Conversational AI
24/7 connectivity is one of the keys defining features of today’s digital economy, with customers expecting to interact with brands round the clock across email, chat, and every social media platform. Although an omnichannel presence is crucial for businesses today, it is touch and resource-intensive to monitor every channel 24/7. This is where conversational AI can be a game changer and a lifesaver.
What is conversational AI?
Conversational AI is a category of artificial intelligence (AI) tools that can leverage natural language processing (NLP) models to simulate human conversations. Conversational AI tools leverage AI and NLP capabilities to analyse large amounts of data to understand and process human language, recognise text and speech inputs, and translate them across different languages to imitate human interactions. In short, conversational AI refers to technologies and tools that can “talk” to people.
Conversational AI is not a completely novel concept. In fact, almost 91% of smartphone users have used conversational AI technology when using voice assistants on their phones. As technology continues to evolve AI and NLP capabilities, conversational AI is moving beyond just virtual assistance and chatbots and finding many use cases for modern businesses. As a result, the global conversational AI market is set to reach $32.62 billion by 2030.
How does conversational AI work?
Conversational AI combines NLP with machine learning (ML) to analyse human language, imitate human speech, and continue to improve its accuracy with learning over time. You can break down how conversational AI works in four steps:
Input generation
The first step is receiving input from the user through a website or app. This can either be a voice or text input.
Input analysis
If the user has provided text-based input, the conversational AI tool will leverage natural language understanding (NLU) to decipher the syntax, context, meaning, and intention of the input to drive the conversation. For speech-based input, it will use combined capabilities of automatic speech recognition (ASR) and NLU for data analysis.
Dialogue management
At this stage, natural language generation (NLG) will formulate a response that imitates human conversation.
Reinforcement learning
The last step propagates the perpetual learning of a machine learning algorithm, where the conversational AI tool will refine its responses over time – based on user feedback and new data – to improve accuracy.
What are some use cases of conversational AI?
Generative AI agents, chatbots, virtual assistants, text-to-speech software, and speech recognition software are some of the most common examples of conversational AI we can see today. Despite its current narrow focus, conversational AI is a highly lucrative technology with exponential potential when it comes to its uses.
Some of the most common examples of conversational AI helping businesses are:
Online customer support
Conversational AI chatbots are replacing human agents when answering frequently asked questions and offering general information to customers. This way, the human representatives are free to focus on more complex customer support issues, and chatbots can offer prompt responses for enhanced customer engagement.
Accessibility
Conversational AI is helping companies bring down entry barriers – especially for users relying on assistive technology. Tools like text-to-speech dictation or translation are making modern businesses more accessible to a wider audience.
HR processes
Conversational AI can simplify HR processes – like employee onboarding, personalised training, and updating employee information – to improve efficiencies.
Healthcare
Conversational AI tools are helping improve operational and administrative efficiencies in healthcare while making healthcare services more accessible and affordable for patients.
Internet of Things (IoT) devices
IoT devices like Amazon Alexa, Google Home, and Apple Siri have brought conversational AI tools into most households, interacting with end-users to help simplify their day-to-day tasks.
Computer software
Conversational AI tools like search autocomplete, spell check, and automatic note (or minutes) taking tools simplify many tasks in office environments, improving overall work efficiency and effectiveness.
These are just some common use cases of conversational AI that go beyond simple practical manifestations of NLP and ML algorithms and are offering benefits like reduced costs, improved productivity and operational efficiency, increased customer engagement, scalability, and enhanced accessibility. And although we must overcome key challenges like language inputs (dialects, accents, background noise, slang, tone, or emotions), privacy, security, and user apprehension, the applications and implications of conversational AI are endless. Working with the right generative AI business operations platform can help you leverage conversational AI tools to build AI-first digital business operations and stay responsive and competitive in today’s digital economy.
