What You Need to Know About the Convergence of Robot Process Automation (RPA) and AI

According to the Robot Report, The International Federation of Robotics recently “noted that the stock of operational robots around the world attained a new record of about 3.9 million units in 2022. The average robot density, or number of robots per 10,000 human workers, rose to 151 and the trend of using artificial intelligence in robotics and automation keeps growing…” In particular, the convergence of robot process automation (RPA) and AI holds great promise for the enterprise. Details here.

Background

Each type of RPA offers distinct advantages and is suited to different operational contexts within organizations, allowing for tailored automation strategies that align with specific business needs and process requirements.

What is Robot Process Automation or RPA?

Robot Process Automation (RPA) is a technology that allows businesses to automate routine and repetitive tasks using software robots or “bots”. These bots can mimic human actions to interact with digital systems and software applications, executing tasks such as data entry, processing transactions, generating reports, and even responding to simple customer service queries. RPA aims to increase efficiency, reduce errors, and free up human employees to focus on more complex and strategic work.

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Three Types of RPA

Attended RPA bots are designed to work alongside human employees, typically on the same workstation. These bots are triggered by specific actions or conditions and are used for tasks that require human intervention or decision-making at certain points. Use Cases: They are often employed in customer service to assist agents with information retrieval, data entry, and executing standard processes quickly while the agent handles the interaction with the customer.
Unattended RPA bots operate independently of human intervention, executing predefined workflows and tasks based on scheduled times or event triggers. These bots are typically used for back-office processes where human decision-making is not required. Use Cases: Common applications include processing transactions, data migration, and batch processing tasks such as payroll or invoice processing, where tasks can be completed without human input.
Hybrid RPA combines both attended and unattended RPA functionalities, allowing organizations to optimize their automation strategy across different departments and workflows. This approach provides the flexibility to automate a wide range of tasks, from those requiring human-bot collaboration to fully automated back-end processes. Use Cases: Hybrid RPA is ideal for processes that start as back-office tasks but require front-office intervention at certain stages. For example, an unattended bot might process an insurance claim up to a point where human judgment is necessary, at which stage an attended bot takes over to assist the employee in completing the process.

Key Features of RPA

Non-intrusive: RPA bots can operate on the user interface level, meaning they can automate tasks without the need to modify existing systems or infrastructure.
Rule-based: Bots follow pre-defined rules and logic to perform tasks, ensuring consistency and accuracy.
Scalability: RPA can easily be scaled up or down to handle varying volumes of work, making it highly adaptable to business needs.
Integration: It can work across different software applications, platforms, and data sources without requiring extensive integration work.
User-friendly: RPA tools often come with graphical interfaces, allowing non-technical users to design and implement automation workflows.

Applications of RPA

Finance and Accounting: Automating invoice processing, expense management, and financial reporting.
Human Resources: Streamlining employee onboarding, payroll processing, and benefits administration.
Customer Service: Handling routine inquiries, updating records, and sending notifications.
Supply Chain Management: Automating order processing, shipment tracking, and inventory management.

Benefits of RPA

Cost Reduction: By automating tasks, businesses can significantly reduce labor costs and operational expenses.
Increased Productivity: Bots can work 24/7 without breaks, enabling higher throughput and faster processing times.
Improved Accuracy: RPA reduces the risk of human error, leading to higher quality outcomes.
Enhanced Compliance: Automated workflows can be designed to comply with regulations, and logs of all actions can be maintained for audit purposes.
Employee Satisfaction: Automating mundane tasks allows employees to focus on more engaging and value-added activities.

Market Size of RPA

According to Mordor Intelligence: “The Robotic Process Automation (RPA) market size is estimated to be USD 4.02 billion in 2024 and is expected to reach USD 14.75 billion by 2029, growing at a Compound Annual Growth Rate (CAGR) of 29.70% during the forecast period (2024-2029). This growth is attributed to RPA’s increasing adoption across organizations of all sizes to generate greater Return on Investment (ROI) and boost productivity.” Forrester puts the size of the current RPA market at $5b with a prediction that “the RPA market will grow to $22 billion by 2025.”

The Convergence of Robot Process Automation (RPA) and AI

“When conceptualizing RPA and AI, it can be helpful to think of AI as the brain, and RPA as the hands. It’s when the two are combined that complex tasks can be completed.”

In a recent report, industry leader UiPath described the convergence of RPA and AI:

Robotic Process Automation (RPA) eliminates tedious work by having software robots complete repetitive, white-collar tasks. This frees up time for teams to devote to work that provides greater value to the business. While RPA can easily capture data and manipulate applications like a person would, more complex and advanced tasks were previously out of reach.

This is where artificial intelligence (AI) comes into play. AI enables software robots to learn how to read, write, listen, recognize patterns, and make complex decisions. AI, together with machine learning (ML) and other advanced technologies, expands automation capabilities and accelerates decision making…with AI-enhanced automations, businesses can identify new opportunities, reduce costs, and improve productivity.

Why do you need AI in automation workflows?

“AI brings RPA to a new level, opening a world of new opportunities for business growth, cost reduction, and improved productivity.”

“RPA allows for repetitive tasks to be completed at scale, freeing up resources and helping to mitigate risks. As impressive as these benefits may be, businesses can see even greater results when they enhance RPA with the power of AI. AI enables companies to automate processes with characteristics that would have been impossible with RPA alone. Here are some examples:

Determine the uncertainty: Use cases where the outcome can’t be determined with 100% certainty. Examples include valuing a property, assessing the risk of a loan default, and forecasting inventory. When valuing a property, many factors are considered, such as the age of the house, the location, and the amenities available. Each value criteria needs to be assessed against each other to predict an accurate value. Previously only humans could make complex estimations like these, but AI now allows robots to accurately determine the results of advanced, uncertain equations.

Automate highly variable work: Processes that have too much variability and many dependencies are not well suited to rules. For example, resume matching and purchase decisions are both loaded with variables. When matching resumes against job postings, there can be significant fluctuations in the skillsets and experiences of job applicants. Recruiters need to sift through this information and manually match applicants to the correct job positions. Using AI, robots can automatically match applicants’ skill sets and resumes to relevant job postings.

Process unstructured data: Information like articles, documents, images, videos, and emails are unstructured. AI enables automatic processing of these documents at scale. Unstructured data is often poorly suited to rules based automations because of the significant differences found from sample to sample. But when enhanced with AI, robots can learn to recognize patterns in unstructured documents and then accurately process the information within them. The result is quick, efficient, and accurate document processing.

AI brings RPA to a new level, opening a world of new opportunities for business growth, cost reduction, and improved productivity. With AI-enhanced automations, businesses can make accurate predictions of sales for a given timeline; and predict which customers are at risk of defaulting on a loan.”

The Convergence of RPA and Automated Machine Learning

“By applying RPA with AI to a myriad of back office processes, the new automators transform their companies, equipping them with digital operating models necessary to thrive in the 21st Century.”

From a recent white paper from Data Robot: “We are at a point of convergence for knowledge work that promises to fundamentally shift the economics of companies in all sectors. Capabilities of RPA (robotic process automation) are converging with AI (artificial intelligence). This expands enormously the range of knowledge work primed for automation and lays the foundation for systems that learn about themselves to improve with experience.

The emergence of high-level, easy to learn and use platforms that abstract away complexity to automate the work of creating RPA and machine learning is liberating legions of automators. Tasks previously consuming hours and days applying programming skills to build RPA and AI are now completed in minutes. The automation of knowledge work, previously the exclusive preserve of a relatively small population of technical specialists, is now undertaken by the hundreds and thousands of technically inclined generalists today working in companies across all sectors of our economy.

Experience and expertise in their organization’s business and its data are invaluable to these new automators as they untangle siloed applications to create new straight through processes. Removing friction to the flow of information improves service to customers and partners, accelerates process cycle times to meet the expectations of our digital age, frees staff to focus on high level activities that create value, and strips away costs as low level tasks are delegated to software machines.

RPA is non-intrusive and leverages existing infrastructure with no disruption to underlying systems. A typical process automation project completes in four to six weeks and return on investment is calculated in months and not years. By applying RPA with AI to a myriad of back office processes, the new automators transform their companies, equipping them with digital operating models necessary to thrive in the 21st Century.”

A case study: American Fidelity

American Fidelity and their RPA partner UiPath turned to DataRobot to add AI to their automation project. DataRobot’s customer-facing data scientists gathered historical emails to which teams had already responded and identified the emails’ texts and the identity of the team which responded to the customer as training data necessary to create a machine learning model. In less than two hours DataRobot trained an optimal routing model. The new model was simply and easily put into production within the UiPath workflow.

Other RPA+AI use cases that are proving valuable in multiple industry sectors include:

loan approvals
call center routing
proactive maintenance
call center staffing
employee attrition

The convergence of RPA with automated machine learning offers companies a low cost and low risk means of transforming to digital operating models. Automation of the work of creating RPA and AI means transformation can be undertaken by technically literate generalists already on staff. Their expertise and experience in your business are critical to your company’s transformation and future success.

What Next? The Future of Robot Process Automation (RPA) and Generative AI

“The emergence of low-code and no-code RPA platforms empowers users with minimal programming skills to contribute to unique business requirements.”

AI and machine learning to aid more robots

According to the Robot Report: “Another example of the combination of technologies is the use of predictive AI to analyze robot performance data and identify the future state of equipment…Predictive maintenance can save manufacturers machine downtime costs. In the automotive parts industry, each hour of unplanned downtime is estimated to cost $1.3 million (U.S.), reported the Information Technology & Innovation Foundation. This indicates the cost-saving potential of predictive maintenance. Machine learning algorithms can also analyze data from multiple robots performing the same process for optimization. In general, the more data a machine learning algorithm is given, the better it performs…”

“…the emergence of generative AI opens new possibilities...this subset of AI is specialized to create something new from things it has learned via training, and generative AI has been popularized by tools such as ChatGPT…robot manufacturers have started to develop generative AI-driven interfaces that allow users to program systems more intuitively by using natural language instead of code. Workers will no longer need specialized programming skills to select and adjust the robot´s actions, predict experts.

The Blockchain Council sketched out this technological future in the following manner: “Robotic Process Automation (RPA) and Intelligent Automation (IA) – In 2024, RPA and IA redefine business operations by automating complex tasks. This advanced solution integrates AI, Machine Learning, computer vision, and NLP to streamline end-to-end business processes. The shift towards cloud environments provides ease of deployment, flexibility, and reduced infrastructure needs. The emergence of low-code and no-code RPA platforms empowers users with minimal programming skills to contribute to unique business requirements.”

“The convergence of RPA and Generative AI represents a frontier in automation technology, offering the promise of more intelligent, flexible, and responsive automated systems that can drive significant advancements in efficiency, customer experience, and innovation.”

The Convergence of Robotic Process Automation (RPA) and Generative AI holds significant potential to transform how businesses automate processes, make decisions, and interact with data and customers. By integrating these technologies, companies can achieve more sophisticated, intelligent, and adaptive automation capabilities. As these technologies continue to evolve, they will likely become a critical component of digital transformation strategies across industries.

Here are several key areas where their convergence could be particularly impactful:

Enhanced Decision-Making: Generative AI can analyze vast amounts of data to generate insights, predictions, and recommendations. When combined with RPA, these insights can automatically trigger specific workflows, leading to more dynamic and intelligent decision-making processes that can adapt to changing conditions without human intervention.

Improved Customer Experience: Generative AI can be used to create natural language processing (NLP) models that understand and generate human-like text. Integrated with RPA, this could automate customer service interactions, creating bots that can handle a wider range of queries with more nuanced and human-like responses, improving customer satisfaction.

Customized Content Creation: The ability of Generative AI to produce text, images, videos, and other content can be leveraged by RPA to automate the creation of personalized marketing materials, reports, and communications tailored to individual customer preferences and behaviors, enhancing engagement and conversion rates.

Process Optimization: Generative AI can identify patterns and optimize processes by suggesting improvements or alternatives that might not be evident through traditional analysis. When these insights are applied through RPA workflows, businesses can continuously refine and optimize their operations for efficiency and effectiveness.

Enhanced Creativity and Innovation: By generating new ideas, designs, and solutions, Generative AI can support creative processes in fields such as product development, marketing, and design. RPA can then operationalize these ideas, automating the implementation of innovative solutions at scale.

Adaptive Automation: Generative AI’s learning capabilities mean that RPA bots could become more adaptive, learning from interactions, feedback, and changing environments to refine their actions and responses over time. This leads to automation that is not just rule-based but is capable of evolving with the business landscape.

Ethical and Compliance Insights: Generative AI can help analyze legal and compliance documents to keep automated processes in line with current regulations. RPA can ensure that all operations are automatically updated to comply with these insights, reducing legal risks.

Challenges and Considerations

While the potential is vast, the convergence of RPA and Generative AI also presents challenges, including:

ensuring data privacy
managing bias in AI models, and
the need for robust governance frameworks to oversee intelligent automated systems.

Additionally, there’s the challenge of integrating advanced AI capabilities into existing RPA solutions in a way that maximizes their potential while ensuring reliability and scalability.

Further Resources

The following articles or reports are chock full of valuable analysis and insights. We were up against copyright restrictions by choosing to quote these resources too liberally in this post (they are that good).

But if our initial analysis has inspired you to take a deep dive to figure out more of the challenges and opportunities ahead for your organization in the convergence of RPA and AI, these resources are a great place to start for that follow up research:

Related References: