Trust in AI in finance: the gap stalling your automation projects in 2026

The executive committee wants an AI plan for accounts receivable. Meanwhile, the team still spends month-end matching grouped payments by hand.

Everyone wants automation. Nobody wants to be the first to let an agent write to the ledger. That tension now has a name, and numbers.

“More than eight in ten respondents expect their organization to further automate AR. Yet 80% currently use no AI in their AR processes.” Source: NACM and BlackLine, The State of AR Automation 2026 (May 2026)

What is the AI trust gap in finance?

The AI trust gap is the space between the automation finance teams ask for and the automation they actually put in production. The survey conducted by NACM in collaboration with BlackLine (2026) among finance and credit professionals measures it bluntly.

Half of respondents named reducing manual, repetitive tasks as their single top operational priority for the next 18 months. The need is immediate, not theoretical.

And yet, when asked about AI handling customer interactions, not a single respondent said they would be very comfortable with it. Not one. The demand for automation is real; the confidence to deploy it is not.

Why do your teams hesitate to trust AI?

Because their caution is built on lived experience, not fear of change. The global study by the University of Melbourne and KPMG (2025), covering more than 48,000 people across 47 countries, quantifies the paradox: 66% of people use AI regularly, but only 46% are willing to trust it.

The same study shows why. 66% of employees rely on AI output without evaluating its accuracy, and 56% say they have made mistakes in their work because of AI.

A controller who has seen an automatic match applied to the wrong invoice knows exactly what a false positive costs to fix.

On the project side, Gartner (2025) predicts that over 40% of agentic AI projects will be canceled by the end of 2027, notably due to inadequate risk controls. Team wariness is not an irrational brake: it is a governance signal worth taking seriously.

How do you build trust without freezing your AI projects?

By making the controls visible: a written scope of action, human approval thresholds, a complete audit trail and one bounded first process that proves the value. Trust is not declared in a slide deck; it is built in mechanisms the team can verify on its own.

We detailed those four controls in our article on AI agent governance in finance. The principle fits in one sentence: what the scope page does not name, the agent does not do.

The choice of first process matters as much as the controls. Cash application remains the most frequent candidate in the AR engagements PlanAxion delivers: a bounded problem, high volumes, years of ERP history and a result measured in hours and dollars. That is exactly the profile of the cash application solutions PlanAxion implements.

The benchmarks to situate your organization in 2026:

• More than 8 in 10 respondents expect further AR automation, yet 80% use no AI (NACM and BlackLine, 2026)
• Not a single respondent is very comfortable letting AI handle customer interactions (NACM and BlackLine, 2026)
• 66% of people use AI regularly, but only 46% are willing to trust it (University of Melbourne and KPMG, 2025)
• Over 40% of agentic AI projects will be canceled by the end of 2027 (Gartner, 2025)
• Manual cash application still ties up about 25% of finance team resources (HighRadius, 2026, a vendor figure)

A note on method: these figures come from public surveys whose scopes vary. None describes your specific situation; they set orders of magnitude, not promised outcomes.

Trust in AI is not requested: it is audited. A trustworthy agent is one whose every action can be verified.

Where do you start to move from wariness to a first measurable gain?

With one high-friction process, before-and-after indicators and approval thresholds validated by finance leadership. A bounded project gives the team a concrete reason to trust: they see what the agent does, what it is not allowed to do and what escalates to a human.

That is the approach of our AI workshop: 4 weeks, 5 steps (prepare, identify, prioritize, validate the data, decide and deliver), with the same filter applied to every idea: problem to solve, expected value, available data, required effort. The investment varies with scope and is confirmed during a short exploratory call.

Frequently asked questions about trust in AI in finance

What is the AI trust gap?

It is the space between the automation teams want and the automation they actually deploy. In accounts receivable, more than 8 in 10 respondents expect further automation, yet 80% use no AI, according to the NACM and BlackLine survey published in 2026. The gap closes with verifiable controls, not promises.

Why don’t finance teams trust AI?

Because experience justifies their caution: 56% of employees say they have made mistakes at work because of AI, according to the University of Melbourne and KPMG (2025). In finance, a misapplied entry commits the financial statements. Caution is a governance signal, not a refusal of progress.

How do you build trust in AI at work?

With four visible controls: a written scope of action, thresholds above which a human approves, a complete audit trail and a periodic review of exceptions. A bounded, measurable first process, such as cash application, demonstrates the value of the agent before you expand its scope.

Which finance process should you hand to AI first?

A repetitive, high-volume process with a measurable outcome. Cash application is the textbook example: matching payments to open invoices, including partial or grouped payments, with only exceptions escalating to a human. The gains show up in DSO days, recovered hours and cleaner accounts receivable data.