The market of conversation artificial intelligence (AI) has immensely grown in the past few years and is expected to exponentially advance in the forthcoming years.
With conversational AI, artificial intelligence can answer queries, execute transactions, collect information, engage customers, resolve problems, and provide services faster and more efficiently compared to traditional methods.
In the future, it’s expected that conversational AI will have a crucial role in the organizational aspects of different businesses. By 2026, it’s expected that the conversational AI market will be worth $18.4 billion and it will only rise. If you’ve still not embraced AI, it’s high time to do so.
How can businesses use conversational AI?
Conversational AI chatbots are immensely useful for diverse industries at different steps of business operations. They help to support lead generation, streamline customer service, and harness insights from customer interactions post sales. Moreover, it’s easy to implement conversational AI chatbots, especially as organizations are using cloud-based technologies like VoIP in their daily work.
Leads and Conversions
Conversational AI chatbots are an important tool for generating leads, and can collect data on website visitors 24/7. Statistics say that people are willing to interact with chatbots if they find some humanness in interactions. This helps to create trust.
It’s worth to note that 55% of businesses that use chatbots generate more quality leads and lower stalled lead conversions.
Bots are nothing less than a boon for businesses since they can analyze enormous customer data amounts, letting them do lead scoring and simplify ideal customer identification, and hence, help the marketing department optimize their time.
Customer Support
With conversational AI software in the picture, customer support will undergo a transformation. Organizations can increase their efforts to help customers 24/7 with their needs via voice AI technology or live chat.
People nowadays prefer real-time support, and chatbots can provide that. Around 42% or customers want live chat, whereas 23% customers prefer email, and 16% customers like social media. Furthermore, 81% of brands expect that messaging will become more important by 2025. 76% brands believe that the same is applicable to live chat. This leads to an increase in the revenue per chat hour by 48%, and an increase in the conversion rates by 40%.
Chatbots are liked by consumers as they are easily accessible and offer quick answers. With chatbots, businesses can save up to 30% on customer support expenses as they cut down the need to hire more people.
AI-based voice bots are also a great tool to create a more personalized experience for your customers. A conversational solution using natural language understanding (NLU) and artificial intelligence (AI), a voice bot helps to interpret meaning and intent in speech commands. For voice bots, it’s not about understanding words only, they comprehend what customers want and help them make an efficient response.
Problems of Current Conversational AI
As human language is constantly evolving, it’s a must for conversational AI to adjust to the emerging speech trends. Customer interactions after a decade may be much different from the interactions today.
For excellent customer support, algorithms and machine learning may be required that can comprehend new word meanings and anticipate the wants of consumers when they use them.
Changing accents could also make understanding human language challenging for artificial intelligence. It’s essential for machine learning to note these differences and update models so as to better customer engagement.
Similar to the human brain, these technologies can learn from new information coming on their way. They can make changes, adjust, and keep up with the interactions with humans. It might be necessary for software developers to step in from time to time for adjusting the software. A great AI system, however, should learn and improve without much assistance.
You might think it’s enough to give well-researched dictionaries to AI systems and let them work. But it doesn’t work like that.
For instance, a simple speech-to-text app is unable to recognize tones of voice. An AI system that’s partially functional might assume that a human saying, “I’m super happy with your product,” is a satisfied customer.
A conversational AI that’s more robust, however, may be able to recognize a sarcastic tone in the customer’s voice. The voice tone will show that the words of the customer are in conflict with their feelings.
As chatbots are getting increasingly sophisticated, they are leveraging the feature of sentiment analysis. This enables them to understand the emotion behind textual or voice customer messages.
Using the sentiment analysis feature helps businesses easily know whether customers are having a pleasant experience with their chatbots.
Conversational sentiment analysis usage lets a chatbot understand a customer’s mood by verbal cues and sentence structures. Bots, using sentiment analysis, can modify their responses according to a customer’s emotions.
Other problems of conversational AI platforms:
- Regional jargon and slang
- Dialects not conforming to standard language
- Background noise distorting the voice of the speaker
- Unscripted questions that the virtual assistant or chatbot does not know to answer
- Unplanned responses by customers
- Gibberish never encountered by conversational AI and it not knowing how to respond to
What happens on failure of conversational AI?
We imagine a near future where conversational AI applications improve social media interactions, give people the services they need without interacting with human agents, streamline workflows, and gently nudge consumers along the customer journey.
Anyone who works with emerging technologies, though, will worry about conversational AI’s barriers to success.
Potential barriers go far beyond the challenges listed above. For example, what happens when a frustrated customer keeps asking an AI tool the same question without getting a satisfactory answer?
What happens when a non-native speaker gets stuck using an AI solution that can’t explain its questions well?
Strategies to address these challenges:
Escalate events to human agents
Currently, chatbots are not capable of answering all kinds of customer queries. As a result, a huge number of customer queries are transferred to human agents, which creates a long queue, making the wait time for each customer as 30 to 45 minutes. Well, customers would want the wait time to be not more than 2 minutes.
To achieve this, chatbots need to be retrained. If a chatbot is unable to answer a question now, it should be retrained so that it is able to answer the next time someone asks it the same question.
Moreover, traditional chatbots are not intelligent enough, and are not completely AI-based. They are workflow-driven. To enhance their performance, they should be AI-enabled. Also, the existing AI-enabled bots should be retrained.
Until these things are achieved, organizations should have some human agents on call so that they can handle any extraordinary circumstances.
For example, when an AI-based chatbot is unable to answer a customer query twice in a row, the call can be escalated and passed to a human operator.
Identify the problem and focus on training
Some AI interactions might fail for unknown reasons. With enough background noise, even a human agent can’t understand what someone is saying.
Other instances will make it obvious that your AI solution needs additional training.
If you see that a high percentage of calls get escalated because the AI assistant did not understand the meaning of a word, you can add that word to its knowledge base.
If your company expands into a new area and your AI assistants don’t understand the local dialect, you can use new inputs to teach the tool to adjust.
Always, keep working with partners that understand the technology and your end goals to keep conversational AI working for you.
Letting the platform learn
Companies often put too much effort on making a conversational AI highly accurate before launching it. Instead, they can launch the platform even if it’s not highly accurate and let it learn. As it keeps learning, its accuracy keeps increasing, and it’s gradually able to handle various forms of customer queries efficiently.
Industries Using Conversational AI
Some of the industries using conversational AI are:
E-commerce
Conversational AI is helping e-commerce businesses engage with their customers, provide customized recommendations, and sell products.
The eCommerce industry is leveraging the benefits of this best-in-class technology for the following:
- Gathering customer information
- Provide relevant product information and recommendations
- Improving customer satisfaction
- Helping place orders and returns
- Answer FAQs
- Cross-sell and upsell products
Healthcare
Conversational AI is having a huge impact on the healthcare sector. Conversational AI has proven to be beneficial for patients, doctors, staff, nurses, and other medical personnel.
Some of the benefits are
- Patient engagement in the post-treatment phase
- Appointment scheduling chatbots
- Answering frequently asked questions and general inquiries
- Symptom assessment
- Identify critical care patients
- Escalation of emergency cases
Insurance
Similar to the banking sector, the insurance industry is also being digitally driven by conversational AI and reaping its benefits. For example, conversational AI is helping the insurance industry provide faster and more reliable means of resolving conflicts and claims.
- Provide policy recommendations
- Faster claim settlements
- Eliminate wait times
- Gather feedback and reviews from customers
- Create customer awareness about policies
- Manage faster claims and renewal
Banking
The banking sector is deploying conversational AI tools to enhance customer interactions, process requests in real-time, and provide a simplified and unified customer experience across multiple channels.
- Allow customers to check their balances in real-time
- Help with deposits
- Assist in filing taxes and applying for loans
- Streamline the banking process by sending bill reminders, notifications, and alerts
Implementation of ChatGPT within an application by aQb Solutions
One of our real estate tech companies was struggling with the tedious process of listing properties on their application. They knew that simplifying the process for his users would increase engagement and help grow his business. That’s when they discovered our solution: conversation AI powered by ChatGPT.
We worked with them to integrate ChatGPT into their application, allowing users to list their properties with natural language conversation. ChatGPT simplified the property listing by guiding the users in crafting captivating content that will attract potential buyers/renters.
The result? The company saw a significant increase in engagement on his application, as users found it easier than ever to list their properties.
Don’t let a complicated listing process hold you back – try conversation AI with ChatGPT and see the results for yourself!
Conversational AI Trends for 2023
ChatGPT
ChatGPT is definitely going to be a buzzword in 2023. Introduced by OpenAI, ChatGPT is a question-answering, long-form AI that provides answers to complex questions conversationally. It’s a breakthrough in the AI world as it’s trained to learn the meaning behind questions asked by humans.
ChatGPT can offer human-quality responses, making users awed at its ability to do so. This makes us wonder that the chatbot may eventually disrupt the way humans communicate with computers and transform how information is retrieved.
A large language model chatbot based on GPT 3.5, ChatGPT has the ability to predict the next word within a series of words. Reinforcement Learning with Human Feedback (RLHF) is another layer of training given to ChatGPT, which leverages human feedback to help the chatbot learn the skill of following directions and generating satisfactory responses for humans.
Automating business processes with AI
If you recall any recent experience of getting a document verified, you will agree that the manual way can be quite time-consuming. These days, be it document verification or payments, intelligent assistants come to the rescue. This software is handy as it can automate repeatable, multi-step business transactions. Let’s take a closer look at social media monitoring, AI-based call centers, and internal enterprise bots.
Social media monitoring:
It’s a well-known fact that any business would like to stay in the know about its industry 24/7. A key question is, how do you manage listening to lakhs of conversations on the web and gleaning opportunities that matter? This can happen via social media monitoring which involves tracking all the elements relevant to your brand (like hashtags, keywords, and mentions). This monitoring is an algorithm-based tool that crawls sites and indexes them, successfully managing online conversations that are important to your business.
AI-based call centers:
You are sure to have seen an AI assistant understanding customer requirements on a call and fetching relevant options automatically from a menu. That’s what happens on a regular basis in call centers powered by AI. AI comes into the picture to help customer service agents target a faster response time and better first-call resolution. This can result in more smiling faces across call centers, since they are less stressed dealing with the more sophisticated calls and can do their job well.
Internal enterprise bots:
A time-saving resource, internal chatbots are AI solutions that automate internal enterprise processes, such as in Human Resources or Operations. The main ‘Why’ for leveraging an internal chatbot is that that task is done rarely and/or is ad hoc, and not very specialized or complex. Thanks to this kind of chatbot, any worries about accessing instructions vanish, because the bot acts as an instruction manual for teams to rely on. These bots are generally set up on platforms that a company’s people use daily, like the company website or the intranet. The underlying natural language processing technology is getting better and better, so the benefits of using this technology will grow as time goes on.
Cognitive chatbots that comprehend context and conversation rather than individual questions
Conversational AI helps in generating comprehensive automatic reports of calls, that can of course be very insightful. A question that often arises is: Humans can fathom emotions – does AI? Doubts like these about whether AI can respond with emotional intelligence and empathy are likely to fade, thanks to sentiment analysis. It can definitely help you recognize your happiest customers, but it can also highlight negative statements that might be expressed by less happy customers who need your attention.
Complex emotions can be anything like joy, anger, surprise, fear and these are reflected in human to human interactions across call centers. The AI will identify these emotions on call and can create a detailed report of how a conversation has gone.
Voice applications:
A voice application, or voice-based application, is an application that depends on speech requests to process a query and reacts to it with the expected action. Voice-enabled devices and the apps that control them are a thrilling new prospect for developers. While businesses think about how they are planning to leverage this new channel, they need to become familiar with some best practices for building and deploying on these various platforms.
Interestingly, voice applications have become multilingual along with a complex understanding of language!
Use of deep learning and deep reinforcement learning in conversational AI
Quite often, chatbots that cover a variety of intents face poor performance because of intent overlap. What’s more, it is tough to autonomously retrain a chatbot considering the user feedback from live usage. Self-improving chatbots are challenging to achieve, as it is not very easy to select and prioritize metrics for chatbot performance evaluation. A dialog agent is needed to learn from the user’s experience and improve on its own.
