SPECIAL FEATURE | AI adoption in telecom rises as network automation takes lead
The findings point to a telecommunications industry moving beyond initial AI experimentation toward broader deployment.
Telecommunications companies are expanding their use of artificial intelligence (AI), with network automation emerging as a leading application as operators seek to improve efficiency, reduce costs and create new revenue opportunities, according to a 2026 industry survey.
The report, State of AI in Telecommunications: 2026 Trends, found that 66% of respondents said their organizations were actively using AI, up from 49% in 2024 and 41% in 2023. The survey included more than 1,000 respondents worldwide, approximately a quarter of whom represented telecommunications operators. Other participants came from network equipment providers, systems integrators, software vendors and other industry organizations.
The findings point to a telecommunications industry moving beyond initial AI experimentation toward broader deployment, although significant challenges remain in data management, workforce capabilities and demonstrating returns on investment.
The report also highlights a shift in priorities. Network automation has overtaken customer experience as the leading AI use case, reflecting the industry’s interest in applying the technology to network operations rather than limiting it to customer-facing services.
AI investment expands as companies report gains
The survey found that 90% of respondents said AI was helping their organizations increase annual revenue and reduce annual costs. Among them, 67% reported revenue increases of more than 5%, while 55% said their organizations had reduced costs by more than 5%.
Employee productivity was another area of reported improvement. Nearly all respondents, or 99%, said AI had enhanced productivity, with 26% describing the improvement as major to significant.
These reported gains appear to be influencing spending plans. Eighty-nine percent of respondents said they planned to increase their AI budgets in 2026, compared with 65% in the previous year’s survey.
The findings suggest that organizations increasingly view AI as an operational investment rather than a technology initiative confined to research and development. However, the survey’s reported outcomes reflect respondents’ assessments and do not independently establish that AI alone caused the financial improvements.
The leading objectives for AI adoption were improving productivity, cited by 43% of respondents; increasing operational efficiency and reducing costs, at 42%; and developing new business and revenue opportunities, at 40%.
The emphasis on revenue generation indicates that telecom companies are looking beyond cost reduction. As network capabilities and digital services evolve, operators are also exploring how AI can support new offerings and business models.
Network automation takes priority
Network automation was cited as an AI use case by 54% of respondents, an increase of 17 percentage points from 2024. Customer service and experience followed at 46%, while internal process optimization accounted for 43%.
Among telecommunications operators, 65% said AI was driving network automation, while 59% identified it as one of the leading AI use cases generating returns on investment.
Automation can help operators manage increasingly complex networks by supporting tasks that would otherwise require extensive manual intervention. These applications are particularly relevant as telecom infrastructure must accommodate changing traffic patterns, expanding digital services and growing demands for reliable connectivity.
The survey also examined progress toward autonomous networks, using the TM Forum’s Autonomous Network Levels framework. Eighty-eight percent of all respondents placed their organizations at Levels 1 through 3.
Among telecommunications operators and multiple-system operators, 19% reported Level 1, 35% Level 2 and 36% Level 3. The report’s broader classification showed that 71% had partial or conditional automation, while 25% had no automation or only basic automation. Just 4% reported high or full autonomy.
These figures indicate that extensive automation remains a work in progress. Although operators are deploying AI in network operations, relatively few respondents reported reaching the highest levels of autonomy.
Agentic AI is also becoming part of this transition. Among respondents who had deployed agentic AI, 41% said they were using it for network automation. Meanwhile, 52% expected network automation agents to deliver significant returns on investment within two years.
The distinction matters because agentic AI is intended to carry out tasks through systems that can make decisions and take actions within defined parameters. In network environments, such capabilities could support more automated responses to operational conditions, although their effectiveness depends on implementation and oversight.
Generative and agentic AI gain ground
The report found that 60% of respondents were using generative AI, up from 49% in 2024 and 43% in 2023. Adoption of data processing and analytics stood at 58%, while 48% said they were using or assessing agentic AI.
Company size also influenced adoption. Among organizations with more than 10,000 employees, generative AI adoption reached 80%.
The findings show that AI adoption encompasses several types of applications, from generating content and analyzing data to systems designed to perform multistep tasks. These technologies have different operational requirements and should not be treated as interchangeable measures of AI maturity.
The report also identified edge computing as an important part of future AI infrastructure. Seventy-eight percent of respondents said they were investing or planning to invest in edge computing to support AI inference, the process through which trained AI models generate outputs from new inputs.
In telecommunications, processing AI workloads closer to where data is generated could support applications that require timely responses. The survey also found that 77% of respondents expected AI-native wireless network deployments before the launch of 6G, anticipated in 2030. Twenty-eight percent said they were already using AI in wireless network research and development.
These findings reflect expectations and investment plans, rather than confirmation that the projected deployments will occur on schedule.
Data challenges remain a major obstacle
Despite growing adoption and reported business benefits, companies continue to face barriers to scaling AI.
Data-related issues were the most frequently cited challenge, identified by 54% of respondents. These included privacy, data sovereignty, isolated data systems, and the size and complexity of available information. The figure was 34 percentage points higher than in 2024.
A shortage of AI specialists and data scientists was cited by 47% of respondents, while 34% pointed to unclear returns on investment.
The prominence of data challenges suggests that deploying AI involves more than selecting models or purchasing software. Organizations must also address how data is collected, governed, accessed and integrated across systems. In telecommunications, where operational information can be distributed across multiple network and business platforms, these issues can complicate implementation.
The skills gap presents another constraint. Even when organizations have access to AI tools, they need personnel capable of integrating the technology into existing operations, evaluating its output and managing associated risks.
Uncertainty over returns also remains relevant as companies increase spending. Although respondents reported revenue growth, cost reductions and productivity improvements, organizations still need to determine whether specific AI projects deliver measurable and sustainable value.
Open-source AI and partnerships shape deployment
The report found that 89% of respondents considered open-source models and software important to their AI strategies, including 46% who described them as very or extremely important.
Organizations are pursuing different approaches to implementation. Forty-two percent preferred to develop AI solutions internally, while 38% co-developed solutions with partners. Thirty-six percent outsourced work to consultants, and 21% used third-party enterprise software.
These approaches indicate that AI development is not following a single model. Some companies are building internal capabilities, while others rely on external expertise or combine in-house development with partnerships.
For telecom operators, those choices can affect the resources required to deploy AI, the degree of control over systems and the ability to adapt solutions to specific operational needs.
The survey also found that 46% of respondents said AI had opened new revenue opportunities, reinforcing the view that companies are considering the technology as both an efficiency tool and a potential source of growth.
The overall picture is one of accelerating adoption accompanied by uneven progress. Telecom companies are increasing budgets, reporting productivity gains and prioritizing network automation, but the low proportion of respondents reporting high or full network autonomy highlights the distance between current deployments and more advanced operations.
The next phase of adoption will depend not only on AI capabilities but also on the industry’s ability to address data governance, workforce shortages and measurable business outcomes. For telecommunications companies, the challenge is to turn growing AI investment into reliable operational improvements and sustainable commercial value.
Full disclosure: All news articles published on the TechSabado website are written by human journalists, unless otherwise specified. Final text editing is also performed by human editors, with artificial intelligence (AI) used only to assist with additional grammar and style guide corrections..
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