By JOLT Experts on Jun 4, 2020 9:46:21 PM
Robotic Process Automation (RPA) is a technology that has been widely adopted across multiple industries and geographies, proven to optimize business processes and services, and enhance the experience of employees and customers alike.
But at its core RPA is a technology designed for automating repetitive, mundane, and rules-based tasks that would have required a human effort. RPA by itself is not intelligent, to truly drive Intelligent Automation enterprises need to converge RPA and cognitive tools such as Artificial Intelligence (AI) and Machine Learning (ML) to augment the doing capabilities of RPA with AI's thinking abilities that mimic human behaviour.
And once organizations have started implementing intelligent automation, they start to set a path towards hyperautomation, where complex and exception-heavy activities could be automated end-to-end across the enterprise.
So, what is AI and how does it complement RPA?
Artificial Intelligence (AI) includes a broad set of cutting-edge technologies such as Machine Learning (ML), Deep Learning (DL), Object Character Recognition (OCR), Natural Language Processing (NLP) and so on, that coupled with RPA can enable robots to mimic human judgment, behavior, and rationale.
With AI, robots can learn from legacy and new data, they can observe how humans solve exceptions and learn from them to further optimize processes. While RPA is process-driven, AI is data-driven, when RPA robots are infused with AI technologies they become 'thinking' robots or cognitive robots and are able to perform reliable data-driven decisions across the enterprise.
When RPA robots can understand data and use AI to make decisions, organizations unlock new possibilities that transform the way they automate their business processes.
Benefits of an AI-powered RPA program
Improving Customer Experience (CX)
One of the main use cases for cognitive robots is to solve business issues that are related to customer experience. According to Gartner, by 2022, 20% of all customer service will be handled by conversational agents.
Conversational agents provide organizations with robots that can intelligently capture the customer’s intent and work with back-end enterprise systems to deliver fast resolutions to their claims and inquiries and substantially improve the CX.
According to a Gartner report, intelligent automation implementation will focus heavily in CX processes since robots allow organizations to stay connected to their customers on a 24/7 basis through text and/or voice all year-round.
Cognitive robots can improve CX by reducing the time to respond to a customer, reducing service costs by implementing self-service channels, boosting multi-channel experiences, and streamlining customer service and contact centers.
AI technologies can be deployed to be used on a variety of front-office applications, including call centers and digital marketing platforms. With intelligent automation organizations can extract data that allows them to create a holistic view of their customer journey and analyze the conversations between the customer and the organization in real-time.
Optimizing Sales Capabilities
Sales teams across industries can benefit from RPA + AI, by deploying cognitive robots that can perform predictive analysis using Machine Learning (ML) models and generate new data that anticipates future market behavior and estimates potential outcomes.
Organizations can leverage intelligent automation to predict outcomes and have a clear path towards the next best action and through data, robots can prioritize leads that are more likely to convert.
Also, cognitive robots can help sales teams with generating forecasts, creating sales presentations that are leadership-ready, and offer analysis and recommendations on what immediate actions to take on new deals and opportunities.
Additionally, RPA + AI can create better sales experiences for the customer by deploying robots that can detect win-win situations, compile customer data to uncover hidden insights, and help the vendor nurture a lasting relationship with the client.
Driving back-office efficiency
Organizations can automate their real-time inventory management using AI-enhanced RPA, a 'thinking' robot that can go into the company CRM and extract the inventory data and then leverage an ML model to predict next week’s sales numbers. With the predictions calculated, the robot will inform a human about the need to increase the inventory and will ask for a purchase order, the human will only need to provide the purchase order so the bot can complete the purchase order on its own.
Use cases like the above have been adopted across the board, RPA + AI can impact multiple areas of an organization, from finance to sales, from marketing to operations, from HR to customer service, each business unit can be intelligently automated.
How to get started with Intelligent Automation?
AI does not function on its own, it would be a brain without the body, RPA acts as the arms and legs of AI to deliver its full potential. Hence, organizations looking to create a smart digital workforce via a combination of RPA and AI need to start by assessing where they are in their RPA journey before diving into the realm of AI technologies. For example, RPA stakeholders need to identify which processes can benefit the most from deploying cognitive robots, such as processes that require prediction and classification.
There are important steps that need to happen in order to secure a well-rounded Intelligent Automation (IA) strategy. First, the definition of the automation operating model that will help drive organizational change and engage Subject Matter Experts (SMEs) to govern and track the progress of transformational KPIs. Next, the selection of technology infrastructure, integration and identity management plans. And last, the creation of a Center of Excellence (CoE) that will lead the implementation and management of automations across the business unit or enterprise.
Leaders driving the deployment of Intelligent Automation need to understand that preparation and time are fundamental to successfully implementing IA. Proper training of the AI system takes time and responsibility, organizations need to make sure the AI system is trained with common sense to prevent it from taking detrimental decisions.
Knowing where to start is a critical part of the journey but organizations must also take into account the potential challenges when deploying AI-powered robots to automate a wide array of business processes.
One of the key challenges is 'robophobia', employees across business units tend to be cautious of technologies that are perceived as threats to their employment. Leaders need to make sure to put in place an effective communications plan to drive the organizational change required to obtain grassroots support for automation. This should include clear details about the benefits that robots will bring to their jobs, so they can regard them as digital assistants that will help eliminate the low-value and tedious tasks from their workloads, and give them the time back to reallocate towards more meaningful and creative work.
Another challenge is the availability of data and know-how, organizations often don't have data scientists or AI/robotics specialists in-house, and DIYing this alone can be a daunting task. Analysts recommend finding the know-how from external consultants to help craft the strategy, design change, deliver training, and provide support and maintenance. JOLT has the experience to help. Contact us to discuss how you can get started on your journey towards creating an AI strategy and fostering an automation-first culture.
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