RPA in the Age of AI: At the Core of Your Autonomous Enterprise

It wasn’t long ago that robotic process automation (RPA) was the cutting edge of enterprise automation. What began as macros and scripts to save keystrokes quickly expanded into scalable, rules-based automations that took on repeatable tasks at speed and accuracy levels unattainable by humans.

Now, with smart AI agents thinking their way through complex end-to-end processes, it might seem as if rigid RPA is on the way out. On the contrary, RPA is more critical than ever, and will be at the core of your autonomous enterprise.

What is RPA?

RPA quickly and reliably automates digital tasks such as collecting customer information, scheduling appointments, managing inventory, and providing customer service. These automations can be triggered by humans to assist with routine tasks, triggered by data, inputs, or schedules to operate without human intervention, or a combination of both — all working alongside your human workers.

RPA remains an enabling technology for enterprise automation because it offers an efficient, scalable way to automate processes and delivers speed, accuracy, cost reductions, and other benefits easily. Examples of RPA applications in action include:

Stant, a manufacturing enterprise, had an invoice backlog of 3 weeks. Using RPA, it automated 80% of invoice processing with zero errors and reduced the backlog to just a few days.
NHS, a healthcare provider in the UK, used RPA to take on repetitive Human Resources tasks and save 7,000 hours annually.
KeyBank, a financial services firm, used RPA to automate mortgage quality checks by processing 40,000 documents automatically for a savings of $5 million.

RPA excels at these types of repetitive tasks, which form the foundation of many enterprise processes. Even the most complex, strategic processes depend on basic tasks to extract and organize information, schedule conversations, assign and track approvals, manage reports and deliverables, and more. These tasks are inherently rule-based and rarely require deep cognitive thinking to execute. However, they remain crucial to informing and advancing higher-level tasks and processes.

The evolution of RPA

Even as the talk turns to intelligent automation, agentic process automation (APA), and the autonomous enterprise, RPA is still there to do the core work needed to support more strategic tasks. Here’s how those innovations build on RPA.

Intelligent automation boosts RPA’s impact and value

Intelligent automation merges AI with RPA to automate complex processes involving unstructured data, decision-making, and learning. RPA might collect structured data required for a weekly report, such as invoice total amounts. But that becomes more complex when the data is unstructured in a form field or requires validation before it can be used with confidence.

AI can step in for these outliers to find keywords in unstructured data that determine things like the order number, vendor details, and invoiced amounts by line item to then find or validate the assumed invoice totals in other enterprise systems. Intelligent automation can even handle RPA exceptions, correct discrepancies, and flag items that require human intervention.

Intelligent automation adds value, scalability, and speed by building on the tasks already automated by RPA and reducing the need for human workers to get involved. If RPA can process, say, 80% of incoming invoices autonomously, intelligent automation enlists AI to increase that number to 95%.

So then, how do you increase that to near 100% and add adjacent tasks? APA gets you there.

APA widens RPA’s scope

AI agents make decisions, adjust to new conditions, and take action to run intelligent automations without human intervention. AI agents further utilize large language models (LLMs) to learn from data and context, engage with humans through natural language, synchronize workflow execution through integrations, and ultimately take action to achieve goals.

APA manages and orchestrates AI agents to handle more complex tasks, collaborate with other specialized AI agents, and complete entire process autonomously. This vastly increases the efficiency and productivity of human workers, giving them more time to use their ingenuity, empathy, and creativity to focus on troublesome issues, high-priority customers, high-value initiatives, and more.

Now, instead of simply minimizing human intervention on a single process, APA lets you expand and scale automation into more end-to-end enterprise processes, all driven and orchestrated by AI agents. But, again, you’re building on the core work of RPA. Even as APA takes on your complete invoice matching and reconciliation process, it relies on RPA to initially match the bulk of incoming invoices with order numbers, for example.

Enterprise autonomy unleashes human potential

Too many headlines have sensationalized the topic of AI displacing human workers. Now, more and more business leaders are see AI as freeing humans to focus on growth, innovation, and speed, and economists predict AI will have minimal impact on employment rates.

These examples underscore the shifting view of AI as empowering people rather than replacing them:

Cisco’s CEO says, "I don't want to get rid of a bunch of people right now. I just want our engineers that we have today to innovate faster and be more productive. That gives us a competitive advantage."
AMD’s CEO says, "We’re still hiring more and more engineers because they’re the final arbiters of our engineering."
Goldman Sachs economists write, "We remain skeptical that AI will lead to large employment reductions over the next decade."
Economic Innovation Group researchers say, "[There won’t be] any meaningful AI impacts in the labor market."
PwC research finds, "In contrast to worries that AI could cause sharp reductions in the number of jobs available – this year's findings show jobs are growing in virtually every type of AI-exposed occupation."

Automating more processes gives your people more time to develop new business models, be creative, uncover new growth opportunities, delight more customers, and discover innovative solutions.

This is the autonomous enterprise: a full embrace of all levels of automation to create self-learning AI platforms that manage and govern processes with minimal intervention. Humans can focus on more strategic, cognitive work while AI agents make decisions and solve problems in the background.

Don’t know how to begin your autonomous enterprise journey? Read "The Capability Maturity Model for Collaborative Intelligence," our white paper detailing a framework for operationalizing AI to elevate human effort by benchmarking your enterprise’s AI-driven maturity.

RPA isn’t dead; it’s at the core of your autonomous enterprise

We live in an age of self-driving electric vehicles and high-tech hypercars with 2,300 horsepower. You know what gets them from point A to point B? Wheels. Yep, every car is built on proven, dependable 6,000-year-old tech.

That’s a bad analogy, but it drives (pun intended) home the point that even the most advanced vehicles rely on a dependable, proven technology to operate.

A better analogy is that RPA is at the core of your autonomous enterprise. And, as you build APA on top of RPA, the people who form the backbone of your organization will be supercharged to apply their intuition, emotional intelligence, and ingenuity in solving problems and making cognitive decisions that catalyze growth and expansion.

You can’t get to the autonomous enterprise without RPA, just like you can’t go for a drive without some wheels.