{"id":4477,"date":"2026-02-19T15:42:56","date_gmt":"2026-02-19T15:42:56","guid":{"rendered":"https:\/\/salarydistribution.com\/machine-learning\/2026\/02\/19\/survey-reveals-ai-advances-in-telecom-networks-and-automation-in-drivers-seat-as-return-on-investment-climbs\/"},"modified":"2026-02-19T15:42:56","modified_gmt":"2026-02-19T15:42:56","slug":"survey-reveals-ai-advances-in-telecom-networks-and-automation-in-drivers-seat-as-return-on-investment-climbs","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2026\/02\/19\/survey-reveals-ai-advances-in-telecom-networks-and-automation-in-drivers-seat-as-return-on-investment-climbs\/","title":{"rendered":"Survey Reveals AI Advances in Telecom: Networks and Automation in Driver\u2019s Seat as Return on Investment Climbs"},"content":{"rendered":"<div>\n\t\t<span class=\"bsf-rt-reading-time\"><span class=\"bsf-rt-display-label\"><\/span> <span class=\"bsf-rt-display-time\"><\/span> <span class=\"bsf-rt-display-postfix\"><\/span><\/span><\/p>\n<p>AI is accelerating the telecommunications industry\u2019s transformation, becoming the backbone of autonomous networks and AI-native wireless infrastructure. At the same time, the technology is unlocking new business and revenue opportunities, as telecom operators accelerate AI adoption across consumers, enterprises and nations.<\/p>\n<p>NVIDIA\u2019s fourth annual \u201cState of AI in Telecommunications\u201d survey report unpacks these trends, underscoring strong AI adoption, impact and investment in the industry.<\/p>\n<p>Highlights from the report include:<\/p>\n<ul>\n<li>90% said AI is helping increase annual revenue and drive down costs.<\/li>\n<li>77% said they expect to see AI-native networks launch before the deployment of 6G.<\/li>\n<li>65% of telecom operators said network automation is being driven by AI.<\/li>\n<li>60% said their organization is using or assessing generative AI, up from 49% in 2024.<\/li>\n<li>89% said open source models and software are important to their AI strategy.<\/li>\n<li>89% of telcos plan to boost AI spending in 2026, up from 65% a year ago.<\/li>\n<\/ul>\n<p>\u201cThere is a seismic shift underway in the telecom industry driven by AI,\u201d said Sebastian Barros, managing director of Circles, a Singapore-based telecommunications provider. \u201cCommunication service providers are converging on a new realization. Their role in society extends beyond moving bits across networks toward moving intelligence across local and regulated infrastructure. That transition defines the move from telco to \u2018AICO\u2019 \u2014 AI infrastructure companies operating at network proximity, not application vendors riding on top.\u201d<\/p>\n<p>Here are some more key findings from the report.<\/p>\n<h2><strong>Tangible Revenue Impact and Return on Investment<\/strong><\/h2>\n<p>The telecommunications industry is seeing a definitive revenue impact from the use of AI. Overall, about nine out of 10 respondents said AI is helping to increase revenue and reduce costs. Telecommunications operators, which represent about a quarter of the 1,000 responses in the survey, are also seeing the benefit, with 90% saying AI has had a positive impact on revenue and costs.<\/p>\n<p>The top AI use cases cited for return on investment (ROI) were AI for autonomous networks (50%), followed by improved customer service (41%) and internal process optimization (33%).<\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter size-medium wp-image-90043\" src=\"https:\/\/blogs.nvidia.com\/wp-content\/uploads\/2026\/02\/top-roi-use-cases-state-of-ai-telecom-2026-960x243.jpg\" alt=\"\" width=\"960\" height=\"243\"><\/p>\n<p>\u201cAutonomous networks deliver immediate ROI by eliminating human effort from repetitive, reactive workflows,\u201d said Barros. \u201cThe fastest impact areas are energy management, fault prediction, configuration drift correction and capacity planning.\u201d<\/p>\n<p>This strong impact on revenue and ROI is leading telecommunications companies to increase their AI budgets in 2026. Overall, 89% of respondents said their AI budget will increase in the next 12 months, up from 65% in last year\u2019s survey, with 35% saying their budgets would increase more than 10% from this year.<\/p>\n<h2><strong>Focus on AI-Native Networks and Autonomous Operations<\/strong><\/h2>\n<p>Network automation has overtaken customer experience as the leading use case for investment, deployment and ROI impact. This signals a bold step toward autonomous networks \u2014 AI-driven, self-managing systems that can self-configure, self-heal and self-optimize with minimal human intervention. Eighty-eight percent of organizations report being between levels 1-3 of autonomy, as defined by the TM Forum, and the use of generative AI and agentic AI is expected to accelerate the shift to level 5 autonomous networks.<\/p>\n<p>\u201cAutonomous networks are delivering return on investment faster than any other AI use case because they directly reduce outages, energy consumption and manual intervention,\u201d said Chetan Sharma, CEO of Chetan Sharma Consulting. \u201cAgentic AI accelerates this by coordinating decisions across domains in real time.\u201d<\/p>\n<p>A surge in edge computing investment is reshaping telecom network architectures, bringing AI inferencing closer to users through a distributed computing infrastructure. Telcos are stepping up investments in AI-native RAN and 6G \u2014 signaling a major industry intercept ahead of the traditional 6G deployment cycle, with 77% of respondents anticipating a much faster time to deployment of this new AI-native wireless network architecture.<\/p>\n<p>The top drivers of investment are using AI to enhance spectral efficiency, improving the performance of the radio access network supporting edge AI applications and accelerating the research and development of 6G.<\/p>\n<h2><strong>A Universal Boost in Productivity\u00a0<\/strong><\/h2>\n<p>AI in telecommunications is advancing autonomous networks and business opportunities as well as improving internal operations. Nearly every respondent in the survey said AI is boosting employee productivity, with 26% citing major to significant improvements to their ability to complete more tasks with higher quality in less time.<\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter size-medium wp-image-90047\" src=\"https:\/\/blogs.nvidia.com\/wp-content\/uploads\/2026\/02\/productivity-state-of-ai-telecom-2026-960x284.jpg\" alt=\"\" width=\"960\" height=\"284\"><\/p>\n<p>The productivity gains are coming from generative and agentic AI solutions deployed across operations, from the back office to networks.<\/p>\n<p>\u201cGenerative AI delivered fast productivity gains, but agentic AI is where telecoms begin to see structural ROI,\u201d Sharma said. \u201cAutonomous agents can act across networks, IT and customer journeys, turning insights into decisions without human delay.\u201d<\/p>\n<p>Download the \u201c<a target=\"_blank\" href=\"https:\/\/www.nvidia.com\/en-us\/lp\/industries\/telecommunications\/state-of-ai-in-telecom-survey-report\/\" rel=\"noopener\">State of AI in Telecommunications 2026 Trends<\/a>\u201d report for in-depth results and insights.<\/p>\n<p><i>Explore <\/i><a target=\"_blank\" href=\"https:\/\/www.nvidia.com\/en-us\/industries\/telecommunications\/ai\/\" rel=\"noopener\"><i>NVIDIA AI technologies for telecommunications<\/i><\/a><i>.<\/i><\/p>\n<\/p><\/div>\n","protected":false},"excerpt":{"rendered":"<p>https:\/\/blogs.nvidia.com\/blog\/ai-in-telco-survey-2026\/<\/p>\n","protected":false},"author":0,"featured_media":4478,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[3],"tags":[],"_links":{"self":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts\/4477"}],"collection":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/comments?post=4477"}],"version-history":[{"count":0,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts\/4477\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/media\/4478"}],"wp:attachment":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/media?parent=4477"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/categories?post=4477"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/tags?post=4477"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}