Hey everyone, Chris here.

It seems that every few months, there is a hot new contact center phrase.

When I started writing Balto content around 2017, speech analytics was hot. Every contact center software company had a speech analytics “perspective” and was competing to rank in the top spot on Google. Speech analytics adorned every eBook title, every conference booth banner, every blog post, and every LinkedIn post.

Soon after—in part because of Balto—the stakes changed. No longer was just speech analytics enough. Instead, real-time speech analytics become the rage. After all, the faster insights become available, the better, right?

Then there was (in no particular order) conversation intelligence, natural language processing, predictive analytics, sentiment analysis…you get the point.

Yet all of these terms pale in comparison to artificial intelligence’s (AI) meteoric rise. We see it everywhere – AI, and AI-powered speech analytics, are the talk of the proverbial contact center town (as an aside, I wonder what this town would be like…?).

So how much do the technical details matter? And what kinds of technical questions should you ask when evaluating new contact center AI tech?

As somebody who’s been on the frontlines of contact center technology for some time, my advice is this – don’t stress over technical details too much.

Ok, Technical Details Still Matter, But Less & Less 

Before the advent of the AI era (let’s simplify and call it the current decade), there really was tremendous variability in how different products and providers got things done beneath the hood. A lot of research was needed to know that you were truly getting your money’s worth as you evaluated new and mostly untested technologies. In this universe, technical details were extremely important.

A baseline of powerful AI has enabled suppliers to provide a stable level of technical expertise to their products, variability has narrowed, and most AI-backed speech technologies can deliver on their promise. 

To illustrate, please allow me to make a cheesy analogy – car buying.

Let’s say you’re shopping for a new car. When comparing your options, you weigh everyday considerations. Is it safe? Is it affordable? Is it good on gas? Can it fit my family? Does it look like a giant Tupperware container? The last one, admittedly, is increasingly challenging.

Unless you’re a die-hard gearhead, you don’t bother to learn about the engine’s intricate details. You probably don’t ask whether or not the spark plugs are made of iridium or confirm the size of the turbocharger (or if it even has a turbocharger). As long as you’re buying a reliable engine that successfully moves the car forward when you press the pedal, you’re good to hit the road. 

Now, if you were buying a car during the Industrial Revolution, those details mattered a lot.

Automotive technology was newer and more fragile, and it wasn’t a guarantee that each car could do what it purported to do. Engines didn’t start reliably, and consumers had to be massively educated and in-the-know if they wanted something that would really deliver on their money. In a broad sense, engineers were the artificial intelligence of their day.

But that’s no longer the case – since virtually all engines achieve bare minimum requirements, the nitty-gritty technical details matter less to everyday buyers.

AI-powered speech technologies have followed the same trend. Just a few years ago, many contact center computers weren’t powerful enough to support new technologies, and legacy platforms couldn’t integrate with AI-powered technology tools like Balto. Many contact centers didn’t even have strong enough internet to handle anything beyond phone calls. 

But now the details matter less and less. Today’s computers can easily handle AI-powered speech analytics and other contact center technology (with one lingering exception – Chrome Books, but even these feeble gremlins have been hitting the gym recently). The point is – AI-powered speech analytics buyers need to worry less and less about what is under the hood. 

So don’t sweat it too much.

Instead, I always suggest simplifying the evaluation down to its most basic parts. These are rarely, if ever, technical, such as:

  • What, specifically, am I trying to achieve? (Unfortunately, AI will not solve all of your problems. Best to define 1-2 key objectives and then expand later.).
  • Do I have the right change management systems in place to onboarding new AI-powered technology? 
  • Does this partner seem like an organization that will stick with me? Will they grow with me? Do I even like these people?

And so on…

These questions are a bit more complex to think through, and they don’t often have Yes or No answers. But they’re worth it. We’re no longer wondering “will this car get me from here to there?” We’ve graduated to “will this car meet my needs for the rest of my life, or for the foreseeable future?” 

So What Technical Details Matter?

Ok, I guess I’m backtracking a bit here. There are still a few important technical questions worth asking before buying Balto, or any other AI-powered Speech Analytics solution. By the way, our sales team proactively confirms these details with each prospective customer.

  • Does it integrate with my CCaaS system? Here is a list of ours. 
  • Is my software up-to-date (i.e. are we using whatever the current version is for Mac or Windows is)?
  • Do we use modern computers? Whatever your gut says is probably right.

Of course, here is a list of our minimum system requirements. That way, we can all sleep at night, and your IT team won’t resent you.

Balto has stayed ahead of the waves of contact center transformation, and our AI-powered speech recognition is built on a state-of-the-art engine. Get in touch to talk through your procurement strategy, we’re here to help you navigate the basics and beyond. 

Good luck!