AI in Telecom
Gen AI eroding critical thinking skills; AI threatens more telecom job losses
Two alarming research studies this year have drawn attention to the damage that Gen AI agents like ChatGPT are doing to our brains:
The first study, published in February, by Microsoft and Carnegie Mellon University, surveyed 319 knowledge workers and concluded that “while GenAI can improve worker efficiency, it can inhibit critical engagement with work and can potentially lead to long-term overreliance on the tool and diminished skills for independent problem-solving.”
An MIT study divided participants into three essay-writing groups. One group had access to Gen AI and another to Internet search engines while the third group had access to neither. This “brain” group, as MIT’s researchers called it, outperformed the others on measures of cognitive ability. By contrast, participants in the group using a Gen AI large language model (LLM) did the worst. “Brain connectivity systematically scaled down with the amount of external support,” said the report’s authors.
Across the 20 companies regularly tracked by Light Reading, headcount fell by 51,700 last year. Since 2015, it has dropped by more than 476,600, more than a quarter of the previous total.
Source: Light Reading
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Doing More with Less:
- In 2015, Verizon generated sales of $131.6 billion with a workforce of 177,700 employees. Last year, it made $134.8 billion with fewer than 100,000. Revenues per employee, accordingly, have risen from about $741,000 to more than $1.35 million over this period.
- AT&T made nearly $868,000 per employee last year, compared with less than $522,000 in 2015.
- Deutsche Telekom, buoyed by its T-Mobile US business, has grown its revenue per employee from about $356,000 to more than $677,000 over the same time period.
- Orange’s revenue per employee has risen from $298,000 to $368,000.
Significant workforce reductions have happened at all those companies, especially AT&T which finished last year with 141,000 employees – about half the number it had in 2015!
Not to be outdone, headcount at network equipment companies are also shrinking. Ericsson, Europe’s biggest 5G vendor, cut 6,000 jobs or 6% of its workforce last year and has slashed 13,000 jobs since 2023. Nokia’s headcount fell from 86,700 in 2023 to 75,600 at the end of last year. The latest message from Börje Ekholm, Ericsson’s CEO, is that AI will help the company operate with an even smaller workforce in future. “We also see and expect big benefits from the use of AI, and that is one reason why we expect restructuring costs to remain elevated during the year,” he said on this week’s earnings call with analysts.
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Other Voices:
Light Reading’s Iain Morris wrote, “An erosion of brainpower and ceding of tasks to AI would entail a loss of control as people are taken out of the mix. If AI can substitute for a junior coder, as experts say it can, the entry-level job for programming will vanish with inevitable consequences for the entire profession. And as AI assumes responsibility for the jobs once done by humans, a shrinking pool of individuals will understand how networks function.
“If you can’t understand how the AI is making that decision, and why it is making that decision, we could end up with scenarios where when something goes wrong, we simply just can’t understand it,” said Nik Willetts, the CEO of a standards group called the TM Forum, during a recent conversation with Light Reading. “It is a bit of an extreme to just assume no one understands how it works,” he added. “It is a risk, though.”
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References:
AI spending is surging; companies accelerate AI adoption, but job cuts loom large
Verizon and AT&T cut 5,100 more jobs with a combined 214,350 fewer employees than 2015
Big Tech post strong earnings and revenue growth, but cuts jobs along with Telecom Vendors
Nokia (like Ericsson) announces fresh wave of job cuts; Ericsson lays off 240 more in China
Deutsche Telekom exec: AI poses massive challenges for telecom industry
Ericsson reports ~flat 2Q-2025 results; sees potential for 5G SA and AI to drive growth
Ericsson’s second-quarter results were not impressive, with YoY organic sales growth of +2% for the company and +3% for its network division (its largest). Its $14 billion AT&T OpenRAN deal, announced in December of 2023, helped lift Swedish vendor’s share of the global RAN market by +1.4 percentage points in 2024 to 25.7%, according to new research from analyst company Omdia (owned by Informa). As a result of its AT&T contract, the U.S. accounted for a stunning 44% of Ericsson’s second-quarter sales while the North American market resulted in a 10% YoY increase in organic revenues to SEK19.8bn ($2.05bn). Sales dropped in all other regions of the world! The charts below depict that very well:
Ericsson’s attention is now shifting to a few core markets that Ekholm has identified as strategic priorities, among them the U.S., India, Japan and the UK. All, unsurprisingly, already make up Ericsson’s top five countries by sales, although their contribution minus the US came to just 15% of turnover for the recent second quarter. “We are already very strong in North America, but we can do more in India and Japan,” said Ekholm. “We see those as critically important for the long-term success.”
Opportunities: As telco investment in RAN equipment has declined by 12.5% (or $5 billion) last year, the Swedish equipment vendor has had few other obvious growth opportunities. Ericsson’s Enterprise division, which is supposed to be the long-term provider of sales growth for Ericsson, is still very small – its second-quarter revenues stood at just SEK5.5bn ($570m) and even once currency exchange changes are taken into account, its sales shrank by 6% YoY.
On Tuesday’s earnings call, Ericsson CEO Börje Ekholm said that the RAN equipment sector, while stable currently, isn’t offering any prospects of exciting near-term growth. For longer-term growth the industry needs “new monetization opportunities” and those could come from the ongoing modest growth in 5G-enabled fixed wireless access (FWA) deployments, from 5G standalone (SA) deployments that enable mobile network operators to offer “differentiated solutions” and from network APIs (that ultra hyped market is not generating meaningful revenues for anyone yet).
Cost Cutting Continues: Ericsson also has continued to be aggressive about cost reduction, eliminating thousands of jobs since it completed its Vonage takeover. “Over the last year, we have reduced our total number of employees by about 6% or 6,000,” said Ekholm on his routine call with analysts about financial results. “We also see and expect big benefits from the use of AI and that is one reason why we expect restructuring costs to remain elevated during the year.”
Use of AI: Ericsson sees AI as an opportunity to enable network automation and new industry revenue opportunities. The company is now using AI as an aid in network design – a move that could have negative ramifications for staff involved in research and development. Ericsson is already using AI for coding and “other parts of internal operations to drive efficiency… We see some benefits now. And it’s going to impact how the network is operated – think of fully autonomous, intent-based networks that will require AI as a fundamental component. That’s one of the reasons why we invested in an AI factory,” noted the CEO, referencing the consortium-based investment in a Swedish AI Factory that was announced in late May. At the time, Ericsson noted that it planned to “leverage its data science expertise to develop and deploy state-of-the-art AI models – improving performance and efficiency and enhancing customer experience.
Ericsson is also building AI capability into the products sold to customers. “I usually use the example of link adaptation,” said Per Narvinger, the head of Ericsson’s mobile networks business group, on a call with Light Reading, referring to what he says is probably one of the most optimized algorithms in telecom. “That’s how much you get out of the spectrum, and when we have rewritten link adaptation, and used AI functionality on an AI model, we see we can get a gain of 10%.”
Ericsson hopes that AI will boost consumer and business demand for 5G connectivity. New form factors such as smart glasses and AR headsets will need lower-latency connections with improved support for the uplink, it has repeatedly argued. But analysts are skeptical, while Ericsson thinks Europe is ill equipped for more advanced 5G services.
“We’re still very early in AI, in [understanding] how applications are going to start running, but I think it’s going to be a key driver of our business going forward, both on traffic, on the way we operate networks, and the way we run Ericsson,” Ekholm said.
Europe Disappoints: In much of Europe, Ericsson and Nokia have been frustrated by some government and telco unwillingness to adopt the European Union’s “5G toolbox” recommendations and evict Chinese vendors. “I think what we have seen in terms of implementation is quite varied, to be honest,” said Narvinger. Rather than banning Huawei outright, Germany’s government has introduced legislation that allows operators to use most of its RAN products if they find a substitute for part of Huawei’s management system by 2029. Opponents have criticized that move, arguing it does not address the security threat posed by Huawei’s RAN software. Nevertheless, Ericsson clearly eyes an opportunity to serve European demand for military communications, an area where the use of Chinese vendors would be unthinkable.
“It is realistic to say that a large part of the increased defense spending in Europe will most likely be allocated to connectivity because that is a critical part of a modern defense force,” said Ekholm. “I think this is a very good opportunity for western vendors because it would be far-fetched to think they will go with high-risk vendors.” Ericsson is also targeting related demand for mission-critical services needed by first responders.
5G SA and Mobile Core Networks: Ekholm noted that 5G SA deployments are still few and far between – only a quarter of mobile operators have any kind of 5G SA deployment in place right now, with the most notable being in the US, India and China. “Two things need to happen,” for greater 5G SA uptake, stated the CEO.
- “One is mid-band [spectrum] coverage… there’s still very low build out coverage in, for example, Europe, where it’s probably less than half the population covered… Europe is clearly behind on that“ compared with the U.S., China and India.
- “The second is that [network operators] need to upgrade their mobile core [platforms]... Those two things will have to happen to take full advantage of the capabilities of the [5G] network,” noted Ekholm, who said the arrival of new devices, such as AI glasses, that require ultra low latency connections and “very high uplink performance” is starting to drive interest. “We’re also seeing a lot of network slicing opportunities,” he added, to deliver dedicated network resources to, for example, police forces, sports and entertainment stadiums “to guarantee uplink streams… consumers are willing to pay for these things. So I’m rather encouraged by the service innovation that’s starting to happen on 5G SA and… that’s going to drive the need for more radio coverage [for] mid-band and for core [systems].”
Ericsson’s Summary -Looking Ahead:
- Continue to strengthen competitive position
- Strong customer engagement for differentiated connectivity
- New use cases to monetize network investments taking shape
- Expect RAN market to remain broadly stable
- Structurally improving the business through rigorous cost management
- Continue to invest in technology leadership
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References:
https://www.telecomtv.com/content/5g/ericsson-ceo-waxes-lyrical-on-potential-of-5g-sa-ai-53441/
https://www.lightreading.com/5g/ericsson-targets-big-huawei-free-places-ai-and-nato-as-profits-soar
Ericsson revamps its OSS/BSS with AI using Amazon Bedrock as a foundation
Agentic AI and the Future of Communications for Autonomous Vehicle (V2X)
by Prashant Vajpayee (bio below), edited by Alan J Weissberger
Abstract:
Autonomous vehicles increasingly depend on Vehicle-to-Everything (V2X) communications, but 5G networks face challenges such as latency, coverage gaps, high infrastructure costs, and security risks. To overcome these limitations, this article explores alternative protocols like DSRC, VANETs, ISAC, PLC, and Federated Learning, which offer decentralized, low-latency communication solutions.
Of critical importance for this approach is Agentic AI—a distributed intelligence model based on the Object, Orient, Decide, and Act (OODA) loop—that enhances adaptability, collaboration, and security across the V2X stack. Together, these technologies lay the groundwork for a resilient, scalable, and secure next-generation Intelligent Transportation System (ITS).
Problems with 5G for V2X Communications:
There are several problems with using 5G for V2X communications, which is why the 5G NR (New Radio) V2X specification, developed by the 3rd Generation Partnership Project (3GPP) in Release 16, hasn’t been widely implemented. Here are a few of them:
- Variable latency: Even though 5G promises sub-milliseconds latency, realistic deployment often reflects 10 to 50 milliseconds delay, specifically V2X server is hosted in cloud environment. Furthermore, multi-hop routing, network slicing, and delay in handovers cause increment in latency. Due to this fact, 5G becomes unsuitable for ultra-reliable low-latency communication (URLLC) in critical scenarios [1, 2].
- Coverage Gaps & Handover Issues: Availability of 5G network is a problem in rural and remote areas. Furthermore, in fast moving vehicle, switching between 5G networks can cause delays in communication and connectivity failure [3, 4].
- Infrastructure and Cost Constraint: The deployment of full 5G infrastructure requires dense small-cell infrastructure, which cost burden and logistically complex solution especially in developing regions and along highways.
- Spectrum Congestion and Interference: During the scenarios of share spectrum, other services can cause interference in realm of 5G network, which cause degradation on V2X reliability.
- Security and Trust Issues: Centralized nature of 5G architectures remain vulnerable to single point of failure, which is risky for autonomous systems in realm of cybersecurity.
Alternative Communications Protocols as a Solution for V2X (when integrated with Agentic AI):
The following list of alternative protocols offers a potential remedy for the above 5G shortcomings when integrated with Agentic AI.
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While these alternatives reduce dependency on centralized infrastructure and provide greater fault tolerance, they also introduce complexity. As autonomous vehicles (AVs) become increasingly prevalent, Vehicle-to-Everything (V2X) communication is emerging as the digital nervous system of intelligent transportation systems. Given the deployment and reliability challenges associated with 5G, the industry is shifting toward alternative networking solutions—where Agentic AI is being introduced as a cognitive layer that renders these ecosystems adaptive, secure, and resilient.
The following use cases show how Agentic AI can bring efficiency:
- Cognitive Autonomy: Each vehicle or roadside unit (RSU) operates an AI agent capable of observing, orienting, deciding, and acting (OOAD) without continuous reliance on cloud supervision. This autonomy enables real-time decision-making for scenarios such as rerouting, merging, or hazard avoidance—even in disconnected environments [12].
- Multi-Agent Collaboration: AI agents negotiate and coordinate with one another using standardized protocols (e.g., MCP, A2A), enabling guidance on optimal vehicle spacing, intersection management, and dynamic traffic control—without the need for centralized orchestration [13].
- Embedded Security Intelligence: While multiple agents collaborate, dedicated security agents monitor system activities for anomalies, enforce access control policies, and quarantine threats at the edge. As Forbes notes, “Agentic AI demands agentic security,” emphasizing the importance of embedding trust and resilience into every decision node [14].
- Protocol-Agnostic Adaptability: Agentic AI can dynamically switch among various communication protocols—including DSRC, VANETs, ISAC, or PLC—based on real-time evaluations of signal quality, latency, and network congestion. Agents equipped with cognitive capabilities enhance system robustness against 5G performance limitations or outages.
- Federated Learning and Self-Improvement: Vehicles independently train machine learning models locally and transmit only model updates—preserving data privacy, minimizing bandwidth usage, and improving processing efficiency.
The figure below illustrates the proposed architectural framework for secure Agentic AI enablement within V2X communications, leveraging alternative communication protocols and the OODA (Observe–Orient–Decide–Act) cognitive model.
Conclusions:
With the integration of an intelligent Agentic AI layer into V2X systems, autonomous, adaptive, and efficient decision-making emerges from seamless collaboration of the distributed intelligent components.
Numerous examples highlight the potential of Agentic AI to deliver significant business value.
- TechCrunch reports that Amazon’s R&D division is actively developing an Agentic AI framework to automate warehouse operations through robotics [15]. A similar architecture can be extended to autonomous vehicles (AVs) to enhance both communication and cybersecurity capabilities.
- Forbes emphasizes that “Agentic AI demands agentic security,” underscoring the need for every action—whether executed by human or machine—to undergo rigorous review and validation from a security perspective [16]. Forbes notes, “Agentic AI represents the next evolution in AI—a major transition from traditional models that simply respond to human prompts.” By combining Agentic AI with alternative networking protocols, robust V2X ecosystems can be developed—capable of maintaining resilience despite connectivity losses or infrastructure gaps, enforcing strong cyber defense, and exhibiting intelligence that learns, adapts, and acts autonomously [19].
- Business Insider highlights the scalability of Agentic AI, referencing how Qualtrics has implemented continuous feedback loops to retrain its AI agents dynamically [17]. This feedback-driven approach is equally applicable in the mobility domain, where it can support real-time coordination, dynamic rerouting, and adaptive decision-making.
- Multi-agent systems are also advancing rapidly. As Amazon outlines its vision for deploying “multi-talented assistants” capable of operating independently in complex environments, the trajectory of Agentic AI becomes even more evident [18].
References:
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- Coll-Perales, B., Lucas-Estañ, M. C., Shimizu, T., Gozalvez, J., Higuchi, T., Avedisov, S., … & Sepulcre, M. (2022). End-to-end V2X latency modeling and analysis in 5G networks. IEEE Transactions on Vehicular Technology, 72(4), 5094-5109.
- Horta, J., Siller, M., & Villarreal-Reyes, S. (2025). Cross-layer latency analysis for 5G NR in V2X communications. PloS one, 20(1), e0313772.
- Cellular V2X Communications Towards 5G- Available at “pdf”
- Al Harthi, F. R. A., Touzene, A., Alzidi, N., & Al Salti, F. (2025, July). Intelligent Handover Decision-Making for Vehicle-to-Everything (V2X) 5G Networks. In Telecom (Vol. 6, No. 3, p. 47). MDPI.
- DSRC Safety Modem, Available at- “https://www.nxp.com/products/wireless-connectivity/dsrc-safety-modem:DSRC-MODEM”
- VANETs and V2X Communication, Available at- “https://www.sanfoundry.com/vanets-and-v2x-communication/#“
- Yu, K., Feng, Z., Li, D., & Yu, J. (2023). Secure-ISAC: Secure V2X communication: An integrated sensing and communication perspective. arXiv preprint arXiv:2312.01720.
- Study on integrated sensing and communication (ISAC) for C-V2X application, Available at- “https://5gaa.org/content/uploads/2025/05/wi-isac-i-tr-v.1.0-may-2025.pdf“
- Ramasamy, D. (2023). Possible hardware architectures for power line communication in automotive v2g applications. Journal of The Institution of Engineers (India): Series B, 104(3), 813-819.
- Xu, K., Zhou, S., & Li, G. Y. (2024). Federated reinforcement learning for resource allocation in V2X networks. IEEE Journal of Selected Topics in Signal Processing.
- Asad, M., Shaukat, S., Nakazato, J., Javanmardi, E., & Tsukada, M. (2025). Federated learning for secure and efficient vehicular communications in open RAN. Cluster Computing, 28(3), 1-12.
- Bryant, D. J. (2006). Rethinking OODA: Toward a modern cognitive framework of command decision making. Military Psychology, 18(3), 183-206.
- Agentic AI Communication Protocols: The Backbone of Autonomous Multi-Agent Systems, Available at- “https://datasciencedojo.com/blog/agentic-ai-communication-protocols/”
- Agentic AI And The Future Of Communications Networks, Available at- “https://www.forbes.com/councils/forbestechcouncil/2025/05/27/agentic-ai-and-the-future-of-communications-networks/”
- Amazon launches new R&D group focused on agentic AI and robotics, Available at- “Amazon launches new R&D group focused on agentic AI and robotics”
- Securing Identities For The Agentic AI Landscape, Available at “https://www.forbes.com/councils/forbestechcouncil/2025/07/03/securing-identities-for-the-agentic-ai-landscape/”
- Qualtrics’ president of product has a vision for agentic AI in the workplace: ‘We’re going to operate in a multiagent world’, Available at- “https://www.businessinsider.com/agentic-ai-improve-qualtrics-company-customer-communication-data-collection-2025-5”
- Amazon’s R&D lab forms new agentic AI group, Available at- “https://www.cnbc.com/2025/06/04/amazons-rd-lab-forms-new-agentic-ai-group.html”
- Agentic AI: The Next Frontier In Autonomous Work, Available at- “https://www.forbes.com/councils/forbestechcouncil/2025/06/27/agentic-ai-the-next-frontier-in-autonomous-work/”
About the Author:
Prashant Vajpayee is a Senior Product Manager and researcher in AI and cybersecurity, with expertise in enterprise data integration, cyber risk modeling, and intelligent transportation systems. With a foundation in strategic leadership and innovation, he has led transformative initiatives at Salesforce and advanced research focused on cyber risk quantification and resilience across critical infrastructure, including Transportation 5.0 and global supply chain. His work empowers organizations to implement secure, scalable, and ethically grounded digital ecosystems. Through his writing, Prashant seeks to demystify complex cybersecurity as well as AI challenges and share actionable insights with technologists, researchers, and industry leaders.
Dell’Oro: AI RAN to account for 1/3 of RAN market by 2029; AI RAN Alliance membership increases but few telcos have joined
AI RAN [1.] is projected to account for approximately a third of the RAN market by 2029, according to a recent AI RAN Advanced Research Report published by the Dell’Oro Group. In the near term, the focus within the AI RAN segment will center on Distributed-RAN (D-RAN), single-purpose deployments, and 5G.
“Near-term priorities are more about efficiency gains than new revenue streams,” said Stefan Pongratz, Vice President at Dell’Oro Group. “There is strong consensus that AI RAN can improve the user experience, enhance performance, reduce power consumption, and play a critical role in the broader automation journey. Unsurprisingly, however, there is greater skepticism about AI’s ability to reverse the flat revenue trajectory that has defined operators throughout the 4G and 5G cycles,” continued Pongratz.
Note 1. AI RAN integrates AI and machine learning (ML) across various aspects of the RAN domain. The AI RAN scope in this report is aligned with the greater industry vision. While the broader AI RAN vision includes services and infrastructure, the projections in this report focus on the RAN equipment market.
Additional highlights from the July 2025 AI RAN Advanced Research Report:
- The base case is built on the assumption that AI RAN is not a growth vehicle. But it is a crucial technology/tool for operators to adopt. Over time, operators will incorporate more virtualization, intelligence, automation, and O-RAN into their RAN roadmaps.
- This initial AI RAN report forecasts the AI RAN market based on location, tenancy, technology, and region.
- The existing RAN radio and baseband suppliers are well-positioned in the initial AI-RAN phase, driven primarily by AI-for-RAN upgrades leveraging the existing hardware. Per Dell’Oro Group’s regular RAN coverage, the top 5 RAN suppliers contributed around 95 percent of the 2024 RAN revenue.
- AI RAN is projected to account for around a third of total RAN revenue by 2029.
In the first quarter of 2025, Dell’Oro said the top five RAN suppliers based on revenues outside of China are Ericsson, Nokia, Huawei, Samsung and ZTE. In terms of worldwide revenue, the ranking changes to Huawei, Ericsson, Nokia, ZTE and Samsung.
About the Report: Dell’Oro Group’s AI RAN Advanced Research Report includes a 5-year forecast for AI RAN by location, tenancy, technology, and region. Contact: [email protected]
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Author’s Note: Nvidia’s Aerial Research portfolio already contains a host of AI-powered tools designed to augment wireless network simulations. It is also collaborating with T-Mobile and Cisco to develop AI RAN solutions to support future 6G applications. The GPU king is also working with some of those top five RAN suppliers, Nokia and Ericsson, on an AI-RAN Innovation Center. Unveiled last October, the project aims to bring together cloud-based RAN and AI development and push beyond applications that focus solely on improving efficiencies.
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The one year old AI RAN Alliance has now increased its membership to over 100, up from around 84 in May. However, there are not many telco members with only Vodafone joining since May. The other telco members are: Turkcell ,Boost Mobile, Globe, Indosat Ooredoo Hutchison (Indonesia), Korea Telecom, LG UPlus, SK Telecom, T-Mobile US and Softbank. This limited telco presence could reflect the ongoing skepticism about the goals of AI-RAN, including hopes for new revenue opportunities through network slicing, as well as hosting and monetizing enterprise AI workloads at the edge.
Francisco Martín Pignatelli, head of open RAN at Vodafone, hardly sounded enthusiastic in his statement in the AI-RAN Alliance press release. “Vodafone is committed to using AI to optimize and enhance the performance of our radio access networks. Running AI and RAN workloads on shared infrastructure boosts efficiency, while integrating AI and generative applications over RAN enables new real-time capabilities at the network edge,” he added.
Perhaps, the most popular AI RAN scenario is “AI on RAN,” which enables AI services on the RAN at the network edge in a bid to support and benefit from new services, such as AI inferencing.
“We are thrilled by the extraordinary growth of the AI-RAN Alliance,” said Alex Jinsung Choi, Chair of the AI-RAN Alliance and Principal Fellow at SoftBank Corp.’s Research Institute of Advanced Technology. “This milestone underscores the global momentum behind advancing AI for RAN, AI and RAN, and AI on RAN. Our members are pioneering how artificial intelligence can be deeply embedded into radio access networks — from foundational research to real-world deployment — to create intelligent, adaptive, and efficient wireless systems.”
Choi recently suggested that now is the time to “revisit all our value propositions and then think about what should be changed or what should be built” to be able to address issues including market saturation and the “decoupling” between revenue growth and rising TCO. He also cited self-driving vehicles and mobile robots, where low latency is critical, as specific use cases where AI-RAN will be useful for running enterprise workloads.
About the AI-RAN Alliance:
The AI-RAN Alliance is a global consortium accelerating the integration of artificial intelligence into Radio Access Networks. Established in 2024, the Alliance unites leading companies, researchers, and technologists to advance open, practical approaches for building AI-native wireless networks. The Alliance focuses on enabling experimentation, sharing knowledge, and real-world performance to support the next generation of mobile infrastructure. For more information, visit: https://ai-ran.org
References:
https://www.delloro.com/advanced-research-report/ai-ran/
https://www.delloro.com/news/ai-ran-to-top-10-billion-by-2029/
Dell’Oro: RAN revenue growth in 1Q2025; AI RAN is a conundrum
AI RAN Alliance selects Alex Choi as Chairman
Nvidia AI-RAN survey results; AI inferencing as a reinvention of edge computing?
Deutsche Telekom and Google Cloud partner on “RAN Guardian” AI agent
The case for and against AI-RAN technology using Nvidia or AMD GPUs
ZTE’s AI infrastructure and AI-powered terminals revealed at MWC Shanghai
ZTE Corporation unveiled a full range of AI initiatives under the theme “Catalyzing Intelligent Innovation” at MWC Shanghai 2025. Those innovations include AI + networks, AI applications, and AI-powered terminals. During several demonstrations, ZTE showcased its key advancements in AI phones and smart homes. Leveraging its underlying capabilities, the company is committed to providing full-stack solutions—from infrastructure to application ecosystems—for operators, enterprises, and consumers, co-creating an era of AI for all.
ZTE’s Chief Development Officer Cui Li outlined the vendor’s roadmap for building intelligent infrastructure and accelerating artificial intelligence (AI) adoption across industries during a keynote session at MWC Shanghai 2025. During her speech, Cui highlighted the growing influence of large AI models and the critical role of foundational infrastructure. “No matter how AI technology evolves in the future, the focus will remain on efficient infrastructure, optimized algorithms and practical applications,” she said. The Chinese vendor is deploying modular, prefabricated data center units and AI-based power management, which she said reduce energy use and cooling loads by more than 10%. These developments are aimed at delivering flexible, sustainable capacity to meet growing AI demands, the ZTE executive said.
ZTE is also advancing “AI-native” networks that shift from traditional architectures to heterogeneous computing platforms, with embedded AI capabilities. This, Cui said, marks a shift from AI as a support tool to autonomous agents shaping operations. Ms. Cui emphasized the role of high-quality, secure data and efficient algorithms in building more capable AI. “Data is like fertile ‘soil’. Its volume, purity and security decide how well AI as a plant can grow,” she said. “Every digital application — including AI — depends on efficient and green infrastructure,” she said.
ZTE is heavily investing in AI-native network architecture and high-efficiency computing:
- AI-native networks – ZTE is redesigning telecom infrastructure with embedded intelligence, modular data centers and AI-driven energy systems to meet escalating AI compute demands.
- Smarter models, better data – With advanced training methods and tools, ZTE is pushing the boundaries of model accuracy and real-world performance.
- Edge-to-core deployment – ZTE is integrating AI across consumer, home and industry use cases, delivering over 100 applied solutions across 18 verticals under its “AI for All” strategy.
ZTE has rolled out a full range of innovative solutions for network intelligence upgrades.
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AIR RAN solution: deeply integrating AI to fully improve energy efficiency, maintenance efficiency, and user experience, driving the transition towards value creation of 5G
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AIR Net solution: a high-level autonomous network solution that encompasses three engines to advance network operations towards “Agentic Operations”
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AI-optical campus solution: addressing network pain points in various scenarios for higher operational efficiency in cities
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HI-NET solution: a high-performance and highly intelligent transport network solution enabling “terminal-edge-network-computing” synergy with multiple groundbreaking innovations, including the industry’s first integrated sensing-communication-computing CPE, full-band OTNs, highest-density 800G intelligent switches, and the world’s leading AI-native routers
Through technological innovations in wireless and wired networks, ZTE is building an energy-efficient, wide-coverage, and intelligent network infrastructure that meets current business needs and lays the groundwork for future AI-driven applications, positioning operators as first movers in digital transformation.
In the home terminal market, ZTE AI Home establishes a family-centric vDC and employs MoE-based AI agents to deliver personalized services for each household member. Supported by an AI network, home-based computing power, AI screens, and AI companion robots, ZTE AI Home ensures a seamless and engaging experience—providing 24/7 all-around, warm-hearted care for every family member. The product highlights include:
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AI FTTR: Serving as a thoughtful life assistant, it is equipped with a household knowledge base to proactively understand and optimize daily routines for every family member.
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AI Wi-Fi 7: Featuring the industry’s first omnidirectional antenna and smart roaming solution, it ensures high-speed and stable connectivity.
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Smart display: It acts like an exclusive personal trainer, leveraging precise semantic parsing technology to tailor personalized services for users.
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AI flexible screen & cloud PC: Multi-screen interactions cater to diverse needs for home entertainment and mobile office, creating a new paradigm for smart homes.
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AI companion robot: Backed by smart emotion recognition and bionic interaction systems, the robot safeguards children’s healthy growth with emotionally intelligent connections.
ZTE will anchor its product strategy on “Connectivity + Computing.” Collaborating with industry partners, the company is committed to driving industrial transformation, and achieving computing and AI for all, thereby contributing to a smarter, more connected world.
References:
ZTE reports H1-2024 revenue of RMB 62.49 billion (+2.9% YoY) and net profit of RMB 5.73 billion (+4.8% YoY)
ZTE reports higher earnings & revenue in 1Q-2024; wins 2023 climate leadership award
Malaysia’s U Mobile signs MoU’s with Huawei and ZTE for 5G network rollout
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Dell’Oro: RAN revenue growth in 1Q2025; AI RAN is a conundrum
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Ericsson revamps its OSS/BSS with AI using Amazon Bedrock as a foundation
At this week’s TM Forum-organized Digital Transformation World (DTW) event in Copenhagen, Ericsson has given its operations support systems (BSS/OSS) portfolio a complete AI makeover. This BSS/OSS revamp aims to improve operational efficiency, boost business growth, and elevate customer experiences. It includes a Gen-AI Lab, where telcos can try out their latest BSS/OSS-related ideas; a Telco Agentic AI Studio, where developers are invited to come and build generative AI products for telcos; and a range of Ericsson’s own Telco IT AI apps. Underpinning all this is the Telco IT AI Engine, which handles various tasks to do with BSS/OSS orchestration.
Ericsson is investing to enable CSPs make a real impact with AI, intent and automation. AI is now embedded throughout the portfolio, and the other updates range across five critical, interlinked transformation areas within a CSP’s operational transformation, with each area of evolution based on a clear rationale and vision for the value it generates. Ericsson sites several benefits for telcos:
- Data – Make your data more useful. Introducing Telco DataOps Platform. An evolution from the existing Ericsson Mediation, the platform enables unified data collection, processing, management, and governance, removing silos and complexity to make data more useful across the whole business, and fuel effective AI to run their business and operations more smoothly.
- Cloud and IT – Stay ahead of the business. Introducing Ericsson Intelligent IT Suite. A holistic end-to-end approach supporting OSS/BSS evolution designed for Telco scale to accelerate delivery, streamline operations, and empower teams with the tools to unlock value from day one and beyond. It enables CSPs to embrace innovative transformative approaches that deliver real-time business agility and impact to stay ahead of business demands in rapidly evolving OSS/BSS landscapes.
- Monetization – Make sure you get paid. Introducing Ericsson Charging and Billing Evolved. A cloud-native monetization platform that enables real-time charging and billing for multi-sided business models. It is powered by cutting-edge AI capabilities that makes it easy to accelerate partner-led growth, launch and monetize enterprise services efficiently, and capture revenue across all business lines at scale.
- Service Orchestration – Deliver as fast as you can sell. Upgraded Ericsson Service Orchestration and Assurance with Agentic AI: Uses AI and intent to automatically set up and manage services based on a CSP’s business goals, providing a robust engine for transforming to autonomous networks. It empowers CSPs to cut out manual steps and provides the infrastructure to launch and scale differentiated connectivity services
- Core Commerce – Be easy to buy from. AI-enabled core commerce. Streamline selling with intelligent offer creation. Key capabilities include efficient offering design through a Gen-AI capable product configuration assistant and guided selling using an intelligent telco-specific CPQ for seamless ‘Quote to Cash’ processes, supported by a CRM-agnostic approach. CSPs can launch tailored enterprise solutions faster and co-create offers with partners all while delivering seamless omni-channel experiences
Grameenphone, a Bangladesh telco with more than 80 million subscribers is an Ericsson BSS/OSS customer. “They can’t do massive investments in areas that aren’t going to give a return,” said Jason Keane, the head of Ericsson’s business and operational support systems portfolio who noted the low average revenue per user (ARPU) in the Bangladeshi telecom market. The technologies developed by Ericsson are helping Grameenphone’s subscribers with top-ups, bill payments and operations issues.
“What they’re saying is we want to enable our customers to have a fast, seamless experience, where AI can help in some of the interaction flows between external systems. “AI itself isn’t free. You’ve got to pay your consumption, and it can add up if you don’t use it correctly.”
To date, very few companies have seen financial benefits in either higher sales or lower costs from AI. The ROI just isn’t there. If organizations end up spending more on AI systems than they would on manual effort to achieve the same results, money would be wasted. Another issue is the poor quality of telco data which can’t be effectively used to train AI agents.
Ericsson’ Booth at DTW Ignite 2025 event in Copenhagen
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Ericsson appears to have been heavily reliant on Amazon Web Services (AWS) for the technologies it is advertising at DTW this week. Amazon Bedrock, a managed service for building generative AI models, is the foundation of the Gen-AI Lab and the Telco Agentic AI Studio. “We had to pick one, right?” said Keane. “I picked Amazon. It’s a good provider, and this is the model I do my development against.”
Regarding AI’s threat to jobs of OSS/BSS workers, Light Reading’s Iain Morris, wrote:
“Wider adoption by telcos of Ericsson’s latest technologies, and similar offerings from rivals, might be a big negative for many telco operations employees. At most immediate risk are the junior technicians or programmers dealing with basic code that can be easily handled by AI. But the senior programmers had to start somewhere, and even they don’t look safe. AI enthusiasts dream of what the TM Forum calls the fully autonomous network, when people are out of the loop and the operation is run almost entirely by machines.”
Ericsson has realized its OSS and BSS tools need to address the requirements of network operators that either already, or will in the near future, adopt cloud-native processes, run cloud-based horizontal IT platforms and make extensive use of AI to automate back-office processes and introduce autonomous network operations that reduce manual intervention and the time to address problems while also introducing greater agility (as long as the right foundations are in place).
Mats Karlsson, Head of Solution Area Business and Operations Support Systems, Ericsson says: “What we are unveiling today illustrates a transformative step into industrializing Business and Operations Support Systems for the autonomous age. Using AI and automation, as well as our decades of knowledge and experience in our people, technology, processes – we get results. These changes will ensure we empower CSPs to unlock value precisely when and where it can be captured. We operate in a complex industry, one which is evidently in need of a focus on no nonsense OSS/BSS. These changes, and our commitment to continuous evolution for innovation, will help simplify it where possible, ensuring that CSPs can get on with their key goals of building better, more efficient services for their customers while securing existing revenue and striving for new revenue opportunities.”
Ahmad Latif Ali, Associate Vice President, EMEA Telecommunications Insights at IDC says: “Our recent research, featured in the IDC InfoBrief “Mapping the OSS/BSS Transformation Journey: Accelerate Innovation and Commercial Success,” highlights recurring challenges organizations faced in transformation initiatives, particularly the complex and often simultaneous evolution of systems, processes, and organizational structures. Ericsson’s continuous evolution of OSS/BSS addresses these key, interlinked transformation challenges head-on, paving the way for automation powered by advanced AI capabilities. This approach creates effective pathways to modernize OSS/BSS and supports meaningful progress across the transformation journey.”
References:
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