Intelligent Process Automation Explained: Merging RPA with AI

Table of Contents

1. Introduction
2. Understanding Robotic Process Automation (RPA)
3. Understanding Artificial Intelligence (AI)
4. Differences Between RPA and AI
5. The Synergy of RPA and AI: Intelligent Process Automation
6. Benefits of Intelligent Process Automation
7. Real-World Applications of Intelligent Automation
8. How RPA and AI Complement Each Other
9. Implementing Intelligent Process Automation
10. Challenges and Considerations
11. The Future of Intelligent Automation
12. Conclusion

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Intelligent Process Automation: Combining RPA and AI for Enhanced Efficiency

Article Summary

• Key Focus: The article explores how the combination of Robotic Process Automation (RPA) and Artificial Intelligence (AI) can revolutionize business processes through Intelligent Process Automation (IPA).

Understanding RPA and AI

• RPA: Used to automate repetitive, rule-based tasks. It’s highly effective for structured data but limited when unstructured data and learning are involved.
• AI: Offers cognitive capabilities, learning, and decision-making with unstructured data. Best for complex scenarios where judgment and adaptability are needed.

Synergy and Benefits

• When combined, AI complements RPA by adding the ability to handle unstructured data, make decisions, and learn from processes, leading to holistic solutions.
• The fusion results in benefits like increased efficiency, reduced costs, better customer satisfaction, and empowering employees to focus on strategic tasks.

Real-World Applications

• The integration of RPA and AI can be seen in sectors like finance, customer service, HR, healthcare, and more, where end-to-end automation is possible from data extraction to decision-making and action execution.

Implementation and Challenges

• Implementing IPA requires a solid strategy, training, and a deep understanding of business processes.
• Challenges include data privacy, integration with existing systems, and ensuring the reliability of AI models.

Looking Forward

• As technologies mature, understanding and leveraging the strengths of both RPA and AI can position businesses to thrive in competitive markets.

Introduction

In a past discussion on Robotic Process Automation (RPA), I mentioned how the right process automation technology can have a dramatic impact on an organization. It can increase efficiency, reduce costs, lift employee morale, and improve customer satisfaction.

RPA is no longer a niche technology. Organizations worldwide across every industry are investing in it. But to get maximum value from process automation, it helps to combine RPA with another emerging technology: artificial intelligence (AI). The combination unlocks even more benefits and allows automation of entire end-to-end business operations.

Understanding Robotic Process Automation (RPA)

RPA is a solution to automate repetitive, rules-based tasks that are usually performed manually by a human. It works with structured data and is commonly used for things like:

• High-volume data entry and validation
• Invoice processing and reconciliation
• Generating routine reports
• Copying and pasting data to and from multiple systems
• Retrieving customer data and managing transactions

Businesses across every department from HR to finance, IT, and customer service have repetitive tasks like these that can be automated with RPA bots.

The benefits of RPA include:

• Efficiency and productivity: Bots work quicker than humans and can be working 24/7 without any breaks.
• Accuracy: Bots won’t make any mistakes when they are performing mundane tasks like data entry.
• Reduced costs: Bots take up fewer resources compared to humans, so they cost less.
• Compliance: Bots stick to the rules, so you can ensure all policies and procedures are followed to the letter.
• Happy employees: Who wants to spend all day reading and sending emails, entering data, and performing other mundane tasks when they could be doing something more strategic? RPA bots can take these boring tasks off employees’ hands.

There are, of course, limitations to RPA. For one, RPA bots are not able to work with unstructured data. This is data that isn’t categorized or organized in a predefined way and typically includes things like emails, voice recordings, and images.

RPA bots aren’t able to make independent decisions, either. They operate based on the rules they have been given, so decision-making tasks get passed back to a human employee. Finally, RPA bots can’t learn from the tasks they perform. So if something changes within a process, the bot can’t work out what to do. It will either make a mistake or pass the task back to a human to take over.

Imagine the process of making a sandwich at home. RPA is like a bread slicer that could repeatedly cut a sandwich to the perfect size in an instant. It’s efficient, it doesn’t tire, and it will slice accurately every time. But it also only solves one small part of making a sandwich. It can’t understand what ingredients need to go in a sandwich, nor can it make decisions about what condiments might improve the overall taste.

Understanding Artificial Intelligence (AI)

If RPA is a bread slicer, artificial intelligence is more like a chef. While AI on its own can’t solve the problem of how to make a sandwich, it can understand what ingredients are needed to make a sandwich, and it can decide what flavors might complement each other. This way it can create lots of tasty sandwich combinations.

While it’s an analogy, that description of AI isn’t far from the truth. But before we get any further, let’s first define AI. Artificial intelligence is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. In simple terms, AI is the simulation of human intelligence by machines.

There are several components of AI, including:

• Machine learning: Algorithms that give computers the power to learn from data without being explicitly programmed
• Natural language processing: The ability for computers to read, understand, and extract meaning from human language
• Computer vision: The field of AI that allows computers to understand and interpret the contents of images
• Intelligent Document Processing (IDP): AI that can “read” and then extract data from unstructured documents
• Large language models (LLMs) like ChatGPT that can generate human-like responses based on vast datasets

We are still a ways off from a real-life robot chef, but AI is already being used by organizations to automate, accelerate, and improve business processes. That’s because it is excellent at processing large amounts of data, making rules-based decisions, and continuously learning over time.

It’s important to note that AI isn’t a direct substitute for RPA. Just like a chef is limited without tools, AI often needs to be combined with mechanized automation in order to unlock the benefits of both technologies. For this reason, the two technologies are highly complementary.

Differences Between RPA and AI

Although the terms RPA and AI are sometimes used interchangeably, there are key differences between the two technologies. RPA is best used when you want to automate tasks that don’t require intelligence. These are repetitive tasks that often work with structured data, require minimal contextual understanding, and need little to no human interaction.

RPA will perform these tasks quickly and efficiently, but it won’t understand what it is doing. It will complete the tasks it has been programmed to do, but that’s all it can do. It can’t improve or take on additional tasks unless it is specifically programmed to do so.

AI, on the other hand, is used when you want to bring cognitive capabilities to your applications. It’s great at working with unstructured data such as emails, documents, and images. It can understand patterns and trends, make decisions, and contextualize data. It can learn, too, meaning it can get better and better at performing a task over time.

Ultimately, AI replicates many aspects of human behavior. This makes it perfect for understanding customer queries, creating tailored responses, highlighting interesting data, and making predictions.

Here’s a simple way to differentiate the two technologies. RPA is your team of hard workers. They aren’t very smart, but they can perform a specific task quicker than anyone else and they can do it all day without breaks. AI is your team of big thinkers. They are amazing at problem-solving and can draw brilliant insights out of thin air, but they aren’t very effective unless they are paired with the right tools.

The Synergy of RPA and AI: Intelligent Process Automation

More and more organizations are realizing the value of combining RPA and AI. Leading process automation vendors that have previously focused solely on RPA are now starting to introduce intelligent process automation features that extend the functionality of RPA tools. This lets users automate even more of their business processes by adding intelligence to RPA workflows.

Adding AI to RPA provides enterprises with a holistic solution where AI facets can do all the thinking, decision-making, and analyzing and pass tasks on to RPA bots for processing and executing. This is typically called intelligent, cognitive, or AI-driven automation.

Intelligent Automation in Practice

Consider the example of an invoice processing workflow. AI has several aspects here, including:

• Reading and categorizing the invoice: Once an invoice is received, AI can read the invoice irrespective of its format, and categorize the invoice without the need for human intervention.
• Extracting relevant data from the invoice: AI tools can extract the invoice number, the amount billed, and understand who the invoice is from and who it should be sent on to.
• Matching the invoice with a purchase order and approving the payment: The tool can then compare the invoice with the relevant information in your system to check it is legitimate and approve it for payment.

AI is critical to this process getting off the ground. Without AI, someone would first have to manually read the invoice, decide what department it is for, and enter all of the important information into a database.

But here’s where things get interesting. Once the invoice has been categorized and approved, an RPA bot can then step in to import the invoice, update the relevant accounts, and email the client that payment has been processed. RPA bots can even perform the payment, too. In this workflow, both AI and RPA are needed in order to create end-to-end automation. In this example, the business can automatically scan and pay invoices with zero human interaction.

Benefits of Intelligent Process Automation

The benefits of intelligent process automation aren’t limited to finance departments. Every team can leverage IPA to improve their processes as they digitize. Some benefits include:

• End-to-end automation: Both structured and unstructured data can be processed without human input.
• Increased efficiency and throughput: By letting AI take control of decision-making, processes are completed faster than with RPA alone.
• Reduced costs: You can achieve more with less using IPA.
• Better accuracy: Automation both increases speed and reduces errors that result from manual data handling.
• Better compliance and audit trails: Automation leaves records that you can use in audits or to ensure you’re following regulations.
• Happier employees: Employees can focus on more strategic work instead of mundane tasks.
• Better customer experience: Faster processing times and fewer resources spent on mundane tasks mean customers get better and quicker service.

Real-World Applications of Intelligent Automation

The potential applications for intelligent process automation within an organization are almost limitless, but several use cases are already emerging. Below are some examples based on industry or department.

a. Finance and Accounting

Finance departments have always been fertile ground for RPA implementation because of the significant number of repetitive and rules-based tasks that go into accounting. But there is only so much RPA can do when processes rely on unstructured data like invoices or require intelligent decision-making. By combining AI technologies with RPA, finance teams can automate many more of their processes.

Let’s take the example of invoice processing that we discussed earlier. An AI tool can be used to “read” an invoice, extract relevant data, and insert that data into a database or application. RPA can then be used to perform any repetitive, rules-based processes that require moving data between systems. Since almost every invoice needs a human to extract the relevant data, this combination of AI and RPA can significantly reduce the time accountants spend processing invoices.

Other potential use cases include:

• Accounts payable and receivable: Machine learning can be used to make decisions about how to handle accounts debts and flag any invoices that fall outside of certain limits. RPA can then be used to send reminders about invoices to be paid.
• Financial reporting: AI can be used to intelligently extract and collate data for financial reports. RPA can then input these data into reporting and visualization tools or even directly create the reports themselves.
• Fraud detection: AI can be used to intelligently monitor transactions and flag any that fall outside of accepted norms. RPA can then be used to help document these transactions and send them to the relevant department for review.
• Payroll: AI can help gather employee data relevant for payroll processing while RPA performs the payroll.

b. Customer Service

More and more businesses are beginning to understand the impact that improved customer service can have on the bottom line. For this reason, there has been significant investment in AI-powered solutions that improve the customer experience.

Conversational AI chatbots are one such solution. Unlike AI chatbots, which run on a set of rules, conversational AI bots use natural language processing to understand the intention of a customer no matter how they communicate. This lets businesses create chatbots that are able to respond to a much more diverse range of questions and serve a broader range of use cases.

AI chatbots can be used to automate a large portion of customer service inquiries, while natural language processing can be used to train the chatbot to automatically understand, categorize, and respond to queries. RPA can be used in combination with AI conversational chatbots, too. While AI chatbots take care of communicating with the customer, RPA can be used to pull up the relevant information, update customer records, and create a new transaction.

Other potential use cases include:

• Automating complex queries: AI technologies like intelligent document processing can be used to understand complex queries. RPA can then be used to automatically respond to the customer or forward the query to the relevant department.
• Prioritizing customers: Sentiment analysis can be used to read and understand a customer query and prioritize angry or upset customers.
• Meeting SLAs: Machine learning can be used to create expected response times to customer queries based on previous data. RPA can then be used to notify or remind customer service reps that SLAs are close to being breached.

c. Human Resources

The vast number of repeatable processes in HR make it an ideal starting point for IPA. Take recruitment, for example. Here, AI can be used to intelligently scan resumes, shortlist potential candidates, and create candidate profiles—not to mention potential applications for minimizing bias in candidate selection. RPA can then be used to schedule the candidate into the appropriate recruiter’s calendar, automatically send a response email, and remind the recruiter of the appointment before it happens.

Other potential use cases include:

• Onboarding: AI can be used to manage the onboarding of new hires. RPA can then be used to create employee profiles, add employees to HR, payroll, and IT systems, and create user accounts for company software.
• Employee data management: AI can intelligently update employee data across systems, RPA will work in the background to manage data.
• Training and development: Machine learning can be used to monitor and create optimal career paths for employees. RPA can then be used to automatically enroll employees in relevant courses at the appropriate time.

d. Healthcare

Healthcare is another booming area of AI implementation. One use case that’s already being implemented nationwide is that of digitizing patient files. Here, intelligent document processing can be used to extract relevant data from paper patient files and create a digital record of the patient. This record can then be updated using an RPA bot, allowing doctors to input patient data and create prescriptions while still using paper records.

Other potential use cases include:

• Diagnosis and screening: Intelligent document processing could be used to scan x-rays, test results, and images to screen for diseases and immediately update patient files with the results.
• Prioritizing patients: Sentiment analysis could be used to understand the severity of a patient’s email and alert healthcare professionals to patients that need to be prioritized.
• Appointment booking: Intelligent chatbots can help patients book appointments. RPA will work in the background to update patient management systems.

e. Supply Chain Management

Supply chain management is one of the most complex and multifaceted parts of running an organization. Its complexity makes supply chains ripe for the combination of AI and RPA. Take ordering new stock, for instance. Machine learning models can be used to forecast demand for specific products in the future based on order history and external factors. When inventory needs replenishing, an RPA bot can be used to automatically create and send the order to suppliers without any human input.

Other potential use cases include:

• Order processing: An RPA bot can be used to handle all of the tasks associated with processing an order, including accepting an order, sending it on to your warehouse, creating an invoice, and updating a CRM. If an order doesn’t match a previous pattern or is too large, machine learning can ensure the order is sent on to a human.
• Logistics: Route planning and delivery schedules can be optimized by AI to make sure they are as efficient as possible. RPA can automate the process of assigning deliveries.

f. IT Operations

AI is playing a bigger and bigger role in IT operations. It can be used to automatically detect malicious behavior, understand and respond to security breaches, and suggest security patches before issues occur. IT departments are also using intelligent chatbots to answer repetitive queries about software and troubleshoot basic problems. RPA is then used in the background to update IT records, create tickets, and notify team members if necessary.

Other potential use cases include:

• Password resets: If a chatbot can’t authenticate a user, the ticket can be passed on to a member of the IT team for review. If they are happy the request is legitimate, RPA can be used to facilitate the password change.
• Employee onboarding and offboarding: RPA can be used to provide and revoke access to company systems when employees join and leave a company.
• IT Support Management: Chatbots can be used to understand and categorize support queries. RPA can be used to send these support queries to the relevant department or the most appropriate member of the support team.

g. Marketing

Data plays a huge role in modern marketing campaigns, making marketing departments another fruitful and highly lucrative area for IPA. Take segmentation, for example. Machine learning can be used to create segments of your customer base and even predict how valuable leads will be with a lead scoring algorithm. Bots can then be used to harness this intelligence and send relevant marketing material to segments of your audience, update your records, and log any transactions that result from the campaign.

Personalization is another example. Here, intelligent A/B testing algorithms can work out the most effective marketing messages to deliver. These messages can then be delivered to customers at exactly the right time using bots. IPA can even be used to create personalized marketing for every customer. Large language models can automatically generate product descriptions, emails, and other texts tailored to a specific customer, while RPA bots fetch customer data from relevant applications and automatically send the personalized message at the optimal time.

Other potential use cases include:

• Social media: RPA bots can be used to automate the posting of social media messages across all popular platforms. Artificial intelligence can then be used to intelligently analyze the performance of these posts and segment your followers into different groups based on similarities.
• Competition analysis: Artificial intelligence can be used to analyze competing products, prices, and marketing messages to make sure you stay competitive. RPA bots can then create and share price or product reports for management.
• Content management: RPA bots can be used to automate the publication of new blog posts, product pages, and landing pages. Bots can also add new posts to your social media scheduling platform, send recent updates to your mailing list, or update your CRM so your sales team knows there’s a fresh blog post to share with prospects. A/B testing can be conducted using AI to learn what kind of content performs best.

h. Manufacturing

Manufacturers can also take advantage of IPA to automate menial tasks. Quality assurance is one such scenario. Here, an AI image processing tool can be used to scan and identify a faulty product. An RPA bot can then be used to log the error and remove the faulty product from the assembly line.

Other potential use cases include:

• Inventory management: Machine learning can be used to intelligently predict which materials need to be ordered. An RPA bot can create the purchase order and send it to the supplier.
• Reporting: RPA bots can collect and combine reports from across the factory floor. AI can be used to summarize the data and even create reports based on the information.
• Maintenance: Machine learning can be used to predict when machinery is in danger of breaking down. An RPA bot can book in a service and order any replacement parts.

How RPA and AI Complement Each Other

As I’ve already described, AI and RPA complement each other beautifully when combined. AI can handle the thinking, reading, and decision-making, while RPA performs mundane, rules-based tasks. Here are several ways in which the two technologies complement each other.

• Data Preparation: As I’ve previously written, the success of artificial intelligence solutions often hinges on their data. Without enough relevant data points, an example size will be too small to develop any actionable insights, and AI won’t be useful. RPA can be used to gather the information AI requires to make decisions and reports and enter it all in one place.
• Legacy System Integration: RPA can be used to integrate AI into systems that don’t have APIs. This lets enterprises make use of cutting-edge AI tools without having to invest in a complete overhaul of their tech.
• Monitoring and Governance: RPA can be used to track the results of AI efforts. This can help to ensure AI solutions are creating relevant and fair results, while organizations also remain compliant with any AI regulation.
• Improve Data Handling: On the flip side, companies may want to use an intelligent document processing tool to read unstructured documents, extract relevant data, and convert that data into a structured format that RPA bots can use to update company records and process transactions.
• Human-in-the-Loop: RPA can help to manage AI performance by providing human input when necessary. For example, RPA bots can flag when AI models aren’t certain of their predictions and escalate the process to a human. The human’s decision can then be used as training data to improve AI performance going forward.
• Intelligent Task Completion: Machine learning can learn to complete specific tasks or fill out specific information. RPA can be used to trigger machine learning when necessary, thereby combining task automation with automatic input completion.

Implementing Intelligent Process Automation

RPA and AI combine to create a compelling automation solution, but implementing it in your organization is not without its challenges. Most process automation solutions will come with some out-of-the-box AI features that make it easy to get started, but creating advanced solutions may require technical expertise. That being said, there are many “no-code” solutions available today that make intelligent automation possible even for people who don’t have a technical background.

Regardless of what tools you choose to implement, getting started is about understanding your business processes and the bottlenecks you need to solve, rather than shoehorning technology into every part of your business. Here are several tips to help you get started.

a. Have a Plan

Implementing RPA alone can be a massive undertaking for large organizations that haven’t digitized their processes before. Adding artificial intelligence into the mix, while incredibly powerful, brings added complexity. It’s important to have a robust strategy in place if you are going to achieve a successful outcome.

RPA is a good place to start, but don’t just think about how individual RPA bots can help individual teams and departments. You’ll achieve much more when you connect your bots and bring AI into the mix to achieve full end-to-end automation. And, given that starting with a proof of concept is always a good idea, make sure you plan for scaling from the outset, and focus on the big picture rather than individual use cases.

b. Train Your Team

Artificial intelligence and robotic process automation both have the potential to dramatically change the way your employees work. That’s why it is critical to provide training to everyone in your organization. Not only will this help with onboarding when it comes to the new tools you are implementing, but it will also help foster an automation-first culture.

c. Create a Center of Excellence

When you are first starting out with RPA and AI, I recommend that you try to get buy-in from every department as soon as possible. It’s rarely enough to focus on just one department; you’ll wind up with small, disparate projects that hit obstacles and peter out. Get all the right people in the business on board from the start so that you can go big and push the technology throughout the organization.

Organizations may want to consider setting up an automation Center of Excellence to drive AI and RPA efforts. A Center of Excellence can provide the framework needed to structure a digital workforce made up of bots and AI tools, and provide a dedicated resource for driving the technology forward.

Challenges and Considerations

Implementing RPA and AI isn’t without its challenges. Two of the biggest issues center around data safety and data privacy. Companies implementing RPA and AI have a duty to protect both employee and customer data while remaining compliant with the latest regulations like the GDPR and HIPAA.

Integrating both RPA and AI solutions into your existing tech stack can also be a challenge, even when your developers have the tools they need to create APIs. Employees may fear for their jobs, too. While RPA bots and AI tools are a long way from replacing employees, there is a risk that workers may feel threatened by the implementation of these tools.

Finally, there’s always a risk that AI models aren’t trained well enough to produce reliable and unbiased results. It can be easy to assume that AI is automatically correct, but it has the potential to provide unwanted results.

The Future of Intelligent Automation

The combination of AI and RPA is already having a significant impact on organizations in almost every industry. Far from being a fad, the intelligent automation market is projected to grow rapidly over the next decade. Both Gartner and Forrester have reported a sharp increase in the number of vendors offering process automation tools with intelligent capabilities. Yet both reports stress that organizations looking to benefit from intelligent automation should be wary of the hype.

“Enterprises should not purchase intelligent automation technologies expecting that all their operations will be able to run ‘lights out’ without human intervention,” warns Forrester. “The goal of intelligent automation is to truly augment human intelligence and capacity—not replace it.”

The report goes on to describe how many of the world’s best companies are already preparing for a world where work is shared between man and machine. By 2030, Forrester believes that automation will create a suite of new human/digital partnerships where digital workers will assist humans, but not entirely replace them.

That will require thoughtful preparation from enterprises today. “Human workers need to learn to manage digital workers, making sure that the bots deliver on their tasks, audit the work that the digital workers complete, and manage human exceptions that digital workers escalate,” the report continues.

I believe there will be no limit to the number of use cases for RPA and AI solutions, both in the home and in the workplace, and I expect further mainstream adoption over the coming years.

“Robotic process automation will take away the drudgery from people’s lives and shift the human contribution to more of IDEO’s approach to innovation: expertise, art, skill, and collaboration,” said Leslie Willcocks, a professor at the London School of Economics. “The robots will take the robot out of the human.”

Conclusion

Process automation technology can have a dramatic impact on an organization, but it shouldn’t be rushed. If you’re considering moving forward with an automation project, it’s worth taking the time to think about what intelligent automation will bring to the table and how you can incorporate it into your strategy from the outset.

Read next:
– 10 Best Document Processing Tools
– A Deep Dive Into Automation Tools