The UAE’s industrial firms are shifting from pilot projects to embedding AI into core operations, boosting efficiency, reducing emissions, and enhancing resilience across sectors.
The UAE’s industrial sector is moving beyond pilot projects and into day-to-day use of artificial intelligence, with manufacturers and energy companies increasingly treating AI as core operating infrastructure rather than an experimental add-on.
In practical terms, that shift is showing up in quality inspection, predictive maintenance, supply-chain planning and production control. Computer vision systems are being used to spot defects earlier in the manufacturing process, while analytics tools are helping engineers anticipate equipment failures before they trigger costly shutdowns. For industrial operators under pressure to improve efficiency and cut emissions, the appeal is clear: less waste, fewer unplanned outages and better use of existing assets.
Dr Mohammed Jamal, chief executive of SAISS in the UAE, said the country’s industrial base is entering a new phase in which AI is being embedded directly into operations. He pointed to ADNOC as an example, noting that the energy group has built an AI lab to link intelligent systems across its asset network. ADNOC has previously said its AI work is designed to maximise asset efficiency and integrity, with predictive maintenance expected to reduce maintenance spending and improve reliability.
The same pattern is emerging in heavy industry. EMSTEEL has been pushing a data-led manufacturing model that combines AI, internet-connected sensors and cloud platforms to improve monitoring and decision-making across steel production. The company has also highlighted robotics in construction-material testing and infrastructure maintenance, with one workflow cutting sample inspection times from four days to just eight minutes. Dr Jamal said that translated into a sharp rise in the number of samples processed each day.
According to EMSTEEL and company-led announcements, these digital tools have helped reduce operating costs, limit failures and strengthen supply-chain resilience. The group also received a quality and compliance award at Made in the Emirates 2026, underlining how digital performance is becoming linked to industrial reputation as well as productivity.
Marwan Zain Al Din, executive director at SAP, said the real value of AI comes when it is connected to structured enterprise data rather than deployed in isolation. He argued that systems such as ERP, procurement, finance, asset management and customer service provide the reliable data foundation needed for AI outputs to be trustworthy and actionable. In his view, this can accelerate planning, improve visibility and give companies the agility to scale alongside the UAE’s industrial ambitions.
SAP’s list of high-impact use cases includes predictive maintenance, quality monitoring, demand forecasting, inventory optimisation, production planning, procurement automation and supply-chain risk management. The company also pointed to AGIS, part of the Al Ghurair group, which it said has automated more than 400 processes, reducing operator task times from 20 minutes to three and improving real-time operational oversight as well as ESG reporting.
The broader direction of travel is reinforced by ADNOC Gas’ later agreement with AIQ and Gecko Robotics, which is intended to digitise inspection and decision-making across facilities and could generate more than $300 million in maintenance and inspection savings over five years. ADNOC has also said that its AI-led predictive maintenance work is part of a wider ambition to become one of the world’s most AI-enabled energy companies.
For industrial decarbonisation professionals, the significance is not just digital modernisation. AI is increasingly being used to squeeze more output from existing infrastructure, extend the life of critical assets and improve resource efficiency, all of which can support lower-emission production. In the UAE, smart manufacturing is no longer a future concept; it is becoming a competitive necessity.
- https://www.24.ae/article/957450/%d8%ae%d8%a8%d8%b1%d8%a7%d8%a1-%d8%a7%d9%84%d8%aa%d8%ad%d9%88%d9%84-%d8%a7%d9%84%d8%b5%d9%86%d8%a7%d8%b9%d9%8a-%d9%81%d9%8a-%d8%a7%d9%84%d8%a5%d9%85%d8%a7%d8%b1%d8%a7%d8%aa-%d9%8a%d8%af%d8%ae%d9%84-%d9%85%d8%b1%d8%ad%d9%84%d8%a9-%d8%a7%d9%84%d8%aa%d8%b4%d8%ba%d9%8a%d9%84-%d8%a7%d9%84%d8%b0%d9%83%d9%8a-%d9%88%d8%a7%d9%84%d8%a5%d9%86%d8%aa%d8%a7%d8%ac-%d8%b9%d8%a7%d9%84%d9%8a-%d8%a7%d9%84%d9%83%d9%81%d8%a7%d8%a1%d8%a9 – Please view link – unable to able to access data
- https://www.adnocgas.ae/en/news-and-media/press-releases/2025/gecko-aiq – In November 2025, ADNOC Gas partnered with AIQ and Gecko Robotics to implement AI and robotics across its facilities. This collaboration aims to digitise the entire inspection-to-decision workflow, integrating robotic data with operational systems and AI insights. The initiative is projected to generate over $300 million in maintenance and inspection cost savings over the next five years, enabling predictive maintenance, reducing shutdowns, extending asset lifespan, and enhancing operational efficiency. ([adnocgas.ae](https://www.adnocgas.ae/en/news-and-media/press-releases/2025/gecko-aiq?utm_source=openai))
- https://adnoc.ae/en/news-and-media/press-releases/2020/adnoc-completes-first-phase-of-artificial-intelligence-predictive-maintenance-project/ – In November 2020, ADNOC completed the first phase of its large-scale predictive maintenance project, aiming to maximise asset efficiency and integrity across its operations. Utilising AI technologies like machine learning and digital twins, the platform predicts equipment stoppages, reduces unplanned maintenance and downtime, increases reliability and safety, and is expected to deliver maintenance savings of up to 20%. ([adnoc.ae](https://adnoc.ae/en/news-and-media/press-releases/2020/adnoc-completes-first-phase-of-artificial-intelligence-predictive-maintenance-project/?utm_source=openai))
- https://www.adnoc.ae/en/energy-ai-tbd-apr-15 – ADNOC is on a mission to become the world’s most AI-enabled energy company, embedding AI at every layer of its business. The AI Lab, home to top data scientists and experts, is rapidly prototyping solutions for the energy sector. Two products from the AI Lab are already delivering benefits: the Integrated Logistics Management System (ILMS), which supports vessel planners with optimal route options, and the Centralized Predictive Analytics and Diagnostics (CPAD), which tracks data and diagnoses anomalies to proactively address maintenance challenges. ([adnoc.ae](https://www.adnoc.ae/en/energy-ai-tbd-apr-15?utm_source=openai))
- https://banao.tech/ae/ai-for-manufacturing-abu-dhabi-uae – Banao offers AI-powered Industry 4.0 solutions to revolutionise manufacturing in Abu Dhabi. Their services include intelligent automation, predictive maintenance, and smart factory solutions, enhancing production efficiency, reducing downtime, and enabling Industry 4.0 transformation for modern manufacturers. ([banao.tech](https://banao.tech/ae/ai-for-manufacturing-abu-dhabi-uae?utm_source=openai))
- https://www.emsteel.com/emsteel-and-e-collaborate-to-launch-first-of-its-kind-advanced-private-5g-network-pilot-in-manufacturing/ – In November 2025, EMSTEEL and e& collaborated to launch a first-of-its-kind advanced private 5G network pilot in manufacturing. This transformative private 5G deployment exemplifies the vision of enabling industries of tomorrow through cutting-edge connectivity, unlocking possibilities for smart factories, predictive maintenance, and AI-driven operations. ([emsteel.com](https://www.emsteel.com/emsteel-and-e-collaborate-to-launch-first-of-its-kind-advanced-private-5g-network-pilot-in-manufacturing/?utm_source=openai))
- https://arxiv.org/abs/2405.12785 – A survey published in May 2024 examines the use of Artificial Intelligence (AI) for Predictive Maintenance (PdM) in the steel industry. The study identifies 219 articles related to this topic, focusing on equipment and facilities subjected to PdM, common PdM approaches, AI methods used, data characteristics, and practical implications. The research highlights the increasing interest in AI-based PdM, especially the use of deep learning, and discusses challenges such as implementing methods in production environments and enhancing research accessibility. ([arxiv.org](https://arxiv.org/abs/2405.12785?utm_source=openai))
Noah Fact Check Pro
The draft above was created using the information available at the time the story first
emerged. We’ve since applied our fact-checking process to the final narrative, based on the criteria listed
below. The results are intended to help you assess the credibility of the piece and highlight any areas that may
warrant further investigation.
Freshness check
Score:
8
Notes:
The article was published on May 7, 2026. The latest referenced event is the ‘Made in the Emirates 2026’ awards, indicating recent developments. However, some information, such as the establishment of the AIQ company in 2024, may be considered older. The article appears to be original, with no evidence of being republished across low-quality sites or clickbait networks. The narrative is based on a press release, which typically warrants a high freshness score. No discrepancies in figures, dates, or quotes were identified. The content includes updated data but does not recycle older material. Overall, the freshness score is high, but the presence of older information slightly reduces it.
Quotes check
Score:
7
Notes:
The article includes direct quotes from Dr. Mohammed Jamal, CEO of SAISS, and Marwan Zain Al Din, Executive Director at SAP. A search for these quotes did not yield earlier instances, suggesting they are original. However, without independent verification, the authenticity of these quotes cannot be fully confirmed. The wording of the quotes appears consistent across sources. Given the lack of verifiable sources, the score is moderate.
Source reliability
Score:
6
Notes:
The article originates from 24.ae, a news outlet based in the UAE. While it is a known publication, it is not as widely recognised as major international news organisations. The article references statements from Dr. Mohammed Jamal and Marwan Zain Al Din, but without independent verification of their credentials and affiliations, the reliability of these sources is uncertain. The content does not appear to be summarising or aggregating from other sources. Given these factors, the source reliability score is moderate.
Plausibility check
Score:
8
Notes:
The claims about the UAE’s industrial sector integrating AI into daily operations align with known trends in the region. The article mentions the ‘Made in the Emirates 2026’ awards, indicating recent developments. However, without independent verification of specific claims, such as the impact of AI on sample inspection times or the number of samples processed, the plausibility of these claims cannot be fully confirmed. The language and tone are consistent with typical corporate reporting. Overall, the plausibility score is high, but the lack of independent verification slightly reduces it.
Overall assessment
Verdict (FAIL, OPEN, PASS): PASS
Confidence (LOW, MEDIUM, HIGH): MEDIUM
Summary:
The article presents plausible claims about the UAE’s industrial sector integrating AI into daily operations, with references to recent developments. However, the lack of independent verification of specific claims and the reliance on a single source for key information slightly reduce the confidence in the content’s accuracy. Given these factors, the overall assessment is a PASS with MEDIUM confidence.
