Demand for immediate, 24/7 support is at an all-time high. With the COVID-19 crisis causing a significant increase in customer inquiries, more businesses are turning to Conversational AI to sustain and improve customer and employee communications.
Conversational AI (aka intelligent chatbots or virtual agents) combines artificial intelligence (AI) and automation to streamline customer interactions across channels. Realizing the importance of transforming customer service or contact center operations and feeling the pressure to accelerate their digital transformation plans, many businesses have begun their automation journeys.
Yet, 95% are not successfully using AI, according to a CCW Digital Market Research study earlier this year.
What should business leaders take into consideration when evaluating Conversational AI solutions in order to increase their chances for success?
What to Look for in a Conversational AI platform:
Ease of Use – For Any User
Simplifying the creation and maintenance of virtual agents across the enterprise is critical. Enabling non-technical users (business users) to help build conversations, rather than only developers and conversational architects and designers, will add greater insights and perspective into end-user experiences. A broader team of users will also tackle the urgency of getting started, scaling and changing strategies faster.
A Platform to Handle Complexity
Customer service teams and contact centers have specific needs when considering new technology solutions. They need to support integrations with internal applications, session routing, speech-to-text and text-to-speech, and visual IVR. Selecting enterprise-grade Conversational AI platforms will meet these requirements, overcome legacy system challenges, and open new doors for future innovation.
Multi-Lingual Capabilities
In today’s global environment, it’s important to consider customer service tools that support bots in multiple languages. Bot platforms should have out-of-the-box Natural Language Processing (NLP) in enough languages and variants to support your customers’ needs. Automate translated conversations across channels, and incorporate fallback languages and local culture dynamics to communicate with your customers in the most natural way.
Accurate NLP
Natural Language Understanding (NLU) is a core component in any Conversational AI platform. Essentially, it is what makes a Virtual Agent smart. A powerful NLU engine achieves a high level of accuracy in understanding user inputs and knows exactly how to respond. A state-of-the-art NLP helps business users and developers stay focused on improving the NLU process and get significantly better results with less work.
Omni-channel and Voice Functionality
COVID-19 has created urgency in shortening the time-to-ROI for Contact Center Automation. Customers today expect a seamless omni-channel experience, whether on WhatsApp or a web chat or a phone call, and they want responses fast. An effective Conversational AI tool should plug into existing contact center solutions and enhance the standard capabilities with AI-driven technology. Particularly with voice channels, reducing call volumes into call centers, is a priority. Look for a turnkey, scalable solution for Contact Center Automation that allows customers to find answers to their questions before reaching a human agent.
Not all Conversational AI platforms are created equally. By aligning your existing requirements and wish list features with the right solution, your organization will not only improve customer service nd CX, but also accelerate time-to-ROI and pave new ways for future growth.
About the Author
Jessica Gopalakrishnan is the Senior Director of Marketing at Cognigy.
Cognigy is a global leader in conversational AI automation for contact centers. Its low-code platform, Cognigy.AI, enables enterprises to automate customer and employee communications using intelligent voice- and chatbots. Start a free trial of Cognigy.AI at cognigy.com.
