by Katie Cavanaugh
We’ve moved far beyond whether AI is an “if”. We’re deep into “how”.
When it comes to AI usage in finance, the real question is how to adopt it without compromising governance, transparency, and accountability.
This shift is happening much faster than many organizations expected.
AI is already influencing reconciliations, close processes, journal entry analysis, reporting workflows, and financial documentation. In many cases, teams are experimenting with AI informally by using general-purpose models to accelerate repetitive tasks, summarize data, or assist with analysis.
At first glance, the results can appear promising. Teams move faster. Outputs look polished. Manual work decreases.
However, finance leaders are beginning to encounter a new category of operational risk: systems that influence financial outcomes without fully exposing how those outcomes were produced.
The challenge is not simply whether AI makes mistakes. It’s whether organizations can explain, validate, reproduce, and defend AI-assisted financial workflows when scrutiny inevitably arrives.;
AI Doesn’t Crash and Burn, It Drifts
One of the more revealing recent examples came from AccountingBench, a benchmark created by Penrose that tested leading AI models against a year of real financial data from a SaaS company.
The findings were striking.
Some frontier models initially performed close to CPA-level accuracy. But over time, material errors accumulated. In certain cases, models invented transactions to force reconciliations to balance when they could not fully resolve discrepancies.
The issue wasn’t that the models failed immediately. It’s that they appeared to work—until they didn’t.
That distinction matters enormously in finance.
Most finance workflows are not evaluated based solely on whether an output “looks correct.” They must also be explainable, reproducible, and defensible under review. A reconciliation that balances for the wrong reason is not a successful reconciliation. A journal entry recommendation without traceability is not a controlled process.
This is where the conversation around AI in finance is beginning to mature. Accuracy alone is no longer enough.
Finance organizations need to understand how conclusions are reached, what source data was used, whether outputs can be reproduced consistently, and how exceptions are handled over time.
Why Finance Requires a Different Standard for AI
Many AI tools were designed for environments where speed and fluency matter more than procedural defensibility. Of course, finance operates differently.
Financial workflows exist inside systems of accountability:
- external audits
- SOX controls
- regulatory oversight
- audit committee scrutiny
- internal governance requirements
In that environment, explainability is not optional.
Finance teams must be able to answer fundamental questions:
- What data was used?
- How was it transformed?
- Why was this conclusion reached?
- Can another reviewer reproduce the same result?
- What controls exist around the process?
- What standards and guidelines were referenced and how?
These are not edge-case concerns. They are core requirements of modern controllership.
The growing use of AI introduces tension because many general-purpose models are probabilistic by nature. They generate responses based on patterns and likelihoods, not deterministic accounting logic. That can create problems when workflows depend on consistency, traceability, and repeatability across reporting periods.
For example, a model may produce a reasonable reconciliation one month, then behave differently when transaction patterns change, new accounts are introduced, or source data becomes more complex. In isolation, each output may appear acceptable. Over time, however, drift accumulates.
That creates risk not only operationally, but from a governance perspective as well.
Finance leaders are ultimately accountable for the integrity of financial processes, especially when AI contributes to the work.
The Rise of “DIY AI” in Finance
At the same time, many organizations are experimenting with AI outside formal finance technology environments.
Controllers and finance teams are increasingly:
- building prompt-driven workflows
- testing reconciliations with general AI tools
- connecting models to spreadsheets or exports
- creating lightweight automations internally
The appeal is understandable. These experiments are fast, inexpensive to start, and often produce immediate productivity gains.
But the long-term governance implications are becoming harder to ignore.
One of the most common failure points is that these workflows are rarely institutionalized. The prompts evolve informally. Logic lives on individual laptops. Documentation becomes inconsistent. When source systems or chart-of-account structures change, workflows break in unpredictable ways.
Eventually, finance teams find themselves recreating manual review processes to compensate for gaps in reliability and control. The productivity gains evaporate as a new layer of review is added in: auditing the AI.
There is also a growing realization that scaling AI inside finance operations is not simply a matter of adding more prompts or larger models. Transaction-heavy financial workflows require infrastructure designed for:
- data lineage
- version control
- workflow reproducibility
- access governance
- evidence retention
- deterministic processing where appropriate
Without those foundations, organizations risk creating shadow AI environments that sit outside existing governance frameworks.
In many ways, the situation resembles the spreadsheet sprawl problems finance organizations spent years trying to control—except now the logic itself may be opaque.
What CFOs Should Consider When Evaluating AI
As AI adoption accelerates, finance leaders should begin evaluating AI platforms less like productivity tools and more like financial infrastructure.
The key question is no longer, “Can this automate a task?”
It is: “Can this support enterprise-grade financial governance?”
Several evaluation criteria are becoming increasingly important.
1. Traceability and Explainability
Finance teams need visibility into how outputs are generated.
AI-assisted workflows should allow users to trace conclusions back to source data, supporting documentation, and applied logic. If a recommendation cannot be reconstructed or explained, it becomes difficult to defend under scrutiny.
2. Deterministic Workflow Design
Not every finance task should rely on probabilistic AI behavior.
There is a growing distinction between workflows that benefit from judgment-oriented AI assistance and those that require deterministic execution. Reconciling large transaction populations, validating structured data, and enforcing workflow controls often demand repeatable logic rather than generative flexibility.
3. Data Governance and Integrity
AI systems touching financial data must operate within robust governance frameworks.
That includes:
- controlled integrations with ERP systems
- role-based access controls
- audit logs
- retention policies
- clear data lineage
- version history
These capabilities are no longer “nice to have.” They are becoming baseline expectations for enterprise finance environments.
4. Workflow Reproducibility
Finance organizations depend on consistency across reporting periods.
If workflows cannot produce stable, explainable outcomes month after month, operational risk increases quickly. AI systems should strengthen process integrity, not introduce variability that requires additional manual oversight.
5. Human Accountability
Perhaps most importantly, finance leaders must ensure that AI enhances professional judgment rather than obscures it.
The goal is not to remove humans from financial workflows. It is to create systems where humans maintain visibility, control, and accountability throughout the process.
That distinction matters.
Reviewing an AI-generated output after the fact is not the same as understanding how it was produced.
AI Governance Is Becoming a Finance Leadership Responsibility
Historically, AI governance was often viewed as a technology or IT concern.
That’s changing rapidly.
As AI becomes embedded within financial workflows, governance responsibility increasingly sits with finance leadership itself. CFOs, CAOs, and controllers will be expected to demonstrate not only that AI improves efficiency, but that the surrounding controls remain trustworthy and defensible.
Boards, auditors, regulators, and audit committees are likely to ask more direct questions in the years ahead:
- How are AI-assisted outputs validated?
- Can financial conclusions be reconstructed?
- What controls exist around AI usage?
- How are exceptions monitored?
- Who remains accountable for final decisions?
Finance organizations that treat AI adoption purely as a productivity initiative may eventually discover they have created governance gaps faster than they created efficiency gains.
The organizations that succeed will likely be those that approach AI differently: not as a shortcut around financial controls, but as infrastructure that must meet the same standards of transparency, integrity, and accountability as the financial systems themselves.
Financial workflows have always been built on accountability.
AI won’t just change that standard, it will raise it.
See What Auditable AI Looks Like in Practice
As AI becomes embedded across accounting and finance workflows, governance, traceability, and explainability can’t be added after the fact. See Trullion’s Auditable AI platform designed specifically for finance teams that need to move faster without compromising control.
Explore Auditable AI for accounting and finance teams.
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