5 Artificial Intelligence Myths Busted

Artificial Intelligence (AI) is one of the hottest things in the tech world these days. But, as is often the case, the hype around AI can result in misinformation and confusion. When investing in a powerful technology like AI, you want to ensure that you know exactly what you’re getting and how you’re going to use it.

Here are five common myths about AI and the reality behind them. We use examples from automated voice applications to help flesh out these explanations, but the ideas here are applicable across different problems, processes, and technologies.

Myth 1 – AI is One-Size-Fits-All

AI is an umbrella term for several different types of technology. These include algorithms and applications that enable deep learning, natural-language processing, big data analysis, and other processes.

Therefore, one doesn’t simply buy “an AI” and plug it into your current tech. Every AI application has a specific purpose. In order to see positive business outcomes and ROI, businesses need to use these applications for the tasks they were designed to complete.

At the same time, because AI is such a diverse collection of technology, you shouldn’t feel the need to commit to a single AI platform for all your AI needs. Each problem and corresponding AI solution needs to be evaluated within its own context. There may be opportunities to use some of the same AI backbone for different processes, but that may not be an option given the tasks under consideration for AI.

Myth 2 – AI Automatically Understands

It’s nice to think that AI automatically understands what users want out of it and can respond accordingly. But that’s not the case. An AI needs to be programmed (i.e. told) what to understand.

For instance, in an AI-powered voice application, if a caller wants to talk to support, a human needs to build a goal or intent that the AI can then use as a benchmark for determining what the caller wants.

If a caller says something that doesn’t fit into one of the pre-determined intents then the AI won’t understand what to do with that person’s call.

Myth 3 – AI Learns by Itself

This myth is related to Myth 2. The pre-determined intents that function as the control room for an AI need to be continuously updated and optimized. It’s true that AI can extrapolate on its own, but actual learning often requires human intervention.

For a voice application, this may involve going through the user phrases that the AI didn’t understand and adding (or removing) them from the pre-determined intents to make the AI more accurate. The upside of this, however, is that over time, and as the AI gets better at assessing caller intent, the amount of time spent on upkeep may decline. What once needed to be done daily or weekly, can be done monthly or quarterly.

So, while AI algorithms extrapolate on data sets faster than humans they need guidance from humans before they can perform those extrapolations.

Myth 4 – AI Will Transform Your Company

It can be tempting to jump into AI technology head-first with the intention that it will completely transform your business. But AI works best when it’s wielded with a tight focus. To be effective, use AI to solve a specific problem.

If you’re able to use AI to reduce customer frustration when they call your company, that may not be the type of improvement worthy of a press release, but incremental changes can have an impact that ripples through your company.

For example, improving customer service and customer experiences can have a measurable, positive effect on customer retention, which in turn affects ROI and the bottom line.

Myth 5 – You Need to be All-In on AI for it to Have an Impact

Commercially available AI (at least solutions that don’t cost millions of dollars) is still relatively new. When adopting AI technology, it makes sense to start with a single problem with a single AI solution. This enables your business to test and experiment with the AI technology until the results are conclusive one way or another.

Remember, AI needs to be trained to understand what your customers are trying to do, so you need to be sure your results timeline is properly calibrated. Don’t expect a huge improvement overnight!

But you are more likely to see positive business outcomes by tackling one problem at a time than trying to implement AI with multiple problems at once.

For information about adding AI to your voice-based customer service, contact us for a free demo and consultation.