Since OpenAI unveiled ChatGPT to the public, AI (artificial intelligence) has become more prevalent in businesses across industries. This trend is corroborated by a Forbes Advisor Survey showing that a whopping 64% of businesses believe AI will boost their productivity.
While detractors may see the widescale adoption of AI tools as potentially controversial and a threat to human employees, the level of efficiency these tools lend to companies utiliding them is undeniable.
Need to quickly create content at scale? There’s CopyAI and BrandWell (formerly ContentAtScale). Want a captivating image to complement an online article? Midjourney can do that in a flash. Specialised tasks like copyediting written articles or coming up with marketing ideas? ChatGPT-4 has your back!
But what about jobs that typically require a human agent, like holding conversations or addressing complex customer inquiries? Two words: conversational AI.
Interested yet? Read on to learn more about conversational AI and the exciting possibilities it brings!
What Is Conversational Artificial Intelligence (AI)?
Conversational AI is a type of technology that lets computer programs “communicate” with end users, who are often the customers.
They leverage advancements in Natural Language Processing (NLP), Natural Language Understanding (NLU), and Machine Learning (ML) algorithms that enable them to handle customer interactions almost as well as a human.
Now, you may ask how this differs from the run-of-the-mill chatbots that businesses already use. The difference between conversational AI and chatbots as we know them today lies in the complexity of the conversations these two programs can carry.
Conventional chatbots typically only answer frequently asked questions with canned responses. This means that your typical website chatbots have a limited scope of customer queries they can address. A common example of these are the chat windows that pop up on the side of a website when you open it.
Conversational AI, on the other hand, isn’t hampered by such limitations.
With advanced conversational AI technologies, AI chatbots can do more than just answer user queries. They can parse or interpret questions or statements from users and glean user intent from these.
How? Through machine learning and Natural Language Processing/Understanding!
ML, NLP, and NLU capabilities allow advanced conversational AI tools to process vast amounts of data and learn how to recognise and interpret nuances in human language. This enables them to hold more than a passing semblance of human conversation with users.
It’s a lot more steps further than asking Siri or Alexa to dial your friend’s number or show you the latest celebrity gossip. With advanced conversational AI systems, you can even have AI act as customer service chatbots that use voice chats.
Yes, you read that right. Conversational AI isn’t limited to only text-based conversations; they can “mimic” human speech, as well!
Conversational AI vs. Generative AI
Conversational AI and generative AI are closely related (and sometimes overlapping) concepts in the artificial intelligence field. These two concepts have such similarities that they can often be confused and (wrongly) used interchangeably.
The table below will help you learn some of the key distinctions between them.
| Conversational AI | Generative AI | |
|---|---|---|
| Purpose | Enable machines to engage in natural conversations with humans. | Enable the creation of new content (e.g., text, images, music, videos, etc.) using AI. |
| Primary Goal | Enable machines to understand and respond to human queries in a way that feels natural and human-like. | Generate creative content that isn’t just a response to an existing input, but actually producing new and original data. |
| Scope | Conversational AIs are often designed specifically for interacting with users in a way that mimics the patterns of natural human conversations. | Generative AI has a broader scope and can produce a wider range of outputs, rather than being limited to text- or voice-based conversations. |
How Conversational AI Works: Natural Language Processing and Natural Language Understanding
You may wonder, how exactly does conversational AI work? How can a machine understand the subtle nuances and idiosyncrasies that comprise the thousands of different human languages? The answer lies in Natural Language Processing and Natural Language Understanding.
NLP is a branch of artificial intelligence that focuses on helping machines understand, interpret, and generate human language.
Think of computers as a foreigner and NLP as a language tutor that helps them learn the local (human) language and talk like a native. It’s not an overnight process; on the contrary, it often takes several stages and involves massive volumes of training data.
Meanwhile, NLU is a subset of NLP. It deals with understanding the meaning and context of language.
Going back to our earlier analogy, you can think of NLU as the foreigner now having a good grasp of the local language, but not yet enough to make them appreciate metaphors and other figures of speech or recognise sarcasm.
These two branches result in what we call the Natural Language Generation (NLG). NLG is how conversational AI “talks” to humans in a way that we understand; it’s how these types of AI generates text that makes sense when read by humans.
Benefits of Conversational AI
Aside from lagging behind your competitors tech-wise, there are many other reasons why you should consider adopting Conversational Voice AI into your business processes.
Here are just a few examples.
1. Increased Customer Satisfaction
This may seem counterintuitive. After all, you’re replacing a key part of customer interaction, the human touch, with a robot.
However, using conversational AI chatbots doesn’t mean you have to shelve your human agents. It simply lets them focus on problems that absolutely require the intervention of a human agent and automate the rest.
Doing this can help you save precious time per customer query and make your customer support process more efficient. It lessens the time your customers spend waiting for an available agent and lets you focus your human resources on where they matter the most.
2. Increased Operational Efficiency
One of the primary reasons you’d want to have conversational AI in your customer support process is to take repetitive tasks off your human agents’ plate. You don’t want your A-team bogged down by the same query from different customers, don’t you?
Why not let conversational AI chatbots take over these calls and reserve your human staff for the calls where they’re truly needed?
Moreover, AI chatbots aren’t limited to providing one-on-one services to customers. One AI chatbot can handle hundreds, if not thousands, of customer interactions simultaneously.
Even better, AI chatbots don’t need to eat, sleep, or rest; this means your customer support is running literally 24/7 nonstop.
3. Reduced Operational Costs
As with all automated processes, turning to conversational AI can help you cut down on overhead costs.
With AI-powered chatbots, you can easily scale up without incurring the high costs associated with hiring additional manpower, training, and recruitment.
Additionally, as conversational AI learn over time through machine learning, their response quality will also continuously improve just by running the tasks they’re designed to do.
The self-improving capability of these conversational AI agents means that they’ll be able to handle more complex tasks with minimal need for human intervention (and therefore less expenses).
Risks of Conversational AI
As beneficial as they are, there are still risks you must proactively mitigate when using conversational AI software.
Take a look at some of these risks.
1. Data Privacy Concerns
Businesses often store the personal information of their customers (e.g., phone numbers, email address, home address, and even credit card information). If not managed properly, these sensitive information can be vulnerable to data breaches and land your company in legal trouble.
To prevent any potential mishap, make sure you have a robust data security systems in place.
2. Miscommunication and Misunderstanding
While conversational AI tools are becoming increasingly sophisticated, they aren’t infallible and errors in communication may still occur. This is why it’s important that you don’t replace human agents with AI tools. Instead, AI tools must be used to complement your human team.
Types of Conversational AI Technology
There are different types of conversational AI models available today.
1. Voice Assistants
You may already be familiar with voice assistants, as these are among the most common types of conversational AI. Google Assistant, Apple’s Siri, and Amazon’s Alexa are all examples of this type of conversational AI.
These virtual assistants use NLP to understand and respond to user commands, from playing music to placing orders online.
2. Interactive Voice Response (IVR)
IVR systems are often used by contact centers and customer service providers that allow businesses to manage large volumes of calls. IVR systems redirect calls to the appropriate departments or points of contact and greatly reduce customer wait times.
3. Multimodal Interfaces
Multimodal interfaces are a newly-emerging trend in the conversational AI space. As the term implies, they combine multiple modes of interactions (e.g., voice, text, and image) to allow users to engage in the way most convenient for them and give the AI more flexibility in how it can respond.
For instance, a user might initiate conversation with the multimodal AI via voice, but the AI will respond with a visual prompt that best satisfies the user’s intent.
Conversational AI in App Development
Integrating conversational AI into your business’s app development process can help you create more intuitive and engaging products.
As enabling communication lies at the core of conversational AI, introducing them into the conceptualization process can help in the development of apps that can interact with users in a more human-like way.
This can pave the way for the creation of apps that can engage in real-time conversations and go “off-script,” not unlike what ChatGPT can do.
With such advancements, you can give your customers more personalized experiences even through digital mediums. Perhaps more importantly, such apps can help you gather better insights about your customers’ behavior regarding the products they consume.
But that’s not all—these conversational interfaces can also be goldmines for understanding customer behaviour and preferences. By looking at how users interact with the app, businesses can spot trends, pinpoint issues, and gauge customer sentiment in real time.
This kind of insight is invaluable; it can guide future product development, shape marketing strategies, and refine customer service efforts. It leads to more meaningful engagement with your customers and boosts their overall satisfaction with your offerings.
Conversational AI: Best Practices
Like all business endeavours, following best practices helps ensure that you can get the best results when using conversational AI.
1. Always Have a Human Backup Available
Although conversational AI tools have become significantly more advanced, there will always be situations where a human touch is needed.
Not having access to a live agent when these situations arise can lead to customer frustration and negatively impact your business’s reputation.
2. Improvements Never Stop
Implementing conversational AI into your processes is not a one-and-done deal; it requires ongoing evaluation and improvement.
By regularly monitoring your AI’s performance, you can identify trends, misunderstandings, and areas where the AI can still be improved. These insights can give you a quantifiable way of improving your AI-enhanced processes.
3. Maintain Transparency
Transparency and consent are a big deal in businesses, even more so when dealing with software that collects and stores potentially sensitive customer information.
Be upfront with your customer base that you are employing conversational AI in your processes and disclose how any of their data are collected, stored, and used.
It’s also prudent to let your userbase know the exact capabilities of your conversational AI. This helps build trust and set a realistic expectation from your customers.
Where to Find Conversational AI that Works
If you’re on the market to find AI tools or professionals with extensive AI experience, look no further than Outsourced Staff’s AI outsourcing solutions!
Whether you’re looking for an NLP Researcher, an AI Systems Analyst, or a Conversational AI Engineer, Outsourced Staff has the right employee for your company’s unique needs.
Curious about the quality of our Conversational AI? Give it a try by calling 02-1234-5647 and test it yourself!
Key takeaways:
- Conversational AI enables machines to communicate with users naturally, leveraging technologies like Natural Language Processing (NLP) and Machine Learning (ML) to understand and respond to human queries.
- Unlike basic chatbots that provide scripted responses, conversational AI can understand context and intent, allowing for more dynamic and nuanced interactions.
- Conversational AI can enhance user engagement and provide valuable insights into customer behavior, informing product development and marketing strategies.