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If you’re a business leader who’s responsible for the employee experience, you’ve definitely heard the words “chatbot” and “conversational AI'' thrown around a lot. So, it’s important to understand the difference.
By reading this guide, not only will you be able to identify the key distinction between traditional chatbots and ones that use conversational AI, you’ll also be able to evaluate which kind of bot will create the most impact for your organization.
Organizations use knowledge-centered service to gain visibility into every aspect of the knowledge support environment within their business. By managing, distributing, and tracking knowledge across the whole enterprise, leaders can more quickly resolve employees' problems — and in many cases, prevent incidents from happening before they arise.
In action, this looks like detailed documentation, knowledge sharing, and data-driven insights into how knowledge actually gets used.
When most people talk about a conventional chatbot, what they’re referring to is a text-only bot that relies on keyword matching to answer the most basic FAQs. This kind of bot relies on a team of engineers to build every single flow, and if the employees deviate from the pre-built script, the bot will not be able to keep up.
There are different levels of sophistication for chatbots — but all conventional chatbots rely on a team of engineers to anticipate employees’ needs. They depend on a pre-deterministic approach. We’ve written in-depth about natural language understanding (NLU) — and why conventional chatbots get it wrong so much of the time.
Conversational AI enables chatbots to mimic the unpredictability of human conversation, keeping up with the concert of human thought. These chatbots can switch topics on the fly without getting confused — and they can identify when you’ve changed subjects immediately. But most importantly, with conversational AI, chatbots can actually learn.
These kinds of chatbots use probabilistic machine learning models to keep up with the users’ needs, hold contextual conversations, and make snap decisions. Conversational AI uses both natural language understanding (NLU) and conversational flow management (CFM) to understand what the user wants, and how to proceed with the next steps.
The employee experience is defined by the support they receive in these three stages:
Chatbots handle these three stages differently. In other words, conventional chatbots take a different approach to this process than bots that use conversational AI. This difference has a big effect on the employee experience. Let’s dig in.
It’s easier to identify the differences between these two frameworks when users first begin a conversation with their bot. When an employee runs into an issue and needs help, a conventional chatbot will present them with options — three to five at least. By surfacing only a few solutions, the bot is effectively telling the employee, “here are the only things I can successfully help you with.”
On the other hand, a conversational AI chatbot will let the user initiate the interaction without limitations. It’s as if you’re talking to your favorite IT agents. Users can submit any issues they have to the bot — they’re not limited to three to five options that exist in the bot’s script. They’ll find that even when AI can’t solve their problems, it can direct them to the right people who can.
As the conversation with their bot continues, the employee may need to add additional context or clarify the issue at hand. A conventional chatbot, even in the best-case scenario, will try to find the right dialogue flow. Let's say you got lucky and what you needed help with was one of the three options presented at the beginning. You clicked into it.
Now, the conventional bot will take you through the decision tree its engineers built for that option. It’ll ask for your role, your location, and any other information the decision tree is required to collect. This whole experience causes a lot of frustration for employees. Why? Because it’s annoying to answer questions that aren’t relevant to your problem. Or to find the right buzzword to be directed to an agent.
Alternatively, conversational AI bots stack probabilistic machine learning models to take the context of employees’ problems into account. A conversational AI chatbot already knows basic information about the user to help answer their questions faster. It knows whether it’s interacting with the CEO or a new hire. And it will adjust its actions accordingly — no scripting necessary.
At the final stages of the conversation, now that the chatbot (hopefully) understands the problem, it needs to work towards a resolution. A conventional chatbot is going to trigger a workflow, depending on which branch of the decision tree you’ve ended up at. Each of the potentially thousands of branches will have an action pre-programmed from the start. It’s impossible for even a team of IT agents to constantly monitor and update these actions — even if they could, there are multiple resolutions for one issue.
The difference with conversational AI — and its probabilistic approach — is that the chatbot can come up with the best solution, tailored for that specific employee, exactly when they ask. It takes into account all contextual information, goes through your knowledge base, and surfaces the best solution — whether it’s an entire knowledge article or a single sentence.
By using probability, a conversational AI chatbot will understand — and react to — all the nuances that are present when an employee has a problem.
The mark of a truly mature support process is a great employee experience. To this in action, read about how Palo Alto Networks leveled up their employee experience.