Why Finance Teams Are Hesitant About AI — and What They’re Missing

The Big AI Question in Finance 

AI dominates business headlines. It writes emails, analyzes contracts, and powers fraud detection tools. But inside the office of the CFO, the conversation feels more cautious than revolutionary. 

Executives know the potential is enormous. McKinsey estimates AI could add up to $1.4 trillion in annual value to finance and risk management functions. Yet adoption lags. A Deloitte CFO survey found that while 85% of finance leaders see value in AI, fewer than 40% have put it to work at scale. 

This hesitation is understandable. Finance runs on precision, control, and trust. A misplaced decimal or an unverified vendor can ripple into millions lost. But while caution protects, excessive hesitation carries its own cost: slower cycles, higher risk, and missed opportunities to elevate finance from a support function to a strategic driver. 

The core question isn’t whether AI belongs in finance. It’s how to overcome hesitation and capture value without sacrificing trust. 

The Problem: When Hesitation Becomes a Liability 

To understand why hesitation has become costly, consider what's happening beneath the surface. On paper, the risks of waiting look small. After all, finance teams are still closing the books, paying vendors, and reporting results without AI. But underneath, the cracks are widening. 

Imagine this: it’s 4:45 p.m. on a Friday. A controller gets a message from Treasury asking why a payment to a key supplier is delayed. The AP team points to an invoice stuck in someone’s inbox, waiting for approval. No fraud, no compliance violation, just a bottleneck. 

Multiply that by dozens of invoices and hundreds of vendors, and the pattern becomes clear: 

• Cash flow visibility shrinks because approvals lag. 
• Supplier confidence erodes when payments aren’t predictable. 
• Staff capacity dwindles as teams spend hours chasing, keying, and reconciling. 

Hesitation around AI keeps these problems alive. While competitors deploy automation to clear bottlenecks and detect anomalies, cautious teams accept delays and errors as the “cost of doing business.” That cost compounds over time, eroding finance's credibility with leadership. 

Why Finance Teams Are Hesitant About AI 

Risk, Trust, and Control 
Finance leaders carry fiduciary responsibility, and every control weakness is a potential headline. That’s why the biggest hesitation around AI comes from risk. Leaders worry about explainability.  

If AI flags a supplier payment as fraudulent, can it show why? And if it can’t, how will that stand up in an audit? 

Regulators also expect a clear chain of evidence, and without audit-ready trails, AI becomes a liability instead of an asset. Fraud risk is another concern. Business email compromise (BEC) scams already cost organizations billions annually, and leaders fear that poorly governed AI could widen the attack surface. 

These concerns aren’t hypothetical. Without transparency and controls, even accurate AI outputs can diminish confidence. Finance leaders hesitate to rely on tools they can’t fully explain to auditors, regulators, or their own boards. 

ROI, Data, and Integration 
Even when leaders feel more comfortable with risk, practical roadblocks remain. ROI is uncertain: one survey found the median return from finance AI pilots is just 10 percent, and more than a third reported limited or no gains. Data quality also limits progress. Many teams still grapple with duplicate vendor records, outdated master data, and fragmented ERP systems. 

Integration is another sticking point. If AI can’t plug smoothly into ERP, payables, or Treasury systems, its impact is capped, and finance leaders fear adding another layer of tech debt. And after years of ERP upgrades and digital projects, many leaders worry AI will feel like just one more transformation without clear payback. 

As a result, many CFOs take a defensive stance: approve limited pilots, keep expectations low, and wait for the technology to mature. But in the meantime, competitors are moving forward. 

Real-World Proof Points: What Peers Are Doing 

While some finance teams hesitate, early adopters are pulling ahead and the performance gap is widening. 

• Adoption is accelerating: Gartner reports that 58% of finance functions now use AI, up 21 percentage points in just one year. 
• Budgets are growing: From 2024 to 2025, 79% of CFOs boosted AI budgets, and 94% anticipated generative AI would deliver measurable benefits in at least one finance activity.  
• Best practices are rare: McKinsey research shows less than a third of companies follow AI adoption best practices such as governance and scaling. 

Early use cases highlight how finance teams are beginning to apply AI in practical ways: 

• AI-based invoice capture that reduces manual entry and speeds approvals. 
• Fraud detection tools that flag duplicate vendors before payments go out. 
• Workflow automation that turns multi-week approvals into days. 

The lesson is clear: hesitation is common, but momentum is building for teams that put the right guardrails in place.

The Solution: How to Bridge Hesitation and Impact 

Start with Defined Use Cases 
Broad AI strategies feel overwhelming. Start small with targeted pain points: invoice capture, duplicate detection, or exception routing. Success in one area builds trust for the next. 

Build Guardrails and Explainability 
AI must be auditable. Every output should leave a trail: what was flagged, why it was flagged, and what rules or models informed the decision. Finance teams gain confidence when they can trace outcomes back to logic. 

Establish Cross-Functional Governance 
Bring finance, IT, and risk together. Pilots should run with oversight, documented results, and clear checkpoints before scaling. Governance isn’t bureaucracy, but insurance against missteps. 

Clean the Data Foundation 
AI amplifies whatever it touches. Standardizing vendor master data, aligning GL codes, and consolidating payment processes ensure AI works on solid ground. 

Communicate Value Early and Often 
Dashboards, benchmarks, and quick wins help secure executive sponsorship. When CFOs can show faster cycle times, fewer errors, and improved fraud defenses, AI moves from experiment to necessity. 

This approach transforms AI from a leap of faith into a disciplined evolution. It builds on finance’s strengths in precision, governance, and control rather than threatening them. 

What Finance Teams Are Missing by Waiting 

The risks of adoption are real, but the risks of waiting are just as pressing: 

• Competitor advantage: Organizations that scale AI early will run leaner, faster, and with fewer errors. 
• Staff burnout: Manual rekeying and rework keep finance talent bogged down, fueling turnover. 
• Lost savings: Missed early-payment discounts and mounting fraud losses add up quickly. 
• Weaker strategic voice: Finance leaders who lack real-time visibility into spend and risk cannot guide executives with confidence. 

Hesitation feels safe in the short term, but it leaves finance vulnerable in the long term. 

From Hesitation to Confidence 

For executives wondering when to act, the answer is clear: the longer the hesitation, the greater the opportunity cost. 

Finance leaders are right to scrutinize AI. Risk, confidence, and ROI all deserve careful attention. But extended hesitation carries its own cost: slower operations, rising fraud exposure, and diminished influence at the leadership table. 

The solution isn’t blind adoption. It’s disciplined, transparent, and governed integration. Start small, add guardrails, and scale based on proven outcomes. Teams that move in this direction already see the difference: faster closes, stronger fraud defenses, and more capacity for strategic work. 

Looking ahead, AI will play a central role in areas that go well beyond invoice capture and fraud detection, reshaping forecasting accuracy, scenario planning, and cash flow visibility. 

These aren't distant experiments, but the next frontier for finance teams that establish strong foundations today. Leaders who move early not only solve today's bottlenecks but position finance as a driver of strategic insight in tomorrow's boardroom. 

Wondering what hesitation really costs?  
AI FOMO: Why Finance Leaders Can’t Risk Waiting on AP Automation explores how waiting puts finance at a competitive disadvantage and why early movers are already seeing measurable impact.