RPA (robotic process automation) uses software robots that mimic what users do in applications, such as clicking, typing and copying data, to run repetitive, rule-based tasks. AI adds the ability to understand unstructured data like emails, documents or voice. As IBM puts it, RPA is process-driven, whereas AI is data-driven. Combined, they form intelligent automation.
Key takeaways
- RPA (robotic process automation) automates repetitive, rule-based tasks with software robots that mimic a user's actions.
- RPA is process-driven and AI is data-driven: the first executes, the second interprets text, images or voice.
- Together they form intelligent automation, which can process emails or documents end to end.
- The best candidates are high-volume, stable processes with structured data, documented and optimized before they are automated.
Automation is rapidly transforming the work landscape. Robotics and artificial intelligence (AI) are gaining traction in businesses, improving processes and making employees' daily lives easier. In this dynamic environment, Robotic Process Automation (RPA) holds a prominent position. Unlike industrial robots or advanced technologies, this solution allows for rapid task automation without modifying existing systems. Its relatively fast implementation and its gains on high-volume tasks appeal to many decision-makers.
This raises a crucial question: what will be the impact of these future technologies on businesses and their employees? This article sheds light on the future of RPA and AI, their joint advancements, and how they are ultimately shaping new forms of work.
What is RPA?
RPA refers to a set of technologies that automate repetitive and time-consuming tasks typically performed by humans. Essentially, software robots take over from employees to perform these tedious activities in their stead.
These robots operate by capturing the actions performed by employees on their workstations. They replicate these actions identically by following well-defined business rules. Unlike AI, classic RPA is not autonomous: its robots merely imitate human tasks to relieve employees of mundane and/or repetitive daily tasks.
Furthermore, RPA continues to evolve. Vendors now add artificial intelligence capabilities to it, which is why people increasingly talk about intelligent automation.
How does an RPA bot work?
An RPA bot works at the user-interface level of existing applications, just as a person would: it opens an application, reads a field, enters a value and moves to the next screen. It can also call APIs when they exist. Bots run in two modes:
- Attended RPA: the bot runs on the employee's workstation, on demand, to handle part of their task.
- Unattended RPA: the bot runs on its own on a server, on a schedule or a trigger, to process large volumes.
The most common use cases involve high-volume processes with stable rules:
- Finance: invoice entry and matching, payment reminders, bank reconciliations.
- HR: creating accounts and files when an employee joins, administrative updates.
- Customer service: order entry, customer record updates.
- Supply chain: inventory tracking, delivery status updates.
Before automating, document the process precisely: a bot faithfully reproduces a procedure, flaws included. These projects also change the daily work of the teams involved and call for proper change management.
The benefits of RPA
Improved productivity
As mentioned, RPA transforms employees' daily work by freeing them from tedious and time-consuming tasks. Employees see their productivity increase as they spend less time on procedural and repetitive activities. This allows them to focus on higher-value tasks that require creativity and analysis.
To go a bit further, we observe that RPA refocuses employees on their core business and customer service. It enables them to upskill in strategic activities: complex project management, predictive analytics, and the creation of new, innovative products and services.
We now speak of "augmented employees" to refer to those assisted by software robots. Thanks to this unprecedented human-machine collaboration, their performance significantly improves.
Automated business workflows
RPA doesn't just automate isolated tasks; it also applies to complete workflows. In this way, it connects different business processes to create optimized end-to-end workflows.
Let's take the example of a company's invoicing process. Allowing a robot to issue a single invoice is good. But interconnecting all the steps – quotes, customer orders, invoice sending, unpaid reminders – to create an end-to-end flow, that's significantly better!
RPA thus orchestrates the sequence of automated tasks and necessary human validations within this overall workflow. Data flows seamlessly between different systems. The result: faster, more reliable and seamless business processes.
This high level of integration brings real added value to businesses. It streamlines information exchange and strengthens operational consistency. Leaders also gain better visibility to effectively manage activities and identify areas for improvement.
A driver for improving working conditions
RPA can also improve working conditions in several ways:
- Increased motivation: Freed from thankless tasks, employees are more fulfilled and engaged in their work.
- Reduced stress: The elimination of repetitive and tedious activities reduces fatigue and psychosocial risks.
- Enhanced job roles: employees focus on higher value-added tasks, which enhances their skills.
- Increased autonomy: by refocusing on their core business, employees gain more responsibility and room for maneuver.
- Fostering creativity: freed from time-consuming tasks, employees have more time to propose innovative ideas.
- Reduced turnover: the reduced arduousness of certain positions can help retain employees.
Thus, in addition to productivity gains, RPA positively transforms the daily lives of teams. This should motivate companies to take the plunge!
AI: the future of RPA?
RPA is currently experiencing a resurgence thanks to recent advancements in AI. By integrating cognitive components into software robots, RPA solution providers are expanding their capabilities. We now speak of intelligent or cognitive RPA.
Specifically, these next-generation robots already analyze unstructured data such as emails, scanned documents, or even voice conversations. They understand the meaning of texts and user requests to trigger the correct automated processes.
For example, a robot with natural language processing (NLP) capabilities can analyze the content of an incoming email to automatically determine its priority level and forward the request to the appropriate department.
Computer vision even allows robots to read and understand paper documents to extract structured data.
Thus, this alliance between RPA and AI is shaping a new generation of intelligent robots, capable of understanding their environment and making automated decisions. It paves the way for end-to-end process automation with minimal human intervention.
RPA vs AI: what's the difference and how do they work together?
RPA and AI are often confused. According to IBM, "RPA is process-driven, whereas AI is data-driven": RPA bots follow processes defined by a user, while AI learns patterns from data.
| Criterion | RPA | AI |
|---|---|---|
| Principle | Executes predefined rules | Learns patterns from data to predict, classify or generate content |
| Data handled | Structured: fields, tables, forms | Also unstructured: text, images, voice |
| Facing the unexpected | Stops or flags an exception | Proposes a probabilistic answer that must be checked |
| Example | Copies an invoice into the ERP | Reads a scanned invoice and extracts the amounts |
Combined, they form intelligent automation, which AWS describes as using AI and RPA to create complex software automation: AI understands the document or request, and RPA carries out the steps in the applications. AI agents, which chain several actions toward a goal, extend this logic but need guardrails: human approval of sensitive decisions, traceability and control over the data they use. To place these tools in an overall plan, see our digital transformation guide.
Conclusion
RPA is already changing the professional world by freeing employees from tedious tasks, enabling companies to increase productivity while improving working conditions. The advent of cognitive robotics equipped with artificial intelligence further pushes the boundaries of automation by introducing more autonomy into administrative processes. Ultimately, this skillful human-machine synergy will reshape our relationship with work, paving the way for a virtuous collaboration where humans and technology work together for the progress of our organizations.
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Frequently asked questions
Is RPA a form of AI?
No. Classic RPA is not artificial intelligence: it follows rules defined in advance and repeats the same actions without learning. AI learns from data and can interpret unstructured content. The two are complementary, and many RPA platforms now embed AI components such as document understanding or natural language processing, which is called intelligent automation.
Is RPA being replaced by AI?
AI is not replacing RPA so much as extending it. RPA remains the reliable way to execute rule-based steps in existing applications, especially legacy systems without APIs. AI handles the parts that require interpretation, such as reading an email or classifying a document, and AI agents can orchestrate several steps. Most organizations combine them rather than choosing one.
Which processes should you automate first with RPA?
The best RPA candidates are high-volume, repetitive processes based on stable rules and structured data: invoice entry, account creation, customer record updates, recurring extracts and reports. A process that changes often, has many exceptions or depends on human judgment is a poor fit for RPA alone and may need AI or a redesign first.
What are the limitations of RPA?
RPA depends on application interfaces: a screen or procedure change can break a bot, so bots need regular maintenance. RPA alone cannot handle ambiguous cases or unstructured data, and scaling from a few bots to an enterprise program is hard; IBM cites a Forrester finding that 52% of customers struggle to scale their RPA programs. Finally, RPA automates a process as it is, so optimize it first.
What is hyperautomation?
Hyperautomation is an approach that aims to automate as many business processes as possible by combining several technologies: RPA, artificial intelligence, business process management (BPM), process mining and low-code platforms. It goes beyond isolated tasks and redesigns end-to-end processes and how they are monitored.
