Introduction
Automation is often introduced as a technology initiative. A company identifies a new tool, approves the investment, and expects employees to begin using it. But buying technology does not create an automated organization.
The more important shift happens when people start looking at their work differently.
Instead of accepting repetitive tasks as part of the job, employees begin asking: Why are we doing this manually? Does this step need to happen at all? Could a system handle it? Could we standardize the process before automating it? And where should human judgment remain essential?
That mindset matters because repetitive work exists in almost every part of an organization. Employees copy information between systems, reconcile spreadsheets, prepare recurring reports, send routine follow-ups, organize documents, update databases, and move information from one workflow to another.
Individually, these tasks may seem insignificant. Collectively, they can consume thousands of hours.
For businesses looking to build an internal culture of automation, the starting point is therefore not accounting, artificial intelligence, or any particular software platform. It is about learning to recognize repetitive work wherever it exists and creating an environment that encourages employees to improve it.
Eventually, that mindset can have a particularly significant impact in accounting, where many processes recur daily, weekly, and monthly.
Automation starts with changing how people see work
The first obstacle to automation is often not technology. It is familiarity.
Employees become accustomed to the way work is done. A spreadsheet is updated because it always is. A report is manually compiled because that is how the previous report was prepared. Someone downloads information from one system and uploads it to another because no one has questioned the process.
Over time, these steps become invisible.
Creating an automation culture means making them visible again.
Employees should be encouraged to identify tasks that are repetitive, predictable, rules-based, and dependent on manual data movement. These are often good candidates for improvement.
That does not mean every repetitive task should be automated immediately. Some processes may be too complex, poorly defined, or dependent on human judgment. Others may become easier to automate after the underlying process is standardized.
The objective is to make process improvement part of everyday work rather than an occasional transformation project.
This distinction is becoming increasingly important as AI changes the nature of automation. Research from MIT Sloan argues that AI’s greatest impact may come not from improving individual tasks in isolation, but from reshaping entire workflows: changing how tasks are sequenced, grouped, and handed off between people and machines.
In other words, companies should not ask only, “What task can we automate?” They should also ask, “How should this process work if we were designing it today?”
Look Beyond Accounting First
One way to build this mindset is to begin with simple, familiar examples outside the finance function.
Consider an employee who spends part of every Friday compiling information from several systems into a recurring management report. Or an HR team that repeatedly collects the same information from employees and transfers it between forms, spreadsheets, and internal systems. Or a sales team that spends time updating customer information across multiple platforms. Or a customer service team that manually categorizes incoming requests before assigning them to the appropriate person.
None of these activities necessarily requires sophisticated artificial intelligence.
They require organizations to notice the repetition.
Once employees start identifying these patterns, automation becomes less abstract. It becomes a practical question about how work can be redesigned.
This also changes the employee’s relationship with automation. Instead of seeing technology as something imposed on them by management, employees can become participants in improving how their work gets done.
That distinction matters. A Massachusetts Business School research study on “task reallocation” describes one potential benefit of automation as shifting routine work to technology, allowing employees to spend more time on activities where human capabilities are stronger, including problem-solving and idea generation. It also found that employees using AI-based accounting software shifted a portion of their time away from routine data entry toward higher-value activities such as business communication and quality assurance.
The goal, then, is not simply fewer manual tasks. It is better use of employee time.
Standardize Before You Automate
There is an important caveat.
Automating a poor process does not necessarily make it a good process. It can simply make a poor process faster.
Before automating a workflow, organizations should understand how the work is actually being performed.
Where does the process begin? How many handoffs are involved? Which steps are duplicated? Where do errors occur? Which decisions require human judgment? What information is being entered more than once?
This process review often exposes opportunities unrelated to technology.
A redundant approval might be eliminated. Two reports might be consolidated. A data field might no longer be necessary. A process might be standardized across departments.
Only after those questions are answered does it become easier to determine which parts should be automated.
PwC’s research on finance and treasury transformation illustrates this challenge. Its 2025 Global Treasury Survey found that organizations are increasingly using AI and automation for activities such as anomaly detection and repetitive reconciliation and payment processes, while data quality, skills, and the lack of a longer-term strategy remain barriers to maturity.
That is a useful lesson beyond treasury: technology adoption does not eliminate the need for process discipline. It makes process discipline more important.
Then Look At Accounting
Accounting provides one of the clearest examples of why this mindset matters.
Financial processes contain a large amount of recurring work. Transactions have to be recorded. Accounts have to be reconciled. Supporting documentation has to be reviewed. Journal entries have to be prepared. Reports have to be generated. Balances have to be investigated.
Then the month ends—and much of the work happens again.
The month-end close is particularly well-suited to examining the relationship among repetitive work, process standardization, automation, and human judgment.
A finance team may spend the final days of the month collecting information, following up on outstanding items, reconciling accounts, preparing journal entries, reviewing variances, and assembling financial reports.
Some of these activities require experienced accounting judgment.
Many others are repetitive.
The challenge is distinguishing between the two.
The Month-End Close: Where Repetition Becomes Expensive
Month-end accounting is laborious partly because it compresses many recurring activities into a limited window.
Bank accounts need to be reconciled. Accounts receivable and accounts payable balances need to be reviewed. Accruals and prepaid expenses may need to be updated. Fixed assets may need to be accounted for. Intercompany balances may need to be reconciled. Recurring journal entries may need to be posted. Supporting schedules need to agree with the general ledger.
The same processes happen month after month.
Yet many organizations continue to approach the close as a recurring manual exercise rather than a workflow that can be continuously improved.
The Journal of Accountancy has highlighted this exact issue in its guidance on streamlining the month-end close, noting that organizations can reduce manual work through more frequent reconciliations, better use of existing systems, and automation. It also emphasizes retraining employees whose data-entry responsibilities are reduced so they can contribute to higher-level activities such as research and variance analysis.
That suggests a more useful question for finance leaders.
Instead of asking, “How can we get through this month’s close faster?” ask:
“Which parts of this close should still require the same amount of manual effort next month?”
That question changes the conversation.
Recurring journal entries may be candidates for automation. Reconciliations involving predictable data may be systematized. Information can be pulled directly from source systems rather than manually re-entered. Exceptions can be routed to accounting professionals for review rather than requiring people to manually inspect every transaction.
KPMG’s 2026 introduction of an AI-powered financial close assistant provides a current example of where this is heading. The system is designed to handle repetitive accounting tasks and first-pass analysis while retaining human review and sign-off.
The underlying principle is straightforward: let technology handle more predictable work while accounting professionals remain responsible for judgment and control.
Automation Should Move People Toward Exceptions And Judgment
This is where the conversation about automation in accounting becomes more useful.
The objective is not to eliminate the accounting team from the process.
It is to change where the team’s time is spent.
A person reviewing hundreds of transactions to identify the few that require investigation is performing fundamentally different work from someone reviewing a system-generated exception list.
In the first model, the accountant spends a significant amount of time identifying problems.
In the second, the system performs the repetitive screening while the accountant investigates what matters.
That distinction becomes increasingly important as accounting organizations grow.
Automation can handle predictable activity at scale. People can focus on exceptions, interpretation, controls, analysis, and decisions.
In this firm’s finance transformation case study of Eaton, automation and standardized processes reduced reconciliation work and manual oversight, while dashboards gave finance teams greater visibility into late reconciliations, trends, and potential issues.
The value therefore extends beyond saving hours. A well-designed automated process can improve the quality of the remaining work.
Make Automation Everyone's Responsibility
An automation culture cannot depend entirely on the IT department or a small transformation team.
The people closest to a process often understand its inefficiencies better than anyone else.
An accountant knows which reconciliation takes hours every month. An accounts payable professional knows where invoice processing repeatedly gets stuck. An HR employee knows which form has to be entered into three systems. A customer service representative knows which requests follow the same predictable pattern.
Those employees should have a voice in improving the process.
Organizations can encourage this by creating simple mechanisms for employees to identify repetitive work, quantify how much time it consumes, and suggest potential improvements.
The goal is not to expect every employee to become a technology expert.
It is to make process improvement part of the organization’s normal operating behavior.
This is particularly important as AI becomes more capable. Organizations should prioritize analyzing how technology transforms roles and overall workflows, rather than focusing solely on automating individual tasks. Employees should understand what the technology is doing, where it can be trusted, where it requires review, and how their responsibilities will change. That creates a more sustainable form of adoption than simply telling employees to use a new tool.
Measure The Work, Not Just The Technology
A company should not measure its automation program by the number of tools it has purchased.
It should measure what changed.
How many hours were previously spent on the process? How many remain? How long does the month-end close take? How many manual journal entries are still required? How many reconciliations are automated? How frequently do errors occur? How much time do accountants spend investigating exceptions versus processing routine transactions?
These measures connect automation to business outcomes.
They also create a feedback loop.
Once a process has been improved, the organization can look for the next source of repetitive work.
That is how automation becomes cultural rather than project-based.
The Bigger Opportunity For Accounting Organizations
For accounting outsourcing firms, this mindset has an additional dimension.
An outsourced accounting team does not simply inherit a client’s existing processes. The strongest operating models continually examine whether those processes can be standardized, streamlined, and increasingly automated.
That matters because accounting volume tends to grow with the business.
More transactions should not automatically mean proportionally more manual work.
If an organization processes more invoices, reconciliations, journal entries, and financial data every year but continues handling them exactly as it did when the business was smaller, its operating model eventually becomes difficult to scale.
Automation provides another path.
Routine work can increasingly be handled through technology and standardized workflows, while accounting professionals concentrate on the areas where expertise adds the most value.
That approach does not diminish the role of accountants. It makes their expertise more useful.
Building The Habit Before Buying The Next Tool
The most important step toward an automation culture may be surprisingly simple: teach people to question repetitive work.
Ask what is being repeated.
Ask why it is being repeated.
Ask whether the process can be simplified.
Ask whether the information needs to be entered more than once.
Ask whether a system can perform the predictable portion of the task.
Then ask where human judgment still matters.
Those questions can begin with something as ordinary as a recurring administrative report and eventually reach one of the most labor-intensive processes in finance: the month-end close.
The technology will continue to change. Today’s automation tools will eventually be replaced by newer capabilities, including increasingly intelligent AI-driven workflows.
The underlying mindset is more durable.
Organizations that build a culture of automation do not simply look for technology to do more work. They continually look for better ways to do the work in the first place.
For accounting teams, that can mean moving away from a month-end cycle dominated by manual repetition and toward a model in which technology handles more predictable activities while skilled professionals focus on exceptions, controls, analysis, and financial insight.
The result is not simply a faster close.
It is an accounting function designed to scale.
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