Industry
Communications, Media and Information Services
Connect. Curate. Comprehend: Unlocking Value Beyond Boundaries
Highlights
- Telecom AI is moving from pilots to scale, with operators focusing on networks, operations, and customer experience, while GenAI and early agentic AI point to a future of more autonomous operations.
- While the ambition to scale AI is clear, legacy IT remains a major barrier. Data quality and integration are the top constraints, cited by 62% of operators, while 47% report a lack of scalable infrastructure.
- AI adoption across telco business functions is maturing, led by networks and operations (81%), followed by customer experience (71%) and service assurance (60%).
- Telecom operators are increasingly focusing on agentic AI across core value chains, while AI leaders stand apart through strong execution using hybrid operating models, clear metrics, trusted partnerships, effective governance and talent readiness.
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From innovation to action
AI adoption in the telecom industry has reached a defining transition point, moving from experimental pilots to enterprise-wide deployment.
Artificial intelligence (AI) is becoming a key driver of change in the telecom industry. It is helping operators modernise networks, improve customer experience, and run operations more efficiently. As AI moves beyond small pilots, telecom leaders are now focused on using it at scale, while dealing with challenges such as legacy systems, governance requirements, and skills shortages. While building rapid prototype models has enabled AI explorations, the focus is now on setting up AI factories and AI Labs to accelerate AI adoption at scale across the enterprise.
To understand how the industry is progressing, TCS conducted a global survey of 300 telecom executives across North America, Europe, the UK, APAC, and MEA. The research explores where AI is delivering measurable value today, how operators are prioritising investments across networks, IT, and customer experience, and what barriers continue to limit adoption at scale. The findings point to a clear inflection point for the industry – one where AI is no longer optional, but central to future competitiveness.
Focus on AI foundation
Scaled and accelerated AI deployments focus on three key elements: systems, infrastructure and data.
Modern IT systems with next-gen infrastructure are the key enablers of future AI value, not merely a supporting layer. Operators increasingly recognise that scalable AI outcomes depend on modernised networks, platforms, and integrated data architecture.
To support this shift, nearly two-thirds of telecom operators are investing in AI infrastructure across cloud, edge, and on-premises environments. Rather than building or expanding isolated applications, operators are prioritising strong foundations over isolated investments.
AI is also rapidly becoming a central pillar of telecom infrastructure modernisation, with 73% of respondents identifying legacy system modernisation as a key focus for AI use within IT.
With data being the fuel for accelerated AI adoption, operators are keen to modernise their data estates with robust data governance to ensure quality and availability of enterprise data for agentic actions and real-time decision making.
Navigating AI at scale
Scaling AI is a capability challenge, not a cost problem.
Data readiness, talent availability, governance maturity, and business-IT alignment, not budgets, are the decisive factors that determine whether AI pilots translate into enterprise-scale value.
The research shows that nearly half of telcos are now scaling AI deployments, signalling a clear shift toward enterprise-wide adoption. However, progress slows sharply once organisations attempt to industrialise AI. The biggest constraints are not budgetary, as only a small fraction cite funding as a barrier; they are structural. Data quality and integration issues, fragmented legacy infrastructure, and the lack of standardised data pipelines continue to limit the ability to operationalise AI consistently across functions.
Equally significant are capability and operating-model challenges. More than half of operators report skill gaps, while a similar proportion struggle with business–technology misalignment when moving from experimentation to production. AI initiatives often stall because ownership between business and IT remains unclear; governance processes are slow, and decision rights are fragmented. As a result, while innovation happens at the edges, scaling requires stronger central coordination, especially for infrastructure, governance, and deployment, while still allowing local teams the autonomy to innovate.
Operationalising success
To overcome these constraints, telecom operators are rethinking how AI is implemented, governed, and scaled.
There is a clear shift away from ad hoc experimentation towards structured operating models, with most operators adopting hybrid AI structures that combine centralised governance with decentralised execution, balancing speed with control and innovation with accountability.
The survey shows that infrastructure and AI governance remain the most centralised activities, as operators prioritise platform stability, data security, and regulatory compliance. At the same time, partnership models are evolving, with greater reliance on outcome‑based engagements to ensure measurable business impact. Growing interest in sovereign and controlled AI deployments further reflects rising regulatory and data‑ownership considerations.
Together, these trends underline a clear shift: AI success in telecom is no longer defined by technology choices alone, but by the ability to orchestrate operating models, governance, and partnerships at scale.
Execution drives AI leadership
As AI adoption matures, competitive advantage will be shaped by execution excellence rather than ambition.
To translate AI intent into sustained business value, telecom operators must focus on a few critical priorities that strengthen foundations, enable scale, and ensure measurable outcomes.
- Strengthen data foundation: Prioritise data quality enhancement, integration and interoperability to prevent fragmented and sub-scale AI investments.
- Build talent alongside technology: Invest in centres of excellence and structured enablement programs to address skills gaps and scale AI effectively.
- Anchor AI to core value pools: Focus initiatives on networks, operations, and customer experience, where impact can be measured against long‑term business outcomes.
- Adopt hybrid operating and governance models: Enable faster innovation with strong oversight across risk, compliance, and strategic alignment.
- Shift to outcome-driven partnerships: Align partner engagement models to business KPIs, ensuring accountability for results, not just implementation.
- Prepare for regulatory and sovereignty requirements: Proactively plan for data control, IP ownership and governance, while leveraging partner ecosystems for scale and innovation.
- Defining success metrics upfront: Move beyond simple ROI to balanced scorecards covering financial, operational and customer metrics.