Intelligent Automation is Marching In
The world has changed rather dramatically during the past year and a half, with a wide variety of activities going virtual. From virtual classrooms to the surge in e-commerce and subscription services (e.g., HelloFresh, Stitch Fix, Harry’s, Peloton, Netflix, etc.), and the exodus from commercial real estate to work-at-home, the effects are evident every day and everywhere. While there are growing signs of a return to “normalcy,” it does not mean that many of these changes will fade away. Forward-thinking organizations around the world are recognizing these changes for what they are – much-needed improvements that are part of their digital transformation.
For many contact centers, technology saved the day as businesses transformed. The cloud helped support the migration from on-site to work-at-home (WAH), analytics provided insight into the voice of the customer (VoC) and employee (VoE), and artificial intelligence (AI) and automation boosted contact center performance. There is no going back. The pandemic changed consumer expectations, customer service and contact center environments in a lasting way, making this the ideal time to adopt intelligent automation solutions.
Robotic Process Automation Trends
Robotic process automation (RPA) has been around for 15+ years in one form or another, but today’s AI-enabled solutions bear little resemblance to the rules-based applications that were once the norm. The value these intelligent automation solutions deliver is significant and is increasingly recognized around the world, as is reflected in growing sales of RPA applications globally. This should not be a surprise as these versatile solutions offer significant benefits to contact centers (attended/unattended RPA) and back offices (unattended RPA).
Multiple AI technologies have found a “home” in RPA solutions, and the list continues to grow. While some of these technologies are applied with a focus on enhancing the core capability of RPA – automation – the wide variety of AI tools being leveraged by these solutions highlights the evolution rapidly taking place in this market. Notable AI additions include computer vision (enabling RPA applications to read and capture data in one system for use in another system without the need for integration); machine learning (used by leading vendors to automatically discover and recommend future automation opportunities); and natural language processing (NLP)/natural language understanding (NLU) (utilized to determine customer sentiment and intent and present agents with real-time guidance based on this knowledge).
Beyond their value as stand-alone offerings, RPA capabilities are increasingly being used to augment the benefits of third-party solutions. Logically, integration with intelligent virtual agents (IVAs) to provide comprehensive smart automation to customers via self-service applications is one of the most common uses. In addition, RPA is being leveraged to automate access to context-sensitive articles in knowledge management solutions and to help speed up the creation and distribution of schedules and reports in workforce management applications.
As RPA vendors strive to make their solutions smarter, they are also creating new ways to make them easier to use. Low-code/no-code design and development environments are a focus for many of the leading and contending RPA vendors, enabling business users and other non-programmers (citizen developers) to deploy and control their software robot workforce.
Robotic Process Automation Challenges
The RPA market is growing rapidly due to the significant value and diverse contributions of these solutions, but buyers and users also need to be cognizant of the growing pains that are part of its success. Contrary to vendor messaging, all RPA solutions are not the same. Unfortunately, some of the marketing is confusing for prospects who are finding it challenging to identify the right solutions for their company. Vendor over-promises are resulting in some false starts when a company invests in a solution that does not yet have the capabilities they need. Adding to the confusion is a lack of clear delineation between attended/unattended RPA solutions and other automation capabilities, including workflow management solutions and IVAs.
Although improving ease of use is a focus for several RPA vendors, many solutions currently require the skills of programmers or RPA developers to design, deploy, monitor and control their automations. Since these resources are limited and can be costly, this negatively impacts the ability of some organizations to get automation programs up and running quickly and also delays the ROI.
And these challenges are not confined to the vendors or their solutions; some of the organizations that are deploying RPA applications are hampering the success of their own implementations. A primary cause is the lack of governance. Without oversight, broken processes are being automated rather than fixed (or fixed and then automated, if appropriate). Additionally, software robots, like all resources, require “management,” including training, onboarding and ongoing support. Automations are not simply “set it and forget it”; they must be monitored and maintained and, when deployed across departments or in addition to IVAs, should be managed through an enterprise-level Center of Excellence.
There is a bot in your future, as RPAs can be very beneficial when they are implemented by experienced resources with proper governance and management oversight. But the true key to success is to find the appropriate uses for these solutions and to optimize the steps in the process prior to automating tasks. This is the case regardless of where (front- and back-office) and how (attended and unattended) RPA is applied.