7 key conversational AI technology trends for 2021

29 Jun · 5 min read

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Learn about the latest technological advances and automation tools for building chatbots and virtual agents

It's no secret, the past 12 months have caused a shakeup in the conversational AI space. The pandemic forced much of the world's customer service and support channels to go almost completely digital overnight. Global lockdowns forced contact centers to shutdown and social-distancing measures restricted in-person access to many businesses.

A survey by McKinsey & Company identified artificial intelligence amongst the key technologies helping organizations to prosper during the pandemic. Executives across multiple industries identified AI as helping to position their companies better than they were before the crisis. Conversational AI was no doubt a driving factor in this. Chatbots and virtual agents present a scalable solution for reducing the customer service traffic spikes businesses faced during the early days of the pandemic.

Similar to other technologies that have become essential to navigating a post-COVID world, conversational AI experienced a period of growth throughout 2020. The simple, rule-based chatbot technology many solutions are built on is no longer sufficient. In order to satisfy consumers, the technology is now expected to deliver customer experiences on-par with in-person interactions.

This need, from both consumers and businesses, to do more with conversational AI has driven vendors to innovate. We are already beginning to see significant leaps forward in the technology in order to satisfy demand.

Here are seven key technological trends that will drive the conversational AI market forward in 2021 and beyond:

1) Self-learning conversational AI will speed up virtual agent deployment

This game-changing advancement makes it possible to scan data from various sources and repurpose it into advanced virtual agents. Using existing data from a company's website, self-learning AI can develop a useable model in a matter of hours. It can also scan chatlogs with similarly impressive results, cutting down development times significantly.

This can help save both time and resources, freeing up certified AI Trainers to concentrate on fine-tuning conversation flows and building meaningful interactions.

2) Advanced Natural Language Understanding will make chatbots smarter

In 2021, there will no longer be any excuse for virtual agents with a limited scope. Expect vendors to begin rolling out proprietary technologies, with NLU able to handle 10,000+ intents while maintaining resolution rates above 90%. After all, if a virtual agent can't understand what your customers are saying, then how effective is it, really?

3) API Integrations will help virtual agents move beyond FAQs

By taking advantage of API integrations, businesses will be able to unleash the true self-service potential of conversational AI. Imagine AI chatbots that do so much more than just answer simple questions. Allowing customers to complete complex transactions that would normally require the assistance of a human customer service agent.

Technologies like Robotic Process Automation and Optical Character Recognition can really open up the possibilities for what an AI-powered chatbot can assist with. We are already seeing successful implementations in the banking sector with virtual agents making it simple to automate the process of home loan forbearance. Insurance companies can also leverage integrations to help to cut down the time it takes to file and process insurance claims.

4) Scalability is king for virtual agents in 2021

Can your existing chatbot scale to handle unexpected spikes in customer service traffic? Seems like a pretty obvious selling point for an automated customer service solution. But, you would be surprised by how many businesses were caught off-guard when the pandemic hit. 

Now, more than ever, it's important for a conversational AI platform to be able to scale up its response capacity. This can result in zero downtime when customers need assistance most.

Additionally, scalability doesn't just stop at support capacity. Ensuring a virtual agent can scale its understanding capabilities as the needs of a business grows is equally important. That means intents numbering in the thousands, not just a few hundred. Virtual agents equipped with pre-built, industry-specific content will be what distinguishes superior conversational AI platforms from lesser solutions.

5) Conversational AI will make employees even more efficient

The advantages of conversational AI, which make it an ideal self-service interface, will be leveraged by businesses to increase operational efficiency. Conversational AI can be used to augment the existing skills of customer service representatives. Businesses can continue to offer human support teams and combine it with AI, working behind the scenes to boost employee productivity.

A good example of this is Tryg, Denmark’s largest insurance firm. It launched an internal virtual agent in 2019 to assist customer service representatives in real-time. The AI bot uses natural language processing and machine learning to offer customer support and human agents helpful suggestions about the company's portfolio of life, auto and home insurance products. This unique implementation reduced calls to the back office by 60 percent and an automation rate of over 85 percent.

6) Using your voice to (successfully) interface with a virtual agent will finally be possible

The synergy between conversational AI and voice-enabled platforms from Google and Amazon is finally beginning to mature. This will result in new avenues of interaction between consumers and businesses, thanks to NLU-powered chat and the latest text-to-speech integrations.

Using conversational AI as the backend for voice makes it possible to remain platform agnostic. A strong NLU foundation can help to eliminate the typical pain points associated with voice assistants such as slang and dialects.

Legacy phone support systems are also due to be disrupted by a new technology called conversational IVR. Conversational IVR uses advanced NLU and integrations to give users the flexibility to speak naturally to an automated phone system.

7) Connecting chatbots across a network will make them even more useful

Virtual agent networks are the next logical extension in the development of conversational AI. Connecting chatbots with different functions together via a single chat window to maximize efficiency.

Businesses will be able to break down departmental and organizational silos, streamlining the customer experience. This implementation of conversational AI has already seen success in Finland, where the government is using a virtual agent network to help foreign entrepreneurs establish new companies in the country.

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