By Sanjay Dhawan, VP of New Business Technology and Operations, SBA Communications
Note: This blog was produced under WIA’s Innovation and Technology Council (ITC). The ITC is the forum for forecasting the future of the wireless industry. Participants explore developments in the wider wireless industry, from 5G network monetization trends and streamlining infrastructure deployment to future spectrum needs and cell site power issues. The group is publishing a series of thought-leadership pieces throughout 2025. These views are not a WIA endorsement of a particular company, product, policy or technology.
Over the past two years, the adoption of Artificial Intelligence (AI) has surged in our daily lives and corporate operations. From interactions with AI-powered language models, image creation, video development, autonomous vehicles and smart glasses, AI has become ubiquitous. As articulated by NVIDIA founder and CEO Jensen Huang at CES 2025, its evolution spans four stages: (1) Perception AI involves tasks such as speech recognition; (2) Generative AI creates text, images and sound; (3) Agentic AI adds perception, reasoning, planning and acting; and (4) Physical AI enables sensors to perceive, understand and reason about the physical world in real time.
Wireless operators are using AI extensively in their businesses in several areas, including network planning, customer service and 5G Radio Access Network (RAN) features, as evidenced by their public communications. The wireless infrastructure industry is no different when it comes to the adoption of AI to enhance overall operations. Generative AI platforms and multilanguage translation services are gaining widespread acceptance, enabling common meetings and webcasts among teams across continents.
At companies such as SBA Communications, the introduction of AI data scientists working side by side with business teams have brought early benefits. AI use cases for the tower industry apply to several areas, including development of tower digital twins, site planning and selection, structural analysis, security and asset protection, energy usage and optimization, site monitoring and remote management, and contract and document management.
Development of a digital twin is one such area of focus. A digital twin is a computer-generated replica of each physical tower and its compound. The traditional way of creating a digital twin requires 360-degree videos of each tower, including detailed measurements of each component mounted on the tower. With each tower upgrade, additional drone flights are required to keep the digital twin updated, with each flight taking several hours. With SBA owning and operating more than 39,000 towers globally, this approach would be expensive, time-consuming and difficult to maintain on an ongoing basis. Adding to the complexity, towers in rural areas are often situated in challenging terrains, such as mountainous regions or places with heavy snowfall, making them difficult to access.
With the evolution of AI models, it is becoming easier to develop a digital twin without undertaking complex and ongoing drone flights for every tower. AI-driven digital twins leverage 3D modeling techniques that integrate sensor data, historical maintenance logs and image processing to create accurate, dynamic representations of infrastructure. With every upgrade on the tower, new images can be incorporated to further refine the model and update the digital twin. Once created, the digital twins can be leveraged for many use cases, including accurate RAD center planning, structural analysis or even troubleshooting aspects such as antenna orientation.
Site planning is another focus area for tower companies. This capability implies enhancing and automating models for identification of new tower locations, considering aspects such as population growth trends, proximity to nearby towers, availability of land parcels and distance from residential units. We can expect this capability to start playing a role in not too distant a future.
Leveraging AI Optical Character Recognition (OCR) is another use case that involves digitizing legal paper documentation, such as leases. In many instances, these documents are in other languages, have handwritten notes, watermarks and stamps with physical signatures. Once digitized, they are easily translated and undergo deeper analysis based on pre-defined criteria; for example, lease expiry dates can be easily identified for thousands of sites in a market within a short time. This AI-driven process reduces the risk of lease expiration, preventing potential service disruptions and legal complications.
As the advantages of AI become more evident, its applications for infrastructure companies are rapidly expanding across different areas of the business, including Network Operation Center (NOC) automation, tower digital twin creation and land and customer lease management. Incorporation of AI into business processes is resulting in shorter time to market, improved planning and anticipated cost savings.
The wireless industry is constantly evolving with enhancement of technology and business models. We in the infrastructure business need to keep abreast of these changes, adapt to them where necessary, and as best as possible, anticipate them and determine how we can enhance and grow our business.
As AI-adoption matures, infrastructure companies will increasingly integrate AI-driven automation, predictive analytics and real-time decision making to drive operational efficiency.
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