As AI takes on more of the repetitive preparation work involved in reconciliations, journal entries and the month-end close, many accountants will spend more time reviewing outputs, investigating exceptions and deciding what is ready to approve.
That transition can be valuable, but it is not always easy.
When you prepare something yourself, you know exactly where the numbers came from and how each step was completed. Reviewing work produced by an AI system requires a new skillset. You need to know what to check, when to investigate further and when there is enough evidence to move forward.
That ability develops with practice. Accountants do not need to trust AI without skepticism or stop paying attention to the nuanced details. They need clear review standards, access to supporting evidence and enough time to become comfortable applying their judgment in a new way.
The following tips can help accounting leaders support that transition while giving their teams the confidence to review and approve AI-generated work responsibly.
Recognize that reviewing is a different skill
It can be tempting to think of reviewing and approving as simpler versions of preparation. After all, the system has already completed much of the initial work.
In reality, reviewing requires a different way of thinking.
A preparer is usually focused on completing a process correctly. They gather the required information, follow a series of steps, make calculations and ensure the final output ties out.
A reviewer needs to step back and assess the result more broadly. They may need to ask:
- Does this output make sense based on what happened in the business?
- Is the underlying data complete and reliable?
- Were the correct accounting rules and policies applied?
- Are there unusual transactions or unexpected changes?
- Is there enough supporting evidence to approve the work?
- Does anything require additional investigation or escalation?
This kind of judgment becomes easier with experience. Accounting leaders should give people room to practice it rather than expecting them to feel fully confident from the first day.
Start with familiar, lower-risk work
One of the best ways to help accountants build confidence is to start with processes they already understand well.
A familiar workflow gives the reviewer a useful point of comparison. They know what a typical result looks like, which issues tend to arise and where errors are most likely to appear.
Good starting points may include processes that are:
- Repetitive and well documented
- Based on clear accounting rules
- Supported by reliable data
- Relatively easy to verify
- Lower in value or financial risk
- Already familiar to the person reviewing them
For example, an accountant may begin by reviewing AI-generated work for a straightforward payment reconciliation before moving into a more complex process involving estimates, unusual transactions or significant judgment.
Starting small does not mean lowering standards. It gives people a safer environment in which to learn what effective accounting reviews look like.
Explain what reviewers should look for
“Review the output” is not a complete instruction.
Without more guidance, some accountants may perform only a quick reasonableness check. Others may redo the entire process manually because that feels like the safest way to confirm the result.
Clear review criteria can help people find a more useful middle ground.
Accounting leaders should define:
- Which data points or calculations must always be checked
- What documentation should accompany the output
- Which variances fall within an acceptable range
- What types of exceptions require further investigation
- When an item should be returned, corrected or escalated
- What must be documented before approval
- Which decisions still require a manager or controller
A checklist can be especially helpful at the beginning. It gives reviewers a repeatable process while they develop stronger pattern recognition and professional judgment.
Over time, the checklist can become less detailed as the reviewer becomes more comfortable with the workflow.
Help accountants review the process without repeating it
Many people initially respond to AI-generated work by recreating it manually.
That instinct is understandable. Completing the same calculations again can feel like the most reliable way to make sure nothing was missed.
However, if every output must be rebuilt from the beginning, the accounting team receives very little benefit from automation.
The goal is to help reviewers evaluate whether the process worked as intended without duplicating every preparation step.
That may include reviewing:
- The source and completeness of the input data
- The instructions or accounting rules used by the system
- Material changes from previous periods
- Transactions that fall outside normal patterns
- Exceptions identified by the system
- Changes made after an initial review
- The relationship between the output and its supporting evidence
This is similar to reviewing a control. The accountant is not expected to reproduce every transaction independently. They are determining whether the process was reliable and whether any specific items require closer attention.
Make the work easy to trace
It is difficult to approve an output confidently when you cannot see how the system reached it.
AI-generated work should not arrive as a final number with no explanation or supporting trail. Reviewers should be able to move from the output back to the underlying evidence.
For a proposed journal entry, that may include access to:
- The source transactions
- The relevant contracts, invoices or supporting files
- The calculation or allocation method
- The account mapping
- The accounting policy applied
- Similar entries from previous periods
- Any assumptions used by the system
- A record of changes and prior approvals
This level of visibility helps reviewers investigate concerns without starting over. It also makes it easier to explain how the work was completed to managers, auditors and other stakeholders.
Traceability is important for trust, but it is also a practical requirement for maintaining strong controls.
Use exceptions as teaching opportunities
Exceptions are often where accountants learn the most.
When an unusual transaction appears, teams have an opportunity to discuss why it was flagged, what information was missing and how the issue should be resolved.
Managers can use these cases to ask:
- What made this transaction different?
- Did the AI system apply the correct rule?
- Was additional business context needed?
- Should the output be accepted, changed or rejected?
- Does the workflow need a new rule or control?
- How should a similar case be handled next time?
These conversations help accountants understand both the strengths and limits of the system.
They also build pattern recognition. Experienced reviewers often notice that something does not look right because they have seen many similar cases before. Accountants moving into review roles need the same exposure, even if they are spending less time preparing every item manually.
Increase approval responsibility gradually
Accountants do not need to move from preparing work to independently approving complex outputs all at once.
A gradual transition can help people build confidence while giving managers continued visibility into the quality of their decisions.
The progression may look something like this:
- Compare AI-generated work with previously completed manual work
- Review outputs using a structured checklist
- Identify and document exceptions
- Recommend whether the output should be approved
- Approve routine items within defined thresholds
- Take on more complex or judgment-heavy workflows
- Help improve the rules, controls and review process
The exact steps will vary by organization and role. What matters is that people have a clear path toward greater responsibility.
This also makes it easier to identify where someone needs additional training or support before their approval authority expands.
Make it safe to question the AI system
Accountants should not feel pressured to approve an output simply because it was generated by AI.
Strong review depends on professional skepticism. People need to feel comfortable pausing the workflow, asking for more evidence or escalating something that does not make sense.
Accounting leaders can reinforce that:
- AI-generated work is not automatically correct
- A reasonable concern will be taken seriously
- Reviewers should document uncertainty rather than ignore it
- Repeated errors should lead to changes in the workflow
- Material or unusual items may require additional human review
- Approval should reflect the reviewer’s judgment, not pressure to move quickly
The objective is not complete trust or complete distrust. It is calibrated trust based on the quality of the data, the strength of the controls and the evidence available for each decision.
Protect opportunities to learn the fundamentals
Preparation has traditionally been an important way for junior accountants to learn.
By completing reconciliations, building schedules and posting entries, they see how transactions move through the ledger and affect financial reporting. They also gain an understanding of common errors, business processes and accounting policies.
If too much preparation work is automated without a plan for training, junior team members may eventually be asked to review processes they have never fully understood.
Organizations can protect foundational learning by:
- Walking through the logic behind automated workflows
- Reviewing representative transactions together
- Rotating employees through different areas of the close
- Using exceptions as case studies
- Comparing current outputs with prior-period work
- Providing access to policies and process documentation
- Allowing employees to complete selected tasks manually during training
- Explaining how individual entries affect the broader financial statements
The goal is not to preserve repetitive work simply because it has always been done manually. It is to make sure future reviewers understand the work well enough to evaluate it.
Redefine what good performance looks like
The shift away from preparation can raise an uncomfortable question: If an accountant completes fewer manual tasks, how will their contribution be recognized?
This is especially important for staff and senior accountants who may have traditionally demonstrated their value through volume, speed and accuracy.
As roles change, performance measures should change with them.
Strong performance may increasingly include:
- Identifying meaningful exceptions
- Resolving issues efficiently
- Applying accounting policies consistently
- Maintaining clear review documentation
- Escalating risks appropriately
- Improving controls and workflows
- Helping the team close more reliably
- Recognizing patterns that require further investigation
- Making well-supported approval decisions
Leaders should communicate these expectations clearly. People need to understand that their value is not disappearing because they are doing less manual preparation.
Their expertise is being applied differently.
Involve preparers in designing the new workflow
The people who perform a process today often know its complications better than anyone else.
They know which source files arrive late, where data quality problems tend to appear, which exceptions recur and which parts of the documented process do not reflect what happens in practice.
That experience should inform the design of AI-assisted workflows.
Preparers can help:
- Define the rules the system should follow
- Identify common and uncommon exceptions
- Test outputs against real scenarios
- Determine what evidence reviewers need
- Set appropriate approval thresholds
- Document escalation paths
- Identify areas where human judgment remains essential
Involving the team can improve the quality of the workflow while making the change feel more collaborative.
Instead of being told that a system will replace part of their existing process, accountants can help shape how the new process works.
Give managers support, too
Staff and senior accountants are not the only people adjusting.
Accounting managers, assistant controllers and controllers may also need to change how they supervise work, provide feedback and evaluate performance.
Managers may need to learn how to:
- Review the quality of an employee’s judgment
- Coach someone through an uncertain approval decision
- Determine when an exception reflects a training issue or a system issue
- Delegate approval authority appropriately
- Maintain consistent standards across the team
- Balance efficiency with financial control
- Explain new career paths and development opportunities
Managers should not be expected to solve all of these questions informally. They need clear policies, defined approval structures and support from leadership.
Treat confidence as something that develops over time
Accountants may understand the purpose of the new workflow before they feel fully comfortable using it.
That is normal.
Confidence often develops through repeated experience: reviewing an output, investigating an exception, reaching a decision and seeing that the process worked as intended.
It can also grow when accountants see that they are still able to exercise judgment, ask questions and maintain ownership of the final result.
The most effective change management does not tell people to trust the technology. It gives them the information, controls and experience they need to decide when trust is warranted.
Moving toward a different kind of ownership
AI may reduce the amount of time accountants spend gathering data, completing repetitive steps and preparing routine work. It does not remove the need for accounting expertise.
Instead, that expertise becomes even more important when evaluating outputs, resolving exceptions and deciding what can be approved.
Moving from preparer to reviewer and approver can feel unfamiliar at first. For some accountants, it may even feel like stepping away from the work that helped them build confidence and establish their value.
With clear standards, transparent evidence, gradual responsibility and supportive leadership, teams can make that transition without sacrificing control.
Accountants are not being asked to care less about the details. They are learning how to direct their attention toward the details that require their experience and judgment most.
