Telco generative AI adoption tracker
The Telco generative AI adoption tracker is a database of deployments of generative AI by telcos around the world. It also features STL’s assessment of key telcos’ progress with AI.
Tag Archives: AI
AIS – Autonomous networks case study
Welcome to this series analysing network autonomy efforts by leading telcos worldwide. In this third edition, we put the spotlight on a leading multinational operator in the journey toward autonomous networks.
Consumer innovation tracker
A global database of innovative telco consumer services beyond connectivity
Orange Insights 2026: Scaling trust
Orange Business is entering a more mature phase of its transformation. This report explores why its pragmatic approach to partnerships, trust and platform orchestration may prove to be a more significant differentiator than any individual product announcement.
MWC Shanghai 2026: Tokens, robots and reach for the sky
At a show led by China’s telecom players, tokenisation, embodied AI and satellite connectivity sat at the centre of the agenda, each revealing how China’s AI and connectivity market is taking its own distinctive shape.
From AI vision to trusted execution: Key takeaways from DTW Ignite 2026
The industry has accepted the destination that is AI-driven autonomous networks. The challenge now is to build systems that operators can trust to make decisions across real networks. And to make every step create actual business benefits.
Chilli bites: STL Partners’ take on the quarterly hot topics in telco – 2Q 2026
STL Partners’ analysts share their bite-sized expert opinions on the key topics in telecoms in the last quarter
NaaS for AI: Takeaways from Mplify Lisbon
How the NaaS world is preparing for agentic AI and its impact on internal operations, customer relationships, security and partnering across the ecosystem.
Embracing AI without alienating customers
How telcos can employ artificial intelligence to enrich their interactions with customers, while protecting their brands and reputations.
How telcos generate AI revenue
How telcos are building revenue from AI-centric solutions for consumers, enterprises and the public sector.
How telcos can deliver for defence
As telcos deploy more resilient and programmable networks, they could become key suppliers to armed forces. Dual-purpose tech solutions by telcos are valuable also to the population and civilian critical services during extreme weather events or energy blackouts, as much as during military conflict.
Liquid cooling: The next consolidation wave in data centre infrastructure
As AI workloads push rack densities higher and liquid cooling becomes essential infrastructure, a fragmented vendor landscape is ripe for consolidation.
MWC 2026: AI but with more realism than hype
AI was again at the centre of MWC. But now telcos are focused on what they can practically achieve with AI rather than on boosting investor perception. Satellites, 5G standalone, sovereignty, security and quantum are other themes we noted in Barcelona.
Telco AI data centre forecast: Where will the compute live in the AI era?
Our telco AI data centre forecast examines the rise of AI factories alongside the evolution of network edge infrastructure, showing how AI-driven demand is accelerating growth in telco compute capacity. How will data sovereignty requirements and new industrial AI applications reshape the telco compute capacity landscape?
The rise of AI at SK Telecom
Following early AI investment, in 2021 SK Telecom announced its intention to become an AI company for the AI era. What actions has SK Telecom undertaken to this end? What role is AI playing across its business? And is AI paying off?
Breaking the RANcuffs: How operators can accelerate RAN Autonomy
RAN autonomy will not deliver impact unless technical progress and value creation advance in parallel. Our dual-axis framework will help operators to assess where they are today, identify constraints across the lifecycle and the investments most likely to scale.
Agentic AI: Making operator data AI ready
To deliver on their AI visions (especially agentic AI), operators must transform their data foundations and capabilities to avoid “garbage in, garbage out”. We identify the steps operators can take to get their data ready for AI on the journey towards level 4+ autonomy.