Software held a 45.58% share of the AI in networks market in 2025, making it the leading component segment as network operators and enterprises continued to prioritize platforms that automate traffic analysis, anomaly detection, performance optimization, and network orchestration. Its leadership is underpinned by the central role software plays in turning network data into actionable decisions at scale, which makes it the core layer through which AI capabilities are deployed, updated, and integrated into existing network management environments. In the AI in networks market, this practical dependence on software platforms supports continued dominance because buyers typically anchor AI adoption around operational control, visibility, and automation workflows rather than around standalone support functions.

Services are the fastest-growing component in the AI in networks market because organizations increasingly need implementation, integration, customization, and ongoing optimization support as AI use cases move from pilot stages into live network environments. Growth is being driven less by basic adoption and more by the complexity of operationalizing AI across heterogeneous network infrastructure, where internal teams often require external expertise to align models, workflows, and performance objectives with real-world network conditions. Compared with software alone, services gain momentum as deployment maturity rises, since successful execution depends on tuning and managing AI-enabled network systems rather than simply acquiring the underlying tools.

Deployment Segment Analysis: Cloud (Largest Segment) vs On-premises (Fastest-Growing Segment)

Cloud accounted for the largest share of the AI in networks market in 2025, reflecting buyer preference for deployment environments that can support scalable data processing, centralized model management, and faster rollout of AI-driven network functions across distributed operations. Its leadership is maintained by the practical advantages of cloud-based delivery in handling large network data volumes and enabling more flexible updates to AI applications without heavy infrastructure changes at each site. In the AI in networks market, cloud remains the leading deployment model because it aligns well with the operational need for agility, especially where network intelligence must be continuously refined and deployed across multiple locations.

On-premises is the fastest-growing deployment segment in the AI in networks market as more organizations seek tighter control over network data, lower-latency processing, and closer integration with existing internal infrastructure. The growth momentum comes from operating environments where data handling requirements, performance sensitivity, or infrastructure control make local deployment more practical than relying fully on external environments. Relative to cloud alternatives, on-premises adoption is accelerating where AI in network operations must function within stricter internal governance and real-time execution conditions.