Mike Burnett: Hello. Thank you for joining. This is Mike Burnett, partner and co-leader of the business services practice here at the Alexander Group. Today, I’m joined by my colleague, Sean Bock, to discuss artificial intelligence and the role of revenue operations in unlocking value. Sean, thanks so much for joining.
Sean Backe: Thanks for having me, Mike.
Mike Burnett: All right, Sean. So, to start us off, you obviously have a lot of experience in revenue operations, working with clients in the space. How would you describe what the role of revenue operations plays or what their role is when it comes to trying to unlock artificial intelligence value?
Sean Backe: Thanks, Mike. In a in a word, it’s what the role has always been. But it’s a little bit juiced on steroids at this point. So, if you think about rev ops historically, what that function has been so good at is being a translator across both the commercial organization and then the corporate in the back office functions and then in particular in terms of the data. Can I take the needs that sellers and marketers have and customer success agents have and translate that into something that a data scientist, a business analyst and others can wrap their head around and then generate insight on? Well, that process is still true, but what AI is doing now is in some areas it’s shortening the turnaround cycle on the entire end-to-end engagement. And in other areas, it’s challenging us to think about new use cases that we’ve never had to consider before. So same as always, but more faster, better.
Mike Burnett: You mentioned use cases and I think to your point, it’s how can revenue operations help us drive some specific revenue motions more effectively and efficiently. And people jump to the tools and the technology rather than thinking through the use case first. So maybe let’s walk through what some of those use cases are as we think about the different ways or the different types of customers we could be engaging with. So, maybe let’s start with identifying and landing new customers. What are some of the most common use cases you’re seeing folks really picking up and running with there.
Sean Backe: So in this area, it’s a it’s a spot where we’re encouraging people. When we’re doing it in our consulting engagements, we help them kind of navigate this process. But if we were just advising, we’d say, think about the level of maturity you’re at, both with your go-to-market motions and with your AI and data science capabilities. If we’re just getting started, if we don’t have a robust demand gen engine in place, SDR, BDR structure, a lot of targeting that we’ve done in the past, we want to be a little simpler, a little more static and using a little bit easier technology. So there we think about a lot around, you know, developing ideal customer profiles, developing opportunity models, focusing on, hey, I can make a judgment about this account, and it’s going to be more or less the same for the next 12 months as shops start to progress and get more sophisticated. The two trends we see with leveraging AI and more advanced machine learning is that speed picks up and personalization picks up. So instead of just saying “hey, this is a high value target,” all of a sudden we’re going to “hey, I’ve got content that I’m personalizing on the fly to this, to this target that’s aligned to the specific use case that they’re going through right now.” And that’s just a lot harder operationally and from a data point of view as well.
Mike Burnett: Right. So, let’s maybe explore the opposite side of the equation. So, we talked about new customers. Now we’ll talk about our existing customers. One of the big things folks are always focused on is – how do we combat churn? How do we maximize retention? What are some of the things you’re seeing folks really deploy there?
Sean Backe: So we’ll again do kind of the two sides of the maturity spectrum on the first at the very beginning. But you got to know about it. So churn risk modeling churn alerts. Right. You got to and the thing that people forget there is a doctor could go down the street and pointed anybody and say that person’s going to die. The question is when and can is it is there something going on that I should do something about. Right? And so being able to bring those turn alerts into an actionable set of tasks and activities, sellers understand that’s the first step. On the flip side, this is where we’re seeing the most advanced use cases of AI. So this is the agentic. We’ve actually got people interacting with machines farther up the kind of customer success journey, so that we are avoiding issues of churn in the first place.
Mike Burnett: Got it. Let’s take it a step further. So, if we retain the customer, how do we expand the customer? What are the things that we’re seeing folks deploy there?
Sean Backe: At the end of the day most of the plays that we have in this space come down to recommendation engines. So, we want to be able to quickly and proactively say “here’s the next best offer that an account or a customer or person might be interested in.” What varies more is as the level of sophistication and speed and real time and granularity, that as you get more and more mature, those next best offers become more and more precise and more and more relevant to the customer.
Mike Burnett: Got it. I like how you kind of broke down. There’s the right path for folks. Kind of depends on the maturity level, especially from a revenue operations team, because these folks are going to be responsible for driving and hopefully forcing along the implementation of the technology. What would be your 1 or 2 pieces of advice for folks as they’re just starting on this journey? What would a newer, less mature organization, what should be the first thing they focus on?
Sean Backe: Well, we’ve already answered it, Mike, or you answered it with your first question and that it’s about think about the use cases. The number one thing that happens with AI models in the go to market space is that people invest money and they sit on a shelf and they’re never used. The reason for it is just having a really smart answer doesn’t do anything unless you’ve created the workflows, the tasks, the sales processes that are all aligned to that so that we can take insight and turn it into action. So, why we really encourage focus on use cases – it helps with that a lot. We kind of get out of the trap of thinking of AI as this sort of magic pixie dust that solves problems on its own and says, hey, wait a second, we’ve got a specific problem, like churn, for example. It’s not enough to just know what customers are leaving. I actually have to tell sales and marketing about that, and then they have to do something. And what is that thing we’re going to do? And so as we get into that area, we start to see a lot more translation into action, which is what actually drives results.
Mike Burnett: Great. Well, Sean, thank you so much for the time. Thank you for the insights today and everyone. Thank you for joining and listening in. If you’re interested in learning more or scheduling time to meet with either myself or Sean on this topic regarding AI and how revenue operations can really play a large role moving forward, feel free to visit our website at AlexanderGroup.Com and we hope to hear from you soon. Thanks again.
