Why So Many Manual Processes Still Exist in Finance — And How Financial Automation Changes This Scenario

The discussion about financial automation has gained traction in recent years, but the reality within companies shows that a large part of financial work is still performed manually. Spreadsheets remain the primary link between systems, validations depend on human review, and critical decisions continue to be based on data consolidated with significant operational effort.

This is not due to a lack of available technology, nor a shortage of skilled professionals. What exists is a structural mismatch between business growth and the way financial processes have been built over time.

To understand why manual processes still dominate finance, we need to look less at the tools and more at the operational history of companies.

Finance Grows by Solving Problems — Not Designing Processes

In early stages, finance is naturally pragmatic. Volume is low, complexity is manageable, and decisions can be made based on simple controls. As the business grows, new demands arise faster than the capacity to structure formal processes.

New products demand new pricing models. New channels create new revenue streams. Geographic expansion adds regulatory and fiscal layers. At each of these stages, finance responds in the most efficient short-term way: by creating manual controls to absorb the complexity.

These solutions work locally but do not scale. What started as an exception becomes routine. What was provisional becomes permanent. Over time, finance comes to operate sustained by human effort, not by process.

Manual work, in this context, is not a mistake. It is a natural consequence of growing without a clear financial automation strategy.

Manual Work Is Not Technological Lag, It's Operational Debt

A common misconception is to associate manual processes with the absence of systems. In practice, most companies already use a combination of ERP, banking systems, payment tools, tax solutions, and analytical platforms. Yet, manual work persists.

The reason is that these tools were rarely designed to operate as an integrated ecosystem. Each system solves a piece of the problem, but the complete flow depends on human reconciliation, validation, and interpretation.

When business rules are not formalized, when exceptions have not been mapped, and when data does not follow consistent standards, manual work becomes the compensating mechanism. It is at this point that manual work ceases to be operational and becomes structural.

Automating, therefore, is not just replacing people with technology. It is transforming implicit knowledge into explicit rules and executable workflows.

The Silent Impact of Manual Processes on Financial Strategy

The biggest cost of manual processes does not appear directly in the budget. It manifests in how finance operates within the company.

Highly manual environments generate:

• long closing cycles
• low cash predictability
• difficulty in scaling operations
• excessive reliance on key personnel
• higher exposure to errors and rework

But the most significant impact is less visible: the loss of analytical capacity. When the finance team dedicates most of its time to execution, little room remains for analysis, scenario simulation, and risk anticipation.

In this context, finance ceases to be a strategic support area and begins to operate as a reactive area, activated only after a problem has occurred.

Why Financial Automation Isn't Advancing at the Expected Pace

If the benefits of financial automation are clear, why is its adoption still slow in many companies?

One of the main reasons is that automation is often treated as a one-off, large, and risky project.

Beyond the time required, there are budget and prioritization challenges: long initiatives that depend on multiple technology stakeholders and consume months of work before delivering any tangible results, while the finance operation continues to run at full capacity.

In practice, finance rarely has this time. Operations continue to demand immediate responses, and any initiative that seems to compete with day-to-day tasks tends to be postponed.

A difficult cycle to break then forms: the department doesn't automate because it's overwhelmed, and it remains overwhelmed because it doesn't automate.

Breaking this cycle requires a change in approach: treating financial automation as continuous development, not as an isolated project.

Financial Automation Reorganizes Work, Doesn't Eliminate People

Another recurring fear is associating automation with staff reduction. In practice, the effect is different. Well-structured financial automation removes repetitive tasks, reduces rework, and increases operational consistency.

Human work remains essential but shifts its focus to:

• data analysis
• scenario interpretation
• decision-making
• exception handling

Automation does not diminish the relevance of finance. It creates the conditions for the department to act with greater depth and impact.

Financial Process Automation Starts Where It Hurts Most

A common characteristic of successful financial automation initiatives is to start with high-volume, high-operational-cost processes. Reconciliations, accounts payable, reimbursements, closing, and data validation are often the first candidates.

These processes, though not glamorous, consume a disproportionate share of the team's time. Automating them provides immediate operational relief and increases the department's predictability.

From this gain, it becomes possible to move towards more analytical automations, involving projections, revenue analysis, and strategic decision support.

AI in Finance Amplifies Structure (or Exposes Fragility)

The incorporation of AI into finance has moved beyond experimental stages and now occupies the center of the area's strategic discussions. A recent KPMG survey, which interviewed nearly 3,000 companies across 23 countries, helps to gauge this shift.

The data is clear. Approximately 71% of companies already use AI in some financial process, virtually all plan to expand this use in the next three years, and 57% of leaders state that AI's return on investment has exceeded initial expectations. Accounting, FP&A, treasury, and risk management consistently appear among the most advanced areas in this adoption.

The message isn't that AI "solved" finance. It's that it has become a strategic priority for teams that have already recognized their operational limits.

At the same time, these numbers help clarify a fundamental point: AI does not create value on its own. Artificial intelligence does not correct inconsistent data, does not resolve poorly defined processes, and does not replace business understanding. When applied to fragile structures, it merely accelerates errors and amplifies noise.

This is why, in organizations where AI generates real impact, it is accompanied by well-structured financial automation. Clear rules, reliable data, and standardized processes create the necessary foundation for AI to function as a lever — whether for analysis, scalability, or risk anticipation.

In other words, AI in finance doesn't change the importance of fundamentals. It merely makes it more visible who has already built them and who still relies on improvisation.

The Future of Finance Involves Less Manual Work and More Decision-Making

The trend observed in more mature companies is not the reduction of finance teams, but the redefinition of work. Automating execution, strengthening control, and investing in data create the conditions for finance to move beyond merely operating as a support area and instead act more closely with strategic business decisions.

Financial automation is neither a fleeting trend nor a one-off response to efficiency gains. It is a structural answer to the growing complexity of organizations. As volume, data diversity, and regulatory demands increase, insisting on manual processes as a permanent solution imposes clear limits on growth.

The transition to a more automated finance doesn't happen all at once. It is built step by step, process by process, with conscious choices about what to automate, when, and with what level of control. Along this path, the impact goes beyond operations: it changes how finance contributes to the business, anticipates risks, and supports decisions.

It is precisely in this context that platforms like :contentReference[oaicite:0]{index=0} gain relevance. By allowing finance teams to structure financial process automation with clarity, governance, and flexibility — combining business rules, data, and AI — Abstra enables the creation of a less manual, more strategic finance function, without relying on large projects or inflated teams.

More than just automating tasks, the goal becomes creating a solid foundation for decision-making. Less operational effort. More predictability. More time for finance to do what ultimately generates the most value for the company: thinking, analyzing, and deciding.

👉 To understand more about how to consistently structure financial automation and financial processes automation, get to know Abstra.

👉 To go deeper into automation, AI, and the evolving role of finance, Catarina Pinheiro shares practical classes and insights on how to structure processes, scale operations, and elevate the finance team’s impact.

🔗 Access the classes and resources, available in portuguese only.