A Perspective from the Digital and Telecom Industry
Over the last five years in the telecom industry, I’ve leaned on a “fail fast” mindset to drive innovation. But with the rapid rise of artificial intelligence (AI) in telecom, the landscape is changing. Today, AI-driven network automation, predictive maintenance, and progress in customer experience are evolving how telecom operators achieve operational efficiency and growth. This mindset encouraged my teams and me to take calculated risks, experiment, and view failure as part of progress. As a result, we developed a culture characterized by speed and curiosity.
But as we enter 2026, I believe the context has shifted. The world around us, especially in the digital and telecom sectors, now rewards not those who fail fast, but those who learn fast.
The shift: from connectivity to intelligent ecosystems
The Telecom sector over the last decade has focused on building the pipes that connect billions of people and devices. The next decade is about analyzing and acting on what flows through those pipes i.e. intelligence.
AI has become deeply infused within every layer of our operations, from network optimization and predictive maintenance to customer engagement and product innovation. Data flows are fast becoming learning flows. Five years ago, decisions on pricing, network rollout, or customer segmentation required weeks of analysis and discussion. Today, AI simulates scenarios in minutes, projects outcomes, underscores trade-offs, and suggests optimal decisions using real-time information.
This has fundamentally changed the tempo of leadership. Speed is still important, but direction is now decisive. “Fail fast” made sense when insights lagged behind action. But when AI can provide near-instant insight, failure doesn’t have to be our teacher anymore.
Where AI performs well and where it still falls short
AI is not a magic solution. It becomes a force multiplier when combined with sound judgment. In my experience in telecom, AI has transformed efficiency. Networks now self heal. Predictive models forecast demand surges before they happen. Chatbots handle millions of queries daily, improving response time and consistency. According to the Ericsson Mobility Report (January 2025), nearly 85% of global telecom operators have integrated AI-based automation into at least one network domain, with most mentioning increased fault detection and energy efficiency as principal benefits.
But I see that AI struggles when context, as well as empathy, matter most. It can process signals, but not always intent. It can predict churn, but it can’t understand why a customer disengaged in the first place. It can surface insights, but deciding which trade-offs are consistent with the long-term purpose is still my decision as a leader. That’s where human leadership still defines the edge. The leader’s job is no longer to spot every pattern, but to ask better questions. To know when the model’s confidence is misplaced, and when human instinct must override algorithmic certainty.
In this new landscape, learning fast means merging the advantages of both worlds, human reasoning and machine intelligence, to create organizations that respond continuously.
Learning fast: the new mindset for telecom leaders
In the telecom and digital ecosystem, “learning” used to be linear: project post mortems, market studies, quarterly reviews. Now, it’s continuous and real-time. Our data infrastructure captures every engagement, transaction, and behavior. The challenge is data conversion: turning information into insight, and insight into action. AI helps by compressing the learning loop. It tells us what’s working (or not) much earlier in the cycle. It gives teams permission to pivot without waiting for failure to make the case for it.
For example, a new product feature once took months of trial before customer response was clear. Today, behavioral analytics powered by AI can signal within days whether adoption is on track. The lesson arrives faster and at a fraction of the cost. That’s what “learning fast” looks like for me in practice: moving from intuition-led iteration to intelligence-driven improvement.
Leadership in the AI era: curiosity over certainty
For CXOs and founders, this shift requires a change in mindset. Our value no longer comes from having the answers; it comes from asking the right questions. AI can offer options, but leadership decides the direction. Curiosity is now my most strategic skill. It drives experimentation, receptiveness to feedback, and the skill to analyze AI insights via the lens of purpose, experience, and people.
When I consider agility, I think beyond speed. True agility is about learning velocity, how quickly my organization and I can sense, interpret, and respond. The companies I believe will thrive in the next decade are those where inquisitiveness is institutionalized and where teams are rewarded for learning loops, not just launch speed.
What’s next: experiment smarter, build smarter
In 2026 and beyond, telecom and digital enterprises will operate as learning ecosystems, combining human insight, customer feedback, and AI intelligence in near real time. Competitive advantage will come from learning effectively from each experiment, not simply conducting more experiments.
Imagine product testing that doesn’t just capture results, but constantly improves the hypothesis. Imagine network planning that adapts alongside customer behavior, not just traffic patterns. Imagine AI copilots that accelerate decision making, not by replacing judgment, but by enhancing it.
That’s the future we’re hurtling toward, where invention is driven by faster learning cycles, not faster failure cycles. So, as a leader, I’ve stopped chasing “failures” as signs of courage. I use technology to close the gap between a mistake and a breakthrough.
If “fail fast” was the motto of the startup era, “learn fast” is the mantra for the intelligent era. AI enables us to anticipate, test, learn, and adapt before obstacles emerge. However, it remains our responsibility to determine how to use that foresight. As someone in the industry, I find that my biggest challenge isn’t technology, but mindset. I believe those who succeed in the next decade will balance the speed of AI with the value of experience.
Let’s make 2026 the year we learn fast!