RPA Use Cases: 20 Examples That Work Across Industries in 2026

Key Takeaways

• RPA fits high-volume, rule-based work with digital inputs and stable screens, but it struggles with judgment calls and changing interfaces.
• Bots work through the user interface, so they can reach legacy and terminal-based systems that have no API.
• Finance, HR, customer service, IT and supply chain hold the most proven use cases, from invoice entry to user provisioning.
• Score each candidate on volume, rule stability, input format, screen stability, baseline data and exception rate before you build.
• Choose an API when one exists and an AI agent when inputs are unstructured, and use RPA for the structured gap in between.
• Kanerika’s UiPath work for Fortegra cut a five-day monthly premium reconciliation to a few hours with 99% extraction accuracy.

When the Morning Starts With 40 PDF Invoices

Picture an accounts payable clerk at a mid-sized distributor at 8:30 on a Monday. Forty vendor invoices sit in a shared inbox as PDF attachments, and each one has to reach the ERP before the payment run.

She opens every file, reads the invoice number, date, amount and line items, then types them in by hand. Then she matches each invoice to its purchase order and goods receipt. By lunch, only about half are done.

A software bot changes the routine part of that morning. It downloads the attachments and extracts the fields. After that, it keys them into the ERP and runs the three-way match. When the numbers agree, the invoice moves on without anyone touching it.

The bot does not settle a price dispute, though, and it cannot decide whether a new vendor is legitimate. Those cases land in an exception queue for the clerk, who now spends her morning on the invoices that need a person.

Routine steps go to the bot, and judgment stays with people. That split is what separates RPA use cases that work from the ones that stall. This guide covers 20 of them by function and industry. It also shows how to pick the first one, and when an API or AI agent fits better.

What RPA Is and What Makes a Good RPA Use Case

Robotic process automation (RPA) is software that works an application the way a person does, through the screen. For example, a bot clicks buttons, reads fields, copies values and types into forms, following steps that someone designed for it. That makes RPA one layer of intelligent automation, the layer that handles fixed, repeatable steps.

How a Software Bot Runs a Process

Every bot starts from a trigger. That can be a schedule, a new email, a file landing in a folder or a person clicking a button. From there it follows its steps through one or more applications, and it also logs each action.

Larger programs feed bots from a work queue, so several bots can share a backlog of invoices, tickets or claims. Some items break a rule, such as a missing field or an unknown customer. The bot then parks them in an exception queue for a person to review.

The reason RPA still matters is the range of systems it can reach. Microsoft’s Power Automate desktop flows documentation lists legacy applications such as terminal emulators alongside modern web and desktop apps. It also uses ERP data entry as a typical example. Many core banking, claims and plant systems still run on screens like these, with no API to call.

Attended vs Unattended Automation

RPA bots run in two modes, and the mode shapes which use cases fit. An attended bot runs on an employee’s own desktop, usually when that person starts it. A service agent might click one button so a bot pulls a customer’s history from three systems while the call continues.

An unattended bot, in contrast, runs by itself on a dedicated machine, on a schedule or trigger, with no one watching. Power Automate’s unattended desktop flow requirements show what that means in practice. They call for the Process plan, a machine where all users are signed out, and a screen that stays locked while the bot works.

Hybrid setups also combine the two modes. An attended bot gathers details while the customer is still on the call. It then passes the case to an unattended bot, which finishes the back-office updates after the call ends.

That relay is common in service-heavy teams, because it keeps the live conversation short while the slower system work happens out of sight. People then see only the cases that break a rule.

Five Traits of a Strong RPA Candidate

Good candidates share a profile that you can check before any build starts. A process that misses two or more of the traits below is a warning sign, even if the work looks tedious enough to automate.

1. High volume. The task repeats daily or weekly, such as keying hundreds of invoices a month.
2. Stable rules. The same inputs always lead to the same action, such as approving any match within a set tolerance.
3. Digital, structured inputs. Data arrives as forms, spreadsheets, emails or machine-readable PDFs rather than handwriting.
4. Stable screens. The applications involved rarely change their layout, so the bot’s screen selectors keep working.
5. A measurable baseline. You know the current hours, cycle time and error rate, so you can prove the gain later.

Exception rate is also worth collecting as a sixth number. When many items need a person’s decision, the bot spends its day handing work back. In that case the process needs a redesign before it needs a bot.

20 RPA Use Cases at a Glance

The tables below list 20 RPA use cases with a proven fit, split into business functions and industries. Each row names the systems the bot touches and where a person stays involved. That handoff decides whether a bot saves time or creates rework.

Read the last column first when you build a shortlist. A use case with a narrow, clear human role tends to automate well. One where people step in on every item is a redesign project with an automation label.

Use Cases 1 to 10: Finance, HR, Customer Service and IT

Use Cases 11 to 20: Supply Chain, Data and Industry

The sections below take each group in turn. They explain what the bot actually does, which systems it relies on, and where it tends to break once real volumes arrive.

RPA Use Cases by Business Function

Function is the easiest way to shortlist, because each department runs its own repeatable back-office work. A finance team and an IT service desk automate very different tasks. Still, both look for the same profile of volume, fixed rules and clean inputs.

Finance and Accounting

Finance is often the first department to automate, since its work runs on rules and its errors cost money. The invoice morning from the opening is the classic case, so the useful question is where it goes wrong. A vendor that changes its invoice layout breaks extraction, which is why teams add document extraction tools and an exception queue.

Duplicates and tolerances need rules of their own. The bot should check each invoice number against recent postings before it keys anything. The three-way match also needs an agreed tolerance for price and quantity differences. Anything outside that tolerance goes to a person rather than being forced through.

Reconciliation follows close behind. A bot downloads bank statements, matches each transaction to the ledger, then posts the result. Insurers apply the same pattern to premium payments, as the Fortegra project later in this guide shows.

Month-end close adds a third use case. Bots post recurring journals and accruals from approved templates, then hand the batch to a controller for sign-off. The approval stays human on purpose, because the person who books an entry should not be the one who approves it.

Human Resources

HR teams use bots for the administrative edges of the employee lifecycle. During onboarding, a bot reads the new-hire record in the HRIS and creates the network account, mailbox, payroll entry and equipment request. Offboarding then runs the same steps in reverse and records each one, which closes a common gap where access outlives employment. Where HR work needs judgment instead of fixed steps, see agentic AI in HR.

Payroll checks and recruitment admin sit at the quieter end of HR, and each has a clear breaking point. Before each run, the bot compares hours, rates and deductions across the time system and payroll, then flags mismatches for a payroll specialist. It cannot judge a mid-cycle rate change, such as a backdated promotion, so that case stays with the specialist. In recruiting, bots schedule interviews, send status emails and update candidate records. A reschedule that juggles several interviewers, like every hiring decision, still needs a recruiter.

Customer Service

Customer service leans heavily on attended bots. An agent on a call often switches between a CRM, a billing system and an order tool to answer one question. An attended bot pulls those records into one view in seconds, so the agent can stay focused on the customer.

Simple transactions then become the next step. Bots apply standard refunds, address changes and plan updates within policy limits, then log the change in each system. Anything outside those limits, such as a refund above a set amount, goes back to a supervisor.

That boundary keeps the bot fast without letting it make decisions it should not own. Supervisors also get a clean list of the requests that genuinely need them.

IT Operations

IT service desks spend a large share of their day on requests that follow a script. Password resets, new user accounts and group membership changes all fit RPA when the request arrives through a ticketing system with the details filled in. The bot checks the request against policy, makes the change, then closes the ticket with a record of what it did.

Access reviews are a second strong case. Before each review, a bot exports user lists from each application, compares them with HR status and builds the evidence file that auditors ask for. Privileged accounts stay with a person, since a mistake there carries more risk than the time a bot would save.

Supply Chain and Logistics

Supply chain work runs across many outside systems, which is why RPA shows up there so often. Carriers, suppliers and marketplaces each run their own portal, but many provide no integration for smaller customers. A bot can log into each carrier site, read the shipment status, then post it to the matching order in the ERP.

Freight invoice audit is another strong case. The bot compares each carrier invoice with contracted rates, then flags any overcharge for review. Sales order entry from emailed spreadsheets works the same way, with credit holds and unusual quantities left to a person.

For a wider view of where bots, analytics and AI meet in this function, see our guide to intelligent automation in supply chain operations.

Data Entry, Migration and Reporting

Some of the most practical RPA work has nothing to do with a single department. Old systems often hold records that no export can reach, especially green-screen applications built decades ago. A bot can read those records screen by screen, then load them into a new platform. That makes RPA a useful bridge during a data modernization program.

That bridge should stay temporary, though. Once the new platform is live, an API or a proper data pipeline should take over. Screen-reading migrations are slow, and they also depend on the old system staying up.

Scheduled report packs are the steadier use case here. Bots run the same reports each week, export them and send them to their owners, while people interpret the numbers and act on them.

Compliance and Audit

Compliance teams spend weeks collecting evidence that already exists in their systems. A bot can gather screenshots, logs, approvals and exports on a schedule and file them by control. The evidence is then ready before the auditor asks. It can also assemble recurring regulatory reports from several sources into a fixed template.

Every bot run also leaves a log of what it did, when, and under which identity. That log becomes audit evidence in its own right. For the wider program of policies, monitoring and reporting, our guide to compliance automation covers the tools and controls.

RPA Use Cases by Industry

Industries reuse the same patterns but apply them to their own forms, portals and regulations. The sections below cover where each sector gets the most from bots, with links to deeper Kanerika guides where one exists.

Banking and Financial Services

Banks run some of the oldest core systems in any industry, so RPA fills many gaps between them. Know-your-customer (KYC) checks are a common case. A bot collects identity documents, runs names against screening lists and assembles the results into one file. A compliance analyst then makes the risk decision.

Statement generation, loan document checks and account maintenance run on the same pattern. The bot gathers data and checks that each file is complete, while credit and risk calls stay with people.

Insurance

Insurers move large volumes of documents through policy and claims systems that often predate modern APIs. Claims intake is a frequent starting point. Here a bot registers the first notice of loss from an email or web form, then attaches the supporting documents. Adjusters still keep the coverage and fraud decisions.

Premium reconciliation is another natural candidate. Payments arrive from several banks in different formats, and someone has to match each one to a policy and update its status. Kanerika’s work for one insurer on exactly this process appears in the case card below.

Renewals add a third use case, with bots preparing renewal notices and pulling the data that underwriters need. Over time, these workflows often grow into hyperautomation, where bots, document AI and workflow tools run together.

Healthcare

Healthcare administration runs on payer portals and practice management systems, which suits RPA well. Bots check patient eligibility before appointments, look up claim status across payer sites and gather the data that prior authorization requests need. Staff then handle denials, appeals and clinical questions.

Privacy also shapes how these bots are built. Under 45 CFR 164.502(b), covered entities and business associates must limit protected health information to the minimum necessary for the purpose. In practice, each bot should get its own identity with access scoped to its task, not a shared administrator login.

Our guide to automation in healthcare covers more of these workflows, from scheduling to revenue cycle work.

Manufacturing

Manufacturers use bots where planning data has to move between the ERP, supplier portals and spreadsheets. A bot can update purchase orders from supplier confirmations, apply approved bill-of-materials changes and reconcile inventory counts between the warehouse system and the ERP. Engineers still approve design changes, and planners still decide how to handle shortages.

Retail and E-commerce

Retailers that sell across several marketplaces keep the same product, price and stock data in many places. Bots sync those values between the e-commerce platform, each marketplace and the ERP. They also process standard returns against policy. Damaged goods, pricing decisions and complaints stay with staff, and our guide to retail automation covers the wider store and supply side.

Public Sector, Telecom and Higher Education

Public agencies, telecom operators and universities share one thread: standard forms, high volumes and long-lived legacy systems. Agencies use bots for benefit application intake, permit processing and records requests, while caseworkers keep the eligibility decisions. Telecom operators automate order provisioning, number porting checks and billing corrections, with customer disputes left to an agent. Universities apply RPA to admissions document checks, enrollment updates and financial aid paperwork, and staff still handle appeals and special cases.

Which Processes Should You Automate First?

The first bot sets the tone for the whole program, so pick a process that will clearly succeed.

Score each candidate from 1 to 5 on the five traits above plus its exception rate, then add up the results. The table shows what low, middle and high scores look like.

The two numbers that matter most are volume and exception rate. A process with high volume and few exceptions is the ideal pilot. High volume with many exceptions is a warning sign, though. The bot will spend its day passing items back, so the team soon loses faith in it.

Fix that kind of process first, for example by standardizing an input form or tightening a rule. Then score it again before you automate.

After the scoring, run a short pilot on the top candidate. Measure the same baseline numbers you collected, and expand to the next process only when the bot beats them.

When RPA Is the Wrong Tool

Some processes look like strong candidates and still fail. The most common reason is an unstable user interface. When a web app redesigns its screens every few weeks, the bot’s selectors break each time and maintenance costs climb.

Judgment-heavy work is the second trap. Approving an unusual claim, reading the tone of a complaint or choosing between suppliers needs context that a rules-based bot does not have.

A clean API is the third reason to skip a bot. When the target system has one, an integration is faster and far less fragile than a bot clicking through screens. Finally, avoid automating a process that is about to change, such as one scheduled for a platform migration next quarter.

RPA, API Integration or AI Agents: Matching the Tool to the Use Case

RPA is one of three ways to automate work across systems, and each fits a different kind of task. Integration gaps explain why the screen-level option still exists. The 2025 MuleSoft Connectivity Benchmark surveyed more than 1,050 IT leaders. It found that the average enterprise runs 897 applications, but only 29% of them are integrated.

With most applications unconnected, a bot that works through the screen is often the fastest route. APIs are better when they exist, while AI agents are better when the input is messy or the task needs reasoning. The table sets out where each approach fits.

How RPA, APIs and Agents Work Together

In practice, the three increasingly work together. UiPath’s September 30, 2025 release expanded its platform for agentic automation and orchestration. It includes Maestro case management for claims, so agents, robots and people share one process. An agent might read a messy claim email, a bot then keys the result into the claims system, and a person approves the payout.

The decision rarely needs a long study. Use an API if a stable one exists. Give an agent the steps with unstructured input or judgment, and let RPA cover the structured gap that remains.

For a deeper look at the combination, see our guide on how AI and RPA work together. Our explainer on agentic automation covers processes where agents take the lead while bots handle the system updates.

Making RPA Use Cases Pay Off: Cost, ROI and Bot Governance

A use case only pays off if the bot keeps running at a lower cost than the work it replaces. That depends on three things: build and run cost, how you measure the return, and how well you govern bots once they are live. For the full program view, see our guide to running RPA as an enterprise program.

What Drives Cost and How to Measure ROI

RPA cost comes from five places, and only the first one appears on the price list.

• Licenses for the platform, the bots and orchestration.
• Bot infrastructure, such as the machines that unattended bots run on.
• Build effort for process design, development and testing.
• Maintenance when applications, screens or rules change.
• Exception handling, the staff time spent on items the bot hands back.

Return comes from the hours a bot gives back, the errors it removes and the cycle time it cuts. The formula is simple. Take the annual value of hours returned and errors avoided, subtract the annual cost of running the bot, then divide the result by that cost.

Measure against the baseline you recorded before the build, not against an estimate. The five measures below make that comparison concrete.

Governance and Bot Maintenance

Bots act inside business systems, so they need the same controls as the people they work alongside. Each bot should have its own identity, with credentials kept in a vault rather than in scripts. Every action should land in an audit log, and the person who builds a bot should not be the one who approves its release.

Maintenance needs a plan as well. Bots break when screens change, passwords expire, input formats drift or the exception queue grows faster than staff can clear it. A small center of excellence (CoE) that owns standards, monitoring and a change calendar can catch many of these issues before users notice them.

Each of those failures has a matching control. Selector tests run before an application release, vaulted credentials raise alerts before they expire, and intake checks reject a file whose format has drifted.

Platform choice matters less than these habits. Kanerika builds on Microsoft Power Automate, UiPath and Automation Anywhere, and picks the one that fits a client’s systems and existing licenses. Teams already on Microsoft 365 often start with Power Automate workflow examples to see what desktop and cloud flows can do.

How Kanerika Delivers RPA Use Cases

Kanerika runs RPA work in four stages. We assess and score candidate processes with the model above, then pilot one process end to end against its baseline.

Once the pilot proves itself, we scale through a small CoE with shared standards, and we run and monitor the bots after go-live. Our RPA services cover each stage on Power Automate, UiPath and Automation Anywhere.

Fortegra: Premium Reconciliation With UiPath

The Fortegra project shows the approach on a real use case. Fortegra is an insurance provider that collects premiums through several banking partners. Bank statements arrived by email as CSV and Excel files, and policy numbers sat inside unstructured comment fields. Staff then updated premium statuses by hand in a web application.

Kanerika configured UiPath bots to pull the emails and download the statements every day. Python scripts using regular expressions then extracted policy numbers, amounts and account details. UiPath then ran the reconciliation through Excel and database activities, while UiPath web automation logged in and updated each premium status.

According to the Fortegra case study, the work delivered 99% accuracy in data extraction and an 85% reduction in reconciliation time. It also brought a 100% elimination of manual web updates. The reconciliation cycle dropped from 5 days a month to a few hours, and automated Excel reports now act as audit-ready logs.

Teams that already run UiPath and want to consolidate on Microsoft can also move their bots with our UiPath to Power Automate migration service. Kanerika’s FLIP accelerator reads UiPath project files and converts the robots into Power Automate flows, keeping trigger logic and business rules intact.

What Actually Works in RPA

RPA works best as a narrow tool used well. Bots earn their place on high-volume, rule-based work with digital inputs and stable screens, but they lose it on judgment calls and fast-changing interfaces.

The 20 examples above also share one more trait: each has a clear point where a person takes over. Start with a process that scores high on volume and low on exceptions. Measure it against a real baseline, govern each bot like a staff member, and add APIs where they exist and agents where inputs get messy.

So which RPA use cases work across industries? The ones with high volume, fixed rules, clean digital inputs and a clear handoff to people.

Frequently Asked Questions