AT&T expands AI partnerships with Wiliot and renews Adaptive ML deal to target new supply-chain growth

AT&T is looking to grow beyond its traditional telecom business by expanding a set of artificial intelligence partnerships aimed at supply-chain operations, according to a report published by Yahoo Finance. The company said it has expanded its client collaboration with Wiliot to deploy Wiliot’s Physical AI platform across large supply chains, adding momentum to a broader push into “intelligent” connected systems that can capture data from the physical world.

Physical AI is generally designed to turn everyday objects into sources of data, often using low-power tags and sensors that can be read through wireless connectivity. In AT&T’s case, the partnership is positioned as a way to apply AI-driven analytics to logistics workflows, potentially helping customers track inventory and improve operational decisions across distribution networks.

AT&T also renewed its partnership with Adaptive ML, a company focused on building and deploying machine-learning models that can make predictions and automate decisions. The report said AT&T plans to double its use of Adaptive ML, indicating the operator expects more of the models’ value as it scales deployments.

While the Yahoo Finance report outlines the partnership themes, it does not provide revenue figures, contract values, or timelines for the expanded rollouts. It also does not specify the number of customers involved or which industries within “large supply chains” are included first, leaving key details about scope and performance unaddressed.

The move fits a wider pattern in telecom as carriers seek growth in areas such as software, data, cloud, and enterprise applications. AT&T has long positioned its enterprise connectivity as a platform that can support additional services, but the company’s emphasis on AI partnerships suggests it is trying to convert that platform into higher-value analytics and automation for business customers.

Supply chains are a natural target because they generate large volumes of operational data and are exposed to disruptions, staffing constraints, and inventory variability. Partnerships like Wiliot’s Physical AI deployment and Adaptive ML’s modeling capabilities can be attractive to enterprises that want real-time or near-real-time visibility and better forecasting. For AT&T, partnering rather than building everything in-house can also reduce development time and allow it to bring specialized vendors’ technology into its customer base.

Still, important questions remain unanswered in the reported announcement. AT&T did not disclose how much the Wiliot deployment and the scaled Adaptive ML usage are expected to contribute to earnings, whether the expansions are priced on subscription, usage, or service fees, or how success will be measured. The report also does not say whether AT&T will expand beyond pilot programs, how quickly customer deployments could scale, or what service levels the company expects to meet.

Investors and customers will likely watch for additional details in future AT&T disclosures, such as metrics that indicate adoption, customer case studies that show operational impact, and any references to how these partnerships fit into broader enterprise revenue strategies. It will also be worth monitoring whether AT&T names more partners in the Physical AI and machine-learning categories, which would announcement a continued effort to build a portfolio of AI-enabled enterprise services.