Research commissioned by the newly reorganised vendor finds most US and European tech leaders believe existing infrastructure is unfit for the next phase of AI – just as Nokia pivots hard toward AI-native networking.
In sum – what to know:
AI pressure test – nearly nine in 10 respondents in the US and Europe say current networks will not scale to meet rising AI workloads, particularly uplink-heavy, low-latency applications.
Strategy alignment – the findings land as Nokia restructures around network and mobile infrastructure to capitalise on the “AI supercycle” and reposition itself as an AI networking specialist.
Investment gap – leaders on both sides of the Atlantic call for faster infrastructure investment, regulatory simplification and more AI-ready networks to avoid future bottlenecks.
News shock: AI network vendor says the world needs more AI networks! Most tech and business leaders in the US and Europe believe existing network infrastructure will need to be upgraded to support the growing demands of AI, according to research commissioned by Nokia and released Thursday (December 16). The research comes, of course, as Nokia undergoes a major strategic overhaul to position itself as a leader in AI networking.
The study surveyed about 2,000 technology and business decision-makers across the US and Europe, including telecoms operators, data center providers, and global enterprise users planning to deploy AI at scale. Respondents broadly agreed that current networks were not designed for the next phase of AI growth and would require substantial investment to keep pace.
In November, Nokia announced plans to reorganize its global operations into two primary business segments – Network Infrastructure and Mobile Infrastructure – as part of its efforts to capture value from the so-called “AI supercycle.” The reorganization, effective from the start of 2026, simplifies Nokia’s operating model to focus on AI and cloud growth and strengthen long-term financial performance, according to the company.
Its new findings point to a shared view across the connectivity ecosystem that next-generation networks are critical to sustaining future AI innovation, creating an opening for coordinated action between industry and governments to modernize digital infrastructure. Pallavi Mahajan, chief technology and AI officer at Nokia, stated: “The first wave of the AI supercycle has already reshaped industries and accelerated innovation.”
Mahajan suggested the new research shows a “clear understanding across the ecosystem that future waves will demand more advanced, AI-native networks and substantial investment to strengthen network requirements”. He said connectivity, capacity and low-latency performance were becoming essential as AI systems increasingly shape how devices communicate, how businesses operate, and how people interact with technology.
According to the research, AI is fundamentally changing network requirements. AI workloads are becoming more uplink-intensive, data flows are increasingly distributed and expectations around latency, throughput, resilience, security and energy efficiency are rising. Those shifts have implications not only for telecom providers and cloud operators, but also for national competitiveness and long-term digital leadership.
AI-geared applications such as autonomous vehicles, smart manufacturing systems, surveillance drones, and remote healthcare diagnostics generate large volumes of data at the network edge that must be transmitted upstream for processing. This places strain on networks originally built for downlink-heavy consumer uses. Ironically, lots of these disciplines also call for private 5G networks, which Nokia has semi-abandoned with its restructure.
But anyway. Nokia said the findings underscore the need for collaboration across the network ecosystem and for more predictable and streamlined regulatory frameworks that support timely infrastructure investment. The research draws on perspectives from operators, enterprises and technology partners and is presented in separate regional reports.
In the US, where AI deployment and mass-market adoption remain strong, 88 percent of respondents said they were concerned that network expansion would not keep pace with AI investment. Respondents identified bi-directional data flow optimization, increased fiber capacity, real-time training feedback and low-latency edge infrastructure as key priorities for modernizing network architecture.
In Europe, 86 percent of enterprise respondents said current networks were not yet ready to support widespread AI adoption. Two-thirds reported that AI was already in live use, while more than half said they had experienced issues such as downtime, latency and throughput constraints as data demands increased.
European respondents highlighted the need for regulatory simplification and alignment across markets, timely spectrum availability, changes to competition policy to allow consolidation and greater investment in energy-efficient, AI-ready networks. The research suggests that without accelerated network modernization, both regions risk bottlenecks that could limit the scale and impact of future AI deployments.
