Telecom operators are entering a new phase of digital infrastructure and AI monetization. That was the clear signal at FutureNet World in April 2026. The conversation is no longer about whether AI belongs in telecom. That question has been answered. The real question is which operators can turn experimentation into measurable commercial value reflected in P&L statements.

What is emerging is not simply a new set of AI use cases. It is a broader redefinition of what telecom providers can offer. For enterprise customers, the product is shifting from raw connectivity to orchestrated outcomes. The winners will not necessarily be the operators with the biggest AI infrastructure footprints or the most GPU capacity. They will be the ones that can combine connectivity, cloud, edge, automation, and governance into trusted, SLA-backed services.

That is the framing from IDC’s panel on April 23, 2026, “Unlocking New Revenue Opportunities by Monetizing AI and Digital Infrastructure” at FutureNet World London. The panelists were senior telecom executives: Emilio Varas Jiménez, Customer Fulfilment Head of AI and Operations Improvement, Vodafone; Franck Morales, Senior Vice President, Marketing and Business Development, Orange Wholesale International; Natali Delić, Chief Strategy and Digital Officer, Telekom Srbija; and Martin Rueckert, Chief AI Officer, Tallence AG.

AI monetization starts at home

External AI monetization has to be built on internal proof points. Before operators can credibly take AI capabilities to enterprise customers, they need to show they can deploy them inside their own business.

This matters because enterprise buyers are increasingly skeptical of AI promises not backed by operational experience. Telcos that can demonstrate AI-driven improvements in field service operations, assurance, employee productivity, and customer experience will be in a stronger position to package that expertise into enterprise services.

For telecom executives, this means the first monetization opportunity is often not a customer-facing AI product. It is the ability to refine, prove, and operationalize AI internally and then externalize that know-how as a service.

From connectivity to business outcomes

The enterprise buying center is changing. Businesses are looking beyond bandwidth or basic cloud access. They want guaranteed performance, sovereign routing, managed AI-enabled operations, and real-time decisioning environments that align with business risk and revenue goals.

This is especially visible in financial services, manufacturing, and retail, where latency, compliance, and uptime have direct commercial consequences. In these environments, value comes from combining multiple layers: network slicing, APIs, edge compute, AI orchestration, and managed service delivery.

Telecom providers are being pulled higher up the stack. The role of the operator is evolving from connectivity provider to orchestrator of digital infrastructure outcomes. 

Orchestration is becoming the real differentiator

Enterprises do not want to decide what runs in the public cloud, what belongs at the edge, and what must stay on-premises. They want those decisions handled for them, governed, compliant, cost-predictable, and reliable.

That changes the basis of competition. Operators that can orchestrate workloads across hyperscaler, edge, and on-premise environments build a durable market position. Those that cannot risk turning AI infrastructure into a commodity layer with limited pricing power.

The long-term value in AI and digital infrastructure will accrue to operators who can integrate, operate, and govern AI-enabled services at scale.

Edge AI will favour smaller, purpose-built models

For most real-time telecom and enterprise use cases, the requirement is not maximum model size. It is deterministic performance, low latency, and auditability.

That favors smaller, domain-specific models and, in some cases, non-transformer architectures, particularly in industrial automation, remote diagnostics, and real-time network decisioning.

This has major implications for investment strategy. Operators that assume the future of edge AI depends on pushing large language models closer to the endpoint may be overestimating both enterprise demand and the technical fit. In many scenarios, the commercial opportunity will come from deploying the right model, not the largest one.

Trust is the missing link in agentic AI

Agentic AI remains one of the most talked-about areas in the market, but many enterprise pilots are still failing to reach production. [Source: attribute to panelist name or add IDC data reference.] The problem is not only technical capability. It is trust.

For agentic AI to be backed with an SLA, enterprises need confidence that decisions are bounded, explainable, and auditable. In regulated and mission-critical environments, free-form reasoning is not enough. Determinism matters. Governance matters. Standards alignment matters.

Telecom providers looking to monetize agentic AI should focus on domain-constrained deployment models. The path to commercial scale is likely to come from tightly scoped, standards-aligned agents that can operate within controlled decision environments.

Sovereign AI infrastructure brings opportunity and risk

Sovereign AI demand is real, particularly in regulated sectors, but that does not mean every operator should rush to build large-scale local AI factories.

There is significant capex risk in overbuilding. If utilization remains low or infrastructure cycles shorten faster than expected, operators could face stranded assets within three to five years. [Per panel discussion, April 23, 2026. Add IDC data reference if available.]

The more sustainable approach is hybrid and multi-cloud by design: combining hyperscaler or neo cloud computing, edge resources, and targeted sovereign deployments where regulation or national security requirements justify them. The key is to align infrastructure investment with verified demand rather than hype-driven positioning.

The bottom line

Telecom AI monetization between 2026 and 2028 will be defined less by model ownership and more by execution discipline. Operators that can prove value internally, orchestrate hybrid environments effectively, deploy trusted and auditable AI, and match infrastructure investment to real demand will be best positioned to capture new revenue and higher margins.

The market is past experimentation. The next phase belongs to operators that can industrialize AI as a commercial capability.

Masarra Mohamed - Senior Research Analyst, Communications Platform as a Service

Masarra Mohamed is an expert in digital infrastructure, cloud, AI, and communications platform-as-a-service (CPaaS). She leads IDC’s global CPaaS research and advisory practice, shaping the firm’s perspective on API-driven communications, customer engagement platforms, and their convergence with contact centre and…