Global carriers are pumping capital into AI at a speed that would only be read as strategic conviction, but the latest documented operational reality showed that more than 70% of telecom executives report that their AI automation in telecommunications is still trapped in fragmented pilot phases, according to GSMA Intelligence and HCLTech research.
HCLTech and Mobile World Live’s “Telecom Pulse Survey Report” for 2026, suggests that the fragmented pilot phases are generating proof-of-concept results that never transition into the core workflows that drive revenue the investment was trying to reach.
Global automation in telecommunications is now caught between ambition and execution, with 60% of telecom leaders identifying AI as a future revenue driver while only 25% feel ready to scale it confidently, believing their organizations can operate it across networks, operations, and enterprise services.
The 35-point gap cuts into TechCo ambitions such as legacy infrastructure, fragmented data, talent shortages, and slow product development delivery. HCLTech’s findings suggest AI connectivity creates value only when operators move beyond pilots toward measurable services, modern platforms and customer outcomes.
AI Ambition Runs Ahead of Telecom Readiness
Carriers have identified network automation, adaptive optimization, and AI customer care as the foundational categories that most likely to deliver the efficiency gains their margin structures desperately demand.
The report calls it an “execution gap,” centering automation in telecommunications to the struggle of turning experimentation into a commercial scale.
The gap between identifying those categories and operationalizing them at production scale is where the telecommunications industry’s AI strategy is currently stalled – where it has been stalled, with exacerbated frustration for the better part of two years.
According to GSMA Intelligence, operators expanding beyond basic connectivity into AI-enabled services can unlock around $400 billion in enterprise value. Yet, 60% of telcos still measure AI through internal cost savings and efficiency rather than customer experience and revenue growth.
The execution gap leaves telco as a service anchored to outdated infrastructure that many operators have not fully modernized. About one in five operators have yet to make significant investments in digital platforms and next-generation networks, creating a weak base for scalable AI products.
Product Velocity is Another Constraint
According to the report, 49% of respondents label slow product innovation as the main barrier to higher-value AI revenue, with 80% launchinge fewer than five digital products last year. For operators driving innovation in telecommunication industry workflows, the pipeline is commercialization rather than ambition.
Fragmented legacy Operations Support Systems (OSS) and Business Support Systems (BSS) environments worsen this hurdle by leaving operational data isolated.
Scaling cloud native telecom depends on integrating these systems, so AI models can use consistent operational context instead of disconnected information.
Talent shortages add pressure across AI, machine learning, data engineering, and cloud architecture. Meanwhile, 49% of respondents rate their transformation culture as moderate, highlighting resistance around intelligent automation for telecom industry as roles, processes and decision-making change.
Industry Collaboration Targets Scaling Problem
The organizational friction is the third layer of the exact same problem, where structural silos between operations, IT, customer experience, and commercial functions mean that AI initiatives launched within one division cannot access data, workflows or budget authority.
GSMA Intelligence data shows only 16% of AI deployments target network use cases even though networks account for 34% of operating expenditure.
That imbalance matters for AI connectivity because generic AI models often lack telecom-specific data, terminology, and network context. Open Telco AI addresses that gap through specialized models and benchmarks, while TM Forum is developing frameworks, standards and deployment pathways around its Open Digital Architecture.
Shared foundations could strengthen AI agents in telecom by giving autonomous systems normalized interfaces and trusted telecom data. Globe Telecom, for example, is exploring multi-vendor root-cause analysis using TM Forum technical solution packs alongside Open Telco AI models and agents.
AT&T’s specialized Open Telecom (OTel[) models are also being adapted into TM Forum environments, showing how agentic AI in telecom can move from operator-specific development toward interoperable assets across multi-vendor settings.
Such work supports telco as a service by connecting models, common frameworks, and practical deployment routes.
It also gives operators a pathway for innovation in telecommunication industry without requiring each company to build every layer independently.
For the emerging AI telco, partnerships may therefore matter as much as model performance. Nearly half of operators surveyed by HCLTech view partnerships as critical to innovation, reflecting the reality that hyperscalers, software providers and vertical specialists will remain part of the stack.
Closing the gap will still require investment, governance, and measurable commercial outcomes. Automation in telecommunications cannot remain an efficiency project if operators expect AI to support new revenue.
The next phase of innovation in telecommunication industry will depend on whether carriers can connect infrastructure modernization with deployable services.
That makes automation in telecommunications a test of execution, while the report’s “once in a generation” opportunity increasingly depends on scale, trust, speed and security.
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