Smart RPA Investments Are Reaping Benefits for Enterprises | Market Insights™
April 29, 2019
About 20% of enterprises that have deployed RPA have implemented AI in conjunction with their RPA deployments*, and the number is rising
About 20% of enterprises that have deployed RPA have implemented AI in conjunction with their RPA deployments*, and the number is rising
Key technologies that form underlying systems for Smart RPA solutions: Key technologies that form underlying systems for Smart RPA solutions: Robotics, Optical Character Recognition (OCR) / computer vision, Process orchestrator / Business Process Management (BPM), analytics, Machine Learning (ML), Natural Language Processing (NLP)
Factors driving the adoption of Smart RPA among enterprises
The contact center industry is changing considerably due to technology enablement. Contact center automation is rapidly becoming a priority as centers increasingly embrace technologies such as artificial intelligence (AI), chatbots, robotic process automation (RPA), and robotic desktop automation (RDA) to handle customer interactions on rote queries like account balances, package tracking, and reservation confirmations.
A similar transformation is also taking place in personal transportation. Advancing technologies and intense competition are driving amazing strides in the autonomous vehicle industry. While cars aren’t yet 100 percent self-driving, companies like Tesla are already offering advanced driver assistance solutions that can pretty much take control of driving, albeit with human supervision.
With the perceived nature of each of these two industries, it’s easy to assume that contact centers will be fully automated in far less time than the two to three years some believe it will take for autonomous driving solutions to get you from one point to another without human intervention.
However, this is an incorrect assumption.
Indeed, counter-intuitive as it seems, it’s much more difficult to completely automate contact centers than it is to automate driving. Why?
Driving involves a large, but still finite, number of scenarios that need to be programmed for. But a contact center environment can throw up potentially infinite unique problem statements and challenges that enterprises cannot possibly predict and program for in advance. Yes, AI helps, but even that can only get you so far. At the end of the day, the human mind’s problem-solving ability far exceeds anything that the current or foreseeable technology can offer. And while most people would be more than happy to let robots take over the wheels on the road, they still expect and require human touch, expertise, and judgment for the more complex pieces that usually make or break the customer experience. Technology just isn’t sophisticated enough to handle these yet.
The degree of contact center automation that can be leveraged within an industry varies by process complexity
Although technology use in contact centers is in the early stages, we are already witnessing higher agent satisfaction and lower attrition rates in an industry that has one of the highest churns globally. And as robots increasingly take care of customers’ simple, straightforward asks, we certainly expect agents’ satisfaction to increase.
Of course, agent profiles will continue to evolve as they are required to deal with more challenging and complex issues leveraging machine assistance. This will, in turn, demand greater investments into talent acquisition and upskilling programs.
It will be interesting to see how all of this plays out in the next few years as technology becomes increasingly advanced and capable. The only thing we can say with certainty is that the customer experience of the future will be much more pleasant as irritations like long wait times, inept IVR responses, and repetitive conversations with agents who hold incomplete information become issues of the past…or, shall we say, smaller and smaller objects in our rearview mirrors?
As we mentioned in our Enterprise RPA Pinnacle report, there are multiple benefits of having an automation CoE:
While many enterprises have established CoEs to overcome challenges and accelerate their automation journeys, not all have been able to extract value from them. Here are some common pitfalls that limit the true potential of an automation CoE.
Automation CoEs work when there is a clear mandate from enterprise leadership. This directive helps ensure that the operating procedures and governance mechanisms are standard across the enterprise. Lack of it could result in the enterprise driving automation initiatives in pockets, leading to potential cost increases, lower license utilization, lower reusability of automation assets, and increased governance challenges.
In its true sense, a CoE represents an entity with the capability to drive automation initiatives across the enterprise independently, with minimum oversight from outside. In many enterprises, however, automation CoEs lack the relevant skill sets – such as developers, project managers, solution architects, and infrastructure support staff – that are critical to driving these initiatives. Successful CoEs typically have three focus areas: day-to-day delivery, operational/tactical decision making, and strategic decision making/providing direction. Each layer, or focus area, has a unique set of roles and responsibilities that are critical to a smooth functioning CoE. Successful CoEs have an intentional focus on developing and nurturing in-house talent to strengthen the capabilities across the three layers. Many CoEs also bring in third-party specialists to accelerate learning. Our blog titled Driving Success in Your Automation Center of Excellence provides more details.
The role of an automation CoE goes beyond just deploying bots into production. CoEs in best-in-class adopters of automation have evolved from executing solutions to empowering businesses across locations to drive initiatives on their own. For instance, the automation CoE in a financial services firm has established standard operating procedures (SOPs) for driving automation initiatives which include a well-defined approach for process selection, evaluation of ROI, talent impact, access to a library of reusable assets, etc. The CoE has created a platform through which business leaders can access these SOPs to evaluate opportunities on their own, and provides necessary governance and execution support, including talent and infrastructure.
As highlighted in Everest Group’s Smart RPA Playbook, the typical roles and responsibilities of automation CoEs include:
Outcomes achieved through automation initiatives are best realized when these investments are in line with the enterprise’s broader digital transformation objectives. Factors such as investments in other digital technologies (e.g., ERP transformation), changes in leverage of Global In-house Centers (GICs) or shared service centers, changes in vendor roles, etc., can have an impact on automation strategy. Automation CoEs need to closely align with enterprise strategy to realize maximum value.
Pinnacle EnterprisesTM – which we define as companies that are achieving superior business outcomes because of their advanced capabilities – have mastered each of these four requirements for successful automation CoEs. To learn more about how they’re maximizing value from their automation CoEs, please see our Enterprise RPA Pinnacle report.
Feel free to share your opinions and stories on how your automation CoE is evolving in its journey directly with me at [email protected].
The HR function faces challenges on multiple fronts, caused by talent-related issues, the need to improve the employee experience, and the high cost of operations
Zomato may be preparing for a future with drone deliveries, but for some of its immediate problems, the food-delivery app’s solution is old-world: bicycles.
The firm is introducing mechanical and electric bikes into its last-mile delivery fleet, it said in a press release yesterday (Feb. 11). Those using e-cycles will make short-distance deliveries of 2.5 kilometres on average.
Bicycles are also way cheaper than motorbikes, which means more job seekers may be able to afford joining the Zomato delivery team (Food-delivery startups in India typically expect executives to bring their own vehicles).
Then there is fuel cost. Food delivery startups can save up to 20% by reducing dependence on fuel, said Yugal Joshi, vice-president at Texas-based consulting firm Everest Group.
When CIO Jason James decided to automate his company’s software deployment process, he tasked his DevOps team with learning everything it could about the process itself.
He says he wanted his DevOps engineers to act like anthropologists, watching how the developers deployed software to the company’s clients, how they scheduled and loaded software updates, and how they used existing tools to get their jobs done.
The goal, James explains, was to have the DevOps engineers understand each step in the workflow so they could first streamline it and then automate it, thereby creating a more efficient process all around.
Sarah Burnett shares her thoughts on automation with CIO (registration required to read the full article)
Application Support Services: 12 fee drivers you must normalize to ensure a like-to-like comparison in benchmarking application support pricing
Many enterprises that have used Robotic Process Automation (RPA) have seen the power of digital transformation, even if only in a small way through a few automated processes. The transformational value they experience is often a tipping point that whets their appetite for even more automation and deeper levels of application integration. But, this creates a quandary about how to maintain the array of automations. Ultimately, their success depends on the scope of the centers of excellence (COEs) that maintain their automations. Let’s explore further.
I believe that RPA has helped companies that previously held back from adopting newer technology solutions see the value of a digital mindset. These converts are now finding more opportunities for automation, and greater conviction in moving to digital-first operating models.
In short, something comparatively simple like RPA helps inspire confidence and vision.
Once this passion is unleashed, organizations come to fully appreciate that RPA is only one tool for automating operations. Many desire to transform their high volume, fast processes, and must confront the reality that surface-level RPA integrations are often not sufficient. The next steps towards more powerful automations often include integration via connectors and APIs.
The following exhibit reflects the diversity of systems which may now need to be integrated in a digital-first operating model world. (Spoiler alert: we’ll be writing a lot more about the Digital Capability Platform in the upcoming months.) And there are many ways to go about creating the needed integrations.
Some enterprises have cast aside the promise of surface-level RPAs, and now use their RPAs more through APIs. This is a bit ironic and worthy of a discussion by itself, but let’s get back to what happens as the types of automations proliferate.
One thing that all integrations – surface, APIs, or connectors – have in common is that they need maintenance. With surface-level RPA, you need to do a lot of robot maintenance when application layouts change. But all integrations, RPA included, require maintenance for other reasons as well. The biggest is the need to resolve data ambiguities, e.g., common customer names (think Jane Smith) with similar account types requesting a temporary address change. Which record should be updated? How can this correctly propagate across all the relevant systems and processes?
This is why a COE should be responsible for all types of automations, whether through surface or other integration methods. By looking across all automations, a COE can not only more accurately maintain the automations, but also identify anomalies and conceive new ways to structure interdependent automations. Of course, adding AI-based tools into the mix adds even more API connections to manage. But AI connections are far from the only ones that will need to be managed; the landscape will become more complicated before it simplifies (yes, I’m trying to be optimistic here.)
I can hear some of you saying that the COE should be an overall digital center of excellence. My answer is a big “no.” Digital is a far broader field that often involves major legacy transformation projects. Automation is clearly a part of digital, but it is operationally focused on the practical realities that come from modernizing processes that still primarily run on legacy systems.
This is a different mindset and a different set of competencies. As a result, it is best to keep a separate automation COE focused on the details of operational processes, while separately working towards the corporate digital objectives in a broader digital office. And that automation COE’s remit should be bigger than just RPA – it must deal with the combination of all types of automations that are enabling the operating processes.
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