Unlocking Business Efficiency with Intelligent Process Automation


The Gist

  • Key distinctions. IPA surpasses RPA by managing unstructured data and making data-based decisions.
  • Tremendous impact. IPA’s predictive analysis and cognitive abilities enhance customer service and efficiency.
  • Vital balance. Despite IPA’s capabilities, tasks requiring empathy, creativity and intuition still need human involvement.

Intelligent process automation (IPA) blends artificial intelligence, computer vision, cognitive automation, natural language processing and machine learning with robotic process automation to enable advanced decision-making automation. IPA excels in customer service, document processing, handling unstructured data and data-driven decision-making. When used for customer service, IPA enhances the customer experience with quick response times, round-the-clock availability and virtually no human errors. It provides customers with a seamless, personalized multichannel customer experience, while also freeing up human agents to focus on more complex, customer-centric tasks. 

A June 2023 Precedence Research report on IPA revealed that the global market for IPA is expected to grow to $51.35 billion US dollars by 2032. The report cited the benefits of IPA to include increasing process efficiency, improving the customer experience, optimizing back-office operations and workforce productivity, reducing costs and risks, enhancing product and service innovation as well as monitoring and fraud detection. This article will delve into IPA, exploring the ways that brands are using it to improve the customer experience, tasks it is suitable for, and processes that are better suited for human agents.

How Is IPA Different From RPA?

While robotic process automation (RPA) and intelligent process automation (IPA) both aim to automate business processes, they do so in significantly different ways. RPA is typically used for automating rule-based, repetitive tasks, whereas IPA, with the help of AI and machine learning (ML), can handle more complex data-based tasks that require decision-making abilities. It is able to learn from unstructured data, adapt to changes and make predictions.

Although RPA works well with structured data, it falters with unstructured data. IPA, on the other hand, adeptly manages both by using AI features such as natural language processing (NLP) and computer vision. This makes IPA ideal for tasks like document interpretation, sentiment analysis or data extraction from complex or unstructured sources.

Additionally, RPA lacks cognitive abilities, and can’t understand or interpret the meaning behind the data it works with. Conversely, IPA can understand, interpret, and make decisions based on data. It is able to comprehend the context, extract insights, and even predict future trends based on historical data.

Cynthia Davies, founder of Cindy’s New Mexico LLC, a fast LLC formations provider, told CMSWire that while RPA can help you fill out forms more quickly, IPA can actually check your information against what’s on the forms to let you know of any mistakes or improved responses. “Essentially, RPA knows what to put in the bubbles — IPA knows what the recipient actually wants, thanks to computer vision and cognitive automation,” said Davies.

Related Article: The Origins, Growth and Challenges of Robotic Process Automation (RPA)

What Kind of Tasks Is IPA Suitable For?

IPA is most suitable for tasks that involve complex decision-making, learning from unstructured data, adapting to new scenarios and improving over time. By using NLP, IPA excels at tasks including language translation and content summarization. These tasks require the interpretation and generation of human language, as well as an understanding of language nuances and context. By using IPA, customer service agents are able to determine the mood and sentiment of customers in real-time, which helps them to better serve the customer.

Rather than using archaic optical character recognition (OCR) technology, the integration of AI technology such as computer vision into IPA makes it well-suited for image recognition and analysis, which is especially useful for processing scanned documents, reading handwriting or identifying objects within images. Additionally, IPA is also adept at predictive analysis. Through the use of historical data, it can spot trends and forecast future occurrences, making it suitable for tasks such as sales forecasting, fraud detection and customer behavior prediction.

“’Autofill on steroids’ may sound a bit mundane, but consider just how much time we all spend inputting, checking, and re-inputting information as part of our jobs,” said Davies. “IPA can essentially do the work for us and turn entire processes into mere approvals. Right now we need to weed through this kind of information looking for errors and fact-checking.” Davies firmly believes that IPA offers a future where information is assured correctly, reducing wait times for responses from the customer’s perspective and streamlining processes. “Think of how powerful this can be when applied to the paperwork-heavy realms of banking and healthcare. Customers can finally get the level of service they deserve without having to pay for a personal concierge.”

Because it is able to work with complex or unstructured data, IPA can extract and interpret information from sources such as emails, social media posts or web pages. It is also useful for cognitive decision-making tasks that rely on data analysis, such as recommending actions based on customer behavior or evaluating risk levels in financial transactions.

Casey Jones, founder, director and head of marketing at CJ&CO, a global digital marketing company, told CMSWire that his company uses IPA to handle routine and repetitive inquiries from its customers via chatbots and voice assistants. “These inquiries include checking the status of a campaign, requesting a quote, or scheduling a meeting. IPA allows us to provide quick and accurate responses to our customers 24/7, without the need for human intervention,” said Jones. “This has improved customer satisfaction and loyalty, as well as reduced our operational costs and workload.”

Additionally, Jones’ business uses IPA to analyze and segment its customer data using NLP and ML. “This helps us understand our customers’ needs, preferences and behavior better. We can then use this information to personalize our communication and offers to each customer, based on their profile and history. This also enhanced customer engagement and retention, which in turn increased our conversion and revenue,” said Jones.

Related Article: Intelligent Process Automation Pushes the Boundaries of Business Process Automation


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