The pandemic year has made it essential to maintain business continuity with customers and conversational AI can be a valuable tool in an organization's customer support arsenal. Apart from cutting costs, conversational AI takes customer experience to a whole new level.
Conversational AI is a subset of artificial intelligence that uses neural networks and machine learning to help build valuable applications, which can interact with customers using natural language. The technology focuses on having a conversation, taking into consideration the variances of natural language inputs, thus allowing it to accomplish meaningful human interactions. Examples of conversational AI at the consumer level include Siri, Google Assistant, or smart speakers, that take in natural language commands and give the required output.
No wonder the conversational AI market is set to grow to$18.4 billion in 2026 at a compounded annual growth rate of 21.8 percent, as per market research.
The Time is Ripe for Conversational AI
The underlying technology supporting Conversational AI was expensive and could not scale up in the past. However, several factors led to an increase in its proliferation. An increase in the adoption of cloud, maturity of Natural Language Processing (NLP), and Speech-to-Text (STT) technologies have made the implementation of conversational AI easier. With the rise of instant messaging and social media apps, people became familiar with short messaging and started using sharper communication. An increasing number of digital natives were comfortable interacting with technology without menu-driven interfaces. This incentivized organizations to switch to technologies supporting natural languages. According to this report, 66 percent of European business executives are planning to launch natural language processing projects by 2023.
Why Conversational AI
Organizations using conversational AI in their customer experience mix can deliver quickly what their customers want and maintain business continuity.
No Learning Curve: The interaction is based on natural language, removing the need for training.
Always-on: Conversational AI works 24x7x365, unlike call centers with fixed working hours.
Cost Savings: A lot of low-hanging tasks and mundane day-to-day queries are prime candidates for automation saving costs and improving the overall customer experience.
Device-Agnostic: Conversational AI can be made available on multiple channels, such as mobile phone, web, IVR, smart speakers, or even smartwatches, making it device/technology-agnostic.
Unified Experience Irrespective of Backend: An organization's data may be scattered across different lines of businesses in the backend. If one can create a conversational AI layer on top of it that takes in natural language input, the customer gets a unified experience on the front-end.
Resilient Against Organizational Changes: Enterprises can transform the backend systems and create a layer of abstraction to ensure that any plans to migrate to another technology will not affect the end customers adversely.
Asynchronous Form of Communication: Conversational AI also removes the rigid synchronous format of communication. Asynchronous formats using text-based or conversational AI are more effective than the slow form-based or IVR modes.
Self-Reliance: Customers can troubleshoot several of their problems using conversational AI
Conversational AI is not just about acquiring technologies but ensuring that you have the right set of talent, designers, and technically qualified people to develop the right experience. Adopting best practices when implementing conversational AI can deliver optimal outcomes.
Best Practices to Follow
Conversational AI should strive to provide intelligent, convenient, and informed decisions at any point in the customer journey. Here are some good practices.
- Conversational AI implementation must be purpose-driven. A conversation-first approach in a natural language format is essential.
- It is critical to understand customer intent as every customer will have their way of framing questions using natural language. If the natural language processing (NLP) tech isn't resilient, it will delay solving the customer's query.
- Organizations must adopt a low code or no-code approach where business owners can manage conversational AI, without depending on developers.
- AI can deliver hyper-personalization by combing through massive data to look for specificities and cater to individual customer preferences.
- An approach that relies on historical insights and continuous post-production evolution using telemetry data on user demands will improve stickiness and adoption.
Organizations must use conversation AI to solve a genuine customer pain point instead of trying to force-fit it. The stickiness factor is paramount to keep the customer experience top-notch. Tasks that can be accomplished using predefined shortcuts should be avoided in conversational AI. But to answer questions like loan eligibility that require processing multiple data points, a natural language-based interface adds significant value to the customer experience.
Strategically speaking, organizations must insist on including governance in the lifecycle automation of a conversational AI. This means irrespective of the technology being used, the underlying architecture must support plug-and-play philosophy and the organization should be able to benefit from using new technology.
Ultimately, you need a holistic and technology-agnostic approach, good governance, and internal lifecycle automation with supportive development operations for successful conversational AI implementation.