The pace of change in the digital workplace shows no signs of slowing down. But legacy systems are still hampering the digital transformation because they leave little room for the integration of new technologies. To overcome this shortcoming, many companies need a platform that acts as an intelligent information conveyor belt, so to speak, enabling and orchestrating collaboration between legacy systems. This is where Intelligent Process Automation (IPA), also known as intelligent automation (IA), RPA 4.0 or hyperautomation, can help.
At its core, Intelligent Process Automation is a set of technologies already used in various Business Process Automation (BPA) places and combined to manage, automate and integrate digital processes. These technologies include Robotic Process Automation (RPA), Digital Process Automation (DPA) and Artificial Intelligence (AI). IPA goes beyond simple process automation, as it is able to process and structure unstructured data by combining the aforementioned technologies. This means that the analysis, management and organization of unstructured information can now be automated.
This is interesting for companies in that up to 90 percent of the data generated on a daily basis, such as scanned documents, e-mails and letters, falls into the category of unstructured data. While structured is uniform, quantitative data that can be easily searched and used, unstructured is more difficult for computers to analyze. This is because it involves data that has a different format or appearance, such as images, videos, spoken text or handwriting.
Intelligent Process Automation is composed of three core business process automation technologies:
RPA brings speed and efficiency to the table. Using robots to mimic human actions and automate processes helps manual, labor-intensive, high-volume, repetitive tasks, such as re-keying data from one system to another, get done much faster and more efficiently.
DPA, also called processor orchestration, has its roots in business process management. It is a set of intelligent process automation technologies that ensure that all tasks, workflows or functions are completed without interruption. DPA can be used to define event-driven business rules that dictate a streamlined next step for all processes in the enterprise. DPA provides the agility and insight required for a holistic approach to business process automation, and is particularly well suited for optimizing diverse intra-business services, as well as managing customer-facing service processes.
AI takes automation to an even higher level, as AI can recognize patterns in data and learn from past decisions to make increasingly intelligent decisions. Machine learning (ML) provides supervised algorithms that learn from structured data sets before they begin to make predictions on their own based on new inputs. Unsupervised algorithms observe structured data and begin to provide insights about detected patterns.
Natural Language Processing (NLP) enables seamless interaction between humans and technology, as NLP enables a computer to understand, interpret, and manipulate spoken or written language. NLP, in combination with ML, is key to developing complex workflows for chatbots and virtual assistants (VAs) that are able to perform tasks, communicate, learn from data sets, and even make decisions based on "emotion recognition." Computer vision tools such as Optical Character Recognition convert scanned documents or photos into text.
With IPA, software robots can replace manual clicks (RPA), interpret text-heavy communications (NLP), make rule-based decisions that don't need to be pre-programmed (ML), make suggestions to customers (VA), and monitor business processes (DPA). Intelligent Process Automation enables companies to rethink existing processes or optimize them with ML, or leverage new technologies like Decision Automation to create innovative new processes through intelligent decision making.
Hyperautomation is a now frequently used term popularized by market researcher Gartner. The term first appeared in Gartner's "Top 10 Strategic Technology Trends for 2020" report. From that point on, hyperautomation was on everyone's lips. Nevertheless, there is still some confusion about what hyperautomation means and how it differs from intelligent process automation. Gartner itself says this in its 2020 Magic Quadrant for RPA:
"Customers are seeking an orchestrated, end-to-end, intelligent, event-driven form of automation delivered with an effective combination of automation tools with multiple machine learning applications and software packages."
Gartner calls this form of automation hyperautomation. In terms of deployment, there is still no difference between this and IPA.
Hyperautomation, however, is viewed by Gartner as an enterprise strategy or initiative with the ultimate goal of automating and transforming as many end-to-end processes as possible through the use of artificial intelligence (AI) and machine learning (ML) and the coordination of multiple automation tools and software. In contrast, intelligent process automation focuses on a narrow set of tasks, combining technologies into a single tool to automate and optimize existing tasks. In many cases, this also lacks a defined strategy to scale automation with tactical and strategic goals.
Also, what is referred to as RPA 4.0 is nothing more than cognitive process automation, where RPA is combined with digital technologies such as artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) to automate tasks to process structured and unstructured data with predictive and prescriptive analytics.
A recent study by McKinsey states that by implementing IPA, organizations can automate over 50% of manual tasks, reduce process times by 50%, and achieve ROIs of over 100%. By combining RPA, DPA and AI, organizations can be confident that they have made the right decision because it is planned in the workflow and AI helps make informed decisions.
Automating end-to-end processes using IPA can reduce the risk of errors such as incorrect data entry. While RPA handles these automation tasks, DPA and AI give the business confidence that business processes are being completed consistently. IPA gives businesses insights into the entire process. This helps identify bottlenecks or points where the customer journey could run more smoothly. IPA increases the agility and speed of process change.
IPA enables companies to automate more complex tasks and perform more adaptive workflows. These tasks include those that RPA cannot handle because they involve many exceptions or rely on unstructured data. Intelligent Process Automation ultimately leads to incremental improvements in customer experience, productivity and efficiency. Instead of simply deploying technologies like RPA in silos and leaving them to individual tasks, IPA can orchestrate work between robots, people and systems.
A typical use case for IPA is a situation where companies need to submit data to their customers, but manually completing these tasks takes a lot of time, such as processing insurance claims or automating customer inquiries. For example, employees in a claims department typically spend hundreds of hours per year entering data from claims forms into the department's CRM. An IPA tool extracts the necessary data from the forms and transfers the information right into the CRM. Integrated into a larger end-to-end process, the relevant information is also sent directly to the customer right away.
In financial services, customer service representatives must spend time collecting customer data from databases, phone calls, emails, and online chats, which can negatively impact the customer journey. An IPA tool retrieves the data from the database and updates the records with additional information from phone calls and emails. IPA solutions can analyze shipping data to optimize shipping routes and schedules to reduce bottlenecks, avoid delays, and optimize available resources.
In accounting, the IPA solution automatically recognizes and posts the invoices. Intelligent Process Automation has all the necessary tools and software to recognize the invoice type, extract all the information such as date, supplier, price, items, payment terms and due date and pass the data on to the ERP, classify it in the correct accounting fields and make the payment at the optimal time for a high cash flow.
In the service center, IPA recognizes the semantic context and thus the customer's concern due to the integrated NLP technology and immediately suggests a solution to the employee. In healthcare, IPA interacts with NLP to provide an approach to data collection, analysis, diagnosis and treatment. Thus, IPA is not only a relief for internal employees, but also beneficial for the service or services that affect the customer.
In its Intelligent Process Automation solution provider landscape report published in 2021, Everest Group examined 27 vendors offering IPA solutions ranging from procurement of IPA technology products to consulting, implementation and maintenance services in terms of their key strengths and weaknesses. These included TCS, EXL, Tech Mahindra, Cognizant, Wipro, Genpact, HCL, Atos, Capgemini, NTT DATA, Hexaware, Infosys, IBM, Mphasis, Exela Technologies, Pricewaterhousecoopers, Data, Sopra Group, Softtek, UST, Persistent Systems, DigiBlu, Digital Workforce, RPATech, Acelirate, Robiquity and SYKES Digital Services.
IPA is the upgrade of any basic automation. As the world becomes more digitized, automation is becoming increasingly important for businesses that want to keep up with customer expectations and competition. By leveraging Artificial Intelligence, an IPA tool can complete more complex business processes that work with a variety of new and emerging technologies.
By combining innovative new techniques, IPA software makes robots smarter so they learn from performing their tasks to work faster, better and more accurately over time. Where basic automation using RPA is not smart enough to manage nuances and exceptions, IPA steps in with its intelligence and flexibility.
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