Telecom AI Readiness Starts with Cloud-Native Transformation

• Mobile operators are eager to embrace AI to improve operations and reduce TCO.
• The readiness status greatly varies between leaders and laggards.
• Open networks and cloud readiness are imperatives for the deployment of an AI native Telecom infrastructure.
• Read the full report.

The gap between AI aspiration and AI execution is one of the defining challenges facing the telecommunications industry today. The Telco AI Readiness Index 2026, conducted by Analysys Mason in partnership with Dell, surveyed 50 Tier 1 operators worldwide. The results are both encouraging and urgent: AI is clearly a strategic priority, but the industry is still far from uniformly ready to operationalize it at scale. It reveals that while AI is firmly on every major operator’s strategic agenda, half remain classified as AI laggards – held back not by a lack of vision, but by gaps in technology maturity, data readiness, skills and cross-functional alignment. The path forward is clear: operators that have progressed furthest in open, cloud-native transformation are also the most prepared to operationalize AI at speed and scale. This is no coincidence. Cloud-native architecture provides programmability, automation and data fluidity that AI-native operations demand. Without it, AI initiatives risk remaining isolated proofs of concept. With it, operators gain the operational base layer needed to scale AIOps, enable agentic workflows and unlock new revenue streams. In this first installment of our blog series, we explore why the journey to AI-native telecom must begin with the cloud-native transformation work already underway. AI ambition is everywhere in telecom. But ambition alone will not build an AI-native network.

AI/ML, GenAI and agentic AI are redefining how telecom networks are built, operated and monetized. They are becoming foundational to autonomous network transformation while also opening new revenue opportunities for operators through GPUaaS, sovereign AI offerings and emerging edge AI services.

Big ambition, uneven readiness

The headline finding is stark. Half of operators are still classified as AI laggards, while only 10% qualify as AI visionaries. What separates the leaders from the rest is not intent. In most organizations, C-level commitment is already there and AI is firmly on the strategic agenda.

The real problem is execution. Gaps in technology and toolset maturity, employee skillsets, cross-functional alignment and ROI clarity are keeping many operators stuck in second gear. In other words, the challenge is no longer whether telecom will adopt AI. It is whether operators can modernize fast enough to turn ambition into repeatable operational and commercial outcomes.

Cloud-native progress is now the clearest predictor of AI progress

One of the most important findings in the Telco AI Readiness Index is the strong link between openness, cloud-nativeness and AI readiness. Operators that have invested further in open network transformation are better positioned to adopt AI at speed and scale.

That conclusion is reinforced by the Open Network Index 2026. The ONI shows that open networks are no longer being pursued simply for vendor choice or architectural elegance. Operators increasingly view them as a cloud-based, AI-driven foundation for greater automation, flexibility, resilience and strategic independence.

This is a significant shift. The industry is moving beyond cloud-native transformation as a standalone objective and toward a broader goal: using openness, programmability and automation as the operating model foundation for AI-native networks, AIOps and, increasingly, agent-based operations.

The ONI data also shows that this transformation is already well underway. Ninety-eight percent of operators have deployed at least one cloud-based mobile core, 80% have deployed cloud-based core in at least two domains and 50% have deployed a horizontal cloud platform for the mobile core domain. In parallel, 70% of operators now consider automation a critically important component of being an open operator, and 90% cite streamlining and automating operating processes as a key benefit of open networks.

Taken together, these findings point to a clear conclusion: cloud-native modernization is no longer just an infrastructure initiative. It is the operational base layer on which AI-native telecom will be built.

Data is now the transformation bottleneck

If cloud-native transformation is the foundation for AI-native networks, data is the control plane. And this is where the industry is least prepared.

The AI Readiness Index finds that preparing and curating data for AI/ML models is the weakest area across the industry. Operators have made meaningful progress in governance and security, but GenAI and agentic AI require much more: model-ready data, common data models, real-time data flows and shared environments across domains and functions.

This is why a domain- and network-level data strategy must become a fundamental pillar of transformation. Operators need to move beyond traditional data availability programs toward a new wave of AI-specific data transformation that removes silos, normalizes telemetry and operational data, enables trusted access and turns fragmented data into reusable AI assets.

Without that work, even the best AI strategy will remain trapped at the proof-of-concept stage. With it, operators can begin to scale AI-driven automation, AIOps and agents across planning, assurance, operations and service delivery.

AI is also reshaping the telecom revenue model

The AI opportunity is not only about efficiency. It is also about monetization.

The Telco AI Readiness Index highlights the growing importance of revenue-generating AI services across both infrastructure and applications, from GPUaaS and AIaaS to vertical-specific offers. At the same time, sovereign AI is emerging as a major opportunity for operators that can combine trusted infrastructure, data residency, regulatory alignment and enterprise relationships into differentiated market propositions.

Edge AI expands that opportunity further. The Open Network Index shows renewed momentum around edge, with 68% of operators already deploying at least one edge use case and 46% deploying at least two. Most importantly, support for edge AI is expected to accelerate sharply: only a small minority support it today, but 56% expect to support edge AI within the next two years.

That matters because it points toward a broader AI Grid opportunity for telecom operators: combining distributed infrastructure, connectivity, data, security and localized inference into monetizable enterprise platforms. For operators, this is where network transformation and AI monetization begin to converge.

Dell’s point of view: from cloud-native to AI-native

At Dell, we see these findings as confirmation that the path to AI-native telecom is not a disconnected AI project. It is the next phase of telecom transformation.

That path starts with open, cloud-native infrastructure and operating models. It requires a deliberate domain- and network-level data strategy. It depends on automation and observability becoming embedded across the environment. And it creates new monetization opportunities in sovereign AI, GPUaaS and edge AI services.

This is where Dell can help. Dell brings open, validated AI-capable infrastructure; Dell AI Data Platform to help operators move from data available to data model-ready; enterprise-grade storage platforms to power high-throughput AI pipelines; and cybersecurity capabilities that help protect data, models and operations as AI becomes more central to the network.

Just as importantly, Dell’s ecosystem spans telecom partners, software providers, hyperscalers, ISVs and AI innovators. That allows us to help operators bridge the gap between network modernization and AI monetization without forcing them into closed architectures or one-size-fits-all models.

The message for operators

The readiness gap is real, but the direction is clear. Operators do not need to choose between cloud-native transformation and AI transformation. The two are now inseparable.

The operators that win in the next phase of telecom will be those that treat openness, cloud-native architecture, data strategy, automation and AI monetization as one connected agenda. The journey to AI-native telecom starts with the transformation work already underway. The opportunity now is to accelerate it with purpose.

Stay tuned for the Telco AI Readiness Index 2026 Blog – Part 2

 The question is no longer whether Telecom operators should pursue AI. It is how they should accelerate the shift from cloud-native to AI-native in a disciplined, monetizable way.

The Telco AI Readiness Index provides six practical imperatives for doing exactly that. In Part 2 of this blog series, we will cover the Six Imperatives to Accelerate the Telecom Journey from Cloud-Native to AI-Native operations.

Read the full Dell – Analysys Mason report here.