This article is by Featured Blogger Muddu Sudhakar from his Blog Page. Republished with the author’s permission.
Robotic process automation (RPA) technology is transforming how businesses operate. Today, RPA software can work with business systems and applications to simplify processes and reduce the administrative burden on employees. Yet, despite its revolutionary potential, RPA has been confined so far to back-office processes. That means it has been missing the opportunity to deliver on the all-important customer experience.
Traditional RPA vendors basically perform what’s known as “screen scraping.” This is merely the process of capturing screen display data from a legacy application and translating it so that a more modern user interface can show it.
The scraped information -- perhaps questions and answers -- is then used by RPA technology to figure out workflows. Thus, screen scraping is essentially a rule-based workflow editor. Screen scraping is not to be confused with content scraping, however, which harvests actual content from a website without the owner’s approval.
Taking RPA To A New Level: Customer Service
RPA has unquestionably advanced processes and lowered the software burden carried by employees in the back office. But now the advances of conversational AI have become available and mature enough to leverage and apply in front office services too.
Conversational AI encompasses natural language processing (NLP) and natural language understanding (NLU). NLU is a branch of AI that understands context and inputs made in the form of sentences in text or speech format. Not surprisingly, the field of NLU is a vital subset of natural language processing. NLU search provides interactive experiences to users, delivering remarkably humanlike dialog to respond to knowledge requests, answer common questions and assist in complex problems to speed up the remediation of issues.
Conversational AI sets the stage for a groundbreaking advance -- namely the ability to leverage back-end RPA tools, applying AI and machine learning (ML) to service-oriented functions in a natural language dialog flow through the omnichannel.
Nowadays, users want to take the path of least resistance and engage with your business using easy, conversational interfaces: voice, text, email or the latest chat tool. The result: Sophisticated automation for business processes, tasks and workflows that drive improved experiences and delight internal and external users alike.
Combining Conversational AI And RPA For Enriched Interactions
Conversational RPA works best when you want more than merely derived information from ticketing. It advances the customer and employee experience with a conversation-driven approach to business processes, letting you address user or customer needs personally, predictively and prescriptively. Business processes that include repetitive, time-intensive customer or employee interactions should use conversational RPA to reduce operational costs and improve the customer experience.
Conversational RPA understands user conversation requests, looks at previous intents and draws from historical findings to automate resolutions and improve employee productivity. This is a dimension ahead of RPA screen scraping a ticket.
Conversational AI, applied to RPA for IT and customer service, offers the ability to verticalize business process automation across IT, customer service, HR, IT ops, cloud services and other departments.
Conversational RPA Solves Business Challenges
Businesses today suffer from a lack of automation in tasks and actions. Accompanying that is a shortage of learning. This deficit translates into long resolution times, manual triage, repetitive tasks and often disjointed and unsatisfactory customer experiences. Add to that the chronic problems of siloed systems and high data volumes, and the result is that users do not enjoy the fast and easy resolution they expect.
Conversational RPA can solve more complex challenges facing IT and cloud services, including the discovery of rare workflows and first steps. On a deeper level, only conversational RPA can deal with such matters as:
- Understanding the intent of a request.
- Integrations from IT and cloud.
- Automation issues.
- Creation of an audit trail.
- Compliance.
- Analysis of the user experience.
According to Gartner, “By 2022, 70% of white-collar workers will interact with conversational platforms on a daily basis.” Van Baker, VP Analyst at Gartner, says, “There has been a more than 160% increase in client interest around implementing chatbots and associated technologies in 2018 from previous years. This increase has been driven by customer service, knowledge management, and user support.”
The Hurdles Of Conversational RPA
When it comes to implementing conversational RPA, there are, of course, certain limitations. For example, conversational RPA is not suited for extremely complex high-touch workflows that require human intervention and judgment. If customer interactions are escalations or exceptions, then humans will be required to resolve these issues. Conversational RPA works for workflows that are parameter-driven and learns from them. If a workflow requires human interpretation to resolve, then conversational RPA won't work.
In other cases, businesses may elect not to digitize certain processes and workflows because the company actually wants to put human agents in touch with customers -- to understand their intent and reasons. In these cases, service agents are given enough leeway to negotiate with customers and determine the best retention actions to take in order to satisfy those customers. If negotiations are too broad and wide-ranging, then this level of interaction cannot be conversationalized with AI.
Starting Off On The Right Foot
If your company is considering RPA implementation, these initial steps can help you get the process started:
1. Understand the existing workflows you have, and establish a baseline. Then determine which can be "RPA-ized."
2. Understand where your workflows are being implemented and which system or service is used to orchestrate and execute these workflows.
3. Understand the steps within a workflow, and implement conversational RPA according to existing workflows and systems.
The end goal of this technology is, again, an enhanced customer experience. Automating tasks, workflows and actions gives users convenience, agility and productivity they could not have imagined under manual models. Just be sure that these processes fit within the parameters of acceptable RPA projects -- those without too many exceptions or escalations. Otherwise, you may find yourself having to fix more problems than were solved.
