How Fast Is the AI in Networks Market Growing?
The global AI in networks market was valued at approximately USD 16.25 billion in 2025 and is projected to exceed USD 245.61 billion by 2035, registering a compound annual growth rate (CAGR) of 31.2% from 2026 to 2035.
This growth reflects rising demand for network automation, real-time optimization, predictive maintenance and AI-driven cybersecurity. Telecommunications operators, enterprises and data center providers are increasingly exploring AI to manage growing traffic volumes and more demanding connectivity requirements.
Yet adoption will not progress at the same pace across every network environment. Operators with large, complex networks may prioritize automated fault management and traffic optimization, while enterprises may focus on security monitoring, application performance and simpler network administration.
For buyers, the key question is not how quickly the market is expanding, but which network problems justify investment today.
1. Why Is AI Becoming Important for Telecom Networks?
Telecom networks must manage changing traffic patterns, service expectations and increasingly complex infrastructure. The expansion of 5G, connected devices, cloud services and AI applications adds pressure to existing network resources.
At India Mobile Congress 2026, industry discussions highlighted AI-native networks, intelligent radio access networks (RAN), network automation and the development of capabilities for future 6G infrastructure. India's telecom industry has also warned that growing AI-related data traffic will require greater network capacity, with spectrum availability remaining a concern.
AI can support telecom operations in several ways:
• Traffic optimization: Predict changing demand and allocate network resources more effectively.
• Fault prediction: Identify unusual patterns that may indicate equipment failures or service degradation.
• Energy management: Adjust network resources in response to traffic conditions, where technically feasible.
• Automated troubleshooting: Help engineers investigate alarms, identify likely causes and recommend corrective actions.
• Service assurance: Detect performance issues that may affect customers before they become widespread.
These applications can reduce the burden of repetitive operational tasks. However, the results depend on data quality, integration with existing systems and the ability to validate AI-generated recommendations.
2. Can AI-RAN Improve Network Performance Without Increasing Infrastructure Spending?
AI-enabled radio access networks are attracting attention as telecom operators look for ways to improve performance and prepare for AI-native network architectures.
Dell’Oro Group projected that cumulative AI-RAN revenue could reach USD 35 billion over 2026–2030. Importantly, its forecast does not assume that AI-RAN will expand the overall radio access network market. This distinction matters: AI-related spending may change the mix of network equipment and software purchases without necessarily creating equivalent growth in total infrastructure spending.
Nokia reported in September 2026 that its AI-RAN platform had delivered more than 20% improvements in spectral efficiency, with further software-driven gains on its development roadmap. This is a vendor-reported result, not a universal performance guarantee across network environments.
For telecom operators, spectral efficiency is important because it indicates how effectively available spectrum can support network traffic. Better efficiency could help operators improve capacity or service performance without relying exclusively on additional spectrum or new sites.
Before scaling AI-RAN, operators should evaluate:
• Performance under real network traffic conditions.
• Compatibility with existing radio equipment and network architectures.
• Computing, power and software costs associated with deployment.
• Improvements in capacity, latency, reliability or spectrum utilization.
• Whether results from controlled trials can be reproduced across multiple locations.
The commercial case is strongest when AI produces measurable improvements in network economics, rather than simply adding another layer of technology.
3. Is Network Automation More Valuable Than Adding More Tools?
Many network teams already use monitoring dashboards, alarms and analytics software. The challenge is that detecting a problem does not necessarily resolve it.
AI-based automation can connect detection, diagnosis and corrective action into a more continuous operational process. For example, a system might identify an emerging congestion pattern, recommend a configuration change and assess whether the adjustment improves performance.
The degree of automation should match the risk of the action. Low-risk, repeatable tasks may be suitable for automated execution, while changes affecting critical services may still require human approval.
A practical implementation approach is to progress through three stages:
1. Assist: Use AI to summarize alarms, identify patterns and help engineers investigate incidents.
2. Recommend: Generate corrective actions for engineers to review and approve.
3. Automate: Execute predefined actions within established policies, monitoring and rollback controls.
This staged approach allows operators to establish trust and measure performance before delegating more consequential decisions to automated systems.
The most useful metrics include mean time to repair, incident frequency, repeat faults, engineer workload and the proportion of incidents resolved without escalation.
4. How Should Businesses Evaluate AI Network Security?
AI can help identify unusual traffic, detect suspicious behavior and prioritize security alerts. It can also help analysts correlate signals across complex environments, reducing the time spent reviewing large volumes of operational data.
However, automated detection introduces its own challenges. Poorly calibrated models can generate false positives, overlook unfamiliar threats or produce inconsistent results when network behavior changes.
Organizations should therefore evaluate AI security tools against practical requirements:
• Detection accuracy across relevant threat scenarios.
• False-positive rates and the workload created for security teams.
• Integration with existing security information and incident-response systems.
• Data privacy, access controls and auditability.
• Human oversight for high-impact decisions.
• Performance when models encounter new or changing attack patterns.
AI should strengthen the existing security process, not replace the need for sound access controls, incident response and continuous validation.
5. Are Telecom Investments Keeping Pace With AI Network Demand?
Growing AI-related traffic creates a need for network capacity, but investment decisions remain constrained by capital budgets, spectrum availability and the economics of upgrading existing infrastructure.
Dell’Oro Group reported that worldwide telecom capital expenditure increased approximately 5% year over year in the first half of 2026. The firm nevertheless described the longer-term capital expenditure outlook as relatively muted, with investment shifting from coverage expansion toward capacity.
This creates an important distinction for technology suppliers. Operators may be willing to invest in AI capabilities, but they still need to decide whether a purchase can improve network performance enough to justify its cost.
For vendors, the opportunity is to demonstrate how AI solutions can help operators:
• Increase the utilization of existing infrastructure.
• Reduce avoidable site visits and manual troubleshooting.
• Improve service quality without proportionate increases in operating costs.
• Prioritize capacity upgrades using more accurate demand forecasts.
• Reduce the time needed to identify and resolve network problems.
For buyers, the priority should be to compare the cost of AI deployment against measurable operational improvements and the alternative cost of conventional network upgrades.
6. What Should Enterprises Consider Before Adopting AI in Networks?
AI networking is not limited to telecom operators. Enterprises, financial institutions, healthcare organizations and data center operators also need to manage growing traffic, distributed infrastructure and increasingly demanding applications.
Their requirements differ. A financial institution may prioritize secure operations and service availability, while a data center operator may focus on congestion, workload movement and predictable application performance.
Before selecting a solution, decision-makers should assess five areas:
• Use-case clarity: Identify the specific problem AI is expected to solve.
• Data readiness: Confirm that network telemetry is sufficiently accurate, consistent and accessible.
• Integration requirements: Determine whether the solution works with existing infrastructure and management platforms.
• Operational safeguards: Establish approval processes, access restrictions, monitoring and rollback procedures.
• Return on investment: Define a baseline and measure changes in downtime, operating costs, capacity utilization and service performance.
A limited deployment with clear performance targets can help establish whether the technology delivers value before a broader rollout.
What Will Determine the Next Phase of the AI in Networks Market?
The expansion of 5G, growing AI workloads and the move toward autonomous networks are creating new opportunities for network optimization, predictive maintenance, cybersecurity and AI-RAN.
But the pace of adoption will depend on more than technological capability. Integration complexity, capital expenditure constraints, data quality and confidence in automated decisions will influence how quickly operators move from trials to large-scale deployment.
For technology providers, measurable results and compatibility with existing infrastructure will be important differentiators. For network operators and enterprise buyers, the priority is to identify applications that improve reliability, reduce operational effort or make better use of existing capacity.
The central business question is not whether AI can make networks more intelligent, but whether it can make them more reliable, efficient and economical to operate.
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