1、Assessing theopportunities andchallenges forAI-RAN strategiesAuthor:Richard Webb,Senior AnalystEditor:Ian Kemp,Managing EditorOctober 2025 inform.tmforum.orgContents3 The big picture 6 Key report findings7 Section 1:Integrating AI into the RAN10 Section 2:AI-RAN drivers and use cases 15 Section 3:Th
2、e value of AI-RAN to Open RAN 20 Section 4:AI-RAN challenges 25 Additional resourcesWe hope you enjoy the report and,most importantly,fjnd ways to use the ideas,concepts and recommendations detailed within.You can send your feedback to the editorial team at TM Forum via editortmforum.orgThe big pict
3、ure3inform.tmforum.orgDriven by these potential benefits,CSPs are starting to position themselves to take advantage of the AI-RAN opportunity.In the US,for example,T-Mobile last year launched an AI-RAN innovation center in partnership with Nvidia.“AI-RAN at T-Mobile will be all about unlocking the m
4、assive capacity and performance that customers increasingly demand from mobile networks,”said Mike Sievert,CEO of T-Mobile,announcing the partnership.“AI-RAN has tremendous potential to completely transform the future of mobile networks,but it will be difficult to get right.”Among the challenges,as
5、we see in section 4,are high upfront costs and as-yet unproven return on investment,high energy consumption,and security and privacy concerns.From an infrastructure perspective,AI-RAN uses a homogeneous,accelerated computing platform without RAN hardware components.This means it can run both mobile
6、network and AI workloads concurrently,with deterministic in other words AI-RAN refers to the full integration of AI into radio access network(RAN)hardware and software in a mobile network.This has the potential to provide communications service providers(CSPs)with transformative efficiency gains in