AI Trust in Finance: The Gap Holding Back Your 2026 Projects

The executive team is asking for an AI plan for accounts receivable. Meanwhile, the team is still spending their month-ends manually matching bulk payments.

Everyone wants to automate. No one wants to be the first to let an agent write to the general ledger. This tension now has a name and some numbers.

“More than 8 out of 10 respondents expect their organization to further automate accounts receivable. Yet, 80% currently use no AI in their 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 divide between the automation finance teams are calling for and what they are actually willing to put into production. The survey conducted by the NACM in collaboration with BlackLine (2026) among finance and credit professionals measures it quite clearly.

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

And yet, not a single respondent says they are very comfortable entrusting customer interactions to AI. Not one. The demand for automation is very real; the trust to deploy it is lagging behind.

Why are your teams hesitant to trust AI?

Because their caution is based on lived experience, not a fear of change. The global study by the University of Melbourne and KPMG (2025), conducted with over 48,000 people in 47 countries, quantifies the paradox: 66% of people use AI regularly, but only 46% say they are ready to trust it.

The same study reveals why. 66% of employees rely on AI results without verifying their accuracy, and 56% say they have made mistakes at work because of AI.

A controller who has seen an automatic matching go wrong knows exactly what a false positive costs to fix.

On the project side, Gartner (2025) predicts that more than 40% of agentic AI projects will be cancelled by the end of 2027, largely due to inadequate risk controls. Team distrust is not an irrational hurdle: it is a governance signal that must be taken seriously.

How do you build trust without freezing your AI projects?

By making controls visible: a written scope of action, human approval thresholds, a complete audit trail, and an initial bounded process that proves value. Trust cannot be mandated in a presentation; it is built through mechanisms the team can verify for themselves.

We have detailed these four controls in our article on AI agent governance in finance. The principle can be summed up in one sentence: if it isn't named in the scope document, the agent doesn't do it.

Choosing the right first process matters as much as the controls. Cash application remains the most frequent candidate in the mandates PlanAxion leads in accounts receivable: a bounded problem, high volumes, years of ERP history, and a result measurable in hours and dollars. This is precisely the profile of the cash application solutions that PlanAxion implements.

Benchmarks to situate your organization in 2026:

• More than 8 out of 10 respondents expect more automation in accounts receivable, but 80% use no AI (NACM and BlackLine, 2026)
• No respondent says they are very comfortable entrusting customer interactions to AI (NACM and BlackLine, 2026)
• 66% of people use AI regularly, but only 46% say they are ready to trust it (University of Melbourne and KPMG, 2025)
• More than 40% of agentic AI projects will be cancelled by the end of 2027 (Gartner, 2025)
• Manual cash application still consumes about 25% of finance team resources (HighRadius, 2026, vendor data)

A note on methodology: these figures come from public surveys with varying scopes. None describe your specific situation; they provide an order of magnitude, not a guarantee of results.

Trust in AI isn't something you ask for; it’s something you audit. A trustworthy agent is one where every action can be verified.

Where do you start to move from skepticism to your first measurable win?

Start with a single high-friction process, before-and-after metrics, and approval thresholds validated by the finance department. A well-defined project gives the team a concrete reason to trust: they see what the agent does, what it is not allowed to do, and what gets escalated to a human.

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

Frequently asked questions about trust in AI for finance

What is the AI trust gap?

It is the gap between desired automation and actual deployed automation. In accounts receivable, more than 8 out of 10 respondents expect to automate more, yet 80% use no AI at all, according to the 2026 NACM and BlackLine survey. The gap is bridged by verifiable controls, not promises.

Why don't finance teams trust AI?

Because experience has taught them to be cautious: 56% of employees say they have made mistakes at work due to AI, according to the University of Melbourne and KPMG (2025). In finance, an incorrectly applied entry impacts financial statements. Caution is a sign of good governance, not a rejection of progress.

How do you build trust in AI within an organization?

With four visible controls: a written scope of action, thresholds beyond which a human must approve, a complete audit trail, and a periodic review of exceptions. A first bounded and measurable process, such as cash application, allows you to demonstrate value before expanding the project's scope.

Which financial process should you entrust to AI first?

A repetitive, high-volume process with a measurable outcome. Cash application is the classic example: matching payments to open invoices, including partial or bundled payments, while only escalating exceptions to a human. Gains are measured in DSO days and hours saved.