We know of the impact that Robotic Process Automation (RPA) has made on multiple industries. However, RPA in itself is undergoing a transformation that is set to bring a wave of unprecedented change. This transformation is known as Intelligent Process Automation (IPA).
What is Intelligent Process Automation?
Intelligent Process Automation is the coming together of Artificial Intelligence with new age technologies such as Natural Language Processing, Machine Learning, Computer Vision, Robotic Process Automation, Cognitive Automation and more. IPA is set to become the core of next generation operating models. Mckinsey has revealed that companies across industries that are still in experimental stages with IPA have already experienced automation of 50-70% of tasks, with ROI growth in triple-digit percentages.
We have known RPA to robotize repetitive tasks. IPA, in addition, throws in fundamental process redesign into RPA and machine learning; and improves upon the robotic activities. The result is simple and effective – improved customer journeys, which is a byproduct of fewer operational risks, better response time, simplified interactions and speedy processes. The deep learning, cognitive technology of IPA gives it decision-making capabilities that makes the difference in the long run.
Benefits of Intelligent Process Automation
- Efficient processing of unstructured data: IPA remarkably complements RPA when it comes to processing unstructured data. Using features such as optical character recognition (OCR), IPA can easily comprehend unstructured data and carry out tasks other than those based on pre-defined norms. IPA systems are smartly built to identify and extract unstructured data from various sources and convert it into structured data, executing difficult tasks with high precision and continuously improving the agility of the business processes.
- Cost and risk reduction: Most automation solutions lack situational awareness and require human intervention at some point. However, smart capabilities of IPA systems enable them to respond to situations when processes deviate and also in case of unplanned scenarios. IPA systems can self-correct from errors and function well without the involvement of humans. With decreased employee input, the operational cost of organizations reduces drastically. Also, by automating end-to-end processes with RPA and IPA complementing each other, there is reduced risk of errors and governance.
- Superior customer experience: IPA has the capability to handle dynamic workflows including uncertain scenarios and streamlines the entire process efficiently. End-to-end visibility of the entire process also improves, making it easier to identify and address the pain points along the customer journey, taking customer experience to an entirely higher level.
- Improved employee morale: IPA generates order where there could be utter chaos by freeing up employees from mundane tasks. Without the need to concentrate on routine manual tasks and the hassle of navigating through various systems, employees can focus on cultivating skills of higher value and sharpening their core competencies. All of this can generate higher employee morale and also reduce employee turnover.
- Advantageous business insights: While IPA tools rely on analytics to analyze the processes and fine-tune them to changing scenarios, they also generate valuable insights based on analytics such as determining the bottlenecks existing in the processes (what works and what does not) for both the process owners and stakeholders. This enables organizations to proactively participate in the automation process by analyzing and measuring the results of automation and identifying opportunities for further automation.
Core Technologies of Intelligent Process Automation
Five core technologies come together in IPA to seamlessly work together and each plays a prominent role in bringing out the real value of IPA. They are:
RPA: This tool or component of IPA is the most well-known automation technology in the market today. RPA tools recognize pre-defined rules and carry out routine tasks. Bots mimic keyboard strokes and clicks to automate mostly computer screen-based tasks such as generating documents and reports, examining files, carrying out calculations, and accessing emails and other systems. This helps speed up processes and employees can focus on higher value activities.
Smart workflow automation: This process management tool connects the tasks performed by employees and machines across platforms. It helps in integrating existing systems, ensures proper coordination, and allows real-time monitoring of workflows with the use of dashboards as well as provides proactive alerts for any bottlenecks within the system.
Advanced analytics and machine learning: Machine learning algorithms identify patterns through supervised and unsupervised learning from structured data offering game-changing predictions that help make informed real-time decisions. With time, the level of advanced analytics gets sharper since the more data it collects, the smarter insights it can produce.
Natural-language generation: Natural-language generation software engines automatically convert observations from structured data into written prose or narrative, eliminating the need for tedious monotonous work for the employees. Natural-language engines can efficiently generate automatic reports, draft email communications, and create descriptions for e-commerce products.
Cognitive agents: Combining two technologies that is machine learning and natural-language generation, cognitive agents, also known as virtual workforce, execute tasks with human-like capabilities. These agents are able to communicate, able to decide using emotional detection, and can also learn new information from data sets. Cognitive agents are capable of taking over the responsibilities of customer support, either through chat or over the phone.
The Future of Intelligent Process Automation
According to Cognilytica, an analyst firm that covers the market on artificial intelligence, machine learning, and cognitive technologies, the total market for IPA solutions will grow to over $8.3 billion by the end of 2023. That said, the question on most minds is whether IPA has reached its pinnacle yet, and does this ring the death knell for RPA.
The answer to both these questions is a “NO’.
While it is true that with IPA getting smarter it is likely to supersede RPA, it is also true that such levels of superior cognitive technology is yet to materialize.
Cognilytica, based on their research and experience in the cognitive technology, have created a scale that measures the capabilities of IPA systems, and have set up four yardsticks for assessment. At Level 0 stand RPA systems that have not been embedded with AI; Level 1 systems have language and context awareness; Level 2 are powered with intelligent process awareness; and finally, Level 3 – the most powerful among them all – offer autonomous process automation. Cognilytica reveals that reaching Level 3 is still a distant dream for RPA vendors, and only a few are at the fringes of Level 2.
However, this development does not mean that RPA itself is on its way out. What is important to understand is that with passing time, cognitive advancement will naturally get embedded into automated processes. RPA and AI are meant to go hand-in-hand and are not to be seen as competitive technologies; rather as enhancements.
Real-life application of Intelligent Process Automation
Case 1:Hollard, a leading insurance company in South Africa implemented iRPA (Intelligent Robotic Process Automation) with the goal to understand and solve complex customer-centric solution objectives. The company added virtual assistants/robots into their end-to-end processes to streamline their operations. The robots were particularly needed to process the high volume of email received from the broker base and other parties, ensuring that they are compliant with the regulatory and statutory provisions at all times. The insurance firm has, over a period of under two years, saved approximately 2000 hours per month in processing time, and reduced cost per transaction by 91% and mean time to execute by 600%, among multiple other benefits. Read the whole iRPA case study here.
Case 2: Kiva Systems, which was recently acquired by Amazon for about $800 million, developed a “mobile-robotic fulfillment system” to automate retail distribution centers for companies such as Crate & Barrel and Walgreens. The robots go back and forth the distribution center, without collision, and transport shelving units loaded with products to workers who are preparing customer orders for shipment. The bots have helped improve worker productivity by a factor of two, compared with its next-most automated facility. After the Amazon acquisition, over 130,000 of these goods-to-worker bots have been put to work in Amazon warehouses and Dcs.
Case 3: Credit Suisse, a Swiss multinational investment bank and financial services company, uses the IPA technology to analyze millions of data points on thousands of companies and automatically write English research reports that assess company expectations, upside and risk. These reports help analysts, bankers and investors make long-term investment decisions. According to Credit Suisse, in comparison with analyst-written reports, the bots have tripled the volume of reports produced, while improving their quality and consistency.
The Beginning of Intelligent Automation
The future of automation is suggestive of cognitive systems that will self-learn and handle processes better, improving the overall workflow, making necessary modifications and optimizing processes in a world where there will be more semi-structured or unstructured data. With RPA’s transformation into IPA comes a smarter system that can handle a wide range of complex enterprise activities unlike ever before. From just simplistic automation of rote jobs and the focus on improving productivity and cutting costs, the emphasis will move to intelligent automation and insightful decision-making that will truly intensify customer experience.
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