RPA (Robotic Process Automation) is a practical and proven way to reduce manual labor in business processes without using AI systems. By using software bots to follow fixed rules, companies can automate some repetitive tasks such as data entry, invoice processing, and report generation. Adoption has been rapid in many areas, especially finance, operations, and customer support.
In recent years, technology has matured. RPA is still being used, but business processes can become more complex. Many systems process unstructured data such as messages and documents. Rule-based automation has a hard time processing these inputs because it relies on predefined steps and structured formats. RPA works best in stable environments where processes do not change frequently. As conditions change or inputs change, bots can fail or require updates, adding maintenance overhead and reducing the value of automation over time.
Gartner pointed to more adaptive automation systems on the market that are designed to handle variability and uncertainty, and that combine automation with machine learning or language models to enable processing a broader set of inputs.
From RPA rules to AI-driven automation
AI has changed the way companies think about automation, as systems from vendors already known in the RPA space, such as Appian and Blue Prism, can interpret task-related context, especially including text and images, and adjust activities.
Large-scale language models have the ability to summarize documents, extract important details, and respond to queries in natural language, enabling automation in areas that were previously difficult to manage. According to a study by McKinsey & Company, generative AI has the potential to automate decision-making and communication tasks, rather than routine data processing.
This change does not replace automation, it modifies automation. Rather than building chains of rules, companies could use AI to handle variations in input media. Automation becomes more flexible as the system can be adjusted to different inputs without reconfiguring it.
That’s the theory. AI systems produce inconsistent output and their behavior is unpredictable. Businesses can combine AI with existing automation tools and use each where it makes the most sense. Getting the balance right – intelligent automation – is a hot topic at industry events and in the pages of RPA and AI media.
Where RPA still fits with AI
Despite these changes, RPA remains important in many situations. Tasks that involve structured data and stable workflows can also benefit from rules-based automation. Common examples include payroll processing, compliance checks, and systems integration.
In these situations, the predictability of RPA is an advantage. Bots follow defined steps and produce consistent results, making them useful in regulated environments. For example, financial reporting and auditing processes often require strict controls and traceability.
Rather than replacing RPA, it is often used in conjunction with AI. Automation workflows start with an AI system that interprets the input, then passes the structured data to an RPA bot for execution. This combination allows companies to scale automation without decommissioning existing systems.
Blue Prism and the change to intelligent automation
Vendors that built their businesses around RPA are adapting to this change. Blue Prism, now part of SS&C Technologies, has expanded its focus to include what the company calls intelligent automation. This approach combines RPA with AI tools that can handle more complex inputs.
Platforms combine automation with features such as document processing and decision support, often through integration with AI tools.
The move towards AI-powered automation will also change the way platforms are used. A workflow combines data sources, decision points, and execution steps into a single process.
Gradual migration rather than complete replacement
Many organizations continue to rely on their existing RPA systems, especially when the processes are stable and well-understood. Replacing these systems is time consuming and expensive, and is not always justified.
Rather, change occurs in stages. While companies can add AI capabilities to expand what can be automated, RPA is still being deployed for tasks that are well-suited. This may change the way automation is designed and deployed over time, but rules-based systems are still needed.
See also: AI agents enter banking at Bank of America
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