About This Episode

Artificial Intelligence is everywhere.

From our phones to our cars, from our email accounts to our facebook accounts. If a company can automate customer support and communication, odds are they’re already doing so. The only question is, to what extent?

But how do you use AI for sales? What is it? What is it not? Where is the difference between AI and machine learning, and what are the challenges that exist in the AI sales space?

On a recent episode of The Sales Engagement Podcast, we sat down with Erroin Martin, VP of Sales at Conversica, to discuss those topics and so much more.

Artificial Intelligence for Sales – What Is It?

AI is a very broad term, encompassing a host of different technologies and capabilities, and it’s important to know which you’re talking about.

• Artificial Narrow Intelligence (ANI) ANI is simply learning a specific task and doing it over and over again. To take specific customer input and return an answer based on that input.
• Artificial General Intelligence (AGI) AGI is being able to carry on some basic question and answer, and some simple back and forth with customers.
• Artificial Sentient Intelligence (ASI) ASI is the kind of AI that people think about when they watch movies. Machines that think for themselves and reason and make decisions, i.e. Wall-E or The Terminator.

Unfortunately, we’re still a long way from ASI.

Forecasting is a fantastic way to use ANI within your sales organization. Forecasting includes a lot of data points: the stories told by the reps, the data in your CRM, these are all factors. You use those data points to tell your leadership, “We’re going to do this number against this goal.”

There’s a lot of room for error in there, but with an AI component to help you determine your chances of hitting those goals, you can take a lot of the human error out of forecasting.

Or knowing the right leads to talk to.

In the old days, you’d go visit the prospect or pick up the phone and call, but now you can tell what the likelihood of the customer buying, based on all of the data points in your CRM, and use the AI to weed out the prospects who are the least likely to convert.

Artificial Intelligence vs. Machine Learning

The terms Artificial Intelligence and Machine Learning get thrown around a lot. It can be confusing to know which of two to implement, or which of the two you’ve already implemented.

So what’s the difference?

Machine learning is very specific data sets that your system uses to learn for itself in order to do something better.

Take a car for instance. Today’s newest vehicles implement a lot of machine learning. The car learns how you drive, and then adjusts based on your driving habits.

It learns how you drive through repetition.

Artificial Intelligence isn’t just testing over and over again. AI looks for things outside of the norm. Once you set machine learning to go, it just keeps going. AI is looking for new data sets to make it better.

AI is your chat platform not just saying, “Yes,” but learning all the different ways to say “yes” and what emotion or attitude each of those convey, and how to respond to them.  

They’re very similar, but also wildly different.

Challenges in Artificial Intelligence for Sales

There’s a lot of noise about AI. The challenge is to tame expectations around what AI is, because according to Erroin, most people drastically overestimate AI, assuming that most companies are operating in the ASI space (which they are not.)

You’ve got to do a lot of convincing to make AI an attractive, presentable option for your customers. To show them that it truly is a machine performing complex, repeated procedures, not just a bunch of programmers in a back room.

No matter how much you show and demo, they’ve got to believe that it’s possible (which of course, it is)

This post is based on a podcast interview with Erroin Martin from Conversica. To hear this episode, and many more like it, you can subscribe to The Sales Engagement Podcast.

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