RPA uses AI and machine learning to super-charge industrial-scale digital transformations
The term digital transformation-defined as technology that fundamentally changes how companies deliver value to customers-has become a household term recently and for a good reason. A wave of new technologies and services such as artificial intelligence, machine learning, augmented and virtual reality, blockchain, cloud, software-as-a-service, edge computing, Internet of Things, among others, are upending business processes and customer relations. And the fifth generation of mobile communications, or 5G, is just around the corner, which is expected to supercharge them all.
Forward-looking companies such as CISCO, Nokia, Telefonica, Vodafone, ABB, have embraced digital transformation to drive product differentiation, improve productivity and customer satisfaction, and boost the bottom line. Of course, digital transformation is highly disruptive, requiring significant changes in business processes, skill sets, capabilities and business models. Companies late to the game risk losing market share, or worse, becoming irrelevant.
One transformational technology that has been getting considerable attention lately is robotic process automation (RPA). RPA uses artificial intelligence and machine learning to handle high-volume, repeatable tasks-including queries, calculations, transactions and maintenance of records-that have been traditionally performed by humans.
However, Altran has found that many companies that start the journey towards intelligent automation are not leveraging the full potential of the technology and so are not reaping maximum business value. We have identified five reasons for this failure.
RPA is gaining popularity because of its potential to improve operational agility and time-to-market. In June 2018, report, Forrester projected that the RPA market would increase from $250 million in 2016 to $2.9 billion in 2021. And a 2017 Gartner study stated that demand for RPA tools is growing at between 20% and 30% every quarter.
Industry frontrunners in the implementation of RPA include aerospace and defense, banking, energy and utilities, financial services, healthcare, information technology, insurance, manufacturing, retail, telecommunications and transportation. Companies are applying RPA to a host of processes from billing, claims processing, customer service, human resources, invoicing, IT and procurement. Advanced use cases include integrating RSA in chatbots.
RPA’s most significant opportunity for improving efficiency and reducing cost is the automation of repetitive tasks. Utilities are using RPA for a host of applications including automated metering, billing settlements, consumption management, automating network operations, customer records management and complaint resolution.
In the transportation and logistics sector, companies are using RPA to improve customer responsiveness by automated ordering and inventory tracking, forecasting, logistics planning, shipment scheduling and tracking. RPA is also used by Airlines to automate fare auditing and passenger data validation checks, and aircraft manufacturers to improve supply chain efficiencies by automating ordering, forecasting, procurement, inventory management and payment processing.
Challenges Implementing RPA
In our experience working with clients to implement RPA systems, Altran has identified a number of common implementation challenges that companies face. While it is not possible to completely avoid these challenges, companies can minimize their impact. Two primary challenges are:
Prepare employees for the transformation. Any company that relies on tried and tested processes that have been in place for years is highly sensitive to change. By definition, RPA is a disruptive technology that requires a cultural shift in how processes, so successfully managing its implementation is paramount to its ultimate success. Changing the work environment, job functions and responsibilities, and introducing new systems-as well as selecting the right processes for automation-requires good internal communication and transparency, and a robust training and education program.
Set realistic expectations. RPA is a powerful tool but there are limits to what it can do. Companies need to set realistic goals during the implementation planning process to ensure success. For example, RPA cannot handle processes that require a lot of manual intervention or oversight, or that are changing frequently and have large numbers of exceptions.
To address these challenges, Altran advises companies to take the time to identify the critical pain points and take a methodical step-by-step approach when implementing RPA. This involves starting with a proof-of-concept review to document the benefits of specific RPA applications. This should be followed by a pilot project that serves as a learning laboratory for the eventual rollout in the production environment.
Along the way, management should conduct regular awareness sessions to communicate the benefits to employees and the company, such as reducing monotonous work and improving the customer experience. One best practice Altran recommends is to create a center of excellence that identifies the processes best suited for automation, develops RPA capabilities and establishes a comprehensive employee training program.
Digital transformation is about aligning the best available technologies to improve the customer experience, boost employee productivity and streamline vendor and supplier relations. Digital transformation has the potential to bring positive change and RPA is an essential part of the process for increasing business value.