Conversational artificial intelligence (AI) strives to replace humans with computers for customer service applications.
See below to learn all about the growing conversational AI market and how it’s fueling a range of customer-facing business applications:
Conversational AI today
Readying and delivering answers
Conversational AI can be used to answer simple questions, screen customer service inquiries, act as a smart voicemail routing system, and answer frequently asked questions (FAQs). The most commonly experienced applications are virtual agents and chatbots.
Conversational AI falls within the machine learning (ML) category of AI that combines self-learning algorithms and human feedback. For human feedback, programmers use prior human responses for training and then the algorithm uses ongoing customer responses for continuous training.
Conversational AI also incorporates natural language processing. NLP starts with converting audio to text, so the computer can recognize it. Next, algorithms parse the text into words, phrases, and ideas. The AI then uses natural language understanding (NLU) to associate meanings and predict intentions. After that, the AI will generate a response and either deliver the response as text or convert the text into an audio response. Finally, the AI will incorporate feedback from trainers and users to improve its algorithms.
For its most common applications, conversational AI builds from a list of FAQs and a database of alternative ways those questions are asked by humans. It then analyzes the text or audio it receives and statistically compares the inquiry against its database of known questions to produce a response.
To reach the next step of its evolution, conversational AI will need to address its main weaknesses. The most basic issue is that AI can currently only handle simple inquiries. However, a more fundamental issue stems from the inability to match a human’s language processing skills.
AI currently lacks human context or empathy. AIs enrage humans by repeating answers perceived to be correct by the AI, even after the human indicated they don’t understand or the answer is inappropriate.
AIs also fail to be without bias. Women and ethnic groups with non-standard accents do not have the same success with AI voice recognition. For example, Google leads the industry in accuracy, recognizing words spoken by the typical male more than 90+% of the time. However, this is 13% higher than what women can expect. English speakers from Scotland suffer even more with a 53% accuracy rate. This disparity involves one language, so there is plenty of work to do for conversational AI to become a universal translator and customer service agent.
Market growth of conversational AI
Estimates for the conversational AI market vary widely, but all estimates recognize a growing market.
The estimated value of the conversational AI market in 2020 varies between approximately $4.7 billion and $5.1 billion dollars. The market is expected to grow from approximately $18.02 billion in 2027 and $46.29 billion by 2028, with a compound annual growth rate (CAGR) from 18.9% to 30.75%.
During the COVID-19 pandemic, many companies could not fill their call centers and instead explored using conversational AI for their voicemail screening, website customer service text, and basic social media interaction. This pushed the market’s growth and extended the use cases pioneered by Apple, Google, and Amazon to power their virtual assistant apps on their phones and home-based devices.
In general, the market is categorized into platforms and services and whether the conversational AI is deployed in the cloud or on-premises. The major adopting industries have been banking, e-commerce, financial services, health care, insurance, retail, and telecom. The automotive industry is also growing fast in adoption, but much of their R&D applications have not yet reached the market.
Regionally, North America has adopted conversational AI the most. Asia-Pacific is estimated to be the fastest-growing region, due to “growing digitization and increasing adoption of advanced ML and AI technologies,” according to Markets and Markets.
The intelligent virtual assistant segment is estimated to hold the highest share of the market, Research and Markets estimates. However, revenue in the segment is also driven by services and chatbots, Emergen Research says.
Despite its growth, the conversational AI market’s growth is limited by two major factors: a lack of skilled professionals and a general awareness of the technology, according to Verified Markets. The market’s lack of accuracy is also “acting as a challenge for the growth, according to Markets and Markets.
Features of conversational AI
Conversational AI features vary depending on the platform or service consumed. Consumers may only interact with the finished product, but corporations can select between turnkey services and multi-component platforms that help build a chatbot from scratch.
Broad features of a conversational AI include:
- Customer intents: pre-programmed replies based on keyword combinations
- Keyword focus: interprets customer dialog, text or verbal, and focuses on key terms to determine appropriate response
- Quick deploy times: integrate and deploy quickly with a range of websites and applications
- Monolingual support: May only work in one language
A more extensive conversational AI includes:
- Actionable reporting and analytics: real-time feedback from customers that can be analyzed and used to further train the AI
- Advanced modules: extensions, plug-and-play features, and other modules that can improve the AI
- Graphical interfaces: eliminates barrier to entry that requires coding through intuitive interfaces
- Multiple languages: supports multiple languages simultaneously
- Training systems: customer trains the AI on more than key terms to help ML algorithms learn a broader array of responses
Benefits of conversational AI
Conversational AI continues to grow rapidly because it provides numerous benefits to customers, according to IBM:
Cost efficiency: Customer service staffing, especially outside of business hours, can be costly for hourly training and pay across multiple time zones.
Customer engagement: Sixty-three percent of customers will leave a company after one poor experience, and almost two-thirds won’t wait over 2 minutes for assistance, according to Forrester. Chatbots and virtual assistants respond instantly, 24 hours a day, to address customer needs.
Increased sales: Conversational AI incorporates personalization to provide chatbots and virtual assistants the ability to provide recommendations that can upsell and cross-sell consumers.
Pooled experience: Employees in a call center learn from their experience but often fail to share that experience for the benefit of others. Conversational AI learns from each interaction, reuses that data to improve the experience, and can share that experience with the human team as well.
Response consistency: Humans make mistakes and don’t always provide consistent answers across agents and calls. Conversational AI provides consistent answers to simple inquiries and repetitive requests.
Scalability: Conversational AI can grow cheaper and faster than growing a call center and can shrink with even more savings. Adding technical infrastructure is required, but cloud-based solutions also make this a rapid and just-as-needed prospect.
Use cases of conversational AI
Here’s a sampling of customer use cases from a variety of organizations and industries in their own words:
“Dialogflow is a framework for natural language understanding that makes it easy to design and incorporate a user interface into your mobile app, web application, server, bot, interactive voice response system, and so on. This makes it artificially intelligent. It can evaluate the customers’ multiple types of data, including text or audio inputs (such as phone or voice recording),” -R&D/product development user at $1 billion-$3 billion finance company, review of Google Dialogflow at Gartner Peer Insights.
“I have been using Watson for about three years now; it’s very flexible, stable, and versatile. On the whole, IBM is a great partner to work with; their engineers are quick to resolve issues, and I have been involved with them for some product development activities as well. IBM truly tries to listen to their customer!”-Knowledge specialist user at $30 billion+ health care provider, review of IBM Watson Assistant at Gartner Peer Insights.
“Creating a chatbot with the help of AWS Lex was quite easy and fast. Its sentimental analysis provision is really helpful in building a perfect user-friendly chatbot without much machine learning knowledge,” -R&D/product development user at $250 million-$500 million services provider, review of Amazon Lex at Gartner Peer Insights.
“Working with boost.ai has expanded our horizon, both in what is capable with conversational AI, but also how far you can push the union between doing business and incorporating technology.” -Ramtin Matin, lead technological strategist at SpareBank 1 SR-Bank, in a boost.ai case study.
“I never thought that having a chatbot integrated into my site answering specific questions of the industry would be so easy; the team at Yellow.ai was so committed that the end result is … unbelievable,” -CIO at <$50 million services provider, review of yellow.ai at Gartner Peer Insights.
Conversational AI providers
The conversational AI market contains many competitors but some consolidation has started to occur.
Microsoft, for instance, acquired one of the larger providers in the market, Nuance Communications, this spring.
Ten notable competitors in the conversational AI market include:
- Artificial Solutions
- Creative Virtual
- Jio Haptik Technologies