When it comes to automation and AI investments, finance teams are leaving the question "Are we using it?" behind. The new question is: which process delivers value, and how quickly? The findings Gartner published on September 24, 2026 give a concrete answer. According to a survey of 160 senior finance leaders conducted between January and April 2026, data extraction, accounts payable and receivable automation, and report creation typically deliver returns within 9-10 months.
In this article, we cover the processes that deliver returns quickly, the longer-horizon ones, and the points to watch when starting finance automation, all with their sources.
Which processes deliver value faster?
The chart Gartner shared shows, for each use case, the average number of months in which all of the expected value is achieved (or is expected to be achieved). The ranking is a useful guide for organizations that want to get started with finance automation:
- Numerical data extraction and retrieval: 9.3 months
- Accounts payable (supplier payments) process automation: 10.0 months
- Report creation (financial commentary, tables and visuals): 10.2 months
- Accounting reconciliations: 10.2 months
- Error and anomaly detection: 10.5 months
- Accounts receivable (collections) process automation: 10.6 months
More complex work requires a longer development period. In the same chart, financial forecasting appears at 12.2 months, data management at 12.3 months, insight generation at 12.4 months and scenario planning at 13.7 months.
The fast-return group shares a notable trait: the work has clear rules, is repetitive and is high in volume. Reading invoice data, carrying out payment and collection steps, matching accounts or preparing regular reports all fit this description. Areas such as forecasting and scenario planning, by contrast, take longer to mature because they require heavy interaction with judgment, data quality and business units.
What are organizations investing in right now?
Gartner's second chart, based on the same survey, shows which use cases finance functions are already running. Today, 43% of executives implement report creation, 38% implement accounts payable process automation, and 35% implement numerical data extraction. For scenario planning, the share stays at 14%.
Gartner reads this picture as follows: AI investments in finance are concentrating on areas that boost efficiency and have a short time to value, such as report creation and process automation. It also adds a caution: the appeal of quick returns should not crowd out the more complex use cases that improve decision quality, manage risk and support revenue growth.
The previous year's data points to a similar trend. In Gartner's 2025 survey, 59% of finance leaders said they used AI in the finance function; among those using AI, the most common areas were knowledge management (49%), accounts payable process automation (37%), and error and anomaly detection (34%).
The picture in Türkiye
In Türkiye as well, finance is one of the main areas where AI is used. According to TÜİK (the Turkish Statistical Institute) data, 33.7% of enterprises using AI in 2025 used the technology for accounting, controlling or finance management. The overall share of enterprises using AI was 7.5% in 2025; according to TÜİK's October 2, 2026 bulletin, it rose to 14.0% in 2026. The barrier most often cited by enterprises considering adoption did not change in 2026 either: at 72.3%, a lack of relevant expertise within the organization.
Read together, these figures suggest that the logical starting point for organizations in Türkiye is finance processes that deliver returns quickly and are easy to measure.
Deployment is not the same as value
Choosing a process that delivers returns quickly is not enough on its own. In its May 2026 assessment aimed at finance leaders, Gartner emphasizes that the number of tools or pilots deployed does not prove they are creating value. According to the same assessment, 66% of finance organizations that have adopted AI cite efficiency and productivity gains as the most important benefit. By contrast, 63% of finance organizations reported that AI implementation progressed more slowly than expected in 2025. Gartner's recommendation is to measure progress by realized value rather than by the number of deployments.
It is also normal for impact to remain limited in the early period. In Gartner's 2025 survey, 91% of respondents reported low or moderate impact in the initial phase; it was noted that the greatest impact emerges once applications reach the production stage.
The real barrier: AI literacy
According to Gartner, the biggest obstacle facing AI initiatives in finance has changed. In the past, bringing the right talent in-house stood out; today, low AI literacy is described as the most significant barrier. Gartner recommends giving employees the chance to use AI in practice through project assignments, sandbox environments and short on-the-job activities.
This finding also lines up with the expertise-gap barrier in the TÜİK data. Whether automation produces lasting value depends on the finance team that knows the process being able to understand the tool and adapt it to its own work.
Getting started: a practical checklist
The steps below are a framework derived from the findings in the sources; each organization needs to adapt it to its own process structure.
- Build a process inventory. List the repetitive, high-volume work with clear rules, such as data extraction, payment and collection flows, reconciliation and regular reporting.
- Measure the current state. For each candidate process, record metrics such as processing time, error rate and workload before automating. An improvement that is not measured cannot be proven.
- Pick one fast area and one long-horizon area. Start with a process that delivers value quickly, but do not leave an area that improves decision quality, such as forecasting, off the roadmap.
- Define human approval points. For critical steps such as payments and accounting entries, decide up front who will review exceptions and under which rule.
- Train the team. Build the team's confidence in the tools with short, role-specific training and a limited sandbox environment.
- Review the portfolio regularly. Decide periodically which initiatives to invest in and when to wind down underperforming ones.
Conclusion
In finance automation, quick returns come not from large transformation projects but from repetitive, measurable processes. Gartner's data shows that data extraction, accounts payable and receivable automation, reconciliation and report creation reach their expected value within about a year. Whether that value actually materializes depends on clear metrics, human approval points and a finance team with well-developed AI literacy.
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If you would like to identify the right starting point for automation in your finance processes and clarify your priorities, let's plan your roadmap together.
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