With the rise of generative AI (hello, ChatGPT) the world of support automation is rapidly evolving. And these advances in AI technology mean the gulf between simple chatbots and the advanced bots that are powered by AI — sometimes called virtual agents or intelligent virtual assistants — is only getting bigger.
If you're on the lookout for an automation solution for your customer support, the first thing you'll need to know is the difference between basic bots and their sophisticated counterparts. So here's a handy guide to what sets the conversational and generative AI-powered best apart from the rest.
What is a chatbot?
A chatbot is a piece of software that has been programmed to recognize and respond to human speech — mimicking a conversation between two people. Simple chatbots are rules-based, meaning they are designed to understand and respond to selected keywords or phrases. When a person uses a keyword that is recognized, the chatbot replies with a preset answer.
In customer service, the chatbots of yesteryear can be used to answer FAQs or carry out simple tasks like placing orders or offering recommendations. Publishing company Harper Collins, for example, has an old-school chatbot that finds customers their next read, based on users’ answers to questions about their tastes and preferences. And fitness brand Peloton uses an entirely button-based chatbot to help solve customer queries around membership, placing an order, returning items, and other issues.
Go deeper on the topic of chatbots.
What is a virtual agent?
A virtual agent (also known as an intelligent virtual assistant, or IVA) is a software program that uses artificial intelligence to recognize human speech in the way it’s really used. As with old-school chatbots, AI-powered virtual agents simulate human conversations. With the help of conversational and generative AI, these bots are able to engage with people in a natural way.
Check out this blog on how to intelligently use generative AI in customer service.
IVAs are also used by customer support teams to automate repetitive tasks — but that’s pretty much where the similarities end. In terms of complexity, AI-powered virtual agents leave rules-based chatbots in the dust.
Learn more about virtual agents.
Virtual agents vs. chatbots
Last decade's chatbots and the virtual agents of today are both designed to facilitate bot-to-human conversations. And within the field of customer support, both can be used to automate simple tasks and resolve repetitive queries — freeing up agent time to work on more rewarding cases.
So what’s the difference?
Virtual agents are powered by conversational and generative AI
People don’t communicate with each other using only a limited set of words and phrases. We talk in text-speech and colloquial phrases, our writing is filled with typos and abbreviations, and there multiple different ways of expressing the same sentiment. This makes rules-based chatbots’ reliance on keywords frustrating.
Say you wanted to check if your favorite airline flies to the Moroccan city of Marrakesh. If you asked their old-school chatbot using the (still correct, but less commonly used by English speakers) spelling “Marrakech” it might not understand your question.
To avoid bot confusion — and human frustration — many rules-based chatbots guide people through a dialogue flow using buttons. This decision-tree model gives users a small number of answers to choose from. With their limited ability to understand natural human language, first-generation chatbots are best suited to taking on simple tasks where a small amount of information is required.
IVAs, on the other hand, are enhanced with conversational AI and generative AI: including natural language processing (NLP) and natural language understanding (NLU). This allows virtual agents to establish what a user is really asking (or their intent) regardless of the exact wording they use. Because these AI chatbots are programmed to understand the overall meaning of a text — rather than specific words — they won’t get tripped up by synonyms, typos, or abbreviations.
More on conversational AI
And bots enhanced with generative AI can even understand context. This means you can ask follow-up questions, and from the previous messages within the conversation these bots will be able to understand what you're asking and give a relevant answer. As well as understanding context, the next generation of AI-powered bots can even adapt to your brand tone of voice — allowing businesses to deliver consistent CX across channels.
Check out the demo below to see our own gen AI bot in action:
IVAs are enhanced with machine learning
Powered by AI, virtual agents have the ability to learn and improve. As IVAs take in new data, they use machine learning to get better at recognizing the various ways different intents are expressed. This means these clever bots become more accurate over time.
Old-school chatbots can only recognize words or phrases they have been specifically programmed to understand. So no matter how many times you ask a chatbot if there are flights available to “Marrakech” — instead of “Marrakesh” — the bot won’t learn that you’re talking about the same city. And if your bot can’t understand what a customer is saying, not only is this a frustrating experience, but a human agent still has to get involved to resolve the issue.
Chatbots are (or used to be) simpler to set up
One of the benefits of using a rules-based chatbot, is that while they are less sophisticated than virtual agents, this means they’re relatively easy to set up, and cheap to maintain. Due to their complexity, it takes a little longer (and requires more resources) to get an AI-powered virtual agent running smoothly.
Or at least it used to take longer.
Generative AI is the fastest, easiest way to get started with automation
Today, generative AI has made it easier than ever to launch an advanced automation solution. Simply connect a gen AI bot to your public knowledge base or help center, let the bot analyze your existing support articles, and start chatting. This means you can start resolving common customer queries almost instantly — and in the most conversational way possible. These knowledge base bots work standalone, or they can be integrated into a comprehensive automation strategy that covers all text-based channels.
Want to know more? Here's an article on how to built an AI chatbot using ChatGPT.
Are all virtual agents the same?
Unfortunately it’s not as simple as choosing between a rules-based chatbot and an IVA… Because not every virtual agent platform has the same capabilities.
Here are some of the features you should look out for when deciding on an automation solution for your customer service (spoiler alert: Ultimate’s AI-powered virtual agent platform has all these features, and more).
1. Omnichannel automation. Customers expect to be able to reach out to brands however they please. The best automation providers will offer omnichannel solutions, automating customer support requests across any digital channel: covering email, chat, and social media or messaging apps.
2. CRM integrations. For customer-obsessed CS teams, a headless automation platform (meaning one that sits inside your existing CRM — just as a human agent would) makes the most sense. Not only does this save your team from having to learn how to use an entirely new system, but it allows your virtual agent to access customer data and personalize interactions. As 80% of consumers are more likely to buy from brands that offer personalized experiences, the headless approach is a no-brainer.
3. Generative AI capabilities. Gen AI is the new kid on the support automation block. With generative AI you can build a bot in minutes, making it the fastest way to get up and running with automation. There are plenty of other gen AI use cases in customer support — from summarizing tickets to generating suggested replies for agents to send to customers. And these use cases will only continue to expand as the technology matures.
4. A no-code platform. No-code platforms are designed to be intuitive, making them simple to use and maintain. Since no-code solutions are accessible to non-technical users, you won’t need to invest in additional IT support, and it’s easy to onboard new bot managers. Using a low-code platform, on the other hand, requires an understanding of programming languages. This means low-code solutions take longer to set up, and you’ll have to hire a developer to take care of the automations.
5. Multilingual AI. If you want to scale your customer service around the world, a multilingual virtual agent is a must. Multilingual AI allows companies to expand into new markets and serve different timezones, while offering localized support at native-level fluency — so your bot will grow alongside you.
6. R&D resources. Want to stay at the forefront of AI innovation? Then choose a provider who has their own in-house team of AI researchers. That way, you can be sure you'll always have access to the most cutting-edge AI technology — as the automation space continues to evolve and progress.
7. Custom solutions. While out-of-the-box automations are faster to implement, creating a custom-built solution using API integrations will allow you to fully automate more processes.
Watch this free event on how to harness the power of integrations.
At Ultimate, we understand that every customer service team has different needs, that require personalized solutions:
“Our priority is giving each customer the time they deserve — not getting rid of them as fast as possible. Ultimate’s solution fits with that need.”
- Naomi Rankin, Global Customer Care Manager, Lush
Read Lush's automation success story in full.
The type of automation solution you choose will depend on the particular needs of your CS team. But regardless of whether you go with a simple, rules-based chatbot or virtual agent enhanced with the latest generative AI technology, automating your support will take care of simple queries, allow you to serve customers around the clock, and free agent time to work on high-value interactions.