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For Middle East oil and gas CIOs, the central digital-transformation challenge is no longer just proving that AI can help. It is scaling AI and other digital programs across assets without compromising cybersecurity, relying on inconsistent data, or getting stuck at the boundary between new platforms and legacy operational systems. ADNOC and Microsoft’s 2025 report points to cybersecurity and data quality as more prominent barriers than cost.
What are the biggest digital-transformation challenges?
The hurdles are connected: an AI project depends on trustworthy data, access to operational technology, secure connections, and people who can use the result in real operating conditions. A 2025 ADNOC and Microsoft report identifies three leading barriers among respondents:
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| Barrier | Share of respondents citing it | What it can mean for a transformation program |
|---|---|---|
| Cybersecurity risks | 49% — ADNOC and Microsoft, 2025 | Connecting systems or expanding access can increase exposure if security controls do not keep pace. |
| Data quality and consistency | 45% — ADNOC and Microsoft, 2025 | Incomplete, inconsistent, or poorly governed data can undermine analytics and AI outputs. |
| Lack of skilled talent | 39% — ADNOC and Microsoft, 2025 | Teams may lack the operational, data, AI, or security skills needed to implement and sustain solutions. |
These are survey findings, not a ranking of every company’s risks. Still, they help explain why a technically successful pilot may not translate into a dependable, repeatable operating capability.
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Cybersecurity across IT and operational technology
Energy companies must manage digital services alongside systems that support physical operations. A transformation plan therefore needs to account for security and resilience across IT and operational technology (OT), rather than treating cybersecurity as a final review before deployment. The Qatar-focused 2025 IEEE Access study also identifies cybersecurity concerns as a critical barrier in the oil-and-gas context.
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Data quality and interoperability
AI cannot compensate for data that is missing, inconsistent, or difficult to connect across systems. Data governance, lineage, shared definitions, and interoperability matter because an operator needs to understand where information came from and whether it is suitable for the decision at hand. ADNOC and Microsoft’s report content recommends unified data foundations and services aligned with OSDU, an industry data platform standard.
Legacy integration and field connectivity
New digital services have to coexist with established control systems and field environments. Integration can be difficult where systems were acquired or deployed at different times, use different data structures, or have limited connectivity. The 2025 IEEE Access study of Qatar’s oil-and-gas context identifies legacy-system integration as a critical barrier; it does not establish that every operator faces the same technical conditions.
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Skills, resistance, and adoption
Transformation requires more than buying technology. Staff need relevant skills, time to learn new workflows, and confidence that digital tools support rather than disrupt safe operations. The Qatar study names organizational resistance and skilled-worker shortages among the barriers. A CIO trade report likewise describes a gap between digital ambition and delivered value, emphasizing cultural transformation as part of closing it.
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ADNOC and Microsoft’s 2025 findings suggest that digital and AI adoption has moved beyond isolated experimentation for many respondents. The report says nearly nine in ten companies increased spending on AI and digital infrastructure since 2024; 73% deploy AI across multiple business functions; and one in five use agentic AI. Separately, 88% of surveyed leaders said scaling AI is essential to energy transformation. These are reported survey findings, not proof that all deployments are delivering operational value.
The practical shift is from asking whether a use case can work to asking whether it can be operated securely, maintained, and extended to other teams or assets. A pilot may rely on a small team, limited data, and one site. Scaling introduces questions about standard interfaces, data ownership, local operating conditions, support, and oversight. For companies working across countries, assets, or joint ventures, those issues can multiply.
How can CIOs scale AI beyond pilots?
A useful sequence is to establish the foundations before multiplying deployments. It is not a rigid checklist: a company may need to address security or connectivity earlier for a particular use case. But each stage should have an owner and an operational test before the next expansion.
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- Choose a measurable operating problem. Define the decision or workflow to improve, such as maintenance, production, energy efficiency, or emissions. Set a baseline and specify what evidence would count as improvement before selecting an AI solution.
- Check the data before modeling. Identify source systems, owners, access rights, quality issues, and lineage. Establish common definitions and integration requirements; consider unified data foundations and OSDU-aligned services where they fit the organization’s needs.
- Design security into the deployment. Assess the IT and OT environment, data flows, access, and operational dependencies. ADNOC and Microsoft’s report content recommends board-level attention to cybersecurity; CIOs can connect that oversight to clear accountability for implementation and ongoing operations.
- Integrate with field and legacy systems deliberately. Map interfaces and connectivity constraints, and decide how the new service will interact with existing systems. Validate the design in the operating context rather than assuming that a cloud or analytics platform will connect uniformly across assets.
- Prepare the workforce and operating model. Identify the skills users and support teams need, explain how the tool affects existing workflows, and build AI literacy. The report content recommends AI literacy and centers of excellence; these can help share expertise and governance across teams, but do not replace site-level ownership.
- Prove repeatability before expanding. Review whether the use case can be supported, secured, and monitored beyond its initial team or site. Expand in stages, checking data suitability, integration effort, and local operating requirements at each new asset or business unit.
At every stage, distinguish a model that produces a plausible output from a solution that operators can rely on in the intended workflow. Scale should follow demonstrated operational readiness, not simply the number of pilots completed.
What do regional examples show?
ADNOC and Microsoft: broadening the AI agenda
The 2025 ADNOC and Microsoft report describes increased investment, use across multiple functions, and emerging use of agentic AI among respondents. Its recommendations—unified data foundations, OSDU-aligned services, board-level cybersecurity attention, AI literacy, and centers of excellence—address organizational capabilities needed to expand AI use. The survey figures describe respondents and should not be read as adoption rates for every Middle East operator.
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Saudi Aramco: a portfolio of digital initiatives
Aramco’s public materials describe work spanning smart cloud, cybersecurity, AI, big data, and industrial IoT. They also describe an eMarketPlace for Saudi supply chains, advanced-computing work with NVIDIA in 2025, and an AI rollout at the Fadhili Gas Plant with Yokogawa during October 2024–April 2025. These examples show the range of initiatives being pursued; the cited materials do not, by themselves, establish comparable business outcomes across the programs.
Qatar: barriers beyond technology
A peer-reviewed 2025 IEEE Access study focused on Qatar identifies organizational resistance, skills shortages, cybersecurity concerns, and legacy integration as critical challenges. Its value is in showing that transformation constraints include people and existing systems as well as technology. Its findings are specific to the study’s Qatar context, not a universal measurement of the region.
How should CIOs judge whether transformation is working?
Track whether the program improves a defined operating outcome and whether it can do so reliably beyond the first deployment. The relevant measures depend on the use case; the available findings do not provide a single regional benchmark or target. A practical review can ask:
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- Operational value: Is there a measured change in the targeted maintenance, production, energy-efficiency, or emissions workflow?
- Data readiness: Can teams identify data sources, quality issues, lineage, and ownership well enough to trust the intended use?
- Security and resilience: Are cybersecurity responsibilities and the relevant IT/OT dependencies understood and managed?
- Integration effort: Can the solution work with the legacy systems and field connectivity at the next intended site?
- Adoption: Are users trained and using the tool in the workflow for which it was designed?
- Repeatability: Can the organization support deployment across additional assets, countries, or joint ventures without losing local fit or oversight?
If the answer is unclear on the intended outcome, security, or repeatability, adding more sites may multiply unresolved problems rather than value. A deliberate sequence—from data and security through integration, people, and staged expansion—gives CIOs a more credible path from experimentation to sustained operations.
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