Conversational AI–What is It and How Does it Impact Modern Communication?
Updated: Aug 13
Communication between humans and computers continues to evolve with the rise of conversational AI. Discover how these AI tools influence daily life.
In a digital culture, it’s increasingly necessary to expand communication methods and strategies in a way that accommodates new technology. Communication is no longer focused solely on human-to-human speech. Now, individuals and businesses require a way to bridge the language gap between humans and computers.
With the rise of artificial intelligence technology, we have more ways than ever to connect with other people and with the devices and systems we rely on every day. One branch of artificial intelligence, conversational AI, provides a pathway for humans to interact with computers intelligently and effectively.
Keep reading to understand what conversational AI is, how it works behind the scenes, and what a future with these tools might look like.
What is Conversational AI?
Conversational AI is any artificial intelligence technology that uses speech and voice recognition to construct reliable communication networks between humans and computers. The purpose of this type of AI is to make interactions between humans and computer systems more natural.
Well-built conversational AI provides such a seamless interaction that it is nearly indistinguishable from communicating with an actual human. While there are different levels and approaches to conversational AI, the goal is to create a fluid and relatable exchange.
What are the end goals of conversational AI?
Computer-based conversational AI programs are usually transactional in nature. This means that there is a desired end result when a human being engages with them. In the case of conversational AI tools, the end goal is to:
Provide new information to the user
Complete basic tasks
Finalize simple transactions (i.e., making a payment on a credit card)
How does it work?
While many conversational AI tools are customized to suit the exact needs of the individual, organization, or business, there are several underlying steps that all speech-based AI systems must perform. The aptitude and quality of each step often indicates how advanced the particular AI tool is.
Conversational AI tools must first be able to identify verbal cues and words via speech recognition. The ability to recognize speech, however, isn’t limited to spoken language. Conversational AI software can also recognize text-based messages.
When input from the person occurs verbally, conversational AI uses Automated Speech Recognition (ASR). ASR is the most common type of voice recognition software, and its purpose is to translate verbal language into text that the computer system can then read.
Dialogue management happens when the conversational AI system processes the input in order to formulate a response. At this step, the software has to interpret and understand what the person is actually saying.
Natural Language Processing (NLP) is what gives AI tools the power to interpret. Being able to collect speech and text is one thing, but translating that text into something usable is the next level in artificial intelligence processing. There are two elements associated with language processing:
Intent – What is the purpose of the communication? What is it trying to achieve?
Entity – In NLP, an entity is the repeated unique identity of a particular message.
The third piece of the puzzle is language output. This step is exactly as it sounds, since the conversational AI tool must be able to provide output in response to human input. Once the system has received and interpreted the message, it must decide what to send back as a suitable response.
Natural Language Generation (NLG) comes into play during the language output step. NLG is the means by which the AI platform creates human language that makes sense. Without this piece, the AI tool would send back a computerized or coded message that had good context without being usable.
Language output is crucial to conversational AI design. Without effective language capabilities, the AI loop is one-sided and not beneficial to human users.
Finally, machine learning and deep learning round out the conversational AI process. These systems operate in the background to help the system learn and adapt as they receive an increasing amount of data.
A good conversational tool AI must have reinforced learning in order to improve. As the system receives more input, the quality of responses becomes more intuitive.
Benefits of conversational AI
Conversational AI has the ability to provide practical solutions to everyday needs, particularly in the business realm. More companies are leveraging conversational artificial intelligence in order to take advantage of the following benefits.
Round-the-clock availability – Intelligent conversational tools can be active outside of traditional business hours. Service can be provided even when staff members aren’t on the clock. High uptime leads to better customer service and expanded global reach.
Widespread business applications – Research from McKinsey has revealed that AI can improve business automation up to 60%. Decision-makers can use AI in a variety of ways to reach more customers, reduce manual workloads, and improve response times in a number of different departments.
Intelligent analysis – Most conversational AI systems receive input and make smart recommendations based on speech. This process results in a substantial amount of data that’s helpful for future decision-making, spending, and growth potential. Since the data highlights the actions customers actually complete, it’s extremely valuable.
Examples of conversational AI
Conversational AI is already a popular way to use artificial intelligence technology. Given the popularity of smartphones and connected home devices, many people interact with this technology on a daily basis.
Two of the most common examples of conversational AI include virtual agents and messenger platforms.
Virtual agents are computer-based systems that many businesses design and develop for the purpose of serving customers with a personalized experience. After receiving verbal answers, virtual agents direct users to specific tasks and functions to help complete their requests.
You might encounter a virtual agent when you dial a health insurance company and want to pay a bill. The virtual agent may collect information about your name, account number, balance, and payment information to complete the request.
Messenger platforms are similar to virtual agents in terms of the back-and-forth nature of communication. Messenger systems are typically more automated and designed to direct customers to another link in the communication chain.
These platforms may also be used for basic social engagement or for entertainment value.
Conversational AI versus common chatbots
Although they are similar in nature and function, conversational AI tools are different from average chatbots. The difference lies in how intuitive the basic software is and what it can do in response to human interaction.
Chatbots mostly rely on pre-built messages and cadences to respond to incoming requests. Their language response is formulated with playbooks set by human users. If a chatbot doesn’t know how to respond based on its internal memory, it may direct the user to a human representative.
Conversational AI tools are more intuitive as a result of their machine learning capabilities. These platforms receive input, interpret it, and make an informed language decision. They also build upon what they already know with the accrual of new data. This type of neural network is not standard in a chatbot.
Are there any downsides to conversational AI?
Users of any technology should be mindful of potential limitations and risks. Some of these shortfalls are hidden in plain sight, while others are only uncovered through regular use of the tool. With conversational AI, some disadvantages include:
The need to constantly supervise performance. Although the uptime for AI tools is high, this means that businesses must be more diligent when monitoring interactions made on their behalf. Regular analysis and calibration is required to keep chatbots or voice assistants in line with brand messaging.
Possible language barriers. Conversational AI tools are built on the premise that they know, understand, and interpret language. To accomplish this purpose in a variety of dialects, the software must be programmed to include multiple languages.
Privacy concerns involving data collection practices. Conversational AI tools constantly intake new data when chatting or communicating with customers. Although this is intended to improve future interactions, some of the data could be sensitive in nature and present red flags for data privacy.
Failure to recognize linguistic nuances. Nearly every language includes slang, jargon, and casual phrases that are part of basic human communication. Unfortunately, computers that operate on strict language processing may not be able to quickly adapt to these colloquial expressions. This limitation may be more relevant with spoken word AI systems.
Future outlook and industry potential
Eventually, technology experts believe that humans will interact with an increasing number of conversational AI tools. In fact, Gartner predicts a future state in which some humans interact more regularly with an AI platform than with their spouse!
Conversational AI has the power to transform business practices, offload routine tasks, and improve assistive technologies. As a result, it’s a highly desirable technology with a bright future outlook.
With the basic framework already in place, the future of conversational AI may be structured around customizations and enterprise-level development. Future opportunities might include:
Internal and cross-department enhancements
Personalized employee engagement
Integration with other apps and systems
Real-time notifications and live data
The prominence of artificial intelligence reminds us that communication is no longer reserved for physical, in-person speech. As global economic and cultural needs change, technology and AI must adapt to keep up with evolving trends and expectations.
While we must remain cognizant of both the advantages and disadvantages of new software, the willingness to embrace conversational AI opens new personal and business opportunities. With such a bright future outlook, we must learn how to leverage conversational AI to achieve goals and solve modern challenges.
If you’re curious to learn more, we have a large AI affiliate network full of companies passionate and willing to explore how conversational AI could affect your business’s future. Reach out to us to learn more.