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Robotic process automation (RPA) uses software bots to carry out repetitive, rules-based tasks in computer systems. It can help organizations handle work faster and more consistently, but it does not automatically redesign a process or guarantee savings. The best results come when teams choose suitable tasks, measure outcomes against a baseline, and govern bots as part of the operation.
What RPA does in business operations
RPA is low- to no-code software that emulates user actions across systems to perform repetitive, rules-based work. A bot might copy information between applications, manipulate a spreadsheet, reconcile records, generate a report, or send a routine communication. It automates task steps; it does not inherently understand the business purpose behind them or fix a flawed process. Digital.gov’s RPA guidance describes these uses and the technology’s potential role in government operations.
The approach can be useful when a task has clear rules, predictable inputs, and a manageable number of exceptions. When a screen, application, or underlying rule changes, a bot may need adjustment. Processes that depend heavily on judgment or frequent negotiation are less straightforward candidates unless people remain involved for those decisions.
Examples of automatable work
- Entering or transferring data between systems
- Reconciling records and manipulating spreadsheets
- Producing routine reports or analytics
- Connecting steps across applications
- Sending standard customer or employee communications
The Federal RPA Use Case Inventory catalogs more than 300 federal use cases, spanning agencies and business functions. Its entries include estimated annualized capacity, but an inventory of use cases does not demonstrate that every deployment achieved those estimates or realized savings.
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What RPA can improve—and how to verify it
Organizations use RPA to increase the capacity available for routine work, shorten cycle times, standardize execution, and make activity more measurable and auditable. Automating repetitive steps may also let employees focus on work requiring judgment. These are possible benefits, not guaranteed outcomes or a universal return on investment. Digital.gov lists them as potential advantages, while APQC has noted the difficulty organizations face in measuring effectiveness.
Before deployment, record how the process performs without automation. After deployment, compare the same measures and account for both the bot and the people supporting it:
- Work volume completed and end-to-end cycle time
- Exception, error, and rework rates
- Service levels and control failures
- Bot monitoring, repair, and maintenance effort
- Total cost of ownership, including implementation and ongoing support
This measurement approach follows the emphasis on transparency, controls, and cost in IMA and COSO’s guidance on internal controls for RPA, alongside APQC’s discussion of measuring RPA effectiveness. A faster task is not necessarily a better process if exceptions rise, rework moves elsewhere, or maintenance costs outweigh the capacity gained.
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Governance and controls are part of the implementation
A bot can affect records, systems, and business decisions, so an organization needs clear ownership and oversight—not just a working script. IMA and COSO’s 2025 guide organizes internal control into five components. Teams can use those components to structure responsibilities for both the process and the automation:
- Control environment: Assign accountable process and bot owners, define responsibilities, and establish who can approve changes.
- Risk assessment: Identify the consequences of incorrect actions, unauthorized access, outages, and exceptions the bot cannot resolve.
- Control activities: Restrict and protect credentials, separate duties where appropriate, document changes, and define how exceptions are handled.
- Information and communication: Make procedures, logs, ownership, and escalation paths available to the people who need them.
- Monitoring activities: Review performance, exceptions, access, and control failures, and confirm that the bot still works as intended.
APQC’s article published March 13, 2024 identifies governance, required capabilities and skills, and effectiveness measurement as challenges. It recommends involving risk management or legal early to help address potential risks.
Adoption figures need their dates and context
APQC reported that 37% of organizations in its survey were investing or planned to invest in RPA in the following 18 months; 13% were operating RPA, 15% implementing it, and 15% piloting it. These are survey findings published March 13, 2024, not current adoption rates for all organizations.
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A separate McKinsey survey-based analysis, published around 2018, reported that 69% of successful advanced industrial companies in its survey involved IT early in automation planning. That figure reflects its specific population and period, not a current cross-industry benchmark. The practical lesson is to involve IT early enough to assess integration, security, support, and ownership before a bot becomes operationally important.
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Automation can become harder to manage as a process crosses more applications, teams, and handoffs. In its January 2025 summary of a 2025 report, software vendor Camunda said organizations handled an average of 50 endpoints to execute a business process, 19% more than five years earlier. The same summary reported that 82% of organizations said compliance risks had risen and 77% were concerned about failures in core business processes. These are findings from Camunda’s research, not universal measurements of every organization.
Best Value
In a complex workflow, RPA can handle deterministic, repeatable steps while orchestration coordinates systems, people, and exceptions. AI may contribute to tasks that involve less structured inputs, but that does not remove the need for controls, clear handoffs, or human decisions where the process requires them. A January 27, 2025 UiPath release summarized a vendor-sponsored survey of 252 U.S. IT executives at companies with more than $1 billion in revenue, conducted in October 2024. Its findings reflect respondents’ views on agentic AI, not proof of realized operational gains. In the release, Wesco Distribution’s Max Ioffe said: “I expect that robotic process automation will orchestrate the agents. For larger scale processes, you need clear orchestration and governance, and that means a deterministic technology like RPA.”
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to choose an RPA approach or platform
There is no single best platform for every organization. Start with the process and its operating constraints, then assess whether the software and team can support it reliably.
- Process fit: Are the rules and inputs stable, and can the business define how exceptions should be routed?
- Integration and resilience: How does the automation interact with each system, and what happens when an interface or workflow changes?
- Security and governance: Can the organization control credentials, permissions, change approvals, audit trails, and monitoring?
- Deployment: Does the available deployment model meet the organization’s data-isolation and operational requirements?
- Support burden: Can the organization monitor failures, maintain bots, and build the skills needed to keep them working?
- Total cost: Do implementation, licensing, infrastructure, oversight, and maintenance costs make sense against measured process outcomes?
Gartner’s August 7, 2024 Magic Quadrant abstract characterizes RPA as a software market for tactical operational efficiency and says it evaluated 13 enterprise vendors. The abstract names Appian, Automation Anywhere, IBM, Laiye, Microsoft, Nintex, Pegasystems, Salesforce, Samsung SDS, SAP, ServiceNow, SS&C Blue Prism, and UiPath. This is a dated market snapshot, not an endorsement or a current ranking. Specific product capabilities and deployment options should be checked against current vendor documentation and the organization’s requirements.
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