I returned from MWC 2026 with an uneasy feeling.
The telecommunications industry has long been defined by its generational leaps—each "G" marking a profound shift in capabilities, use cases, and societal impact.
2G brought reliable digital voice and SMS, enabling mass mobile communication. 3G introduced mobile data and picture messaging, laying the foundation for internet on the go. 4G powered the explosion of social media, apps, and always-on connectivity. 5G delivered massive bandwidth, fueling high-definition video streaming.
These evolutions followed a predictable cadence governed by 3GPP standards, with operators methodically upgrading infrastructure, spectrum, and devices in multi-year cycles. Parallel to this, the network itself transformed through virtualization: from SDN separating control and data planes, to disaggregating hardware from software, and evolving VNFs (Virtual Network Functions) into cloud-native CNFs (Cloud-native Network Functions). These shifts improved flexibility, scalability, and cost efficiency but remained incremental within the familiar "G" framework.
AI is entering telecom in silos—AI-RAN for spectrum and energy optimization, agentic AI in OSS for autonomous operations and predictive assurance, customer service copilots for intent-based support—delivering proven cost savings (e.g., 25-40% OPEX reductions in network ops, up to 35% energy efficiency). Yet these domain-specific wins rarely connect into a unified, end-to-end intelligence layer. Data stays fragmented across RAN, core, edge, OSS/BSS, leading to duplicated efforts, incomplete visibility, and "agent sprawl" risks. Industry sources highlight how silos impede multi-agent ecosystems and true autonomous networks.
This misconception manifests in several ways:
Viewing AI as incremental tech add-ons — Operators often pursue isolated pilots (e.g., AI-RAN trials, genAI copilots, or agentic OSS agents) expecting quick wins without addressing deeper structural issues.
Underplaying organizational and cultural complexity — AI demands far more than engineering upgrades. It requires breaking down legacy silos (RAN/IT/OSS/BSS), fostering cross-functional agility, upskilling thousands in ML ops/data governance, and driving cultural shifts to trust agentic systems. Cultural resistance, job security fears, and fragmented skills often stall progress, with many projects failing to move beyond pilots (only ~30% of genAI use cases reach production in some analyses). Organizational challenges—including change management and silo-breaking—as top barriers, yet leadership frequently delegates AI to a separate function rather than owning it as a CEO imperative.
Misjudging the scale of change needed — Unlike past "G" evolutions (hardware/spectrum-driven, standardized via 3GPP), AI is a software-defined, data-hungry, adaptive intelligence layer that reshapes workflows, decision-making, operating models, and even business identity (from connectivity provider to intelligent platform). Treating it as "just tech" ignores the need for unified data fabrics, intent-based orchestration, governed multi-agent ecosystems, and radical process redesign—efforts that can take years, not quarters, and demand massive internal rewiring.
New vendors (hyperscalers, specialized AI-RAN players, agentic platforms) disrupt legacy supplier models, while operating models evolve toward intent-driven, cloud-native, agent-orchestrated environments requiring cross-functional agility and new skills. Massive CAPEX uncertainty surrounds compute (GPUs, accelerators), high-bandwidth memory, power, and cooling—often in the hundreds of billions globally—amid unclear ROI timelines and risks like underutilization. AI excels at cost management through optimization, but revenue-generating services (e.g., enterprise AI platforms, GPUaaS, network APIs for AI workloads, personalized offerings) remain nascent for most operators. This imbalance—cost wins without broad revenue upside, vendor shifts, and compute investment risks—demands an AI strategy that starts with organization and operational models, not technology.
This underestimation risks turning AI from a greenfield opportunity into added complexity: persistent silos, agent sprawl, duplicated investments, and missed revenue potential. Proven cost optimizations are real, but without holistic transformation, operators may achieve efficiency gains while remaining commoditized pipes in an AI-driven world. Warning to operators: AI is not "plug-and-play." Underestimating its demands—starting with organization, leadership alignment, operating model redesign, and cultural renewal before heavy technology scaling—will lead to stalled initiatives, wasted CAPEX (especially on compute/infra), and competitive disadvantage. Frontrunners recognize AI as a radical reinvention requiring bold, enterprise-wide commitment; the rest risk being left behind as the intelligence generation unfolds.
