3 strategies for bigger and better results from Conversational AI
Companies are seeing positive results from Conversational AI; however, many believe greater outcomes are possible by extending the reach, functionality and intelligence of their solutions.
Integrating robotic process automation (RPA), connected products (IoT) and evolutionary computation* with Conversational AI allows companies to deliver enhanced customer experiences, automate deeper into core processes and derive more meaningful insights from their data.
Industries that can benefit immediately from this approach to Conversational AI include healthcare, retail, hospitality, banking, insurance and consumer goods.
Inspired by natural evolution, evolutionary computation refers to algorithms that repeatedly operate on data that, over generations, determines optimal procedures and methodologies to solve the most complex problems that exist today.
For most companies, the prevailing Conversational AI strategy has been to focus on relatively narrow use cases addressing customer experience and internal efficiency through voice-based virtual agents, chatbots and digital assistants. This thinking can certainly yield positive results, such as the development of best practices and plans to further operationalize, scale and accelerate these solutions across the organization. Businesses are realizing even greater results when conversational AI is deployed, not as a discrete, stand-alone system but instead applied with other powerful technology approaches, such as RPA, IoT and the emerging area of evolutionary computation.
At present, most companies that have deployed Conversational AI solutions are satisfied with their results. They are developing a foundation of best practices and are planning to further operationalize, scale and accelerate these solutions across their organizations with these top objectives in mind:
Creating more revenue opportunities.
Improving customer care and support.
Enhancing customer experience.
Leaders across industries are seeking the same goals, whether they’re focusing on web,contact center, mobile apps, smart speakers, messaging or, increasingly, all of the above. However, there is a growing sentiment that more should be possible from Conversational AI.
Next-level 3 Strategies: Specifically, to extend the functionality, reach and intelligence of chatbots and voice and virtual agent programs, they are being aligned with RPA, connected products and evolutionary computation.
How do these technologies work together with Conversational AI to deliver broader and more substantive results than when each is deployed as a discrete, stand-alone strategy?
1. Conversational AI and RPA: Delivering True Intelligent Automation
Conversational AI is following a similar trajectory as did RPA just a few years ago. It’s evolving quickly from smaller pilots to more integrated, transactional solutions as organizations find more ways to optimize engagements for hands-free and screenless user interfaces. While chatbots and voice solutions are obvious to the end user, RPA typically operates unseen, addressing friction points in middle- and back-office processes that customers may feel but not see.
At the edges of a customer-facing process, Conversational AI automates by delivering personalized, one-to-one interactions to prospects, clients and end users. RPA picks up behind that customer interaction point by automating previously manual, as well as labor-intensive, screen and keyboard tasks. Here are examples of how both are being applied:
In contact centers—handling “how-to” questions, booking reservations and scheduling service calls—or support centers—providing remote support, diagnostics and troubleshooting. RPA also enable sales and order processing, as well as payments, returns and credits.
For insurance, banking and financial services—finding branches, agents and brokers; checking balances and moving funds; initiating loans, accounts and policy applications; opening claims and validating policy coverages; investment and portfolio reporting; initiating fund and stock research; and tracking and delivering performance results.
2. Conversational AI and Connected Products: Contextual, Hands-Free Interactions
Even beyond smart speakers, voice-controlled lights and equipment sensors, connected devices are everywhere in our consumer lives and increasingly in our offices and industrial workplaces. These always-on connections from billions of endpoints allow businesses and their products to perform better, run more efficiently and operate more safely. Conversational AI adds greater value when combined with complementary hardware, such as far-field microphones and interactive displays. These advances allow product engineers and designers to embed voice-controlled, hands-free user interfaces into more and more and varied types of “things.”
Conversational AI provides contextual and personalized capabilities via voice and screen interfaces, and the addition of a far greater array of physical products, equipment and devices can offer even more interactive ways for people to obtain information, conduct transactions and request support. Together, they deliver a richer customer experience in everything from home appliances to industrial machinery to building lobbies to virtual medical care.
Conversational AI and connected products can be applied for optimal success in the following ways:
Virtual healthcare is reducing hospital stays with monitored in-home care via connections to tele-nurses or virtual physicians to discuss and monitor conditions, recording biometrics and physical activities, and updating caregivers and healthcare specialists on out-of-tolerance ratings or other indicators.
Drive-throughs and kiosks are improving by allowing quick-serve restaurants, retail stores, banks, pharmacies and dry cleaners—essentially any business that relies on drive-through windows, menu boards, displays or kiosks—to increase sales and deliver ”segment-of-one” personalization with natural language technologies embedded into intelligent displays.
Retail sales floors, building lobbies and meeting spaces are offering hands-free, personalized and zero-wait channels for customers and visitors to search, shop and get support. This is applicable to retail, financial services, healthcare facilities, transportation hubs and terminals, and entertainment venues.
3. Conversational AI and Evolutionary Computation: Humanizing Artificial Intelligence
What don’t you know about your competition, customers and business? What are your barriers to higher sales, engaging more customers and improving productivity? Success in business requires the insight to derive meaning from data on interactions, transactions and operational metrics to draw conclusions as well as predict business and behavioral outcomes. Evolutionary computation will advance Conversational AI by greatly multiplying the intelligence value over deploying either separately.
Cognizant’s Evolutionary AI offering combines AI and evolutionary computation to drive AI adoption by gleaning real meaning and insights from vast amounts of raw data.
Conversational AI brings virtual agents, chatbots and connected devices to life and allows natural language interactions to enhance user experience and improve outcomes, creating a universe of training data with all the digitized conversations for Evolutionary AI to optimize.
Bringing these capabilities together allows contact centers to “infer” human emotions such as angst, frustration or concern from chat or virtual agent dialogue. That insight can be enhanced with contextual and referential data to recommend real-time actions agents can take to positively influence customer interactions. Here are examples of how Cognizant’s Conversational AI and Evolutionary AI solutions can be more effective when applied together:
In healthcare, AI services such as natural language understanding, sentiment analysis and machine learning, can help identify potential drug misuse and addictive behaviors—alerting caregivers to at-risk patients, improving health outcomes and lowering treatment costs.
In the fields of information, media and entertainment, mass media is being replaced by segment-of-one (personalized) media. Both content generation and distribution are increasingly more contextual, personalized and interactive. Product engineers and news and entertainment services can leverage natural language interfaces and Evolutionary AI to analyze usage, duration, location data, media preferences and channel trends to continuously enhance individualized experience and relevance.
With this integrated strategy, more substantive outcomes are possible; customer experiences can be significantly enhanced, and competitive advantages are magnified.
Matt Smith is Associate Vice-President at Cognizant and Conversational AI Practice Leader, where he leads a team dedicated to helping companies understand, prepare for and benefit from a new class of technologies.
Before Cognizant, Matt was a practicing entrepreneur, first launching a company providing sales and marketing programs for IT firms, and later a firm delivering robotic process automation services for outsourcers.
Matt holds a bachelor’s degree in business administration from Stetson University, where he majored in Marketing.