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The Gist
- Industry impact. Conversational AI tools are revolutionizing customer experiences across various industries including ecommerce, healthcare and banking.
- Growing market. The global conversational AI market is projected to grow at a rate of 23.6% annually over the next seven years.
- Learning curve. Implementing conversational AI requires specific skill sets and an understanding of data security, making it a complex but rewarding endeavor.
Conversational AI tools used to improve and enhance customer experiences may not perform tasks at the intellectual level of humans, yet.
But the pros of conversational AI outweigh the cons. While advanced chatbots and virtual assistants don’t always understand regional dialects and are prone to technical glitches and downtime, they compensate for these shortcomings with 24/7 support, instant responses, cost savings, and the ability to gather valuable real-time data about customer behavior.
And conversational AI will only improve as the technologies that drive it — machine learning, natural language processing (NLP), natural language generation (NLG), and deep learning — continue evolving.
Over the next seven years, the global conversational AI market is forecast to grow at a rate of 23.6% per year, according to Grand View Research.
“Conversational AI is a happening space and we’re seeing increased use of chatbots, virtual assistants, and automated voice systems,” said Inge De Bleeker, head of conversation design at OpenDialog AI. “In many call centers today, the interactions are moving away from the stilted ‘press 1’ decision trees and are becoming voice-driven experiences akin to human conversations.”
Various industries have capitalized on these maturing conversational AI tools, with each industry having its own unique CX needs and goals.
“Industries that rely on contact centers [banking, healthcare, etc.] have been on the front lines of conversational AI,” said Bradley Metrock, general partner of Project Voice Capital Partners, which invests in conversational AI startups.
“But at this point, conversational AI has impacted every industry out there.”
Let’s explore three industries that have been early and ongoing adopters of conversational AI tools to streamline customer requests and lower costs.
Conversational AI Tools in Ecommerce
Thanks to the increase in online purchases, the use of chatbots in retail that simulate human conversations has surged, with Gartner predicting that chatbots will be the main customer support tool for one-quarter of companies by 2027.
Ecommerce chatbots are used through text chats, voice commands, or both and they serve a variety of duties, including:
- Saving retailers money and time by serving customers 24/7 (eliminates the need to hire full-time staff)
- Answering FAQs and guiding customers to the next step of a purchase
- Recommending products based on a customer’s previous purchases
- Generally helping customers find what they’re looking for
Most ecommerce chatbots can handle rudimentary customer inquiries, but the more advanced chatbots, often referred to as “virtual assistants,” use AI technologies to respond to customer questions in a more human-like way.
Cosmetics retailer Sephora is a “virtual assistant” innovator. Sephora’s website chatbot answers questions about returns and exchanges. But it’s also a shopping assistant that asks customers questions about their skin tone and makeup preferences and then gives tailored recommendations.
H&M, Walmart, eBay and Amazon are other retailers known for using conversational AI to provide 24/7 customer support.
Related Article: What Is Conversational AI? More Than Just Chatbots
Conversational AI Tools in Healthcare
Healthcare is another industry that uses conversational AI to provide timely and accurate information to patients and streamline administrative tasks.
The main conversational AI use cases in healthcare are:
- Patient education and engagement – Virtual health assistants/chatbots deployed on websites and mobile apps use conversational AI to answer common medical questions and offer guidance on managing chronic conditions. For example, a patient with diabetes would use a virtual assistant to help them track blood sugar levels and get reminders to take their medication.
- Appointment scheduling – Patients can also engage with online chatbots to check physician availability, book appointments, and receive appointment confirmations and reminders.
- Remote patient monitoring – RPM tools integrate conversational AI to collect and deliver patient data to healthcare providers in real-time. For example, a patient with heart disease may wear a device that monitors vital signs and sends the data to a virtual assistant, which can then alert the healthcare provider if any abnormalities are detected.
Related Article: How Will Conversational AI Transform Customer Experience?
Conversational AI Tools in Banking
Conversational AI is widely used in the banking industry to improve customer support efficiency and response times.
The three most common use cases for conversational AI in banking are:
- Virtual assistants for general customer service and financial advice
- Alerts for potential fraud or security breaches
- Guidance with account openings and loan approvals
The most well-known conversational AI virtual assistant in banking is Bank of America’s “Erica.” Erica, which recently surpassed 1.5 billion client interactions, is accessible through the bank’s website and mobile app. Customers can engage with Erica through text or voice commands to make inquiries about:
- Account balances and recent transactions
- Bill payment history
- Budgeting tips to help customers manage their finances
- Credit scores and factors affecting credit
- Transferring funds between accounts and depositing checks through mobile capture
- Potentially suspicious account activity
Other banks such as Wells Fargo, Capital One and Truist Bank have conversational AI-based virtual assistants similar to “Erica.”
Challenges of Implementing Conversational AI Tools
For marketing, data, and engineering teams, deploying conversational AI tools into team workflows will come with learning curves.
One common mistake teams make is they underestimate the skills needed to implement conversational AI technologies.
“You’ll need people with very specific skill sets such as a conversation designer and an NLU [natural language understanding] engineer,” said Inge De Bleeker of OpenDialog AI.
“Conversational AI technology is still very new to most teams. Everyone, from product managers to QA engineers, will need to educate themselves on how to execute conversational AI projects. Ease into this learning curve with a well-defined use case and make sure it clearly illustrates the value of conversational AI to get buy-in from leadership.”
In addition to honing AI talent and winning over business leaders, make sure you have clarity from your AI service providers about the security of the data that’s being collected and used.
All the industries mentioned in this article handle sensitive personal information. Customers and patients will be typing or speaking about confidential details in an AI conversation. Without proper safeguards, customers’ information could be exposed and their trust in your brand broken.
“Service providers should be able to speak effortlessly on what training data was used to power their conversational AI technology, where it came from, and whether it complies with laws and regulations,” said Metrock. “Any partner not engaging in the topic of data privacy is one you probably don’t want.”
