Vanessa Chambers is Vice President, Operations & Product Management at Martus Solutions.

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Software companies have traditionally treated trust as a matter of brand reputation, security certifications and privacy policies. Those factors still matter, but AI has fundamentally changed the equation. Organizations are accelerating fast to “win” the AI race, yet most discussions center on capabilities. What can AI automate, predict or do next?

Those are the wrong questions.

What really matters is whether users trust the actions AI takes on their behalf.

When AI is influencing budgets, transactions and financial reporting, trust becomes more important than tasks. In the AI era, trust is no longer a marketing promise; it’s a product requirement. An inaccurate recommendation in a financial workflow, for example, can create compliance issues, reporting errors or misallocated funds.

As AI becomes embedded and more autonomously driven, trust must be intentionally designed.

The AI Problem Nobody Is Talking About

One of the biggest barriers to trustworthy AI is surprisingly simple: language.

General-purpose LLMs are reasonably good at understanding everyday language, but they struggle with product-specific terminology, industry jargon and context-dependent definitions.

Take a term like "allocations." Within the world of budgeting and planning alone, that single word can carry several meanings: allocating overhead costs across departments, allocating employee costs across positions or departments, or spreading an annual total (e.g., utility expenses) in the same seasonally-driven pattern as prior years.

The AI must understand the context before it can provide a reliable answer.

For finance professionals, these distinctions matter enormously. If the AI doesn't understand the user's intent, it has two options: ask for clarification or make an assumption. Too often, today's LLMs default to the latter.

Trustworthy AI requires developers to create a deep understanding of that vocabulary and the context in which it is used. AI and LLMs need to be trained not only on general language but also on the specific terminology, workflows and business logic of the platforms they serve.

When AI Gets It Wrong, Who Is To Blame?

As AI shifts from answering questions to making recommendations or taking actions, it becomes more important to consider who is accountable when AI makes a mistake. This is the elephant in the room that product leaders and developers are not talking about enough.

In financial systems, AI is not always a passive assistant. Increasingly, it can update records, reclassify transactions, identify anomalies, even alter the general ledger.

Consider this real-world scenario: A company discovers it has been paying for software licenses for months that nobody is using. Charges for it continued for four months before anyone noticed. The team wasn't using the software, yet the expense remained in the system.

A sophisticated AI should be able to identify that anomaly and investigate further. It might ask questions like who is using this software? Which department owns it? Who has active access?

Based on that information, the AI could recommend or even execute a correction by moving the transaction to the appropriate department or flagging it for review.

But autonomy without accountability is dangerous.

Any AI capable of making changes to financial data must be able to explain its reasoning, document what it changed, record why it made the change, and understand when the situation requires human review.

Trustworthy AI is not defined by how much it can do. It is defined by how responsibly it acts.

The Human-in-the-Loop Imperative

Large language models are remarkably confident in their outputs, whether those outputs are accurate, hallucinated or based on stale data. In a low-stakes context, a confidently wrong answer is an inconvenience. In a financial workflow, it can mean misreported numbers, a misallocated budget or a compliance issue that nobody catches for weeks or months.

In many consumer applications, AI is rewarded for providing an answer, even when uncertainty exists. In finance, that approach is risky. When context is unclear, the correct response should be to ask for clarification. The goal is not to slow AI down, but to make its actions auditable, explainable and reversible.

Not all AI decisions carry the same level of risk. A $12 expense reclassification is vastly different from a $12,000 adjustment.

Rather than applying the same level of oversight to every action, organizations should use product workflows to build tiered human review processes based on materiality and risk. Low-risk, reversible actions may be automated with minimal intervention. High-impact decisions should require human approval before execution.

Human users should—and must—have the ultimate control. They should always understand what happened, why it happened and how to undo it if necessary.

Trust By Design, Not Disclaimer

Treating trust as a product feature changes how AI workflows are designed.

This means building transparency into workflows from the beginning rather than relying on disclaimers after launch. It means prioritizing explainability, audit trails, user controls and contextual language definitions alongside product performance.

The companies that succeed with AI solutioning will not necessarily be the ones with the most advanced models. They will be the ones that give users confidence in the outcomes their models produce.

This is particularly true in the nonprofit and mid-market sectors. These organizations often operate with limited resources, lean finance teams and little tolerance for risk. They are unlikely to adopt AI-powered systems they can’t fully trust.

The Bottom Line​

Trust is not a soft concept. It is an engineering decision, a design decision and, ultimately, a product strategy decision.

For these customers, the barrier to entry is not AI-powered features. It is confidence.

The product leaders who understand that distinction will shape what AI in finance looks like over the next decade. The ones who do not may discover that the smartest AI in the world is useless if users do not trust it enough to use it.

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