Will telco shift to AI-native platforms and localised services in 2026?

As 2026 approaches, telecoms leaders are converging on a shift from connectivity to AI-driven platforms, emphasising localisation, security, and open architectures to unlock new growth avenues beyond traditional revenues.

As 2026 approaches, telecoms executives and technology vendors are converging on a common theme: the industry must pivot from treating connectivity as the sole product to becoming platforms for AI-driven services, localised digital offerings and AI-native network operations. According to the original report, leaders from Qvantel, Cerillion, Netcracker, Whale Cloud and Nvidia agree that the coming year will be defined by a rapid scaling of AI across customer, business and network functions, paired with a renewed commercial push into “beyond connectivity” products. ^[1]

Commercialisation beyond pure connectivity is already underway. The company claims Qvantel’s Flex BSS now includes modules for partner management, B2B sales force automation, enhanced configure‑price‑quote, dynamic pricing and project management , capabilities designed to let CSPs package and deliver complex technology solutions to enterprises. Qvantel positions this as a route to tap a large enterprise technology services market, a shift operators see as essential given flat connectivity ARPUs. ^[2][6]

That commercial imperative intersects with a localisation imperative. Industry voices argue that local‑language AI and culturally relevant services will unlock new user bases in emerging markets where coverage exists but adoption lags. According to the original report, Cerillion and others point to initiatives such as India’s AI4Bharat and wider efforts to build models and content for Indic and African languages as evidence that locally relevant AI will be central to closing the “relevance gap.” GSMA and related industry data are invoked to highlight the scale: connecting billions more users could yield trillions in GDP uplift by 2030 if services become accessible in local contexts. ^[1][3]

Vendors are responding with BSS/OSS platforms that embed AI and enable open integration. Cerillion’s recent product updates , including a Promotions Engine, an MCP (Model Context Protocol) Server, and a suite of AI agents for billing, sales and workflow , are presented as examples of how BSS/OSS can move from back‑office systems to AI‑orchestration layers that support natural‑language interaction and flexible model choice. The company says these tools reduce complexity and accelerate time‑to‑market, while enabling operators to integrate multiple large language models rather than being locked into a single ecosystem. ^[3][4][7]

Yet the rapid adoption of agentic AI exposes new security and governance challenges. Netcracker warns that autonomous agents making real‑time decisions expand the attack surface and that “traditional security models designed for human‑driven workflows fundamentally break” when agents provision services, modify configurations or process payments autonomously. The report stresses the need for continuous authentication, zero‑trust architectures, encrypted operations and auditable governance to prevent a compromised agent acting at machine speed. ^[1]

The security requirement is not merely defensive; it is commercial. Netcracker argues that operators who embed end‑to‑end security and auditable autonomy will gain durable advantages, while those treating security as an afterthought risk costly breaches and regulatory penalties. The vendor also highlights that autonomous operations can materially reduce operating costs and enable emerging market operators to offer sophisticated B2B services such as 5G slicing and edge compute without the legacy burdens of mature incumbents. ^[1]

On the network side, Nvidia and Whale Cloud see 2026 as the start of an AI‑native era for RAN and edge infrastructure. Nvidia frames the shift as networks built for AI traffic , bursty, iterative, latency‑sensitive conversations driven by on‑device and network‑delivered inference , and advocates for distributed AI grids that place inference close to data to preserve data sovereignty and lower cost per token. Whale Cloud highlights multi‑agent systems and domain‑specific LLMs trained on telecom data as enablers of self‑monitoring, self‑adjusting and self‑healing networks, particularly valuable where engineering resources are scarce. ^[1]

Despite the promise, vendors caution that scaling AI remains constrained by weak data foundations, skills shortages and immature regulatory frameworks in many markets. The original report emphasises that next‑generation BSS/OSS must provide open, modular integration to let operators choose the best models for each task while enforcing data governance and local compliance. The combination of flexible monetisation systems, local‑language AI and secure, agentic operations is presented as the practical roadmap for operators seeking growth beyond flat connectivity revenues. ^[1][2][3][4]

For telecoms executives, the practical takeaway heading into 2026 is clear: the industry needs to deploy production‑grade AI across product, network and operational layers while simultaneously hardening security and embracing open BSS/OSS architectures. If those pieces come together , domain LLMs and AI agents, monetisation platforms that can charge for services rather than bytes, and distributed AI infrastructure that respects sovereignty , the sector’s transition from connectivity seller to digital services platform could become mainstream next year. ^{[1][2][3][4][6]}

📌 Reference Map:

##Reference Map:

• ^[1] (Developing Telecoms) – Paragraph 1, Paragraph 3, Paragraph 5, Paragraph 6, Paragraph 7, Paragraph 8, Paragraph 9
• ^[2] (Qvantel blog) – Paragraph 2, Paragraph 9
• ^[3] (Cerillion press release 25.1) – Paragraph 4, Paragraph 9
• ^[4] (Cerillion press release 25.2) – Paragraph 4, Paragraph 9
• ^[6] (Qvantel blog on B2B opportunity) – Paragraph 2, Paragraph 9
• ^[7] (Cerillion AI Hub) – Paragraph 4

Source: Fuse Wire Services