of Autonomous Finance

To understand the current hesitation surrounding artificial intelligence, it is necessary to examine the evolutionary timeline of corporate finance technology.

The Era of Paper and Manual Entry

For decades, corporate expense reporting and spend management were defined by analog processes. Employees routinely collected paper receipts, taped them to physical sheets of paper, and submitted them to internal accounting departments for manual review. This process was prone to human error, lost documentation, and significant administrative delays, often stretching the monthly financial close timeline to its absolute limit.

The Digital Shift and Software Silos

As enterprise resource planning (ERP) software and digital spend management platforms emerged, companies began digitizing workflows. While this reduced physical paperwork, it often created isolated data silos. Finance teams found themselves managing disconnected systems for corporate cards, receipt uploads, travel bookings, and invoice processing.

The Advent of Modern Automation

In recent years, spend management providers began introducing basic rules-based automation. These tools could flag duplicate receipts or enforce preset company spending policies, reducing some of the manual friction. However, these systems still required heavy human oversight and lacked the contextual reasoning capabilities associated with modern generative AI and machine learning models.

The AI Inflection Point and Current Hesitation

Today, the finance industry stands at a critical juncture. Generative AI and advanced machine learning agents have matured to the point where they can analyze unstructured data, generate complex predictive reports, and autonomously flag anomalies. Yet, because these capabilities are relatively new to the enterprise landscape, finance leaders find themselves caught between the promise of hyper-efficiency and the deeply ingrained instinct to manually verify every financial movement.

Supporting Data: Breaking Down the PEX Benchmark Report

The PEX survey of 687 finance and operations leaders offers granular insights into how organizations are approaching, or shying away from, artificial intelligence integration.

• Adoption Rates: 31% of surveyed leaders are currently utilizing AI in some capacity, while 66% are interested in doing so.
• Autonomous Trust: Only 28% feel comfortable allowing AI systems to make routine financial decisions independently.
• The Reporting Chasm: A 47-point gap exists between those wanting AI-generated financial reports (61%) and those actually using them (14%).
• Primary Impediments:
  • 36% cite a lack of trust in AI output accuracy.
  • 20% cite interoperability issues with legacy systems.
  • Corporate inertia and entrenched manual processes round out the remaining barriers.
• The ROI of Early Adoption: Among the 340 respondents who are actively piloting or utilizing AI tools:
  • 69% reported a measurable reduction in manual review time.
  • 51% reported a faster month-end financial close.

These metrics suggest that while the technology successfully delivers on its efficiency promises for early adopters, the broader market remains trapped in a state of cautious observation.

Official Responses and Expert Insights

To bridge the gap between AI aspiration and enterprise adoption, industry leaders are urging a fundamental shift in how finance departments evaluate automation. Toffer Grant, CEO and founder of spend management platform PEX, emphasizes that the core issue preventing widespread adoption boils down to a single word: trust.

"Though finance teams might be intrigued by AI, they still want to be able to have control over financial processes," Grant said in an interview with CFO Dive. He noted that integrating automation into traditional workflows inevitably creates a "level of tension" that individual professionals must work through.

Grant brings deep industry perspective to the discussion, having founded PEX in 2007 after serving in senior executive roles, including Vice President of Sales at TSYS Prepaid and Clarity Payment Solutions. Drawing from nearly two decades in the spend management sector, Grant understands why early-stage adopters are hesitant to hand over critical operational keys.

"I think a lot of the folks who are kind of on the early end of their adoption are not going to let a tool like PEX just pay a $15,000 invoice," Grant explained. "You have the vendor relationship to manage. You need to know that the system isn’t going to pull $150,000 instead of $15,000."

Rather than diving headfirst into high-stakes automated payments or autonomous financial structuring, Grant advocates for securing "easy wins" that lay the psychological and operational foundation for trust. These low-risk applications include using AI to review lengthy credit card statements, parse unstructured receipt data, and automatically apply tracking tags for internal reporting.

By deploying AI agents to handle the frustrating, repetitive aspects of compliance—such as tracking down employees for missing receipts—organizations can alter the internal dynamic of the finance department.

"Now, what somebody is doing is getting very comfortable on relying on a platform to do the annoying work of being the enforcer, and also the annoying work of doing the chase down," Grant noted.

Implications: The "Crawl, Walk, Run" Roadmap for Finance Leaders

The reluctance of finance teams to fully embrace artificial intelligence carries significant strategic implications. Organizations that fail to adopt automation risk falling behind competitors in operational speed, data-driven decision-making, and labor productivity. Conversely, rushing into complex AI deployments without establishing internal governance can lead to catastrophic financial errors and fractured vendor relationships.

To resolve this tension, PEX’s benchmark report outlines a structured, phased implementation strategy known as the "crawl, walk, run" approach. This methodology is designed to help businesses at varying stages of digital maturity build confidence and technical competence incrementally.

1. The "Crawl" Stage: Low-Lift, Zero-Project-Team Capabilities

For organizations at the beginning of their AI journeys—where the report notes that 77% currently run no automation tools at all—the priority should be simplicity. Finance teams should target single-use, low-risk administrative burdens that require no dedicated IT project team to deploy. Automating receipt uploads, standardizing expense categorization, or flagging simple policy violations serve as ideal starting points. These initial steps remove the friction of manual data entry without exposing the firm to financial risk.

2. The "Walk" Stage: Expanding Interoperability and Workflow Integration

Once basic trust in AI accuracy is established through successful low-risk deployments, organizations can progress to the "walk" phase. Here, finance leaders should focus on integrating AI tools with existing enterprise software, addressing the interoperability hurdles cited by 20% of survey respondents. During this phase, AI can begin assisting with preliminary cash flow analysis, mid-level spend variance reporting, and routine audit documentation preparation.

3. The "Run" Stage: Autonomous Operations and Advanced Forecasting

In the final phase, organizations can leverage AI for complex, high-impact financial workflows. With a proven track record of accuracy, established internal trust, and seamless system interoperability, finance teams can safely explore AI-generated financial reports, predictive cash flow modeling, and autonomous vendor payment execution.

The Path Forward

The narrative surrounding artificial intelligence in corporate finance is shifting away from breathless hype toward pragmatic evaluation. While the path from a paper receipt to an autonomous ledger is paved with understandable skepticism, the data is clear: organizations that successfully navigate the "want-versus-have" gap are reaping tangible rewards, including dramatically reduced review times and accelerated financial closes.

Ultimately, artificial intelligence is not positioned to replace the financial professional, but to serve as an indispensable copilot. By adopting a methodical, trust-building approach to technology integration, finance leaders can finally bridge the divide between what AI promises and what their operations are ready to deliver.