How AI Will Help Revenue Teams Do More With Less

In this Revenue Mavericks episode, Nathan Clegg shares valuable insights into the role of AI in revenue operations, emphasizing its potential to drive revenue by optimizing processes and enhancing collaboration between sales and marketing teams.

 

About this Mavericks episode

Nathan is a seasoned professional with over two decades of revenue executive experience. He is currently the CRO of Squeeze, where they use artificial intelligence (AI) to help clients land and close more deals. He made an early entry into the world of AI as the CRO of BoomAI, which has made him well equipped with the nuances of AI and how it can help sales and revenue teams generate growth. For this reason, he is also a sought-after keynote speaker on AI and its future impact on revenue and sales.

In this episode, he talks about how AI can be implemented in organizations from a RevOps perspective. He shares his knowledge on how revenue teams can reap maximum returns from AI, which tasks can be assigned to AI and what revenue teams need to be careful about when implementing AI into their processes. Towards the end, he also shares advice for revenue professionals to succeed in their careers.

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Key takeaways from this episode

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Simplify processes and leverage AI to automate repetitive tasks

Simplify operations and utilize AI to streamline repetitive tasks. By offloading mundane activities to AI systems, teams can focus on more critical and creative aspects of their roles, ultimately driving efficiency and productivity.

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Patience is key when it comes to the ROI of AI

Because AI depends on Machine Learning (ML) and ML depends on the number and quality of inputs the system is trained on, teams have to be patient for the AI system to develop the right knowledge base to be able to output the right actions and predictions.

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Start small with AI and grow at a steady pace

For career advancement, individuals should educate themselves on AI technologies and experiment with them in their roles. Starting with small, low-risk initiatives allows for early wins and builds confidence in utilizing AI tools effectively to drive revenue growth.

 

“AI will identify activities which generate the most revenue”

Nathan talks about the implications and benefits of AI and how it can help to not just identify activities that generate more revenue, but also repeat those activities such that maximum revenue is realized.

 

“Get an easy win with AI before you go wholesale”

Nathan shares tips on how to get started with implementing AI in your revenue organization and use it to its full potential.

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Full transcript of this episode

Matt Durazzani: Hello everyone, and a special welcome to Nate Clegg. We're excited to have you, Nate. Um, you're a great addition to the Revenue Maverick program. So, welcome. I'm Matt Durazzani, I'm your host. And I am a Revenue Maverick Advisor. And for the audience who's listening today, I would love to introduce you to Nate.
He is currently the Chief Revenue Officer at Squeeze. And Nate and I go back a very long time, nearly 25 years. And so it's been fun to watch his career progression. Nate has a very unique background when it comes to revenue and revenue operations. Not only has nearly two decades of direct experience as a revenue executive, But he also has been exposed to a number of operational experiences that are not common for most leaders, such as exit of a business, M&A, remote operations, and also international operations. ... And, um, he's also often sought as a keynote speaker, and one of the fields of expertise for him as a speaker has been artificial intelligence, a topic that is very hot in these days. So we will hear more about that today in our podcast, but we are very thrilled to have him on the program. So welcome Nate, and we're excited to have you here.
Nathan Clegg: Thanks, Matt. Uh, I'm excited to be here. I'm glad you mentioned how long we've known each other. Most people look at me and think I'm a small child because of my face. I am experienced, I promise. That's a battle I've had my whole career that I looked too young, but, uh, but yeah, thanks for having me here.
I'm super excited to talk to you.
Matt Durazzani: Well, I have probably enough gray hair for the both of us, so we're good. Yeah. We're good. So before we get started, uh, uh, in the area of AI, uh, Nathan will probably be helpful if you take a moment and tell us about your current role in, in the organization.
Nathan Clegg: Yeah, here at Squeeze, I'm Chief Revenue Officer, like you said, and I'm in charge of all things sales and all things marketing.
So that's my world right now. Uh, Squeeze, in particular, does really lead, uh, work. So we get leads, and then we help people further down the funnel. It's mostly direct to consumer work. So we work with banks, mortgage companies, insurance groups, and home service companies. And we'd get more out of their leads.
Really, we try to help marketers get a higher return on their ad spend. And that more or less is where I've been sort of a lead generation space off and on over the last 15 or so years. And that's what Squeezed does.
Matt Durazzani: Awesome. That's very interesting. So when it comes to the fact that, uh, um, you were one of the early adopters of artificial intelligence, in fact, you build companies around and like, uh, boom AI, can you maybe share with the audience today what you learn about AI, uh, with regards to revenue and go to market strategies during that journey?
Nathan Clegg: Yeah, for sure. Boom AI was an awesome experience. It was an offshoot of boom sourcing and boom sourcing sold off. Boom AI went off on its own, own route, but I got to be a part of that initial setup of boom AI. And what I learned about emerging technologies in general is. Patience. I think that's the number one thing.
Um, and you just have to take your time and make it work and be ready for some bumps, but that's just the necessity of new technologies. You have to let it simmer. You have to let it, Learn and have it take time and, and it's almost a cliche to say this at this point. Uh, most of the time when we talk AI, it's not really AI, right?
It's machine learning and machine learning takes time. You have to train it. It's like this little genius baby. And you have to teach this genius baby how to like interact with normal human beings or do some simple functions. So the more patient you are, the better the outcome is going to be with
Matt Durazzani: AI.
Patience. That's that. That's a perspective that I've not heard before, but, um, it sounds very true when it comes to the way AI technologies today learns, right? Uh, you ask them a question, uh, they come back with an answer. It's not the answer you want exactly, so you keep going back. It's almost like you keep re explaining yourself until the AI is in a position to understand more context.
And then give you more the type of answers that you were hoping for.
Nathan Clegg: Yeah, for sure. When we first started deploying our, our instances for boom AI, because we did consumer outreach, boom sourcing as well, and boom AI. And every time we deployed a new instance of the AI, the clients we interacted with that had the greater amount of patients had the better feedback loop to us, where they were the clients that we liked the best and were the most likely to see success because we communicated often and we continually updated the AI and it got better over time as we really had better input into the machine learning.
Matt Durazzani: That's a very good point. Uh, what comes to my mind is, uh, what are some of maybe. Uh, some, some of the best use cases for AI to help businesses generate more revenue with less. Today there's less resources, sometimes there's less budget, um, and so maybe AI offers something that we can tap in to get additional help and support.
Any ideas or suggestions on use cases that are worth considering?
Nathan Clegg: The two areas that I think you want to look for the most One is going to be the things that have a high value individual doing something repetitive. and something simple at the same time. So if you have, if you're looking at it as an executive and you're saying, Hey, I have this person, I'm paying them a pretty hefty salary to do something over and over and over again, the same thing.
And that thing is really simple. Those are the two main qualifiers. And that's when you look to AI to fix that. Um, one of the things That that I look at it with my hat on was Chief Revenue Officer because I'm wearing a marketing hat and a sales hat. A lot of times I go to cadencing and emails, right? So if you're going to do cadencing and emails and outreach, if I can have AI do that and have it be done well, instead of having four or five SDRs, On an, on a campaign, maybe I only need two or three, but because I have the AI doing the outreach, the email, um, there are some either coming up or existing demo softwares that are gonna get better and better as the machine learning increases, so I can do kind of some upfront demoing and some upfront email cadencing.
If I can do that with AI, that's better than me having that much higher of a, of a cost. To do those kind of simpler tasks at
Matt Durazzani: the gate. Okay. So, uh, trying to, if I'm getting the message, right, it's trying to automate some of the, uh, basic tasks that, uh, oftentimes will reduce the bandwidth of your employees.
And and so if that automation comes into place, if we leverage that side. Then what do you have them focus on more?
Nathan Clegg: There's two options. If you don't want to decrease your employee cost, which is the more altruistic version of this, maybe you have some great people. You have them focus on the things that an AI is not going to be able to do yet.
The things where you're going to have to really do some critical thinking. A lot of times when you do data analysis, for example, you might be looking at some trend lines. But you don't understand all of the human element involved. You're still going to need human eyes to focus on that. And that's usually where you get your bigger lifts more than anything else.
So if you're doing the human, if you're allowing your human beings to really look at the data once it's been analyzed a bit and to truly understand it, they're going to spend more time finding Critical input or critical analysis that will allow you to have some better inputs as a result of it. I think of oftentimes if you if you have a fairly large sales team, you have a number of people on their operation side looking through what has just happened.
Well, if they can not have the data crunch, but I have the data crunch from, for them and have it pop up in front of them. Um, they're going to be better off for sure. If they can just start really digesting the higher level information and not just that lower level information. So their, their mission critical things that they should be doing will become more abundant.
And that's what people want to be doing. Anyway, they don't want to be doing the same repetitive things over and over again. They want to be able to make a difference. They want to have insight. They want to get. Maybe even a little more creative than they were before. So AI will enable that more and more as you go forward.
Matt Durazzani: So this kind of ties, what you're saying kind of ties to what you were saying earlier about this element of patience, right? Um, one of the things that comes to my mind, uh, as I listen to that is, Alright, you have a certain number of people on the team. Uh, we are automating some of their tasks. And which frees up their, or almost shift a little bit their, um, repetitive tasks.
Almost like the grind, let's call it the way a lot of them refers to, right? The grind is changing from just constant repetition to be a little more intentional, to think more, to be a little more creative and, uh, personalizing. And maybe that's the process of patients that if they do that, that allows then the outreaches to be, um, again, more intentional, more, more thought through.
Which today. That thinking process, that strategic patience, if you want to call it that way, is held back to the need that they need to go fast and they need to do a lot of these tasks in a continuous
Nathan Clegg: mode. Yeah, definitely. And that's certainly the mindset of the operational side of it, which makes sense for our target demographic here that we're talking to on the same side.
If you're talking about AEs and even SDRs to an extent, the more critical conversations they're having, the better on the, I've done a lot of direct to consumer sort of marketing and a lot of those transactions are a little bit lesser, but they can, they can be high dollar amounts as well. Yeah. And if my, if my agents who are selling something over the phone, well, if I can drive inbound calls to them and keep them busy that whole time, that's much better than doing outbound dialing.
And it's the same thing with an SDR. If an SDR is just doing constant outbound outreach, they're going to get burned out. It's going to be harder. They're going to have all kinds of groups like they do on LinkedIn just to like help them emotionally through the day, you know, whatever it may be. But if I can use AI to generate those interested prospects and Essentially land them in their lab right then and there to talk to them, they'll become more efficient, they're having those conversations I want them to have, and the revenue will increase over time as a result of it, for sure.
Matt Durazzani: I like that. I like that. Um, since you've been in the space for, for a long time, maybe can you paint a picture for us. Let's say five years from now, how will a CRO be using AI tools to better manage revenue. And the teams and what will be the norm, um, at that point for revenue teams,
Nathan Clegg: things that we'll start to see, if we're not already seeing, uh, AI is going to be able to really understand or help us understand all of our interactions.
And it'll allow us to identify what thing is generating the most money. I was talking to a friend not too long ago about some sales operations and, and a perspective tool that he wants to create. Yeah. Which would basically do what we're talking about here, which is do a whole bunch of outreach that would then generate into opportunities for a sales team.
And in the end, he, he said, more or less, there's going to, I want to get it to the point where there's just one button that you push and that button when you, as soon as you push it, all of a sudden, you're starting to make money. And I told him that button, no matter what, should have a dollar symbol on it.
You just push the dollar symbol button and all of a sudden there's activity that's happening, and then you'll have people to talk to, you'll have numbers in front of you that'll help you understand what's happening and it'll allow you to make key decisions. I don't think we're very far away from that dollar sign button, but once again, with anything, AI, anything, machine learning, you have to have really good inputs to train it, to have the right sort of outputs that you want.
But people will crack this code and we'll get to the point where they'll be able to put that push that dollar symbol and there's opportunities happening. And on a higher level as an executive, you're going to be able to see all of that because right now, historically, sales has been a really kind of gut feeling sort of thing.
Like, hey, which thing are we going to do? Obviously that's advanced significantly as time has gone on. There's still a lot of that kind of gut check. Well, the gut check will be able to be validated with actual, actual numbers. And it'll come down to every little activity that you do will produce revenue or be more likely to produce a revenue.
And then we'll narrow in on, and each company will be different. But you'll narrow in on the thing that's going to make you the most money and AI is going to allow you to do that. And then really it'll exponentially grow that activity that's going to generate the revenue for you. So that, that's kind of what I see is happening is, is this really heavy sales enablement for the sales teams and the operations teams and the management team to understand what makes me money.
And we're going to do a whole bunch, a whole bunch more of that thing for sure.
Matt Durazzani: Yeah, that's a very interesting perspective. I like it. And, um, I love the idea of that button. So when you, whenever you find that button, let us know
Nathan Clegg: for sure. Well, I'll be pushing that button. A whole bunch myself been
Matt Durazzani: going at it.
Yeah. Um, so when it comes to maybe processes and systems. And then we look at that vision that you just painted. Um, what do revenue operation teams need to change today?
Nathan Clegg: Number one thing always is simplify. Simplify over and over again. The simpler you can make your process, the better. And when I say that, I don't want you to give up some of the critical information, um, and activities that you need in order to better understand the sales process and get those inputs that machine learning in the future is going to need to make it better.
But I mean, I'll just tell the story. The first time I ever interacted with Salesforce, I about threw up. I hated it so much. And, and I know Salesforce is widely adopted. Everybody loves it. And I still have this and I understand the need for it in large part, but I still still kind of hate it. And the reason why is, uh, No matter where I go, I'll always carry some sort of pipeline.
I think that any revenue leader should, or, or your fake, you know, your fake sales leader, um, that means I'm interacting with our CRM and anytime I'm interacting with my CRM, the more complicated it gets, the angrier I get. And that's just my problem, I guess. But, um, the, the more your team is, uh, able to do without interacting with the technology, the better.
So my suggestion is simplify as much as possible, and then when it comes to AI and testing AI, especially if you're if you're in management and you're looking to increase your prospects in your career and move along, or you go wholesale into an AI solution. Find something simple that you can do first.
Get an easy win. Find something that's not mission critical so you can have easy buy in. That way you can fix something simple, easy, get an early win on your AI usage. Then people are going to trust you. And that way you can leverage greater and more significant AI tools as time goes on. So start super simple, start on something that's low risk, get your interaction done with that.
Uh, and it depends on the tool that you use. Some tools will be kind of. Uh, user friendly and very easy to use, but the ones that are going to be the most robust are going to be the ones that allow you to put input into it and get output out yourself. So, basically, keep it simple and make sure you get familiar with it before you go wholesale at it.
For sure. Yeah,
Matt Durazzani: that's really good advice. Thanks for that. You mentioned earlier, um, enablement now, and that's something that. It is not often discussed when it comes to revenue operations in some organization, revenue operations, um, bridges over enablement in other. Sometimes there's a separate team.
Regardless, there's always an interaction, uh, little or a lot with certainly revenue operation. So, um, in terms of enablement, where do you think organizations today can leverage AI for enabling benefits? Give us
Nathan Clegg: some ideas. Yeah, I think that's a very good question. Enablement is really the key. I think AI really will be enablement for a long time.
It will be the sort of tools that will allow your, or your workers to be more efficient, enjoy life better. I mean, that should be the goal with a lot of this AI as well as to, to make jobs more efficient, but also more enjoyable so that you have better retention. Um, so the need is certainly there. When it comes to just pure enablement, once again, I'm gonna go back to the beginning and say, find the things that your people are doing over and over again that are, uh, repetitive and simple.
As soon as you find those things, use AI to replace that human effort. That's what it comes down to is, is simplifying, repetitive, simplifying, and, uh, those repetitive tasks. And then having AI do it and then your human being that you're working with, they can then do the more creative things they can do the things that will help your organization do more and be more and, uh, push them down the road a little bit further.
So that's what I would say. Every, or every organization is different, right? So I can kind of give some general ideas, but for the most part, just look for those elements, um, and get it to the point where they're doing. Using their brains as often as possible instead of just doing those mundane tasks. Any mundane task at this point is a waste of time and get rid of it.
So that would be my suggestion on enablement as much as possible.
Matt Durazzani: Um, let's talk for a 2nd and another typical interaction that it occurs in, uh. Um, in a space of revenue operations across the board, and that is a very strategic partnership between the marketing team and sales team. Um, to give a piece of context before I ask, the question is, oftentimes, uh, leaders struggle with.
Um, marketing sees things a little bit different and they are having some different agendas. Sales wants to be a little more on a different execution path. And organizations that are really, um, aligned, uh, work very closely with these two organizations, right? They very work hand in hand. So in terms of marketing and sales teams, where, or what are some principles or guidelines that you will give both of them to help them align, to help them support each
Nathan Clegg: other better?
Definitely. Yeah, I think that's very culture specific. Everywhere I've, I've gone of late, I've been able to do both sides, marketing and sales, and we have not had a hard time bridging the two. I think part of that is what I mentioned earlier, I typically carry some sort of sales pipeline. The larger the order, the smaller that pipeline gets, just 'cause my.
Life is a little more complex, but it allows me to wear that hat. At the same time, I'm in with the marketing team and the marketing team tends to be a little more creative. They want to do things. They want to try new things. The sales team has quota. They have to really hit. Now a lot of times marketing does too, of course, but the sales team wants to go back to those tried true methods that they know.
will create the output that they need. If I'm able to create the culture where the sales team and the marketing team are working together and looking out for one another and they all win together, which is usually how I create some compensation structures, by the way, is, is there, there's some almost joint compensation structures where if one side's winning, the other side's winning.
As long as they're all aligned and there's this culture of, of friendship and really winning together, those, those really, those two will bridge the gap between those two organizations really pretty well. Um, I think that they're meant to work happily together. And if they're not, you got to look at your culture a little bit and you have to deep dive what that is.
So my sales and marketing teams meet often together. We meet all together. We work together. I get my own hands dirty. I think that helps a lot. Um, so I'm very familiar with both what both sides are doing. And as a result, as I'm talking to marketing, I'm making sure that I'm wearing my sales hat. And as I wearing my sales hat, I make sure I wear my marketing hat at the same time.
And then we ended up bridging the gap. Pretty easily between those two organizations.
Matt Durazzani: Excellent. Excellent. Well, I know we're almost at time here. So I kind of want to. Wrap things up here for, for this podcast. But, um, one of the things that, uh, we often ask, uh, our guests is to provide some, uh, um, career advice for those that are listening, um, for those people that are earlier in their career, there may be, are looking for experience or, um, really looking for those positions that will help them move forward.
What are some of that guidance that you will give in relation to incorporating. Um, AI in their profession, what, what would you tell them to start?
Nathan Clegg: Educate yourself as much as possible. Test things out. Uh, like I said earlier, start small, start simple, but the more you educate yourself on AI and the AI tools that are available, And the more you experiment with it yourself, don't be afraid to experiment with it.
Just see what happens, you know, and then find something simple that you can test it with at work and get some early wins with it. The better off you're going to be. And the more valuable you're going to be eventually AI tools, uh, are going to be everywhere. And right now there's a million of them already that just kind of popped up out of nowhere.
Um, and which ones are best. I can't always tell you. The ones that probably invest the most in their machine learning, but I try to identify what those are and look for those companies that are really trying to do some new and interesting things and experiment with them. And then you, then you'll be able to put on your resume what you can do.
If you can speak to it with a certain amount of experience and expertise, when you interview at a new place, the better off you're going to be for sure. And then I guess some general advice to your network is everything. So continue to expand your network, reach out, get to know people. That's, that's always how I've ever gotten new opportunities is my network.
That's really
Matt Durazzani: the only way. Now that's great advice and maybe we'll flip the coin for a second. And for those leaders that are maybe more senior, more towards the executive level, maybe their bandwidth to educate themselves and do that may not be as available as maybe some of the younger roles. What advice would you give them in the way they manage and the way they look from a strategy perspective to their teams and their organization and how to incorporate that AI piece?
Nathan Clegg: I tend to be a kind of warm and fuzzy leader. So my advice will be, you know, in context with that, I do as much soft skill training as I possibly can. I try to self duplicate as much as I possibly can, and I think it's going to be more and more necessary as things like AI will take away those mundane and simple tasks.
You're going to have all these human beings with a lot more time on their hands. Well, they're going to need to be able to adapt to a constantly changing environment around them. And the people who are the most adaptable are the people that you've trained the most and you've created the most relationship with.
And if you do that, if you spend your time investing in your people with your own time and energy and training them and making sure that they feel appreciated and making sure that they're upskilling as much as possible, the better off you're going to be and the better they're going to make you look for sure.
And, uh, the, the more credit you give them, the better you look too. So create teams that, that are going to be like you duplicate yourself. If you're up there, it's because you did something good, usually. Um, and so teach, teach your team how to do it too, and you will succeed more often than not. And you'll have teammates that you will look to you for years to come and reach out to you for years to come and ask for advice and, and build them up as much as you can, and it'll be something that you will always be grateful that you did.
Matt Durazzani: Thank you. Um, I'm walking away with the key, some key words in my mind. One is patience. One is, uh, not being afraid, test things, you know, um, experiment, um, train, invest in your people, enable them, empower them. And so, I think, uh, these are all, uh, very good points that are worth both leaders and employees of those teams as they listen to this, uh, program today.
So, Nate. Super awesome. Thanks again for your advice today. We loved having you and, uh, uh, for any of you that have listened, that it would like to get ahold of Nate and maybe connect with them and pick his brain on what you're doing within your organizations, I'm sure he will be happy for you to reach out via LinkedIn and, uh, connect with him offline that way.
And, uh, but we appreciate all of those that listened and, uh, we wish you a great day. Thank you.