The Top 5 Industry Use Cases for Conversational AI

Artificial Intelligence (AI) is one of the most pervasive technologies in use today. With the human language being the medium to how we communicate, it is no surprise that Conversational AI (CAI) is becoming the most prominent frontier of this technology. Many businesses today are enlisting the help of conversationally intelligent chatbots to stay competitive.

According to Markets and Markets, the expected global Conversational AI market size is set to grow from USD 4.8 billion in 2020 to USD 13.9 billion by 2025, at a Compound Annual Growth Rate (CAGR) of 21.9%.

Therefore, companies, industry leaders, and employees need to understand precisely what Conversational AI is, why it’s essential, and the many use cases of this AI application disrupting Healthcare, IoT Devices, Retail, HR, and Finance and Banking Industry. 

An audio version of this article.

What is Conversational AI?

Conversational AI is a subset of AI that focuses on imitating conversations with humans to deliver a human-like conversational experience. The goal of these types of AI is to replicate the human experience as closely as possible. Through Machine Learning technologies like Natural Language Processing (NLP) and Natural Language Understanding (NLU) – Conversational AI aims to process language data (what you say) and understand it (what you mean).

Other technologies like speech recognition, sentiment analysis, and dialogue management are also used to provide Conversational AI with the ability to respond accordingly. To do so successfully, Conversational AI needs input data that humans curate to learn from how we communicate and understand one another naturally.

Conversational AI is seen as a successor to chatbots, one of AI’s first applications. While chatbots strictly follow a script, Conversational AI allows for a more contextualized conversation. The application of Conversational AI is far more complicated and nuanced than chatbots simply because of its ability to understand language on a deeper level.

Why is Conversational AI important?

As the adage goes – it’s not a matter of if, but when.

And with Chatbots, it’s not hard to see why. According to Juniper Research, Conversational AI solutions can scale, provide 24/7 service and asynchronous conversations, and are forecasted to have operational cost savings in industries like banking of $7.3 billion globally by 2023.

However, it is not enough to have a chatbot on your site in 2021. Businesses need to have intelligent chatbots with natural language processing and understanding for the best customer support experience. In fact, with the emergence of Conversational AI, more and more people expect chatbots to understand them and assist them beyond what they can find on a FAQ. Conversational AI can deliver a customer experience equal to or better than a live chat when done correctly.

In addition, One cannot overlook the importance of the handshake between a bot and a website. A bot cannot replace or compete with a website: the best chatbot designs are ones where the site and bot work in tandem. The trend for Conversational AI bots is now increasingly beyond solutions for just reducing operation costs of call centers, but instead adding to the customer experience and providing better engagements.

Armed with the machine learning technologies, it is not surprising that Conversational AI applications are behind many chatbots and devices that exist in the market today, proving to be a core component to social success

What are the 5 Industry Use cases for Conversational AI?

The Conversational AI industry is currently undergoing explosive growth as it becomes increasingly applicable for more use cases. Here are the top five sectors and their use case of CAI technologies to improve user experience and interactions and provide excellent customer support.

1. Healthcare usage cases for Conversational AI

The healthcare industry is undergoing a paradigm shift as it becomes increasingly apparent that Conversational AI can help cut costs and streamline the patient experience. Patient care has not been immune to this new age of technology, with chatbot ai solution now playing an essential role in cutting down on unnecessary time from human medical assistants to helping patients understand their treatment plans through natural dialogue – albeit digitally enhanced.

Use cases for Conversational AI for the healthcare:

  • Diagnosis: Conversational AI can help diagnose conditions online by asking the patient a series of questions and learning based on their responses to provide insight into any potential health issues they may be experiencing.
  • Medical scheduling: Conversational AI can streamline a patient’s medical appointment by providing them with general information about their next visit before they even arrive at the hospital. It can also handle the patient’s paperwork and help them schedule appointments.
  • CBT: Cognitive Behavioral Therapy is an effective way to treat mental health problems like anxiety. Conversational AI can provide a completely immersive CBT experience with the help of NLP and NLU.
  • Therapy: Conversational AI can help fill the gaps in care that mental health patients receive from human clinicians. Conversational AI can provide a 24/hour service, which means it can be provided for as long as necessary without needing breaks or days off. Additionally, bots have no judgment and won’t stigmatize patients when engaging in a conversation- something significant for mental health.
  • Mental Health: Bots like Replika, which are conversational chit-chat bots, can help with emotional counseling, providing a safe and private space for people to share their feelings. Conversational AI can also aid in therapy sessions themselves- such as assisting a therapist by taking notes or summarizing the session.
  • Medical assistant: Conversational AI can be a virtual assistant to support patients and their carers, helping them understand health-related topics. This tactic often helps reduce the stress levels associated with healthcare services by freeing up human medical assistants for more high-level work that is best left in their hands.
  • Data Collection: Conversational AI is also being used by pharmaceutical companies as a method for gathering user feedback on their products via surveys or focus groups – all without the need for an interviewer. This saves both time and money spent on hiring human data collectors while still collecting valuable information from consumers that can be analyzed using Conversational AI’s Natural Language Processing capabilities.

2. Internet of things (IoT) devices

Perhaps becoming the more useful helper in the household in this day and age are Conversational AI-enabled devices that use automatic speech recognition to engage with users. These include voice assistants such as Amazon’s Echo and Google Home and mobile, smartwatch, and desktop assistants like Apple Siri and Cortana. Conversational AI can bridge the gap between humans and non-human interfaces by understanding natural speech patterns and allowing context without a rigid conversation structure.

Meadow F7 Micro

Many of these devices use unsupervised machine learning – meaning Conversational AI’s abilities are self-learned through trial and error in response to user input.

Some of the use cases for this industry include:

  • It can control home appliances through google or Alexa.
  • We are getting any devices to “dial” phone numbers and send messages on the user’s behalf.
  • Ordering food or grocery items through Conversational AI-enabled devices and apps like Amazon’s Alexa while simultaneously learning what the user likes to suggest better products that they may be interested in.
  • It is remotely actioning tasks such as turning on the lights or air conditioner.

3. Retail use cases for Conversational AI

Conversational AI in the retail space is an emerging trend: lead generation, lead qualification, lead nurturing to 24/7 concierge services, faster order fulfillment, and amplifying marketing messages.

Human Resources Industry Use Cases for Conversational AI

Through smarts like API integrations, other use cases for retailers with conversationally enabled applications include:

  • Product Recommendations: Conversational AI is leveraged by retailers as a customer service chatbot to provide product recommendations based on user interactions and search queries.
  • Customer Data Insights: Conversations with customers are recorded digitally, eliminating the need for humans to manually input every word spoken during an interaction or call center conversation. A simple data analysis into the type of search queries asked can provide businesses with further insights into their products and services.
  • Scalability and Multi-Channel Integrations: Conversational AI can scale conversations across different channels simultaneously (i.e., email to web assistance to Facebook) without human intervention. This provides increased opportunity for conversions and sales while at the same time reducing costs associated with traditional methods of communication that require human involvement, such as phone calls.
  • Better User Experience and Engagement: Conversational AI can be used in retail settings not just as one-off ‘conversations’ but as ongoing conversations. Maintaining context and holding data from previous conversations will translate to better customer experience, engagement, and conversion rates.
  • Inventory tracking: Conversational AI provides the ability to track inventory and offer availability to customers.

Through all these use cases, Conversational AI provides the foundation to excel in online retailing in 2021 – not only by providing the information that customers need immediately and 24/7 but gives businesses the data from their customers to provide better and more optimized products and services.

4. Human Resources

Companies are also leveraging conversational AI for HR purposes – from recruitment to documentation directory. Companies can find that their resources can be allocated more efficiently, boosting productivity, maintaining staff satisfaction, and saving time and money.

Human Resources Industry Use Cases for Conversational AI

The most common use cases for an HR bot are:

  • Recruitment: Conversational AI can be used to sift through CVs and job applications, eliminating a need for HR staff to go through every application manually. The ability to analyze data such as keywords found on the CV compared to other applicants’ search terms means that these AI can provide companies with a more accurate list of possible candidates than any HR could.
  • Onboarding: Conversational AI can be used to automate onboarding and orientation processes, including advising new employees with any necessary information they need about their work before they start work- such as where bathrooms are located. AI chatbots also have a user interface that is more intuitive than any HR staff member will ever have and can remember every conversation with an employee.
  • Documentation: AI can automate documentation processes, which means that HR staff will not constantly update their records. Conversational AI’s memory function also ensures all employees’ documents are up to date in real-time.
  • General staff advisory: Acting as a concierge service or a help desk, the use of Conversational AI extends to answering any questions, filling out leave applications for employees, and automated shift date and reminders.

5. Finance and Banking

According to a recent study by Juniper Research, the success rate of bot interactions in the banking sector will reach 90% in 2022. This means that the most robust way for financial institutions to stay competitive is by embracing digitalized customer experience strategies such as Conversational AI.

Banking Assistant Industry Use Cases for Conversational AI

Conversational AI is currently making waves in the world of finance and banking, with use cases including:

  • Banking virtual assistant bots can check user balances or process a transaction across any bank accounts.
  • It prevents fraud with automatic speech recognition, detecting any keywords or phrases that could signify fraudulent activity on the user’s account. Conversational AI also can detect any anomalies from normal behavior, which could be indicative of fraud.
  • Finance bots can process any transactions to provide you with an accurate picture of your finances. Conversational AI will help access and analyze data, such as trends in spending habits or bank accounts, to recommend how best to spend money.

The Bottom Line

Conversational AI promises a lot of potential in 2021. There are many industries with excellent use cases for Conversational AI: Healthcare, Public Sector, HR, IoT devices, Retail and Banking, and Finance – each one promising to change how that industry operates. Conversationally enabled applications have the potential to take customer service from good to great – chatbots can assist companies in saving up to 30% customer support costs and answering up to 80% of routine questions

Let’s take a look at how far AI has come. Enjoy the video below – it might change the way you think.

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Now back to the topic. While it may seem that Conversational AI is an easy to implement, all-encompassing force to be reckoned with, it is worth noting that a chatbot is only as good as its solution and conversation design and the platform that facilitates it.

Ultimately, Conversational AI chatbots still need to be human-curated simply because humans understand how humans communicate best. While Conversational AI can never truly replace human-to-human conversations and interactions, it’s certainly getting close.

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Author Info

Jessica La is an AI Consultant with over six years in the start-up, data, and content industry. In her blog, she helps individuals develop their online careers and provides the tools and research for innovating and growing.