Improving operational efficiency with AI is non-negotiable in telecommunications. Discover how targeted AI use cases can deliver immediate value.
In a recent conversation with Kuba Smolorz from STL Partners, we explored their latest research on how telcos can navigate the AI era. The insights from their report, "The telecom industry in the AI era: How telcos can follow the money" offer a pragmatic roadmap for operators facing pressure from the boardroom to "do something with AI."
Here's a scenario many of you will recognize: Your CEO hears about AI at an industry event. Hyperscalers are pushing the message hard. Your competitors are talking about their AI initiatives. The pressure cascades down through the organization until everyone receives the same task: "What can we do with AI?"
As Kuba and I discussed, this creates an interesting challenge. Suddenly, the method becomes more important than the outcome. Teams are asked to demonstrate AI implementation rather than solve actual business problems. But as STL Partners' research makes clear, this approach gets things backwards.
STL Partners' analysis reveals a fundamental truth: improving operational efficiency through AI is non-negotiable. According to their AI, analytics and automation (A3) research, AI-driven networks can deliver approximately $8-16 million in annual value for an average telco, roughly 5% of current revenues. Nearly half of this value comes from resource management - optimizing how you use assets, people, and processes.
The key is identifying specific use cases that will improve your processes, save money, and enable growth. Yes, better data quality and improved access to data are important benefits. Yes, top management is demanding AI adoption. But the real question is: what problems are you actually trying to solve?
Through our work with telcos at Polystar, we've identified four core machine learning functionalities that consistently deliver value:
These technologies share a crucial advantage: they suppress noise and help you focus on what matters. This dual benefit allows you to drive automated workflows more confidently while also enriching human decision-making.
Here's the approach that works: Look at your operational problems and cost drivers. Identify the specific processes where AI can help. Pick the achievable wins first and create that foundation. Then gradually evolve your capabilities. This is where AI can truly transform efficiency, automate complexity, ensure the transition to an infraco model and free up resources to reinvest in growth.
This might not sound as exciting as solving your most challenging problems with AI overnight. But trying to solve simple problems with overly complex AI solutions often creates complicated systems that don't work. The sensible path is solving basic problems first with appropriate solutions, then building toward more sophisticated use cases.
Yes, you need reliable, accessible data. Our platform helps you collect and organize network data from multiple sources, ensuring you have actionable information to make confident decisions. But data quality and accessibility aren't the end goal - they're the foundation that enables the use cases that matter.
At Polystar, we focus on practical solutions that deliver value today while building for tomorrow. Our approach centers on those foundational technologies - anomaly detection, forecasting, clustering, and segmentation - because we know they can support multiple use cases for you as your AI journey evolves.
Whether you're focused on identifying network issues faster, reducing downtime, improving customer satisfaction, or optimizing resource allocation, the key is starting with specific business outcomes and working backward to the AI capabilities that enable them.
The AI era is here. The question isn't whether to adopt AI, but which use cases will move the needle for your business. That's where your journey should begin.
For more insights on telcos in the AI era, see STL Partners' October 2025 report "The telecom industry in the AI era: How telcos can follow the money" by Amy Cameron, Managing Director, Research.
