Developing Telecoms Global Forecast 2026 Part 1: Qvantel, Cerillion, Netcracker, Whale Cloud, Nvidia

Details: James Barton; Category: Trends & Forecasts; 10 December 2025; 17762 views

The telecoms sector is always looking to the future – but as 2025 draws to a close, thought leaders across the industry are of course considering what 2026 will have in store. For our Global Forecast 2026, Developing Telecoms reached out to luminaries from service providers, manufacturers, AI specialists and operator groups to hear their thoughts. Over the next two weeks, we’ll share their responses.

This week’s article features insight from Qvantel, Cerillion, Netcracker, Whale Cloud, and Nvidia.

Jukka Heiska, CMO, Qvantel: 2026 will see an Increased Focus on AI, Data Sovereignty and Beyond Connectivity Growth

Over the last two years the move by CSPs (communications service providers) to increase their offers beyond connectivity and sell a wide range of digital services has become an increasingly urgent priority, as connectivity revenue growth has been almost flat. At the same time, AI has moved from small scale proof of concepts aimed at improving customer care to a critical priority in multiple business functions, and will also play a fundamental role in moving the industry towards the vision of fully autonomous networks.

2026 will see a major push for CSPs to monetize beyond connectivity services. We’ve already seen tier 1operators like Jazz in Pakistan undergo successful transformations and move from being a telco to becoming a digital services company. In Q3 2025 Jazz’s revenues from digital services accounted for almost 30% of total revenues. This is just one example of the implementation of a digital operator strategy, and we are seeing many other operators looking to up their digital services game, providing a wide range of offers from education to healthcare to entertainment. But to succeed, it’s important that these services are relevant to the country they are offered in, and this means working with local and regional partners to produce and deliver meaningful and relevant digital services.

To market and sell these new digital services, many CSPs are increasingly turning to AI-driven solutions. CSPs have a wealth of data about their customers and are using this to provide intelligence that can help drive marketing and sales efficiency. These AI-driven solutions will use real-time data from BSS and monetization solutions to enable personalised, context-aware customer engagement.

The data from BSS and monetization systems that CSPs use to drive AI-driven sales and marketing campaigns can be extremely sensitive, and includes data on customers and usage patterns, financial and payment information. Many countries now enforce strict data sovereignty rules that require this data to remain within national borders. To ensure regulatory and legal compliance many CSPs are working with AI technology providers to provide in-country solutions. Some CSPs are also developing local Large Language Models with their technology partners to better support local and regional languages, dialects, mixed-language usage and culturally specific expressions common in customer interactions. To accommodate varying local strategies for AI and data sovereignty, BSS platforms must provide CSPs with the flexibility to use the AI technologies best suited to them.

One of the biggest changes underway is in monetization. CSPs are upgrading their billing and charging systems from ones that were built to collect, rate and monetize voice calls, messaging and MBs of data to ones that can monetize everything and anything – from remote health services to a live stream of a football match to an AI assisted on-line maths tutor service.

This change is not just for consumer digital services. Many CSPs are also expanding ‘beyond connectivity’ service offerings to sell technology services and solutions to their B2B customers. These include edge and cloud-based computing, AI and security solutions, and a wide range of ICT offers. Several CSPs have also developed ICT solutions for specific vertical markets such as manufacturing, logistics, healthcare and construction. By targeting growing industries that are undergoing digital transformation, rely heavily on connectivity, and need scalable infrastructure, security, and data capabilities, CSPs are transforming to become technology companies. CSPs have an advantage here in that they are not starting from scratch: they have core assets, such as infrastructure, customer base, data, and trust that they can build upon to increase revenues from selling ICT solutions to their B2B customers. As data sovereignty is becoming a global priority, CSPs are in a unique position to address the increasing needs and drive new revenues since they are established and trusted providers of critical infrastructure.

In 2026 we will see an increased focus on selling to B2B customers. As 5G SA roll out gains momentum, we will see more ICT solutions supplied that feature network slicing, allowing the CSP to include SLA backed connectivity with fixed throughput and latency rates. Including differentiated connectivity as part of a solution gives CSPs a significant advantage over other technology suppliers.

With connectivity ARPUs flatlining, price-based all you can eat models becoming the norm, and the demand for data set to quadruple by 2030, CSPs need to go beyond selling connectivity services to consumers and business customers if they are going to grow. With the introduction of AI-driven sales and marketing solutions, and the appetite for change, 2026 could well be the year when we see the transformation of CSPs into digital services companies become a mainstream reality.

Dominic Smith, Marketing Director, Cerillion

In 2026, a key trend in emerging markets will be the rise of locally relevant digital services powered by local-language AI. As operators and developers build content and AI models in local languages, the next wave of services will finally become accessible to non-English speakers.

India’s AI4Bharat initiative shows this in action, with open-source models for major Indic languages powering new digital services.

This push for locally relevant content will make the internet more useful to new users and spur adoption. By expanding access to online services in education and health, operators can boost innovation in sectors that rely on network access.

By the end of 2025, nearly 5 billion people globally will have mobile internet access, yet many still struggle to access online services; for example, around 50% of people in Africa remain unconnected to mobile broadband, despite living within network coverage. Most online platforms currently only support a tiny fraction of the world’s 7,000+ languages, risking the exclusion of billions.

The main challenge for operators in 2026 will be overcoming this relevance gap – ensuring services are usable in the languages, contexts and economic realities of their target customers.

Closing this digital divide is also a major economic opportunity. The GSMA calculates that connecting the 3.1 billion people who have coverage but are not yet online could generate an additional $3.5 trillion in GDP between by 2030, with 90% flowing to low- and middle-income countries. For operators, this represents a significant commercial opportunity as well as a development priority.

As Crystal Rugege, Managing Director of the Centre for the Fourth Industrial Revolution Rwanda notes, linguistic diversity is “both a challenge and one of the greatest opportunities.” If AI and digital services reach people in their own languages, many more can join the digital economy.

Local-language AI will be central to turning this opportunity into growth, opening digital services to millions more users.

Collaborations between operators, startups and policymakers will help bridge the digital divide and extend services to the next billion users. By reducing costs, embracing partnerships, and investing in locally relevant solutions, telcos can secure their future growth while reducing digital exclusion.

Realising this opportunity will require BSS/OSS platforms that are open, flexible and built to integrate with multiple LLMs rather than locking operators into a single ecosystem. An open integration architecture allows operators to choose the best model for each task – from customer engagement and service automation to network intelligence and revenue operations – while ensuring these AI capabilities operate within secure governance frameworks.

By enabling secure API-driven integration with a range of LLMs, MCP servers and AI agent orchestration tools, next-generation BSS/OSS platforms will give operators the agility to adopt the best model for each use case, while retaining control of customer data and meeting regulatory requirements. This open, modular approach will be crucial for scaling digital services and complex pricing models, and driving the industry’s next phase of growth.

John Byrne, Director of Strategy, Netcracker: AI-Native Operations Drives New Focus on Locking Down Security in 2026

The narrative around telecoms in emerging markets has tended to be about their role as “fast followers,” tending to deploy the latest technologies several years after operators in mature markets. However, 2026 will mark a year in which that narrative shifts, with emerging market operators in a unique position where they will be better able to embrace agentic AI-powered autonomous operations than some operators in mature markets that may be contending with the challenges of legacy infrastructure. However, this heightened focus on autonomous networks will need to be accompanied by a greater focus on end-to-end network security.

The Critical Challenge: Security in an Autonomous World

As operators look to agentic AI and embrace autonomous operations, 2026 will reveal an uncomfortable truth: traditional security models designed for human-driven workflows fundamentally break when AI agents make real-time decisions affecting millions of subscribers.

The challenge isn't theoretical. When autonomous agents provision services, modify network configurations, process payments and resolve customer issues without human intervention, the attack surface expands exponentially. A compromised agent doesn't just leak data—it can execute malicious actions at machine speed across entire operational domains.

Emerging markets face this challenge with particular intensity. Regulatory frameworks lag behind technological deployment. Cybersecurity expertise remains scarce and expensive. Networks frequently interconnect with less-secure partner systems. Yet the consequences of breaches—financial losses, regulatory penalties, reputational damage—hit resource-constrained operators disproportionately hard.

In 2026, the industry will recognize that end-to-end security can no longer be bolted on as perimeter defense or compliance theater. Security must become embedded into network architectures as never before and baked into every agent interaction, every data flow and every autonomous decision. This requires AI agents that authenticate and authorize continuously, zero-trust architectures that assume breach and limit damage, encrypted operations across all domains and governance frameworks that make every autonomous action transparent and auditable.

Operators that get this right in 2026 will establish competitive advantages that compound over time. Those that treat security as an afterthought will face increasingly costly consequences.

The 2026 Opportunity: Affordable Excellence Through Operational Innovation

The convergence of agentic AI and comprehensive security architecture unlocks emerging markets' greatest opportunity: delivering world-class telecommunications services that drive genuine digital inclusion, particularly in emerging markets. Autonomous operations reduce operational costs by 40-50% compared to traditional models. AI-driven assurance improves service quality while cutting support costs. Automated fraud detection protects both operators and subscribers. Self-optimizing networks deliver better performance with less infrastructure investment. All of these factors represent vital puzzle pieces in the push to make universal connectivity economically sustainable.

By late 2026, we'll see emerging market operators launching sophisticated B2B services such as 5G network slicing, edge computing and advanced IoT connectivity that have typically been associated with Tier 1 carriers in developed markets. The difference is that emerging market operators can achieve comparable service quality and operational efficiency without the century of accumulated infrastructure and workforce that challenge many mature market operators.

Zhengcang Xiao, Chief Technology Officer, Whale Cloud International

In 2026, the telecom industry in emerging markets will be defined by a new level of AI-driven transformation. After several years of experimentation, AI will begin to scale across all core telecom functions—business operations, customer operations, and network operations. Instead of isolated pilots, operators will see mature, production-ready AI scenarios emerging in marketing automation, customer care, predictive maintenance, and network optimization. This marks a shift from AI as a support tool to AI as a foundational capability across the operator value chain. 

A central part of this transformation will be the rise of AI multi-agent systems, which Gartner identifies as a technology poised for rapid adoption in the next one to three years. These agents will fundamentally reshape how networks are managed. By coordinating multiple intelligent agents—each responsible for a specific function such as traffic forecasting, fault detection, or energy optimization—operators can build truly autonomous networks. These networks will be able to self-monitor, self-adjust, and self-heal, dramatically reducing operational complexity and cost. For emerging markets, where engineering resources are often limited, multi-agent autonomy will become a critical accelerator for both efficiency and network resilience. 

At the same time, the industry will enter the era of domain-specific large language models. Unlike general-purpose LLMs, telecom domain models can be trained on network traffic patterns, geospatial data, customer behavior, and capacity dynamics. These models will allow operators to predict network demand with unprecedented accuracy, detect fraud or anomalies more proactively, and orchestrate services and resources more intelligently. Domain LLMs represent the shift from generic AI capabilities to industry-specific intelligence—an essential step for operators that need AI to deeply understand their business context. 

Despite this momentum, significant challenges lie ahead. The most critical bottleneck will be the lack of AI capabilities and weak data foundations. Many operators still lack the necessary skills in AIOps, data governance, and model lifecycle management. Without strong data infrastructure, even the best AI systems cannot scale. Another key challenge will be making AI truly industry-specific—embedding telecom domain knowledge in a way that general models cannot achieve. Finally, regulatory environments in many emerging markets remain underdeveloped, creating additional risks in AI deployment, data usage, and cross-border model governance. 

Ronnie Vasishta, SVP of Telecommunications NVIDIA

The defining trend in 2026 will be the shift from networks tuned for voice, data and video to networks built for AI traffic. As more consumers use generative AI and assistants on their phones, traffic becomes bursty, iterative and harder to predict, with many short exchanges instead of steady streams. This wave of new AI apps and use cases will only accelerate, so mobile networks in emerging markets must prepare for much higher, more volatile AI traffic with tighter expectations on responsiveness and reliability, even where power and spectrum are constrained.

To meet this demand, wireless networks will start to evolve into software‑defined, AI‑native assets, underpinned by AI‑RAN. In 2026, AI‑RAN will move from lab trials into real‑world pilots, using live sites to test edge AI services and AI‑powered radio algorithms that lift performance, efficiency and user experience. Fast innovation will happen across AI‑for‑RAN (using AI to improve network performance), AI‑and‑RAN (sharing AI and network workloads on a common compute) and AI‑on‑RAN (running new AI services on top of the RAN). This is the beginning of the AI‑native era, with increasingly autonomous networks and AI embraced as a core engine for performance, efficiency and growth across infrastructure, operations and services.

The biggest shift for operators will be moving from selling connectivity to becoming national AI infrastructure and platform providers. This means not only deploying AI infrastructure and embedding AI deeply into networks and operations, but also rethinking their role in the digital economy.

To succeed, telcos will need a mindset shift. From aggressively upskilling talent, to developing new partnerships with application and software ecosystems, from exploring new commercial models and rapidly experimenting with AI-powered services for consumers and enterprises to championing local AI innovation - Telcos must move at the pace of AI to capture and operationalize the opportunity ahead of them.

As AI inference grows, the biggest opportunity for telcos is to turn their networks into distributed AI grids that bring intelligence much closer to users. By leveraging their nationwide footprint – sites, power, data centers and fiber – operators can deploy distributed AI infrastructure that processes AI inference workloads from anywhere in the network, while optimizing the cost and performance of every token. This unique AI grid architecture ensures data sovereignty by keeping sensitive data local while still tapping high‑density compute when needed. Compute moves closer to the data instead of forcing data to traverse long distances, which is essential for scaling latency‑sensitive AI use cases across bandwidth‑constrained geographies.

In many countries, especially mobile‑first developing markets, this model will become the primary way people and small businesses engage with AI in a cost‑effective manner – through affordable, network-delivered intelligence rather than expensive devices or overseas clouds. In this model, connectivity plus AI becomes the growth engine for inclusion: farmers get crop and weather analytics, clinics access diagnostics without sending data offshore, enterprises use agents delivered over the network, and cities tap network‑embedded sensing for traffic, safety and energy management.