The Delegation Era Starts with Trust

According to Gartner, 84% of surveyed finance organizations have implemented AI or plan to, yet only 7% report a high or very high impact from their investment. That gap is not a technology problem. It’s a trust problem.

AI isn’t leaving the finance function any time soon. OpenAI and Anthropic are pushing further into financial services, and with this shift, AI is moving deeper into day-to-day workflows. For CFOs, the pressure is on to put these tools to work, scale productivity, and expand adoption across their teams.

Why the gap between AI adoption and AI impact? Finance teams require precision and aren’t always comfortable relying on newer technology to complete tasks correctly, especially when the results affect financial reporting or planning. That hesitation is understandable. There’s a difference between asking AI to prepare an analysis and trusting it to change the numbers.

For finance organizations to confidently delegate real financial work to AI, they need clear guardrails around what the technology can access, what it can do, and when human oversight is required. Understanding where that trust breaks down is the first step toward putting those safeguards in place.

“Close Enough” Doesn’t Work in Finance

There are a couple of reasons why finance teams might not place much faith in AI. Most of it centers on the nature of the finance function and how that intersects with the way AI systems are designed. It’s easier to explain this with an example:

Take someone who works in the controller’s office. For the last 15 years, they’ve completed the same 82 steps each week. Each step requires precision, which they consistently deliver. Those established processes have earned their trust by producing accurate, consistent results. If AI is going to take over parts of that work, finance teams need to understand how it will maintain the same standards and where human oversight is still necessary.
Now introduce AI into the mix. Large language models (LLMs) are probabilistic. An AI platform predicts outputs based on the given requirements. Yet, finance requires deterministic operations and accuracy. A “close enough” output won’t suffice. That’s why, according to KPMG’s 2026 AI in Finance survey, 48% of surveyed companies cite the accuracy of AI-generated financial outputs as a top concern. That person in the controller’s office sees these discrepancies and thinks they can do the job better.
But, for argument’s sake, let’s say that person in the controller’s office decides to try AI out. The chatbots respond like humans, so the employee talks to them like one. But at the end of the day, they aren’t human and they don’t understand the same rules and boundaries we do. Ask a financial analyst to review a client’s financial statements and put together a report, and they understand what information they should and shouldn’t use. But without explicit instructions, an AI agent won’tnecessarily recognize those boundaries. Its only job is to complete the task, which can easily put sensitive financial or personal information at risk if the right guardrails aren’t in place. At the end of the day, that inconsistency can lead finance teams to phase the technology out of their workflows.

So, how do finance organizations answer the demand for AI while getting their teams to use the technology? They become more thoughtful about where they use AI and how they build it into workflows. Essentially, they build guardrails that foster trust.

Structure Comes Before Delegation

Here’s a closer look at what those safeguards mean for finance teams:

1. Defining rules around data access: Finance teams need clear permissions and access controls that define how financial information can reach the model.

2. Specifying what steps AI can execute: Finance teams need to define which actions AI can perform independently and which require approval. An agent preparing a financial report, for example, should have different permissions than one updating a forecast or changing financial data.

3. Creating human-in-the-loop checkpoints: People still need to be part of the process. Finance workflows should account for a built-in review layer that can route exceptions to controllers and pause for verification before anything posts.

4. Establishing auditable records: Finance teams can only trust AI if they can explain what the technology did and why. That means clear audit trails, visibility into decision-making, and traceable outputs.

These guardrails put boundaries around technology that is probabilistic by nature and shape it into something that finance can actually use. Ultimately, they create the conditions for trust, so organizations can confidently hand over real financial work to AI. The next step is putting those safeguards into practice. Gartner says that successful AI use cases in finance are directly connected to structured and disciplined roadmaps. That means accounting and finance teams need a plan of action.

Put the Controls into Practice

AI adoption frameworks should start with creating a single source of truth. AI can’t safely operate if the underlying information is fragmented or unclean. It’ll lead to unreliable analyses and leave finance teams doing extra cleanup downstream.

Once a data foundation is in place, CFOs can define the business requirements. Technology teams can then turn those priorities into an agentic roadmap, put the right controls around it, and connect AI to live financial data.

From there, AI can move into practical applications, starting with lower-risk tasks. Think of an AI agent like a junior analyst. You give them clear instructions, review their work, and expand their responsibilities as they demonstrate they can handle the job. The same approach applies to AI. Agents can start with smaller jobs like summarizing financial documents or preparing reports. As they prove their reliability, finance teams can begin delegating more complex work, including compressing the financial close, modernizing accounts payable, or supporting tax research.

Delegation Without Losing Control

Every CFO I talk to wants the capacity AI promises. Deloitte’s Q4 2025 CFO Signals survey found that 49% of CFOs cite automating processes as a leading priority to create more capacity across the finance function.

But capacity only shows up when you delegate work to a capable party, and that takes controls. Your team can trace every action and understand every calculation. Get that right, and agents can take on more across closing, planning, and reporting processes. Your team spends less time on the close, and more time steering the business. That’s the ultimate goal: a finance function that doesn’t just report on the business, but helps lead it.

ABOUT THE AUTHOR:

Alok Ajmera is president and CEO of Prophix. He is a recognized visionary, guiding Prophix to become a global leader in financial performance management by empowering the office of finance. Alok’s passion is for building exceptional operating teams, driving customer satisfaction and inspiring colleagues to create positive change.