As operators continue to invest heavily in 5G, many are still searching for ways to translate that spend into measurable returns. In this interview, Shaun McCarthy, president and chief revenue officer at Spectrum Effect, explains why interference management and spectrum utilization remain among the most complex challenges in the radio access network. He outlines how Spectrum Effect uses the RAN as a sensor, applying machine learning and AI to analyze network data and drive real-time actions that improve performance, efficiency and user experience across large-scale deployments.
McCarthy also shares how the role of AI in telecom is evolving beyond trials and pilots into day-to-day operations. He highlights two critical paths: using AI to deliver actionable insights that empower engineering teams, and leveraging closed-loop automation to resolve network issues autonomously. As operators shift focus from overanalyzing ROI to deploying and scaling AI-driven solutions, McCarthy sees growing opportunities for monetizing 5G through enterprise use cases, spectrum coexistence and edge-based AI. The message is clear: moving faster, deploying sooner and iterating at scale will define the next phase of 5G success.
Linda Hardesty:
Hi everybody. My name's Linda Hardesty and I'm Chief Analyst for Communication Technologies at Fierce Network. I'm here today with Shaun McCarthy. He's President and Chief Revenue Officer at Spectrum Effect. We're going to talk today about the challenges that operators face in maximizing 5G investments, the growing role of automation and intent-based networks and how emerging approaches like spectrum coexistence and AI-driven optimization are shaping the future of mobile networks. So welcome, Shaun.
Shaun McCarthy:
Hey, Linda. Thanks for having me.
Linda Hardesty:
To kick it off, for listeners who may be new to Spectrum Effect, how would you describe what you do and your role in today's networks?
Shaun McCarthy:
Spectrum Effect was founded by industry experts in the RAN space, radio access network space. And they went out to tackle what they viewed as the most challenging problem in the RAN, which is really interference and interference's impact on the use and utilization of spectrum. So the company kind of had its learnings over the years, and really I'd say in the last two years or so, really hit its stride with deployments in all three US tier one operators that we completed last year. And basically what we ended up building and the product sort of evolved to is a platform that uses the RAN as a sensor. We use machine learning and AI algorithms to do analysis and look at the data that we get from these sensors coming from the RAN. And then we drive some sort of actions back into the network, whether it's providing insights or doing closed loop automation where we're reprogramming the RAN to sort of optimize a user's experience.
So we kind of started there, really focused on RAN optimization. And over time we've sort of evolved as customers have said, "Hey, you're looking at this, you've got this sensor network out there, you've got these AI models. What else can you do?" And we've kind of evolved some new applications around spectrum sharing and we're in the process of evolving these new applications around some of these enterprise 5G use cases that are going to leverage the macro network instead of private 5G networks.
Linda Hardesty:
Wow, interesting. The RAN as a sensor. Okay. And so AI is everywhere right now, as we all know. How do you see telcos approaching AI today?
Shaun McCarthy:
Yeah. So AI is certainly, you can't turn without hearing that acronym. I think about AI for telco really from two lenses. One is AI for the telcos themselves. Right. So that's pretty obvious. Like every company on the planet, all of the telcos are looking to integrate AI to every aspect of their business, right, from HR to operations, to internal operations, sales marketing, the chatbots when we call into these call centers. But obviously they're rethinking how they use AI to build and how they use AI to operate networks. And so that's kind of the space that we play in. Right. And there's really two areas that we're pretty active with operators is, one is there are these really important engineering teams that are keeping these networks alive, that are optimizing these networks, that are evolving these networks to customer requirements. And AI can deliver insights to these folks to let them do what they do in a much more effective, efficient, and highly scalable way. Right.
So one is using AI to deliver insights and enable your people. And the other is there's really a lot of things that people are doing today that could be automated. Right. And so with our solution, we're optimizing a network. Right. And so rather than tell the engineering team, "Hey, this is how you should optimize the network, or this is the problem you have in the network," we can just go reprogram the network ourselves. And so that's really where you get the closed loop automation to drive resolution of these issues and to free up... Your people are your most important resources. So free them up to go work on other higher order problems, right? But the other is about telco for AI, right? And so that's really about how are operators going to take advantage of this new wave and how are they going to monetize this wave of AI and the new use cases that are coming from this AI movement.
And I think telcos have a really great opportunity and I think we're still really early in the process. So the one that everyone's talking about today is this AI RAN concept and we're distributing AI inferencing at the edge using the same hardware for the actual RAN as we are going to use those GPUs when they're not active in the RAN and how we're going to build AI into the RAN itself. But the other one, like I mentioned, is we're seeing these 5G use cases evolve around AI that are sort of... I think it's what we talked about with 5G years and years ago, but it's finally coming to fruition. So we've got this huge sensor network and as enterprises are looking to turn up fleets of drones and vehicles and robots that are going to interact with the mobile network, you start to ask yourself, what are the things that operators can do to help monetize these services and how can we help them do that and also deliver value to those enterprises? So it's a really exciting space right now.
Linda Hardesty:
Yeah, for sure. Well, many operators are still struggling to fully monetize their 5G investments. From your perspective, what are the key challenges in realizing that value at this stage?
Shaun McCarthy:
Yeah, it's funny, right? So before we launched 5G, as an industry, we said, "Hey, 5G is going to be all about the enterprise. It's all about the enterprise." And then we poured billions of dollars into these macro networks, which are largely built to deliver consumer services today. And we didn't really deliver much more for those consumers, therefore the consumers didn't want to pay more. And we all kind of looked at ourselves and said, "Hey, this is not a good business case for 5G." But listen, I think part of the problem is all of these initial enterprise five use cases, which are really compelling, they're all being served today via private 5G networks. Right. So warehouses and mines and factories are all deploying 5G and these use cases are real and there's strong value there, but the revenue for those use cases isn't correlated with the investment we made in the macro networks.
But like I mentioned, I mean, we're still early, right? We're still early and we're seeing more and more of these use cases start to get turned up where these enterprises are going to be leveraging the macro network. So like I mentioned, think about fleets, fleets of drones, fleets of vehicles, fleets of robots, right? These are devices that are going to live outside of this boxed in private network. And so we're seeing that happen. So I think it's not typical of us in the telco space to get really, really excited about a technology and kind of give ourselves some unrealistic timelines. So I think it's all coming to fruition and I think operators are going to continue to have opportunity to grow their revenues based upon the 5G use cases that were promised. It's just coming a little bit later than I think we originally envisioned.
Linda Hardesty:
A lot of AI in networking is still discussed in terms of trials and pilots. What's been most challenging about moving AI into the actual day-to-day network operations?
Shaun McCarthy:
If you go back to really 2024, which was probably kind of the breakout year for AI, it was really a year of excitement, but also a year of analysis paralysis, right? We were really over-engineering these business cases and so much energy was spent on these trials and what's the ROI of this and what's that? And part of the problem was, at least from my lens and from my experience in the space, there was a lot we didn't know. So if you don't know what you don't know, how do you build this business case, right? And so what we learned is when we started doing deployments, we were able to solve problems with our deployments and with AI that we never would've imagined it, right, if we purely just did that ROI beforehand. And we did, of course. And so the ROI got much better after the deployment. And I think the tone changed quite a bit in 2025.
So we transitioned a bit from over rotating on the business case to folks saying, "Hey, look, we just got to get moving." Right. This is clearly the direction we need to go. How do we move? How do we go faster? And I think that's kind of where we're at now. It's really about scale and speed of deployment. And it's about trial. It's more about like, let's get something deployed, let's iterate on it and let's scale it. Really, that's kind of the mindset I think going into 26. So I think we're past that sort of over analysis paralysis phase.
Linda Hardesty:
That's all the questions I have, Shaun, but I really appreciate you chatting with me today.
Shaun McCarthy:
Great. Thanks for having me.