The global Conversational AI market size will reach USD 32.62 Bn by 2030. By 2023, more than 60% of all customer service engagements will be delivered via digital and self-service platforms, up from 23% in 2020. The pandemic has accelerated the usage of Conversational AI, which is cutting costs for companies and taking customer experience to the next level.
Many digital natives were comfortable interacting with technology without menu-driven interfaces incentivizing organizations to switch to natural language supporting technologies. According to a survey, 66% of European business executives plan to launch natural language processing projects by 2023.
The potential stems from technology focusing on conversations considering the variances of natural language inputs. The time is ripe for Conversational AI to accomplish meaningful human interactions. The adoption of Cloud, the maturity of Natural Language Processing (NLP), and Speech-to-Text (STT) technologies have made Conversational AI implementation easier and inexpensive.
Why Conversational AI?
A recent survey showed 98% of enterprises believe that being an ‘intelligent enterprise’ is beneficial. They utilize intent classification and contextual understanding to learn about customers and improve conversational ability.
- It offers no learning curve for users as it uses natural language.
- It works for 24 hoursX365days and saves costs as it is device agnostic.
- It is available on multiple channels.
- It unifies different lines of businesses and unifies the experience.
- It is resilient against organizational changes. So, migrating to another technology will not adversely affect consumers.
- It enables customers to troubleshoot several of their problems.
Conversational AI is the force behind a growing industry. The management must ensure their company has technically qualified people and the right technologies to develop the right experience. Adopting best practices when implementing Conversational AI can deliver optimal outcomes.
Voice is one of the favorite interfaces for commerce and communication. Since talking is simply more convenient than typing, the shift makes sense. 54% of Americans have used voice commands, with 24% doing so daily.
Taking Conversational AI to the Next Level
When you layer in cognitive search, structured and unstructured data can be pulled from various enterprise data sources and help chatbots provide faster and more thoughtful responses, elevating the customer-service experience. It is an advanced version of enterprise search powered by AI, bringing together numerous data sources while providing automated indexing and personalization.
The cognitive search solutions use natural language understanding (NLU) and machine learning (ML) to ingest, understand, and gather digital content from multiple sources. They also use ML to understand and organize data, predict users’ search queries, continuously learn, and improve answers based on user feedback.
Cognitive search-powered conversational AI can derive insights from a consistently growing data collection for use across the company and potentially improve information discovery internally. For instance, agents can enter a query in natural language, and conversational AI will understand the context and invoke the cognitive search for more insights.
Agents can have these insights to visualize the selective options and provide feedback on the retrieved information, improving adaptive learning and the AI engine.
Conversational AI Applications Across Industries
The below table shows the extent of Conversational AI-related potential use cases in several industries. Though these industries used chatbots extensively, they would provide ready answers to queries. With artificial intelligence implemented into customer support solutions, its utility value would increase up a notch.
Imagine a healthcare company using a cognitive-search solution to provide sales agents access to enrollment options, medical supplement details, etc., allowing quicker, more efficient responses and resolutions. The company could reduce average call handling, faster information access, improve sales opportunities, and dramatically enhance users’ call-center experience.
Companies can use customer insights to become personalized and predictive and proactively pitch products aligning with customer requirements. Combining predictive ML models and cognitive search with conversational AI can deliver precisely the type of hyper-personalized customer experience necessary to capture these opportunities.
Intelligent product recommendations provide natural and logical upselling and cross-selling opportunities resonating with customers. Product recommendation tools use historical data and provide further suggestions. Data-driven predictions make customer interactions more meaningful while helping conversational AI deliver hyper-personalized, intuitive experiences to customers that improve quality and operational efficiency.
Where are they using Conversational AI?
As per experts, Conversational AI’s current applications are focused on performing a very narrow field of tasks. Strong AI, a theoretical concept, focuses on a human-like consciousness that can solve various tasks and solve a broad range of problems. Conversational AI has the following use cases.
- Customer support – Chatbots are replacing humans along the customer journey. They answer frequently asked questions (FAQs) around shipping, provide personalized advice, cross-selling products, or suggest sizes for users. It changes what we think about customer engagement across websites and social media platforms.
- HR processes – Human resource teams use Conversational AI to optimize onboarding processes, employee training and updating employee information.
- Accessibility – Companies reduce entry barriers and make their management more accessible. Commonly used features of Conversational AI for these groups are text-to-speech dictation and language translation.
- Healthcare – Conversational AI can make healthcare services more accessible and affordable for patients while improving efficiency and streamlining administrative processes such as claim processing.
- Software – Conversational AI simplifies tasks in an office environment. It can help you auto-complete sentences in word processors and do a spell check. Even Google enables you to autocomplete searches.
Lastly, we already have many Internet of Things (IoT) devices used on smartwatches or Alexa speakers. These devices use automated speech recognition to interact with end-users.
Moreover, cognitive search-driven Conversational AI enables a more personalized virtual assistant addressing every user request. Multiple chatbots will converge to a single, more efficient, and decisive virtual agent, paving the way for a more interactive user experience.
The ability to identify a user’s mood with voice modulation, body language, and emotional signals makes it possible for evolved chatbots to handle complex questions and carry out multifaceted conversations.
Additionally, companies will predict customer churn using big data analytics and provide recommendations from user data available on multiple data sources, including social media. In short, by revolutionizing their contact-center automation, companies can drive efficiency and revenue by moving beyond chatbots.
Draup’s sales intelligence platform can enable enterprises to build deeper relationships with stakeholders and customers with cutting-edge solutions. It assists sales teams with real-time insights and signals and identifies the trends through rigorous analysis of data points. With the insights, the management can learn to use conversational AI for easier operations.
Draup conducted a detailed analysis of the conversational AI market. The report details the current scene of the Conversational AI market, focusing on the significant industry adoption and use-case landscape. Learn about key players, the engagement analysis, and the opportunities for stakeholders.