Robotic Process Automation

Shortly

In this article, I’ll explore the evolution of automation — from the early days of rule-based RPA to the modern, AI-driven systems reshaping workflows today.

The question we’re really asking is: is automation still evolving, or has it reached a turning point?

So, what is RPA really first?

Robotic Process Automation (RPA) uses software “bots” to handle repetitive, rule-based tasks across enterprise systems, freeing people to focus on higher-value work.

According to Wikipedia:
Robotic process automation (RPA) is a form of business process automation that is based on software robots (bots) or artificial intelligence (AI) agents. RPA should not be confused with artificial intelligence, as it is based on automation technology following a predefined workflow. It is sometimes referred to as software robotics (not to be confused with robot software).

Originally celebrated for its rapid deployment and clear ROI, RPA is now evolving alongside advances in AI, orchestration, and cloud-native platforms.

This evolution raises an important question: what role will RPA play in the age of intelligent automation?

Brief history lesson

Most of us deal with tasks that could easily be automated, whether through AI-driven tools or simple rule-based systems.

While exploring this topic (to be transparent, it was more of a deep Google dive than formal research), I came across eight different sources.

Strangely enough, they all felt almost identical — like a copy-and-paste chain passed from one website to another, just under different names.

Still, I’ve shared them as references at the end of this article.

Interestingly, most of these sources trace the roots of RPA back to the 1990s.

But there’s one exception:
https://www.nandan.info/history-of-robotic-process-automation-rpa/

This article traces RPA back to the early 1970s, showing that it’s far from a new trend — it has been evolving for more than five decades.

Anyway, let’s focus on summarizing its history:

Computerized Automation (1970–1990)
Roots and Precursors (1990s → early 2000s)
Commercialization and the First Vendors (early-to-mid 2000s)
Enterprise Adoption and Maturity (2010s)
Shock, Scale, and the Pandemic (2020)
From Rule-Based Bots to Intelligent/Agentic Automation (late 2010s → 2020s)
Current State and Outlook

For more details on each stage, you can refer to the sources I’ve shared below.

Popular RPA Tools

When it comes to RPA, there’s no shortage of tools out there — each with its own use cases.

Some of the names you’ll often hear include UiPath, Automation Anywhere, Blue Prism, and Microsoft Power Automate.

Personally, exploring them feels a bit like walking into a candy store: each one promises to make repetitive work vanish, but in slightly different ways.

The key is finding the tool that fits your workflow, team skills, and the type of tasks you want to automate.

What happened to RPA?

After our brief introduction, I want to turn to the main focus of this article: what actually happened to RPA?

I first heard about RPA in 2023, through a job announcement — one of the banks was looking for an RPA developer.

Naturally, this caught my attention, and I decided to dig deeper.

The role sounded genuinely interesting: as an RPA developer, you research which tasks can be automated, determine how to automate them, identify the necessary resources, and evaluate which tools are best suited for the job.

It’s a mix of strategy, technology, and problem-solving, which immediately intrigued me.

In recent years, automation has taken a new direction. Today, we hear a lot of buzzwords — AI, intelligent agents, smart bots, and more.

Despite the different names, they all share the same fundamental goals as RPA:

Cutting costs
Increasing workflow speed
Boosting productivity and revenue
Reducing human errors

The tools and terminology may have evolved, but the core purpose remains the same.

Perhaps I’m not the only one who finds the lines a bit blurred.

If AI and RPA are essentially achieving the same goals, why do we give them different names?

And why do we treat them as separate terms?

At the end of the day, all of these technologies are implemented with a single aim: to increase efficiency and, ultimately, revenue.

For a closer look, I’ll reference Wikipedia as an authoritative source:

In traditional workflow automation tools, a software developer produces a list of actions to automate a task and interface to the back end system using internal application programming interfaces (APIs) or dedicated scripting language.

Even wikipedia confirms that similarities to other tools:

RPA tools have strong technical similarities to graphical user interface testing tools. These tools also automate interactions with the GUI, and often do so by repeating a set of demonstration actions performed by a user.

So, why are we still treating them as separate terms?

During my research, I noticed several emerging trends:

New fields for automation — automation is expanding beyond traditional boundaries.
It’s no longer limited to rule-based systems — the scope has grown far beyond simple scripted tasks.
AI as a tool in RPA development — artificial intelligence is increasingly integrated to enhance automation capabilities.

These trends show that while the terminology may differ, the underlying goal of improving efficiency and productivity remains the same.

Today, we’re no longer limited to classic RPA tools when it comes to automating tasks and workflows.

Alongside the big enterprise platforms, we now have a wide range of options — from free, open-source solutions like n8n, to paid, closed-source services like Zapier.

This variety makes automation more accessible than ever, whether you’re a developer building custom flows or just someone looking to simplify everyday work.

I’m also aware that some people might be rolling their eyes at this point, thinking: “What is he even talking about? AI, agents, reinforcement learning, automation, RPA — aren’t these all completely different concepts?

What kind of article is this?”

And honestly, that reaction makes sense.

These are distinct terms in the technical world, each with its own definition and scope.

But from a practical perspective — especially for people just trying to get work done — they often blur together.

I completely agree with and respect all of these perspectives — each concept has its own place and meaning.

But at the same time, let’s pause and reflect: aren’t all of them, at their core, about the same thing — automation?

At the end of the day, whether we call it RPA, AI, or something else, the common goal remains: making our processes faster, more efficient, and less dependent on repetitive human effort.

So why do we keep creating endless boxes of terminology, debating definitions, and separating them as if they belong to different worlds?

The answer, I believe, is simple: money.

New terms create new markets, new job titles, and new ways to sell the same underlying idea.

At last, what we truly want are technologies that deliver real value.

And like every other technology, automation has not disappeared — it has transformed.

In the first stage of automation, we were limited to programming rule-based systems that followed predefined, deterministic outputs: if the result is 1, do X; otherwise, do Y — and so on.

It was straightforward, but also very rigid.

Today, the picture is completely different.

We now have tools that can handle almost any kind of output, and in many cases, they don’t even rely on strict predefined rules.

Instead, they can adapt and solve problems dynamically within their environment, opening the door to far more flexible and intelligent automation.

We automated chess as early as the 2000s with Deep Blue, and for a long time many believed that games like Go were beyond the reach of machines. Yet even that fortress eventually fell with AlphaGo.

Fast forward to today, and we’re seeing automation reach an entirely new level — this time in software engineering itself, an industry worth more 50 billion USD.

What once seemed unimaginable is now becoming reality.

When we step back and look at this entire evolution from above, it feels like watching history unfold from the sky — one breakthrough after another, each expanding the horizon of what machines can do.

At its core, this article is about automation.

In my opinion, the evolution of automation can be seen like this:

Rule-based systems (scripting languages, RPA)
Workflow automation (tools like Zapier, n8n)
Reinforcement learning (minimum human in the loop)
Agents in the loop (workflows enhanced by LLMs)

But it’s important to note that this progression hasn’t been strictly chronological.

These areas are developing asynchronously, often in parallel, influencing and overlapping with each other rather than following a neat, linear path.

So, we are still witnessing the dominance of rule-based systems as a primary technological layer, even while other approaches — like Agentic AI and robotics AI — are still in their emerging stages.

But this naturally leads to the question: what comes next?

With the rapid evolution of AI, the lines between automation and standalone decision-making have become increasingly blurred.

This means we should start approaching these terms in a way that goes beyond traditional academic definitions.

Instead of separating them into rigid categories, we need a more fluid perspective — one that reflects how these technologies are actually converging in practice.

Let’s finish

So, is RPA dead? Not really.

What we are witnessing is not the disappearance of RPA, but its transformation into a broader landscape of automation where AI, agents, and intelligent workflows play alongside traditional bots.

From the rigid, rule-based scripts of the past to today’s adaptive, AI-powered systems, automation has constantly reinvented itself.

Each stage brought us closer to tools that can handle more complexity, with less human intervention, and deliver greater value.

The terminology may keep changing — RPA, AI, agents, automation — but in practice, they are all part of the same continuum.

What matters is not the label, but the outcome: technologies that free us from repetitive work, reduce errors, and help us focus on creativity, strategy, and innovation.

In that sense, automation is very much alive.

It hasn’t met its asteroid — it’s still evolving, reshaping industries, and redefining the way we work.

The real challenge for us is not to debate the terms, but to learn how to harness this transformation in a way that creates meaningful, lasting value.