Across the GCC, public infrastructure organizations are operating under an unprecedented level of political commitment to AI. National transformation agendas and leadership mandates have elevated AI from a technology priority to a governmental imperative. Yet commitment and capability are not the same thing. As delivery expectations intensify, the distance between strategic ambition and operational readiness is emerging as the defining challenge for public sector leaders in the region.
AI across the Gulf
Industry deep dive: Technology, Media & Telecoms
By Nizar Hneini and Jawad Shaikh
From rapid adoption to architectural coherence
Telecom, media and technology organizations should, on paper, be the easiest places to scale AI. They are digitally native, data rich, and technically literate. Leadership trust in AI is higher here than in any other sector.
Organizations show high levels of AI adoption and maturity: just over half (53%) report having fully documented digital and AI strategies, and alignment with national strategies is stronger than the cross-industry average (62% vs 49%).
Despite this, scaling outcomes are mixed, with around one-third of respondents reporting AI as fully scaled, compared to 46% who report that scaling is underway. AI prioritization in this sector is frequently leadership-driven, a higher share than the cross-industry average report leadership-led, ad hoc prioritization of AI initiatives (56% vs 43%).
The result is a sector that moves fast, experiments broadly and struggles to convert activity into consistent enterprise-scale impact.
Where AI is creating value today
TMT organizations report widespread AI activity across network operations, customer engagement and internal productivity. 58% cite customer and citizen experience and 55% cite cost savings and productivity gains as the primary business value they expect AI to deliver in the next two years, both the highest of any sector.
End-user adoption: where scaling succeeds or stalls
Trust in AI outputs is highest in this sector. Just over half of respondents (51%) report full trust in AI-generated outputs, while only 3% report no trust.
At the same time, adoption is frequently driven by leadership decisions rather than bottom-up demand. AI initiatives are often launched quickly across multiple areas, reflecting strong top-down momentum. This results in broad uptake, but also increases the number of parallel initiatives.
What is holding AI back from enterprise scale
Technology fragmentation is the most prominent scaling challenge in TMT, cited by 47% of respondents. This is compounded by tech readiness and integration challenges, which 62% identify as a barrier to scaling specific use cases.
Data coherence is a second structural constraint. 43% cite limited access to unstructured data and 44% cite compliance and privacy restrictions on data usage, both above the cross-industry average. Critically, 41% report that data is siloed across departments, meaning that even where data exists in volume, it cannot be accessed and combined effectively across the organization.
What this means for TMT sector leaders – the 3 imperatives
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1. Proliferation of AI activity, but limited enterprise scaling
Across the TMT sector, the race to leverage AI has triggered a wave of experimentation across network operations, customer engagement, and internal productivity, yet these efforts remain fragmented. There is a proliferation of use cases, but organizations continue to struggle to scale these efforts and translate them into measurable value.
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2. The bottleneck is data coherence
Even in data-rich industries such as telecom, players struggle to access and combine the right data at scale. Fragmented data sources, unclear “single source of truth”, and limited access to unstructured data impede AI initiatives from scaling and prevent models from being reused across functions.
As a result, teams often struggle to access to the right data and frequently rebuild data pipelines and models for individual initiatives, slowing scaling and limiting impact.
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3. Strong AI governance is required to enable scale
To scale effectively, TMT organizations need a clear governance structure, a central AI hub with sufficient seniority to overcome barriers, help prioritize investments, and arbitrate on AI topics across different functions.
A key priority for such a hub is to address the data challenge by instituting common data standards, clarifying access protocols for structured and unstructured data, and enabling a single source of truth. It should also define a consistent methodology to evaluate and prioritize AI use cases, underpinned by a clear business case and a roadmap to scale.
In addition, the hub can serve as a central point of expertise, supporting business units in translating ideas into structured use cases with defined value and scaling potential.
With the right governance and data foundations in place, organizations can move beyond fragmented experimentation and focus AI investments on scalable use cases that deliver measurable business value.
To explore the full data and insights behind these trends, download the complete AI across the Gulf: From ambition to scalable impact report here
Explore our other AI across the GCC sector insights
Further readings
