Although enterprise interest in bots seems to be at an all-time high, Gartner reports that 68% of customer service leaders believe bots and virtual assistants will become even more important in the next two years. As bots are called upon to perform a greater range of tasks, chatbots will increasingly rely on back office bots to find information and complete transactions on behalf of customers.
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Connecting the Front and Back Office With RPA
Chatbots — and other customer-facing front office bots, such as speech bots and natural language voice response systems (IVRs) — have been the focus of customer experience transformation efforts. These bots interact directly with consumers on websites, phone systems, mobile apps, messaging apps and home assistants. They listen to customer requests, gather details, look up information, perform operations and present results. These capabilities are enabled by conversational AI that includes natural language processing, intent prediction, conversation management and response generation.
Depending on the customer’s intent, a bot may need to access a variety of enterprise systems, such as CRM, order entry, inventory, billing and payment, help desk, service provisioning and others. If these systems have modern APIs, then front office bots can get the information required themselves. However, these bots are powerless if APIs are not available — a common situation due to IT constraints. Fortunately, a solution is at hand: robotic process automation (RPA) is skilled at navigating difficult-to-access enterprise systems.
RPA has been deployed in the back office for a while, helping to automate repetitive tasks in finance, accounting, operations, HR and IT. RPA bots may touch five applications and automate hundreds of clicks that a human would otherwise do manually. The bots can connect to web applications, Windows applications, and primitive client-server applications. They can identify the buttons or links to be clicked on, and the fields on a page where data should be entered or retrieved.
RPA bots can serve human agents as well as virtual agents. These bots specialize in accessing disparate, sometimes legacy, enterprise systems that have user interfaces (e.g., agent consoles, portals and applications) but lack APIs. Using rules engines, the bots gather data across multiple systems, make decisions and execute processes in a repeatable, auditable manner. RPA bots allow agents to spend more time on relationship building and problem-solving activities rather than mundane, routine actions such as looking up and copying information. For example, bots help agents at a large global bank manage complex transactions like retirement rollovers and setting up new accounts. The bot gathers client data, fills out the needed paperwork, and uploads the completed forms to a client portal.
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Areas for RPA Improvement
RPA can bridge the gap between front office agents (humans and bots) and complex enterprise systems, making it easier to deliver effortless customer experiences. To realize this potential, RPA vendors should consider the following challenges and opportunities:
- Faster Turnaround. Most RPA bots work in batch mode, and the turnaround time for an individual item (such as processing a single invoice) can be minutes up to hours, depending on the number of items in the queue and the computing resources available. In customer service, where someone may be on the phone or waiting on live chat, RPA response times will need to be short and predictable. Live customer interactions should be given a higher work scheduling priority than less time sensitive tasks. Furthermore, RPA bots should be over-provisioned (so that some bots are always available to accept a new request).
- Proactive Service. Most RPA tasks today are reactive and transactional: the bots wait for a customer request to be made, and then fulfill the request. However, the ability to access and combine information across enterprise systems can also be leveraged for proactive service and marketing automation. RPA bots can identify opportunities for personalized offers, such as suggestions for proactive service (e.g., a periodic review of the customer’s financial plan) or recommendations for up-sells and cross-sells (e.g., a loan to consolidate credit card debt). These offers can be presented during a customer conversation, or can be pushed as a notification or reminder.
- Pricing Flexibility. In RPA, a bot instance is typically priced as a digital worker on an annual contract. The bot instance has the user credentials and computing resources to mimic a single human worker, can work 24 hours a day, but is limited to performing one task at a time. This pricing is well suited for back office functions where processing load can be distributed evenly during the year. In customer service, however, contact volumes can vary greatly by the hour, day, and season. More flexible pricing models will be required to allow digital workers to scale up and down on-demand, allowing companies to maintain response times without paying for underutilized workers.
As we step into a future where man–machine conversations are on the rise, RPA bots will play an important role in improving the quality and productivity of chatbots and human agents. Moving RPA bots from back office batch processing to customer-facing support will empower front line bots and agents to handle more customer intents and free them to focus on what they do best: provide the personalized attention that creates memorable experiences.
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