The fear that robots will steal our jobs has been around for years, and let’s face it: they probably will. We all know it. What many don’t know is that the inevitable disruption caused by artificial intelligence (AI) and robotic process automation (RPA) will entirely redefine the role of humans in work processes. But will that change be for better or worse?
While we await the answer, businesses fall prey to the hype around generative AI and RPA and rush to implement them in pursuit of efficiency, cost savings, and other enticing promises. Brands crave GenAI-powered applications and RPA software, but many still lack an understanding of how to capture their value.
Without doubt, merging RPA with AI technology has opened a whole new world of intelligent automation that was once out of reach. RPA was already making big strides before AI came to the fore, but AI has introduced a layer of intelligence, elevating RPA to hyperautomation and positioning the RPA market to surpass $14 billion by 2029.
In this article, we explore how you can effectively combine RPA and AI to achieve tangible business outcomes – and where humans fit into the equation.
Evolution of RPA technology: From desktop automation to intelligence process automation and beyond
From the invention of the wheel to robotic process automation (RPA), technology has evolved through the ages to relieve people of mundane, repetitive work by offloading human labor to mechanical and autonomous systems. Today, almost every business function involves tons of operations that organizations seek to automate by introducing new solutions.
Let’s take a walk down the evolution lane to see how business process automation started and what’s next for the world of intelligent automation.
2000: Desktop automation
Back in the early 2000s, business process automation was barely on the radar. Its infant form, desktop automation, came down to simple, tactical approaches where specific actions or tasks were automated with the help of macros and scripts.
2010: Robotic process automation
Over time, those early automation efforts evolved into a more comprehensive back-office business process which was, however, entirely rule-based and still relied heavily on manual human intervention.
Just a quick example: Suppose your company receives thousands of invoices that must be manually entered into the accounting system. An RPA bot could automatically approve and process all invoices under, say, $1000. Invoices exceeding that amount were sent for manual approval, requiring human judgment for more complex decision-making.
Another limitation of early robotic process automation was that it only allowed for processing structured data (for example, from spreadsheets or form fields). Before an RPA tool could handle a task, you needed to standardize every document. Dealing with unstructured information formats (emails, chats, handwritten and scanned documents, natural speech) demanded cognitive capabilities, so only partial automation of document processing was possible. Until artificial intelligence (AI) and machine learning (ML) came along.
2015: Cognitive automation
AI and ML technology made it possible to work with unstructured data, allowing organizations to significantly improve process automation. Combining RPA with AI not only enhanced back-office operations but also opened the door for front-office automation. This was when the first chatbots and virtual assistants cropped up, powered by natural language processing (NLP).
If RPA imitates what people do, AI imitates how they think. The key difference between RPA and AI is that while RPA tools rely on static bots to perform repetitive high-volume tasks, AI solutions, powered by machine learning and neural networks, learn and evolve over time as they acquire more data and experience. AI can make cognitive decisions, such as sorting incoming emails and directing them to the right teams, interpreting visual information much like a human would, and processing voice input to interact with customers.
2020: Hyperautomation
The success of early automation programs gave impetus to organizations to automate more processes, using AI-enabled RPA technologies to achieve end-to-end journey automation for comprehensive document workflows and streamlined operations.
Meanwhile, the outbreak of the COVID-19 pandemic struck a severe blow to the global economy, posing unexpected challenges for business. Suddenly, companies were forced to reinvent their workflows and optimize costs by adopting enterprise-wide automation strategies.
The interplay of these two powerful drivers gave rise to hyperautomation, an approach focused on automating every possible process within an organization. Hyperautomation brought with it a host of intelligent automation methods and solutions – such as low-code/no-code apps, smart analytics, intelligent document processing, process mining, and human-in-the-loop decision intelligence – enabling companies to take their operations to a whole new level.
For example, process mining, based on tracking the actions of operations specialists and analyzing data in internal systems, reveals the current state of workflows within an organization. By using audit trail logs, this method provides insights into how a process unfolds, detects anomalies and deviations, and enables restructuring and improvement of the process to optimize expenses for operational activity.
2023: Generative AI automation
When GenAI stormed into almost every aspect of business, a new epoch of intelligence process automation started. GenAI-powered solutions drive today’s most advanced technologies, including natural language understanding (NLU), sentiment and tone analysis, autonomous decision-making, predictive analytics, automatic generation of content (text, emails, code, images, and videos), and software source code generation.
Deemed as a technology with nearly endless potential that pushes the boundaries of what’s possible, generative AI has set the expectation bar high. Businesses are diving headfirst into the new opportunities and business models that GenAI offers. However, new technology doesn’t guarantee instant results: it’s a tool, not a magic button.
Here’s what the synergy between GenAI and RPA can look like – and the impact this powerful combination can have if used effectively.
Generative AI and RPA: Two halves of the brain
It is easier to understand the differences between RPA vs AI and how they work together if you think of them as two sides of the human brain. The left hemisphere is responsible for logical thinking, executing detail-oriented tasks, following methodical step-by-step processes, and performing complex calculations. In contrast, the right brain plays a major role in perceiving, processing, and interpreting information, recognizing patterns, and connecting ideas to the bigger picture.
Just as the two brain hemispheres work in harmony to create a balanced cognitive experience, RPA and AI can collaborate to deliver holistic automation strategies and intelligently orchestrated workflows. While RPA handles structured, rule-based, and mechanical tasks, AI agents bring human-like intelligence to enable dynamic and flexible decision-making.
Here’s a quick overview of these two approaches to business process automation, each with its unique features and capabilities.
RPA vs APA comparison
Source: Bot Nirvana
We may still be some steps away from the future of touchless automation, where RPA and AI models can flawlessly manage all enterprise operations with zero human oversight. But we’re well on our way, advancing through key transitional stages on this journey.
Tactical RPA
Putting RPA technology in the driver’s seat of your operations, with GenAI as a supporting tool, allows you to efficiently handle a variety of tactical tasks. For example, you can add a large language model (LLM) tool to your RPA solution to perform text-related tasks, including generating content, analyzing text, and extracting insights.
Another effective use of GenAI on the tactical level is for self-healing automation. When a human interacts with a system through a user interface, any modifications of UI elements – such as changes to a button or form field – are intuitively recognized by the human operator. However, with automated UI processes, these changes require code updates so the automation system can understand them.
This was the case with one of our clients who faced ongoing issues with unreliable UI integration. Every time the interface changed, their automated workflow would fail, as it was tied to the UI’s underlying code. Our team developed a GenAI-driven solution that dynamically detects modified UI elements and regenerates the necessary code, restoring automated workflows and allowing the UI to easily adapt to changes.
While AI-enabled RPA excels at resolving various tactical tasks, its impact remains limited compared to the transformative potential of the next frontier: agentic process automation.
Agentic process automation (APA)
Since GenAI entered the scene, business process automation has taken a new turn. LLMs have evolved from supplementary tools to the core decision engines driving RPA workflows. RPA and AI agents still operate in tandem, but AI takes the lead in deciding how to streamline operations and dynamically adapt workflows.
Instead of giving an AI agent a sequence of steps to follow, you simply equip it with all the necessary tools and assign it a task to complete independently. The AI then decides for itself which tools to use and which steps to take to achieve the desired result.
Let’s say you need to create a consolidated quarterly financial performance report. You give your AI agent access to the financial system, providing it with ETL (extract, transform, load) tools for data collection, NLP tools for handling unstructured data formats, and analytics tools for data processing and analysis.
The ML model then builds its own approach, creating steps and workflows to efficiently handle this task.
- First, it gathers structured data from databases, Excel sheets, and CSV files, as well as unstructured data from scanned documents, PDFs, or emails.
- Then, the model applies anomaly detection to catch any unusual transactions, trend analysis to identify revenue trends, and predictive modeling to forecast future financial performance.
- Finally, the model generates a comprehensive financial report, combining data tables with text summaries, visualizations, and key insights derived from both structured and unstructured data sources.
GenAI streamlining a traditionally time-consuming task like financial reporting is only scratching the surface of what APA has the potential to achieve.
A truly representative example of how AI agents can handle drudge work is Anthropic’s Claude 3.5 Sonnet model with its groundbreaking new beta functionality: using a computer like a human to automate operations. This is the first instance of GenAI interacting directly with a UI, where it looks at the screen, moves the cursor, clicks buttons, and types text.
When the model receives text instructions — such as a request to import new contractors into an ERP system — it autonomously performs all necessary operations: searching through tabs, collecting the requested information, filling out forms, and even taking screenshots for oversight. This approach could be scaled for similar tasks across various domains.
However, deploying APA systems might present challenges and must be strategically managed to maximize long-term benefits.
Implementing APA: Challenges and solutions
APA technology is taking off fast, but it still has room to mature before becoming a fully trusted tool for businesses. At Intellias, we’re rigorously looking into the potential challenges that organizations might encounter on their digital automation journey and proactively developing solutions to support our clients every step of the way.
The first challenge is ensuring the reliability of your model. AI in general, and GenAI in particular, are inherently non-deterministic in terms of their work and approach. This means they can’t guarantee 100% accuracy and there’s always the chance that your model will produce incorrect results. Because LLMs are prone to hallucination and can generate inaccurate outputs, they can’t be treated as the ultimate source of truth.
To mitigate the impact of AI hallucination, you need to clearly define the boundaries for a model to help it better understand its tasks and complete them more effectively. Another vital step is to thoroughly test the model before deploying it as well as to continuously evaluate its performance, retraining the model as data evolves or becomes obsolete.
The second challenge is ensuring that GenAI agents make accurate and safe decisions. If you give AI complete freedom to use your tools and make real-time decisions, it may end up deleting important transaction information, sending money to the wrong account, or disrupting your business operations in any other way.
Safe and transparent decision-making requires proper governance, continuous monitoring and testing, and thoughtful human supervision.
To address the risks associated with AI and protect citizens from its potentially adverse effects, the European Parliament adopted the EU AI Act, the world’s first comprehensive legislation that fosters responsible AI implementation within the European Union. Conversations about the responsible use of AI are now taking place globally, with many countries looking to the EU AI Act as a model for their own regulatory frameworks.
The principles of responsible AI by design must be hardwired into your APA system from the outset. Every decision your AI agent makes should be tracked to maintain a comprehensive audit trail of its steps, and every critical action should be approved with human oversight. In addition, integrating fail-safes and fallback mechanisms enables seamless error handling. Finally, rigorous testing is an indispensable part of the implementation process, ensuring reliability and precision.
As a committed advocate for responsible AI, Intellias heavily invests in overcoming AI-related challenges, actively examining industry regulations, and promoting ethical AI concepts and practices. To support businesses in their AI initiatives and projects, Intellias offers guidance in strategic AI governance, helping companies establish responsible business practices and innovate safely.
From IntelliAssistant to AI agents
Intellias has always been in the business of empowering clients with the latest tech innovations. So when AI and RPA took center stage, our R&D team responded by creating IntelliAssistant, an AI-powered copilot platform.
Today, IntelliAssistant is an award-winning AI solution that demonstrates how state-of-the-art technology and meticulous software engineering approaches can come together to help businesses achieve unprecedented levels of effectiveness and reliability.
Here are some of the ways how IntelliAssistant optimizes our business processes and increases workflow efficiency for our clients.
- IntelliAssistant does all the resource-intensive work to enhance our sales team’s productivity in processing requests for proposal (RFPs). It efficiently analyzes documents and accurately extracts data insights, eliminating the need for manual document review and creating tailored, high-quality RFP responses that fit evolving requirements.
- The bot effectively processes client requests, scanning our enterprise database of 7,000 skills and 140,000 employee references to identify top candidates with relevant project experience, knowledge, and certifications. It then generates detailed candidate profiles and can even estimate an approximate budget and timeline for a project based on past data.
- IntelliAssistant advances career growth for our employees by processing career development requests in just 30 seconds, compared to the 25 minutes it typically takes an HR manager to handle each request manually – increasing response and processing speed by 50 times.
- Notably, IntelliAssistant serves as an accelerator for building AI tools, allowing companies to automate workflows and streamline productivity. The solution provides our clients with cloud-driven benefits such as cost savings, enhanced performance, and scalability while accelerating the development and launch of AI tools.
The capabilities of IntelliAssistant keep growing as our engineers continue to innovate and enhance its functionality. At the same time, we’re building on the experience we’ve gained with this tool and moving forward to create AI agents.
One of our recent developments is an AI trading agent designed to connect all the dots in the complex procurement process for a large-scale manufacturer. Managing high volumes of incoming data and the urgency of operations made it a major challenge for our client to efficiently and consistently coordinate contracts, orders, purchases, and deliveries.
Any errors or delays came with a steep price, costing the company millions of dollars each year. If an operator mistakenly placed an order for the wrong date, it could result in higher prices that harmed the company’s bottom line. Or if trade records were not accurately maintained, it could cause lost data and delays in contract payments, ultimately leading to financial losses.
Our team developed an AI-powered agent that not only orchestrates all trading operations, automatically optimizes costs, and ensures compliance but also takes charge of the decision-making process.
The agent includes a chatbot interface that consolidates order requests from all of the company’s departments. It then decides whether an order meets the criteria for price and total amount and can be approved automatically or if it needs to be forwarded to the procurement team for final approval. The agent also analyzes market prices, ensuring materials are purchased at the most competitive rates.
Our AI agent solution serves as an integral part of the client’s team, significantly reducing the workload for human trading agents and allowing them to focus on higher-level work. It’s also an invaluable tool for standardizing integration processes where diverse, distributed operations need to be unified – a task that would be quite a feat to handle manually.
Rethinking the human role in the age of GenAI and RPA
If RPA software can replace intellectual human labor and AI agents can make autonomous decisions, what is the role of humans in the future of work?
While APA offers cognitive, dynamic decision-making and minimizes the need for human intervention in routine tasks, it can’t replicate the intuition, experience, and discernment that only humans can bring to the table. The challenge lies in adapting to the new normal and learning how to configure and integrate AI into our systems so we can work in tandem with it.
This collaboration will amplify the strengths of both AI and humans, with APA augmenting our capabilities by managing complex data-intensive tasks and humans bringing their unique intelligence, insights, and potential to unlock capabilities that have yet to be discovered.
RPA and AI: Better together
AI and RPA each drive fundamental changes in business processes on their own, but together they form a powerful synergy that redefines the efficiency and scope of business operations. Although there is a long journey ahead to fully touchless automation, Intellias is making headway in exploring and hitting pivotal milestones along the way, including self-healing automation and APA.
As more and more companies (your competitors included) tap into the efficiency gains offered by automation technologies, APA will serve as a catalyst for further progress in this field, creating a more streamlined and intelligent workflow ecosystem. If you want your business to become part of this ecosystem, we know how to guide you through the transformation.
Ready? On your mark, get set, automate!
Want to combine GenAI and RPA to make your business processes smarter, faster, and more accurate? Contact our experts to find out how.
