Only 2% of 55 Asia-Pacific telco respondents reported live agentic AI at scale
TM Forum's wider survey covered 216 IT executives from 111 operators in 72 countries. Its 55-person Asia-Pacific subset points to a gap between strategy and production use—but the self-reported regional result is directional, not an audited market estimate.
The short version
- What happened
- TM Forum says 2% of 55 Asia-Pacific survey respondents reported that AI agents were operating at scale in live core systems; 72% described AI as experimental or attached to specific tasks.
- Who it affects
- Telecom workers may gain tools that diagnose faults or assemble customer information, but live agents can also change roles, decision authority and accountability. Customers will feel the result through service reliability, complaint handling, privacy and whether a human can intervene when automation is wrong.
- What remains unproven
- The Asia-Pacific analysis has 55 respondents and is explicitly described by TM Forum as directional, not statistically definitive.
Keep in mind
The Asia-Pacific analysis has 55 respondents and is explicitly described by TM Forum as directional, not statistically definitive.
The public release does not disclose fieldwork dates, response rate, sampling frame, weighting or the distribution of respondents by operator and country.
Responses are self-reported and mix executive, architecture and engineering perspectives; the study does not independently audit live agents or measure incidents, productivity, service quality or customer outcomes.
The reported 2% is rounded, so it should not be converted into an exact operator count without the underlying data.
Source analysis · 1 linked source. Primary analysed source date: 28 October 2026.
Read the full report · 4 min The evidence, methods, implications and limitations
The production gap is larger than the strategy gap
Telecommunications companies increasingly describe AI as central to network operations and customer service, yet TM Forum's Asia-Pacific results suggest that production-scale agents remain unusual. Among 55 regional respondents, 89% called AI critical to competitive strategy, while 2% reported AI agents operating at scale in live core systems. A much larger 72% said their use remained experimental or bolted onto particular tasks, and 55% placed core-enterprise integration among their top three priorities for the next 18 months.
Those figures do not show that agent projects have failed. They show different stages of adoption being discussed in the same market. A chatbot, a technician copilot and an agent allowed to change a live network carry very different operational consequences. Moving from recommendation to action requires reliable data, documented decision rules, identity and access controls, observability, rollback and a clear point at which a person must intervene.[1]
The survey identifies barriers, not their measured cost
Regional respondents most often cited shortages of skills as a major obstacle, at 49%, followed by low-quality data at 43%, cost at 42% and accuracy or hallucination concerns at 39%. Another 38% said critical human decision-making was not documented well enough to scale AI safely and consistently. That last finding is practical: an agent cannot reliably reproduce or escalate a judgement that the organisation itself has never made explicit.
The denominator also limits the claim. The global research surveyed 216 IT executives from 111 operators across 72 countries, including C-suite leaders, network architects and software-engineering practitioners. The public release does not give fieldwork dates, a response rate, sampling frame, operator-size distribution or weighting, and respondents may not represent every operator. TM Forum itself describes the 55-person Asia-Pacific view as directional. Stronger evidence would independently verify deployed agents, the tasks they perform, intervention and rollback rates, incidents, customer effects and productivity over time.[1]
The distinction that matters
What 55 Asia-Pacific respondents reported
The percentages describe organisational views and claimed adoption stages, not audited market shares.
2%
Reported live agentic AI at scale in core systems
Rare in this directional regional sample
72%
Described AI as experimental or limited to specific tasks
Most reported activity remained before broad core integration
49% / 43% / 42%
Cited skills, data quality and cost as major obstacles
Organisational foundations may constrain deployment more than model access