Up until relatively recently, the only option end-users had for receiving IT support were phone calls, tickets or emails. Now, thanks to rapid iterations of artificial intelligence and machine learning technology, IT departments are able to leverage the power of intelligent bots to offer round-the-clock, automated (read: agentless) support.
But not all bots are created equal. One of the biggest differentiators is whether they are rule-based or true AI. Understanding the key differences here will help organizations make more informed decisions when adopting a virtual support agent (VSA) model.
Rule-based chatbots are capable of answering end-user questions based upon a predefined set of rules that they have been programmed for. This isn’t to say they’re necessarily basic. In fact, with the right programming, rule-based bots can be built to be relatively complex (at least, to some degree). And because they are built on if/then conditions, they are much easier to train than AI bots, which means they can be implemented extremely quickly. That being said, they are far more cumbersome to maintain over time, as every new piece of information must be programmed as it’s needed.Where these chatbots fall short, however, is in their inability to understand context and learn on their own. As such, there is often a disconnect between the end-user and the bot, which can lead to frustration and delays. For more complex issues, bots can hand over the conversation to a human agent who can provide a higher level of service and support. This means that rule-based bots cannot operate completely autonomously. They must rely on human intervention whenever anything outside of their database arises.
While the human/computer interface of rule-based vs. AI bots is relatively the same, the major difference between the two technologies is their self-learning capabilities (or lack thereof). AI bots are programmed with machine learning (ML) and natural language processing (NLP) so that they can read and comprehend context and continuously learn and improve on their own. The key to success with AI bots is access to rich, relevant data.
While there is certainly an investment of time, resources and money upfront, AI-bots are generally much more cost-effective in the long run, because they require far less ongoing maintenance than rule-based bots. They are also more resource-efficient, since they can handle highly complex support needs without requiring any human input. This enables organizations to optimize their staff numbers, either trimming down or reallocating human resource to more meaningful, revenue-generating projects. Meanwhile, end-users receive the on-demand support they need, maximizing satisfaction levels.
Which Type of Bot is Right for You?
Reach ANALYB today so we can guide you with your selection process and provide you the optimal solution with lower TCO.