Conversational AI in 2022: Trends, Challenges, and Techniques

Hey Siri, can you search me for a good blog post that enlists the top Conversational AI trends. Or, Alexa, can you simply play me a song that takes my mind off the mundane daily tasks. Well, these aren’t just rhetorics but standard drawing-room discussions that validate the overall impact of a concept called Conversational AI.

But that’s standard personal examples. Keeping the inclusivity of Conversational AI in mind, it is important to note that even businesses are steadily acknowledging the relevance and importance of CAI (Conversational AI). Meanwhile, in an effort to incorporate CAI into the mix, businesses are also keeping their focus on a few relevant trends that might take bigger forms as we move into 2022.

However, the keenly contested AI space isn’t too welcoming towards new players. Business planning to build CAI-powered interfaces and models must steer clear of several challenges that come across as major roadblocks in AI adoption. But then, moving into 2022, following the trends would mean subverting the challenges and discovering the true potential of Conversational AI as a resource.

And in the subsequent sections, we shall discuss all that and more to ensure that personal and commercial CAI projects are developed to perfection whilst keeping relevance, sustainability, and compliance in mind.

Conversational AI Trends that are going to Explode in 2022

Before we jump right to the trends, it is important to establish a fact about Conversational AI. As a technology, Conversational AI is a self-learning entity that is scalable, interactive, and capable of gaining perspective using Reinforcement learning. Therefore, every voice assistant you come across isn’t a CAI brainchild, as the concept needs massive data repositories and high-end labeling skills to become functional.

But we shall explore the nitty-gritty aspects of CAI in a separate topic. For now, let’s keep the focus on the more evolving AI trends:

Slew of Intelligent DAs

If you have been regularly interacting with the likes of Siri, Bixby, and Alexa, 2022 might see these and even new digital assistants getting more intelligent and proactive in time. Despite the stipulated YoY growth expectation of almost 34%, 2022 will see DAs evolve in an unprecedented manner. And the reliance on work-from-home setups is further expected to pave this adoption in a more comprehensive manner.

But that’s just speculation, right! Not exactly as according to Gartner, by the end of 2022, almost 70 percent of white-collar corporate workers will be regularly interacting with diverse conversational platforms.

Improved In-Vehicle Experiences

Even though autonomous vehicles rely extensively on Computer Vision to identify road-based entities, in-car experiences are primarily influenced by Conversational AI and its accurate implementation. With cars and other vehicles, autonomous or not, getting more intelligent by the day, improved voice solutions will certainly have a role in play in 2022. And most car manufacturers are expected to sweeten the pot further with facial recognition. But that’s a discussion for another day.

Top-Notch Customer Service

Have you ever wondered how conversational AI can automate customer service? Well, this question brings us to a discussion concerning intelligent chatbots capable of handling and responding to intelligent conversations.

With businesses planning to scale faster than their competitors, conversational AI and powered chatbots will give them an edge by letting businesses keep the shop open 24 x7 when it comes to interacting with the customers and generating leads.

Going into 2022, the focus of the proactive chatbots will not be limited to responding to user queries. With CAI as the underlying technology, chatbots will be able to understand intent, learn progressively, manage and work around spelling mistakes made by customers, and offer real-world contexts that have been impossible for machines to replicate.

Challenges that might slow down Trend Adoption

Implementation of chatbots and voice assistants in eCommerce setups, in-car infotainment, and security systems, and customer service process integrations seems the more productive way to go. However, CAI implementation for training the mentioned models properly continues to be a challenge for businesses.

Here are some of the major challenges that might hinder trend adoption in the next few months to come:

Building Complex Workflows: This challenge concerns adhering to the more granular aspects of CAI, including establishing content relevance, implementing NLU labeling strategies, and getting hold of accurate speed-to-text converted data.
Handling Noisy Data: CAI is a powerful resource, but it is necessary to feed accurate data sets by scaling beyond the clutter to make the models responsive enough.
Personalization: This implementational challenge is about the inability to prepare data sets accurately. And the lack of segmentation, classification, and improvement ensures that the CAI models are never as good as you expect them to be.

AI Data Collection and Scaling Beyond the Challenges

As a business, adopting any of the mentioned CAI trends to develop the desired model needs you to scale beyond the existing challenges. However, to mitigate bottlenecks concerning complex workflows, noisy data, and lack of personalization, it is important to onboard specialized data partners that can help you with accurate, multi-faceted, and top-of-the-line data collection and annotation.

If and when a credible and experienced service provider is connected with, it becomes easier to access the following resources that can cut out each of the aforementioned challenges with relative ease:

Improved and accurate data inputs
Improved dialogue management
Cohesive NLU implementation for accurate domain identification and intent recognition
Accurate conversion of structured data into user-specific natural language

With the following data collection and annotation resources, it becomes possible to develop CAI models that are good at defining intents to the T, extracting semantic entities with accuracy, and collecting utterances to understand the context better.

Wrap-Up

Moving forward, Conversational AI is going to be a norm for businesses looking to implement or develop intelligent and proactive DAs, VAs, chatbots, in-car interactive setups, and more. And what’s interesting to note is that even the conversational AI space is upbeat about the mentioned trends, which can be followed only if the collected and annotated data adhere to the quality and quantity-specific standards.

And while keeping up might be hard for disparate businesses, experienced data partners with tested data handling, collecting, and annotating strategies can always be onboarded to make Conversation AI projects and AI agent creation successful, regardless of the business domain and specialization.