What is robotic process automation?
Governed by business logic and structured inputs, robotic process automation (RPA) employs software “bots” to automate repetitive tasks within business process workflows. RPA tools enable companies to configure these bots to capture and interpret applications for processing a transaction, manipulating data, triggering responses, and communicating with other digital systems. RPA scenarios range from generating an automatic response to an email, to deploying thousands of bots, each programmed to automate jobs in an ERP system.
Many CIOs have turned to RPA to streamline enterprise operations and reduce costs. With RPA, businesses can automate mundane rules-based business processes, enabling business users to devote more time to serving customers or other higher-value work. Others see RPA as a stopgap en route to intelligent automation (IA), which, rather than take prescriptive actions, leverages machine learning (ML) and AI to make intelligent judgments about future outputs.
What are the benefits of RPA?
Implemented successfully, RPA enables organizations to reduce staffing costs and human error. Intelligent automation specialist Kofax says the principle is simple: Let human employees work on what humans excel at while using robots to handle tasks that get in the way.
Bots are typically low-cost and easy to implement, requiring no custom software or deep systems integration. Such characteristics are crucial as organizations pursue growth without adding significant expenditures or friction among workers.
When properly configured, software robots can increase a team’s capacity for work by up to 50%, according to Kofax. For example, simple, repetitive tasks such as copying and pasting information between business systems can be massively accelerated when completed using robots. Automating such tasks can also improve accuracy by eliminating opportunities for human error, such as transposing numbers during data entry.
Enterprises can also supercharge their automation efforts by injecting RPA with cognitive technologies such as ML, speech recognition, and natural language processing, automating higher-order tasks that in the past required the perceptual and judgment capabilities of humans.
Such RPA implementations, in which upward of 15 to 20 steps may be automated, are part of IA.
RPA vs. intelligent automation
Intelligent automation builds on RPA to create self-improving software automation that enables RPA bots to tackle more complex tasks and use cases with minimal human intervention. To do so, intelligent automation leverages AI technologies, including machine learning (ML), natural language processing (NLP), gen AI, and optical character recognition (OCR).
RPA vs. agentic AI
Agentic AI goes beyond traditional RPA. Whereas RPA bots follow explicit instructions using pre-defined scripts, AI agents can make autonomous decisions.
According to IBM, RPA excels in structured environments with defined, predictable workflows. It is especially good for automating manual, repetitive processes such as high-volume data entry and migration, invoice and purchase order processing, standard report generation, and customer onboarding. When these processes encounter an error or exception, they require manual intervention.
Rather than following a script, AI agents have an objective (e.g., optimize supply chain logistics for the next quarter) and can act autonomously to achieve that objective. Use cases suited to agentic AI include intelligent customer service (e.g., drafting personalized responses), dynamic workflow management (e.g., prioritizing critical tasks based on real-time data), and autonomous research and data synthesis (e.g., answering a complex query by searching multiple data sources, synthesizing the information, identifying patterns, and drafted a structured recommendation).
Real-world RPA examples
Organizations across all industries are leveraging RPA to streamline operations and reduce costs. Here are four examples:
Close Brothers augments RPA with document understanding
UK-based merchant banking group Close Brothers has been on an RPA journey for more than six years, mostly focused on automating rules-based, structured processes. Now it’s working close with AI-powered RPA platform vendor UiPath to leverage UiPath Document Understanding in the company’s wholesale finance operation.
By integrating Document Understanding with RPA, Close Brothers is automating the reconciliation of documents submitted by customers, and enabling automation of the end-to-end payouts process.
US Marine Corps retools its fighting force with RPA
The US Marine Corps (USMC) is leveraging RPA as part of a massive digital transformation of the Marine Depot Maintenance Command (MDMC), which is responsible for maintaining, repairing, and calibrating Marine Corps equipment.
As part of the effort, the MDMC is using RPA and Microsoft Power Apps to make daily operations more efficient and reduce manual labor.
US Med-Equip streamlines hospital bed rentals
Houston-based US Med-Equip, which rents, sells, and services a range of movable medical equipment, has developed a one-click solution to order hospital beds. The solution involved retraining an existing ML model from accounts payable, and adding Microsoft code and RPA. With 93 locations nationwide, US Med-Equip can now deliver rental beds for patients more efficiently. The solution combines RPA and ML with robotic faxing called eFax, Microsoft Power Automate RPA, a Power AI OCR tool, and a retrained Microsoft Azure-based ML model to process the documents involved.
Siemens Mobility automates more than 700 processes
Since 2017, Munich-based Siemens Mobility has automated more than 700 processes and transformed its business along the way. Most of the work has focused on automating processes involving different software applications, with an emphasis on SAP integration.
RPA is now a full corporate unit at Siemens Mobility under the CFO. It has adopted a strategy with three primary approaches: the initiative approach (under which the RPA unit provides other business units with a framework, tools, and training to apply automation to their processes), a citizen developer approach (which focuses on supporting individuals in automating their personal processes), and a robotics-as-a-process approach (for developing and managing solutions to automate large, cross-departmental processes dealing with things like financial data, information security, and data protection).
What are the top RPA tools?
The RPA market consists of a mix of new, purpose-built tools and older tools that have added new features to support automation, some of which were originally business process management (BPM) tools. Some vendors position their tools as “workflow automation” or “work process management.” Overall, research firm IDC forecasts the market for standalone automation will grow to $77.5 billion by 2028, with AI-powered automation growing to $19.9 billion.
“Business automation platforms are at a pivotal juncture,” says Maureen Fleming, program VP of Worldwide Intelligent Process Automation research at IDC. “As AI and automation converge to support agentic automation, market leadership is not just about streamlining work, it’s about reimagining it. The future belongs to those successful in reimagining what their customers can build and deliver with agentic workflows, rather than focusing on incremental improvements that produce only incremental benefits.”
Top RPA tools vendors, according to Gartner, include:
- Appian RPA
- Automation Anywhere Agentic Process Automation System
- AutomationEdge
- IBM Robotic Process Automation
- Microsoft Power Automate
- Rocketbot
- SAP Build Process Automation
- SS&C Blue Prism Intelligent Automation Platform
- TruBot
- UiPath Platform
For a closer look at these vendors’ RPA offerings, see RPA buyer’s guide: Top 24 robotic process automation tools available today and 20 AI workflow tools for adding intelligence to business processes.
What are the criteria for choosing RPA tools?
There are 10 key factors to consider when choosing RPA tools:
- Ease of bot setup
- Low-code capabilities
- Attended vs. unattended
- ML capabilities
- Exception handling and human review
- Integration with enterprise applications
- Orchestration and administration
- Cloud bots
- Process and task discovery and mining
- Scalability
For a more in-depth look at these selection criteria, see RPA buyer’s guide: Top 24 robotic process automation tools available today.
What are the top RPA certifications?
As organizations increasingly adopt RPA, they also need individuals with expertise in RPA tools and implementations. Certifications can validate knowledge and proficiency in RPA tools and platforms, and many of the most popular RPA certifications are offered by vendors including:
- Appian Certified Lead Developer
- Automation Anywhere Certified Advanced Automation Developer
- Blue Prism Developer Certification (BPDC)
- Microsoft Certified: Power Platform Fundamentals
- UiPath Certified Professional (UCP)
8 tips for effective RPA
Implementing RPA can be challenging, given both the potential complexity of legacy business processes and the level of change management that can be required for RPA to succeed. The following tips can help your organization on its way.
1. Set and manage expectations
Quick wins are possible with RPA, but propelling RPA to run at scale is a different animal. Many RPA hiccups stem from poor expectations management. Bold claims about RPA from vendors and implementation consultants haven’t helped. That’s why it’s crucial for CIOs to go in with a cautiously optimistic mindset.
2. Consider business impact
RPA is often touted as a mechanism to bolster ROI or reduce costs, but it can also be used to improve customer experience. For example, enterprises such as airlines employ thousands of customer service agents, yet customers are still waiting in queues to have their calls fielded. A chatbot could help alleviate some of that wait.
3. Involve IT early and often
COOs were some of the earliest adopters of RPA. In many cases, they bought RPA and hit a wall during implementation, prompting them to ask for IT’s help (and forgiveness). Now citizen developers without technical expertise are using cloud software to implement RPA in their business units, and often the CIO has to step in and block them. Business leaders must involve IT from the outset to ensure they get the resources they require.
4. Poor design, change management can wreak havoc
Many implementations fail because design and change are poorly managed. In the rush to get something deployed, some companies overlook communication exchanges between the various bots, which can break a business process. Some CIOs will neglect to negotiate the changes new operations will have on an organization’s business processes. CIOs must plan for this well in advance to avoid business disruption.
5. Project governance is paramount
CIOs must constantly check for chokepoints where their RPA solution can bog down, or, at least, install a monitoring and alert system to watch for hiccups impacting performance.
6. Build an RPA center of excellence
The most successful RPA implementations include a center of excellence staffed by people responsible for making efficiency programs a success within the organization. Not every enterprise, however, has the budget for this. The RPA center of excellence develops business cases, calculating potential cost optimization and ROI, and measures progress against those goals.
7. Don’t forget the impact on people
Wooed by shiny new solutions, some organizations are so focused on implementation that they neglect to loop in HR, which can create some nightmare scenarios for employees who find their daily processes and workflows disrupted.
8. Incorporate RPA into your whole development lifecycle
CIOs must automate the entire development lifecycle or they may kill their bots during a big launch.
AI automation and the talent pipeline
AI automation is allowing companies to increasingly automate entry-level and routine work, which is reshaping the IT leadership pipeline. According to a recent report from SAP, the early-talent job market has experienced a 10% drop since 2021, with entry-level software engineer, customer support, and data analyst job openings dropping 35% from 2024 to 2025.
Organizations that reduce their entry-level hiring without rethinking how early-career talent develops risk creating a gap in their future leadership pipeline. Investing in early talent development programs may prove an important element of AI transformation.
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