While the Robotic Process Automation (RPA) market is heating up, many say that RPA is not AI per se, but rather a gateway on the path to cognitive automation. Automation Anywhere certainly agrees with this perspective, and in this exclusive interaction with CPO Innovation, Milan Sheth, EVP-IMEA at Automation Anywhere shares insights into the RPA industry, the overlap with AI efforts, the new release around autonomous process discovery bots, and the future of cognitive automation.
Intelligent Automation has gained adoption in many sectors in recent years, and one of those places where people may be familiar with Intelligent Automation is Robotics Process Automation (RPA). So, many see RPA as a gateway to more cognitive forms of automation such as AI. What role does RPA play to move the company’s ladder of intelligent automation?
The current situation is critical for any company to operate business as usual, companies have to deal with accelerated pace and high customer expectations. The Holy Grail is customer experience, and this can be achieved through automation. While RPA is being widely implemented, Intelligent Automation (IA) is the next logical step. Many are speculating that RPA may have already reached its tipping point since it represents the workhorse of operations. RPA is usually the entry point into automation and it deploys software to replace receptive tasks done by humans. Once deployed, RPA can be left to operate with minimal supervision.
However, what happens when enterprises want automation to take on more complex operations? This is where IA comes into the picture – it mimics humans, learns from them, and over time, outperforms them. This is done by incorporating RPA, machine learning, reasoning, problem-solving, natural language processing and cognitive abilities. Thus, IA is the next step in the evolution of RPA as it can read unstructured data, extract information and determine what to do next – all at a rapid pace. We consider RPA to be the foundation of an enterprise’s automation system that enables bot creation and digital worker deployment. But by adding sophistication in the form of AI modules, API integrations, security and governance controls, enterprises can further expand the capabilities and effectiveness of RPA to reach the next level of automation aka Intelligent Automation.
Can you share with us some examples where RPA & other approaches are used to address the challenges in automation?
In the current business scenario, that has been severely disrupted, we can see both the limitations of traditional automation and the benefits of cognitive Intelligent Automation. For instance, safeguarding supply chains during unplanned global events such as a global pandemic or natural disaster cannot just rely on simple automation. A more advanced and intelligent form of automation is needed for better supply chain access and insights. IA can take over Business as Usual (BAU) operations and free up resources for diverting towards areas of supply chain that require more manpower and expertise. Tasks here include material requirements planning, supplier onboarding, contract management, logistics, purchase order processing and more – all tasks that need some form of cognitive Intelligent Automation to keep moving without intervention.
Moreover, such systems can also enable quick switches to alternative suppliers by simplifying information sharing and uploading. Each of these tasks generates insights and data models that traditional RPA would be unable to provide. The benefits are reflected in the bottom line by saving downstream costs and enabling faster response times. This is yet another set of factors why intelligent automation is becoming the norm, and enterprises are integrating AI, NLP, Machine Learning and other cognitive abilities into their RPA solutions.
Another example is how RPA can reduce acquisition risks, especially in cases of public sector contract acquisitions. One of our clients opted for an RPA bot to perform low-value administrative tasks, while human workers could focus on negotiations and cost analysis instead. While a human worker took one hour to create a contractor responsibility determination, a bot was able to achieve the same output in two minutes with zero error. This time savings led to 13 extra days for each of the 7,000 contracting professionals over a year, thus highlighting how RPA can address business challenges.
Tell us something about the interesting use cases of automation processing that you have seen or encountered in different industries and your organization.
Enterprise technology companies are built on customer success stories, and we have seen our fair share of RPA-led customer success stories. Our bots are designed with the customer in mind at all stages – right from product creation to delivery and support. One successful automation implementation worth highlighting has been with a global information services company that wished to implement RPA. While we enabled scale from pilots in four months, within 12 months the implementation really started to display long-term benefits. The enterprise was able to enjoy 6x growth in cost savings, 98% transaction success rate and 100% reskilling of impacted employees. The productivity difference was massive, with more than 5,00,000 routine tasks at the organization subject to automation.
We have several such examples that highlight the impact of our customer-centric approach delivers. These range from manufacturing companies reaping the soft benefits of enabling workers to devise better solutions to insurance companies reducing employee stress by improving the efficiency of their tasks.
Please tell our readers something about the highly innovative products of Automation Anywhere, especially about the Discovery Bot?
Process discovery is one of the key aspects of effective automation initiatives. It helps discover automation opportunities within an enterprise, and upon discovery, it helps increase the ROI of automation projects. This is where Automation Anywhere’s Discovery Bot comes into the picture. It’s an intelligent process discovery solution that leverages AI and Machine Learning to analyze and consolidate multiple workflows to identify common patterns. It goes through 6 steps – Capture, Map, Identify, Prioritize, Generate and Deploy – to ease the deployment of automation for enterprises.
By leveraging its inbuilt AI features, Discovery Bot pinpoints automation opportunities within the enterprise workflows and saves time and resources diverted towards bot development. Indicators have shown that this enables modern enterprises to accelerate their automation cycle by 5x, which makes it simpler for end-to-end automation that can be rolled out quickly. When merged with IA, this allows enterprises to find automation opportunities and create RPA bots to perform repetitive actions in record times.
How do you see the future of RPA? What are the exciting things or upcoming projects that Automation Anywhere is working on or planning to work on soon in the field of intelligent automation?
Until recently, we have been in the initial phases of automation seeping in at the workplace. The next step in this evolution is ‘Hyperautomation’, and this will include the end-to-end automation of office work leveraging various technologies including RPA, AI and Machine Learning. These developments demand high flexibility and adaptability from enterprises and this will mean that they need to be open to automating less visible and less standard processes as well. Such an approach requires a culture and mindset shift amongst enterprises too. Automation Anywhere is contributing to this evolution in iterative steps, and the latest technology in this process is Intelligent Automation. This implies the deployment of trainable, intelligent digital assistants for specific roles, rather than purposes.
We are building technologies that can automatically analyze human behavior and then pinpoint opportunities for automation. This is done through sophisticated pattern recognition and optimization algorithms that leverage the power of AI. As higher data volumes and complicated global business operations become the norm, self-learning Hyperautomation programs are the future.