The market value of conversational AI crossed USD 4.8 billion by the end of 2020. It is expected to cross 13.9 billion USD by the end of 2025.
Even though Alan Turing built the ‘Turing Test’ in 1960, a test to determine whether a machine can be called intelligent, it took the human race 64 years to find something to beat it.
Nevertheless, it gave rise to the ‘smart’ category of products and services that had a brain of their own and could not only deduce human commands but also engage in conversations like humans do.
One such offering we are about to discuss here is conversational AI. This article discusses what is conversational AI, what makes it different, and how this helps business.
What is Conversational AI?
Gone are the days when brands had to employ several employees merely to cater to their customers’ most basic queries. A few years ago, we saw the rise of decision tree bots that solved a plethora of issues for companies. But companies soon realized that these pre-programmed bots were linear and could only undertake a specific set of tasks. So there began the quest for finding something more worthy and efficient.
The quest led to the development of conversational AI. It is a branch of AI (artificial intelligence) focussing on enabling software to interact with humans as any other human being would, that is, in the most organic and intuitive way possible.
For that to happen, it uses a mix of technologies like machine learning (ML), speech-to-text recognition, intent and domain prediction, and natural language processing (NLP). Now, the question is what is the key differentiator of conversational AI? Conversational artificial intelligence uses a host of principles generally used by people conversing with each other to ensure everything feels natural to those interacting with it.
How does Conversational AI Work?
Instead of being linear, conversational AI can learn with time and optimize its results. We can break down its operational capabilities and function into four steps –
- Receiving inputs – Here, it allows users to enter the requisite information via text or speech.
- Analyzing data received – Depending on the type of input received, conversational AI uses either NLP or ASR automatic speech to understand the meaning of the individual words and the sentence as a whole.
- Constructing a reply – After understanding the input from the user, conversational AI prepares a response to it.
- Reinforcement learning – Post replying, it stores the input received and analyses it to ensure the response is in line with the theme of the user’s query.
What is an Example of Conversational AI?
Chatbots are the commonest examples of conversational AI. We all have come across them on a plethora of websites. These are bots programmed to answer a specific set of customer queries.
While these are examples of the most basic type of conversational AI, the next step is the more complex virtual personal assistants or VPA, such as Google Assistant, Alexa, and Siri.
These are much more powerful but are linear, meaning they cannot carry context from one conversation to another. These solutions answer queries as they come and use a mix of ASR and NLP to increase their accuracy.
The most complex forms of conversational AI are a virtual employee assistant (also known as RPA or Robotic Process Automation) and a virtual customer assistant. These are scalable and highly flexible solutions deeply knit within the organization’s data hub, allowing them to draw the requisite information. In addition, it helps them provide context to a conversation and offer solutions to customers and employees similar to a human assistant.
What Principles Apply in Conversational AI?
For businesses looking to integrate conversational AI in their operations, it is imperative to have a set of guiding principles for optimized performance. If you are merely adding it to your armory just because it is new and fashionable, we suggest you take a step ahead and understand these principles –
Conversational AI should be available to customers, irrespective of the medium they use for reaching out to you. Being omnipresent would allow the solution to understand the context better and provide a viable solution for visitors' queries.
Understanding intent better
It is imperative for the conversational AI solution to combine multiple data points, such as transactional, behavioral, and external, to gauge the intent of the conversation and reply to the customers accordingly.
Agent blending and optimized escalation
The primary aim of having conversational AI integrated into your system is to curtail the work handled by human agents. But it should not be at the expense of keeping customers from being fully satisfied.
So it is imperative for businesses to ensure that these bots escalate the issue to a human agent at the right time. It should also allow the company to have the flexibility to re-induce these bots once the situation normalizes.
Conversational AI collates a wide range of data for optimizing its functions. But that also makes it vulnerable to possible security breaches. With half of the customers having questions pertaining to their data security while dealing with smart assistants, companies must forge a tight set of norms and guidelines to ensure data safety.
How does this Help in Business?
An Accenture report states 56% of companies believe conversational AI-based bots are disrupting the way they conduct their business.
The integration of conversational AI is helping every business in more ways than one. Here are some of them-
- It enables businesses to improve lead generation ratio by providing intuitive solutions to their website visitors in real-time.
- It is also optimizing the business’ lead conversion ratio by actively asking questions that excite customers and generate curiosity to try the company’s offerings.
- Thanks to its high processing speed, it is able to cater to even the clients who are averse to waiting for response and improve their experience with the company.
- In addition, it also helps customers to reach out to the relevant departments based on their query. It has also enabled businesses to handle call volume spikes better and resolve issues faster.
- Conversational AI has not only reduced the monotonous parts of a human assistant’s job profile, but it has also been instrumental in diverting a majority of other queries. It has allowed human assistants to focus on the most critical aspects and be more productive.
- A report by Juniper Research, an analysis firm, states that conversational AI will help businesses save over USD 8 billion annually by 2022. It will do so by automating manual processes to optimize the time and resources required.
How to Choose the Right Conversational AI Platform for Your Business?
There are a plethora of players touting their conversational AI solutions to be the best. Such hype has made it difficult for businesses to choose the right tech for their business model. If you, too, are in a fix as to which service to choose, here are some criteria to help you judge if a solution is a good fit for your company –
Every business operates with a motive to expand and increase its customer base. So irrespective of the stage you are in presently, it is only fitting for you to choose a conversational artificial intelligence solution that can scale seamlessly with you.
The same customer may use different channels for their varying needs. So it is necessary for a solution to have the same proficiency across channels to bring value and reduce the gap between human and machine-based assistance.
Today, more and more conversational AI solutions are directed towards enabling even your non-technical workforce to manage and use them with ease. So you must choose a solution that would allow your employees to optimize customer experience seamlessly.
Conversational AI collects a vast range of data. But it is more important that it should allow you to harness the same for improving your business functions and bringing about the necessary changes.
Conversational AI is the Way ahead for Excelling in Customer Engagement
With every passing day, the world is discovering a new use case for AI and NLP. So today, brands are in the conundrum of ensuring that AI does not get out of their control while finding ways to use them to their benefit.
With Conversational AI going mainstream, it has opened a new vista altogether to customer interaction. At the same time, customers are also adapting to the changing ways of communication and are looking to change their behavior patterns. If enterprises ignore paradigm shifts like Conversational AI, it can harm their business in more ways than one.