UST and AWS Get Real: Moving AI From the Lab to Live Telecom Networks

COPENHAGEN, Denmark – June 23, 2026 – The air at TM Forum's DTW Ignite event is thick with the usual buzz of 5G, cloud-native architecture, and digital transformation. But today’s announcement from UST, a technology transformation firm, in collaboration with Amazon Web Services (AWS) feels different. It’s less about a new product and more about a new production model for innovation itself.

UST has launched an AI Cloud Center of Excellence (CCoE) on AWS, but the key phrase here isn’t “Center of Excellence”—a term so overused it’s become corporate wallpaper. The key is what they’re calling it: an “AI-driven delivery engine.” This isn't just advisory work or another pilot program. It's a factory for building and deploying practical, scalable AI solutions, with the beleaguered and complex telecommunications industry as its first proving ground.

The AI Delivery Engine: Beyond the Hype Cycle

For years, communications service providers (CSPs) have been caught in an AI catch-22. They sit on mountains of network, operational, and customer data, yet struggle to convert that asset into tangible value. The reasons are familiar to anyone watching the space: complex legacy systems, siloed data, a persistent skills gap, and the difficulty of proving ROI on ambitious AI projects. As one industry analyst noted, “Telcos have been in a state of perpetual AI experimentation, but 2026 is the year they must shift to operationalization or be left behind.”

This is precisely the gap UST and AWS are targeting. The CCoE is designed to move CSPs from this cycle of experimentation to measurable outcomes. Instead of offering bespoke consulting on a project-by-project basis, the goal is to create a repeatable, industrialized process for solving the industry's most pressing challenges—from modernizing archaic Business and Operations Support Systems (BSS/OSS) to improving network resilience and customer experience.

“Telecommunications providers are looking for more than simply the latest AI technology, they need practical solutions that can help modernize operations, boost efficiency, and improve customer experiences,” said Aravind Nandanan, General Manager for Telecommunications at UST. He describes the CCoE as an evolution “from a traditional services model to intelligent, AI-driven delivery engines.” This shift is critical. It’s the difference between selling a blueprint and running the factory that builds the prefabricated components for the entire city.

A Strategic Synergy of Expertise and Scale

The power of this new model lies in its fusion of two critical elements: deep vertical expertise and horizontal cloud-scale power. UST brings decades of experience navigating the labyrinthine world of telecom operations. This domain knowledge—understanding the specific compliance frameworks, security standards, and operational workflows of a CSP—is the secret sauce that generic AI platforms often lack.

AWS provides the industrial-grade kitchen. The CCoE is built upon a formidable stack of AWS services. This includes Amazon SageMaker for building and training models, but more revealingly, it leverages advanced services like Amazon Bedrock, a platform that provides access to a range of powerful foundation models. The collaboration also utilizes AWS Transform, an agentic AI service designed to automate complex cloud modernization tasks, and Kiro, an AI coding assistant that can orchestrate these agents. By using these tools, UST can effectively “codify” its telecom expertise, turning decades of human knowledge into repeatable, scalable, and intelligent software agents.

“UST's new AI Cloud Center of Excellence on AWS reflects our shared commitment to helping telecommunications providers move from AI experimentation to real operational outcomes,” commented Amir Rao, Global Director for Telco Solutions at Amazon Web Services. “By combining deep telecom expertise with cloud-scale AI capabilities, UST is building a repeatable model for delivering intelligent, autonomous solutions at scale.” This synergy addresses the core challenge of enterprise AI: technology alone is insufficient without the context and nuance of the industry it serves.

Agentic AI in Action: The Dawn of the Self-Healing Network

The first product to roll off this new production line is UST IntelliResQ, and it offers a compelling glimpse into the future of telecom operations. Unveiled here in Copenhagen, IntelliResQ is designed to slash the time it takes to resolve network incidents, a critical metric for any CSP known as Mean Time To Resolution (MTTR).

This is not just another dashboard that flags alerts for human operators. IntelliResQ is built using what the industry is calling “agentic AI.” Powered by AI agents developed on Amazon Bedrock AgentCore, the solution can perform both autonomous and semi-autonomous tasks for incident management and remediation. These agents can sift through torrents of data to perform root cause analysis, initiate remediation workflows, and learn from each event to prevent future occurrences. The solution incorporates six distinct AI agents, seven AWS services, and three industry compliance frameworks, demonstrating the complexity being abstracted into a single, outcome-focused tool.

This is the first step toward the long-held vision of the self-healing network. While traditional solutions rely on rule-based automation for known problems, agentic AI can reason and act on novel issues, a necessity in today’s increasingly complex 5G and cloud-native environments. The business case is undeniable; experts project that this level of AI-driven automation can lead to a 30–50% reduction in MTTR and 20–25% in OPEX savings, all while improving the customer experience by minimizing service disruptions.

Industrializing Intelligence for a Resilient Future

The UST-AWS collaboration is a microcosm of a larger trend. It’s an acknowledgment that for AI to fulfill its transformative promise, it must become a utility—reliable, scalable, and deeply integrated into the fabric of business operations. This CCoE aligns perfectly with AWS's broader strategy of creating industry-specific clouds and fostering a partner ecosystem that can translate its powerful general-purpose tools into vertical-specific solutions.

To power this engine, UST is investing heavily in its people, with plans to train 300 associates on advanced AWS AI courses by the end of 2026. This focus on human capital is essential; even an AI-driven factory needs skilled engineers and architects to run it.

While telecom is the first stop, the model UST is pioneering is broadly applicable. Imagine similar AI delivery engines for healthcare, manufacturing, or financial services—each codifying the unique expertise of its domain into intelligent agents running on scalable cloud infrastructure. This is how digital transformation finally moves from a series of ambitious, one-off projects into a continuous, industrial-scale process of improvement. This is the “why behind the buy” in 2026.