The world of telecommunications is entering one of its most exciting chapters yet. As we move beyond the era of 5G and begin to imagine the possibilities of 6G, the industry finds itself balancing extraordinary opportunities with new layers of complexity. Telecom operators are being asked to do more than ever, expanding into enterprise solutions, powering the Internet of Things, and embracing AI-driven innovations, while still navigating the weight of legacy systems, rising energy demands, and evolving regulations. 

At the heart of this transformation is agentic AI, a powerful new force that can do everything from fine-tuning bandwidth in real time to predicting system failures and crafting customer experiences with remarkable intelligence. 

In this moment of change, we sat down with Ulf Ewaldsson, President of Technology at T-Mobile, and Pranay Agrawal, CEO of Fractal Analytics, to hear their perspectives on what the future holds. Their conversation paints a vivid picture of an industry on the cusp of reinvention one that must be faster, smarter, and more resilient, while never losing sight of the trust that binds technology to people. 

Pranay: What are the biggest business opportunities and challenges in telecom today? 

Ulf: I’ve been in the telecom industry for 35 years, watching it evolve from state-owned utilities into private successes, largely driven by the mobile phone.  

Yet telcos have digitalized much more slowly than other industries. Even with 5G, customers often still need to visit a store to change plans or resolve issues, unlike airlines or hotels, where nearly everything is online. 

The biggest challenge and opportunity now is accelerating digitalization. Telecom generates massive volumes of data, more transactions than almost any other industry. That makes AI both the greatest opportunity and the greatest challenge as telcos move into a new phase of transformation. 

Pranay: You summarized it well. AI really is both the opportunity and the challenge. It’s not just about how telecom companies run their businesses more efficiently or create additional value; it’s also about how the industry itself is turning into a catalyst for AI adoption across so many other sectors. That’s what makes this moment so exciting. 

You talked about faster and deeper digitization. In your view, where do the biggest opportunities sit right now? Is it in reimagining customer experience, in transforming network operations, or in modernizing core systems like OSS and BSS? Where do you think the most urgent need is today? 

Ulf: I love your comment, Pranay, that we are both the problem and the solution to AI.  

Connectivity is central, and mobile phones will play a huge role as AI spreads. We already see it happening with ChatGPT’s mobile app, one of the most widely used applications, which is reshaping how connectivity is delivered.

Inside our company, there’s huge potential to streamline processes, but the real priority of the AI revolution is not cost savings. It is customer experience. Focus there, and efficiency will follow.

Telecom is uniquely positioned to lead because operators possess behavioral data on a large portion of the population. This creates extensive datasets for training AI models and providing more personalized services. The shift also transitions us from rigid, rule-based systems to intelligence-driven ecosystems. Processes such as BSS and OSS, including billing, charging, and service delivery, can be reimagined.

Ultimately, this transforms operators from telcos into tech-driven businesses, what we call the move from Telco to TechCo. Too many still start with the wrong question: “How can I cut costs with AI?” The right one is, “How can I use AI to improve the customer experience?” If you get that right, cost savings will follow naturally.

Pranay: Exactly. Focusing on customer experience, improving service, products, and interactions naturally enables everything else to follow.

In markets such as the U.S., growth no longer relies on adding subscribers due to already high penetration. With subscriber growth stabilized, the emphasis shifts to preserving market share and enhancing customer retention, making customer experience the key focus. The subsequent question is whether future growth will stem more from increased consumption or from launching new services. Is this a reasonable perspective?

Ulf: I’d say that’s partly true, but look at other industries. Take automotive: almost everyone already has a car, yet innovation still drives growth as new products win customers from competitors. Any industry with strong product innovation has opportunities, and telco has been stagnant, especially in how it uses customer understanding.

Think about hotels asking, “Have you stayed here before?” or grocery stores that don’t anticipate your buying patterns. AI will fix that, and the businesses that adopt it will win. The same logic applies to telcos.

Customers shouldn’t have to repeatedly provide IDs or social security numbers when smartphones already verify identity with face recognition. People expect to be recognized.

That is the golden opportunity for telcos. Customers already pay us to authenticate their communication in real time on trusted devices, and they trust us. If we combine that trust with AI’s ability to deliver smarter, more seamless experiences, we can unlock enormous opportunities across industries.

Pranay: In that context, could you tell us a bit about Intent CX? I understand T-Mobile is launching it now. What is it, and how does it work?

Ulf: Intent CX is something we launched at our capital markets day last September. We spoke about the shift from Telco to TechCo (Telecom Company to Technology Company) and introduced AI-RAN, our AI-driven approach to building radio access networks. On stage with us were Nvidia’s Jensen Huang and Sam Altman from OpenAI, announcing partnerships focused on using AI and data to determine the next best action in any interaction with T-Mobile. That is the essence of Intent CX.

This opportunity exists for any operator. Large language models bring new ways to understand language, but it is not about replacing people. It is about combining AI with human expertise for better results. At T-Mobile, we are using AI to supercharge customer care. When you call, we already know who you are and what your recent experience has been.

Operators can identify dropped calls, network problems, or poor coverage during hikes in national parks, and use that information to predict issues or provide solutions like satellite connectivity. The purpose of Intent CX is to make every interaction more precise, proactive, and personalized.

Pranay: That’s fantastic. Across touchpoints like contact centers, chat agents, and service, we are now able to predict, modem by modem, the likelihood of an outage within 24 hours. We can identify whether the issue is at the customer’s home or upstream and deploy technicians accordingly. This improves the customer experience while also reducing costs.

Ulf: Exactly. These are the kinds of things I implemented while running operations at T-Mobile. We worked on predicting the lifetime of every radio and the performance of every tower. AI proved especially valuable during unexpected events. Take hurricanes in Florida: we use AI not only to predict storm paths but also to determine which towers will withstand the impact, how to reroute traffic, and how to adjust antennas to extend their reach or reduce interference.

Rule-based algorithms struggled with accuracy in the past, like writing a football strategy, getting some parts right while missing others. AI differs because it processes vast data, learns, and continuously improves. As a result, operators globally are increasingly adopting it. An additional advantage is automation: with AI managing these tasks, large teams of engineers aren't needed to manually solve every issue. This leads to significant cost savings.

Pranay: Exactly. In that context, you mentioned unexpected events and how to prepare for them. Could you share a bit about self-optimizing networks and the role AI plays in enabling them?

Ulf: The idea of self-organizing networks has been around for 15 or 20 years. Early versions were rule-based: you set rules, and the network followed them. That is how most telcos historically operated.

AI changes everything. With a strong data layer and open APIs, networks can now make real-time adjustments such as antenna tilts, traffic rerouting, or slicing while optimizing for customer experience and measuring outcomes. That is the future of self-organizing networks.

As we move toward 6G, industry groups are codifying this vision. The best analogy is the aviation autopilot. At one point in time, it was resisted as unsafe, but it is now standard. Pilots monitor and adjust as needed. Telecom networks will evolve the same way, largely automated, with humans overseeing and guiding.

Pranay: Excellent. Organizations may be moving at different speeds, but everyone agrees this is the future. As you accelerate digitization and AI, what organizational challenges, guiding principles, and people practices are you putting in place to drive the transformation?

Ulf: Across telcos worldwide, I see recurring challenges. These are large organizations rooted in traditional engineering disciplines. Radio engineering has always been central, but that legacy makes it harder to embrace a world where automation must be trusted as much as people. Ultimately, it comes down to skills, and the real challenge is finding talent to drive transformation.

The industry also faces a structural aging problem. Deregulation and the mobile boom of the 1990s brought in a wave of leaders and engineers.

The iPhone launch in 2007 turned the phone from a gadget into a lifeline, but much of today’s leadership reflects that earlier era. As a result, structures have grown rigid and slow to change.

Between 2010 and 2020, operators lagged behind other industries in digitalization, and that still weighs them down. If you built a telco from scratch today, you could run a large network with just a few hundred people. Legacy structures prevent that.

Pranay, I know you see this tension too: between revolutionary opportunities to start fresh and the reality of change within complex systems.

Pranay: In our process, of course we are not starting from scratch. Large businesses already have customer bases, networks, CRM systems, and operating platforms. Success and speed of implementation depend on a few key ingredients.

The first is leadership commitment. CEOs and senior teams must be fully invested, or the transformation stalls. Second, when people ask, “What is your AI strategy?” I reframe it as, “What is your business strategy, and how can AI advance it?” That creates better answers and focuses on outcomes such as stronger networks, better service, greater efficiency, and new products.

Transparency is also critical. People must understand how decisions are made and have the ability to step in if needed. Finally, skills.

Organizations need AI fluency at every level. Not everyone must be a programmer, but people must know how to apply AI, build trust in it, and move past fear of the unknown. That is one of the most critical drivers of success.

Ulf: Yes. Also, when I was speaking before, I clarified that the challenge is not age; it is that organizational structures grow rigid over time. I often see senior employees eager to change, but transformation requires upskilling. My question is, how bold are your customers? Do they truly reimagine systems with AI, or just layer it onto old structures?

Pranay: There is a wide spectrum. Some want to move quickly, others prefer a slower pace, and some build entirely new AI-native businesses while allowing legacy systems to continue. However, no one is ignoring this trend. Taking bold steps is crucial. The companies that are willing to take risks are the ones that succeed.

Ulf: Exactly. The more you experiment, the faster you move. AI cannot be a side project; it has to be the main show.

Pranay: Absolutely. Boldness and commitment are what separate the winners. 

Conclusion 

The perspectives shared by Ulf Ewaldsson and Pranay Agrawal reveal a telecom industry standing at the edge of profound reinvention. Agentic AI is no longer just a tool for efficiency; it is quickly becoming the catalyst for redefining the very essence of connectivity. With its power, operators are poised to evolve from transactional service providers into trusted technology partners, enabling networks that are predictive, self-optimizing, and deeply attuned to the needs of both individuals and enterprises. In this new era, the true measure of leadership will be found in those who embrace transformation with both boldness and responsibility. 

But this shift is about more than algorithms or infrastructure. It calls for visionary leadership, unwavering ethical foundations, and a workforce that speaks the language of AI with fluency and confidence. It means letting go of outdated models, nurturing new capabilities, and weaving transparency into every layer of decision-making. The organizations that pair daring innovation with thoughtful guardrails will define the future, one where networks are not silent backbones of society but intelligent collaborators that anticipate, adapt, and earn trust at scale.