Six Imperatives to Accelerate the AI Native Telecom

• • Mobile operators need to embrace AI Native networks.
  • Analysys Mason and Dell Technologies conducted research to identify the key milestones operators must reach to successfully embrace AI.
  • The Telecom AI Adoption Index 2026 highlights the key actions telecom operators should take to move forward on this journey.

In Part 1 of this Dell Technologies blog series, we explored the main conclusions from the Telco AI Readiness Index 2026 report.

 Telecom operators are highly ambitious about AI, but many still face deep execution gaps in technology, data, organization and commercial readiness.

We also connected that finding to the Open Network Index 2026 (ONI, which shows that open, cloud-native modernization is creating the architectural and operational foundation for AI-native networks.

This is the critical context for Part 2 of the blog series.

The question is no longer whether 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 2026 report provides six practical imperatives for doing exactly that. Here is what it means in practice, and how Dell is working with network equipment providers and mobile operators to enable each of these findings.

1. Transform organizational structure and capabilities – and build the business case from early wins

The Telco AI Readiness Index shows there is a small group of operators qualifying as AI visionaries, while half sit in the laggard category. That gap is not primarily about strategy. It is about execution capability — employee skills, operating model readiness, cross-functional alignment and the ability to prove value quickly.

To move towards becoming AI visionaries, operators should prioritize high-impact, near-term use cases that build confidence and create reusable patterns. In practice, that means focusing on AI-driven automation in areas such as network planning, assurance, security, energy optimization and service operations before attempting broad AI transformation everywhere at once.

Dell’s view is that Centers of Excellence, structured operating models and rapid early wins remain essential. AI-native transformation will not scale if it is treated as a series of isolated experiments.

2. Make data strategy a first-order AI transformation priority

This may be the most important imperative in the report. Preparing and curating data for AI/ML models is the weakest area across the industry. Operators need a new wave of data transformation that is purpose-built for AI rather than inherited from older analytics programs.

That means creating a domain- and network-level data strategy that spans governance, model-ready data preparation, common data models, telemetry normalization, silo removal and real-time processing. This is foundational to AIOps, closed-loop automation and future agentic implementations because AI systems are only as effective as the data they can observe, reason over and act upon.

Dell is strongly positioned here with Dell AI Data Platform, enterprise storage and cybersecurity solutions that help operators build trusted, high-performance data foundations for AI-native operations. For telecom, this is not just a data-lake discussion. It is about making network and operational data consumable, secure and reusable at scale.

3. Extend AI partnerships beyond technology alone

The Readiness Index makes clear that technology partnerships are necessary but insufficient. Operators need partners that can support transformation across infrastructure, operating model, ecosystem integration and commercial execution.

The ONI reinforces this point by showing that operators are working with more partner categories than before as they pursue open network ambitions and automation benefits. That matters because AI-native telecom will require coordinated innovation across network infrastructure, software, models, data services, operations and vertical go-to-market motions.

Dell’s open ecosystem approach is designed for exactly that reality. Operators need neutral partners that can connect cloud-native modernization work already in flight with the next wave of AI-native use cases and services.

4. Monetize AI through GPUaaS, sovereign AI and edge AI services

AI transformation must create top-line outcomes, not only OPEX benefits. The Telco AI Readiness Index highlights growing operator interest in revenue-generating AI services ranging from infrastructure offers such as GPUaaS and AIaaS to higher-value application and vertical solutions.

Sovereign AI is a particularly important opportunity. Operators can bring together trusted infrastructure, regional presence, regulatory positioning, security and enterprise relationships to create differentiated sovereign AI propositions for government, regulated industries and large enterprises.

Edge AI extends the opportunity further. The ONI shows that edge investment is moving into a more practical phase, with 68% of operators already deploying at least one edge use case and 56% expecting to support edge AI within two years. That opens the door to AI Grid-style opportunities in which operators combine distributed compute, data locality, connectivity and inference to support enterprise AI workloads closer to where data is created and decisions must be made.

Dell’s perspective is that operators should not stop at GPUaaS. The longer-term value lies in moving the stack toward sovereign AI platforms, verticalized offers and edge AI services that bundle infrastructure, operations, security and telecom-grade service delivery.

5. Start with SMO/RIC-led AI for near-term RAN Value

The Open Network Index is especially helpful in showing where operators are placing their bets in the Radio Access Network (RAN). While multi-vendor Open RAN has progressed more slowly than many expected, operators still see strong value in the automation capabilities of the Service Management and Orchestration and Radio Interface Controller (RIC) layers, and in the longer-term potential of AI-RAN.

That aligns closely with the AI Readiness findings. Most operators are concentrating AI RAN investment where the value is clearest and disruption is lowest: the SMO/RIC layer, for use cases such as closed-loop optimization, energy management and predictive maintenance.

As confidence grows, extending AI toward the edge of the RAN, where the Base Band Units (BBUs) are located, using GPUs or custom ASICs, can unlock deeper network intelligence and open the door to enterprise far-edge services.

Dell’s purpose-built telecom and edge servers are designed for these workloads. Validated with NEP software stacks across key silicon partners, they are optimized for space and power constraints and built to support the convergence of RAN and AI compute.

6. Make the “Metro Edge” the near-term priority for AI-native growth

Metro edge is emerging as one of the most important bridges between network modernization and AI monetization. It is where distributed cloud-native infrastructure, local data processing, low-latency connectivity and enterprise AI demand begin to intersect.

The ONI shows that support for enterprise and B2B services is nearly universal in operator edge strategies and that AI-enabled use cases are becoming a more prominent driver of edge investment. As edge AI adoption ramps and new solutions like AI Grid are implemented, operators have an opportunity to create differentiated offers for manufacturing, public sector, healthcare, smart environments and other latency- and sovereignty-sensitive use cases.

This is why Dell believes metro edge should be treated not as a side initiative, but as a strategic initiative for the AI-native era. It connects the infrastructure transformation operators have already been making with the new AI-driven services and distributed edge inferencing capabilities they now want to monetize.

From transformation to acceleration

Taken together, these six imperatives describe a single transformation arc. Operators need to keep modernizing their networks toward open, cloud-native architectures while accelerating toward AI-native operating models built on automation, AIOps and agents.

That means developing a network/enterprise-wide AI data strategy and treating it as core infrastructure, not back-office plumbing. It means using AI to drive both operational improvement and new monetization. And it means building on the transformation progress already visible in the industry: cloud-based mobile cores, horizontal platforms, automation frameworks, open ecosystems and edge expansion.

Dell’s role is to help operators turn that vision into an executable roadmap: bridging infrastructure, data, automation and ecosystem integration so they can move from pilots to production and from modernization to monetization. With open platforms, AI-ready infrastructure, data foundations and the ability to support deployment from core to edge, Dell helps operators accelerate the journey from cloud-native transformation to full AI-native operations.

The bigger opportunity is not simply to deploy more AI tools, but to re-architect the operator for the AI-native era. Operators that modernize around open, cloud-native platforms, common automation and observability layers and a cross-domain data strategy will be best positioned to scale AIOps, agent-based operations and new revenue streams such as GPUaaS, sovereign AI and edge AI services.