Inside Libya’s Oil Industry: AI, Digital Leaders and Sustainable Digital Innovation

Research examining Libya’s oil and gas sector connects the use of AI decision support with better managerial decisions and stronger sustainable digital innovation.

An AI system can flag a maintenance problem or uncover wasted resources, but the value of that information depends on the decisions people make with it. Research examining Libya's oil and gas sector connects the use of AI decision support with better managerial decisions and stronger sustainable digital innovation. The findings put leadership and everyday working practices at the centre of a discussion often dominated by technology.

The study, "From AI Decision Support to Sustainable Digital Innovation: The Mediating Role of Decision-Making Quality and the Moderating Role of Digital Leadership in Libya's Oil and Gas Sector," was published in the journal Sustainability. Researchers Nawal Almabrouk Masoud Khaleel, Sami Mohammad, and Ayse Arslan examined how AI use, decision quality, and digital leadership relate to innovation. Their analysis draws on responses from 326 managers and decision-makers working across Libya's oil and gas industry.

Libya provides an important setting for this research because its energy industry faces ageing infrastructure, institutional instability, and shortages of digital expertise. Sustainable digital innovation means developing digital solutions that support business performance and address environmental, social, and economic needs. Applications discussed in the paper include predictive maintenance, emissions monitoring, smarter production systems, and improvements to supply chains.

How the Researchers Examined AI Use

The researchers collected survey responses between June and August 2025 from organisations involved in exploration, production, refining, distribution, oilfield services, and energy support. They distributed 520 questionnaires and retained 326 usable responses after screening incomplete or unsuitable submissions, producing an effective response rate of 62.7%. Sampling covered production, refining and processing, and support divisions within the National Oil Corporation and its affiliated companies.

Participants rated statements about their companies' reliance on AI, leadership practices, decision quality, and sustainable digital innovation using a five-point scale. Decision quality included effectiveness, meaning whether decisions delivered the desired results, and efficiency, meaning whether those decisions avoided unnecessary time, cost, and staff effort. The questionnaire was reviewed by specialists, tested with 12 managers outside the final sample, and prepared in English and Arabic.

The team used SmartPLS 4 to examine relationships among the survey measures and checked the estimates through 10,000 resamples of the data. Their framework drew on the Dynamic Capability View, which describes how organisations recognise opportunities, act on them, and reshape resources to meet changing demands. The Digital Leadership Perspective helped explain how leaders' skills and behaviour could influence the connection between AI information and managerial action.

Better Decisions Connect AI With Innovation

The results supported all six proposed relationships, including positive connections between AI decision support and sustainable digital innovation, AI use and decision quality, and decision quality and innovation. The model accounted for 40.2% of the variation in decision quality and 52.8% of the variation in sustainable digital innovation within the sample. These figures describe how well the model explained differences in survey responses, rather than percentage improvements caused by introducing AI.

Decision quality explained part of the relationship between AI use and sustainable innovation, suggesting that useful information needs effective decision processes to become practical change. A company might use AI analysis to identify an operational problem, assess possible responses, and choose a digital project that makes better use of resources. The connection between AI and innovation remained after accounting for decision quality, leaving room for other routes such as generating ideas or automating processes that support innovation.

The findings treat decision quality as a combination of achieving worthwhile results and reaching decisions without unnecessary demands on people or resources. A fast decision offers limited value if it directs investment towards an unsuitable project, and a sound recommendation can lose usefulness if approval takes too time. The study's prediction checks produced lower errors than a simpler linear benchmark across all measured decision-quality and innovation indicators.

Digital Leadership Strengthens the Connection

Digital leadership was associated with a stronger relationship between AI decision support and decision quality. The leadership measures covered communicating a clear digital vision, developing employees' skills and mindset, encouraging transformation, explaining digital values, and demonstrating personal competence with technology. These behaviours describe leaders who help staff understand how digital tools fit into their work and how their outputs should inform decisions.

The indirect connection between AI and innovation through decision quality was nearly twice as strong at high digital leadership levels as at low levels. Its estimated coefficient increased from 0.079 under low leadership conditions to 0.154 under high conditions, with an estimate of 0.117 at the average leadership level. This comparison concerns the strength of a modelled relationship and does not mean that stronger leadership doubled actual innovation output or financial returns.

The authors recommend combining AI investment with clear procedures for reviewing recommendations, approving decisions, and assessing results. Their proposals include leadership training for senior and middle managers, staff development, collaboration across departments, and opportunities to test digital ideas. For policymakers, the paper proposes stronger digital infrastructure, data governance, guidance on responsible AI use, and partnerships that support sustainable innovation across the energy sector.

What the Findings Can and Cannot Tell Us

The research captures participants' views at one point in time; it cannot establish that AI use caused better decisions or sustainable innovation. All measures came from respondents' own assessments, creating a risk that shared perceptions made some relationships appear stronger than they were. The researchers' checks found no strong indication of severe shared-response bias, but those checks cannot eliminate the concern.

The focus on one country and one industry limits how confidently the findings can be applied to other settings, and the survey did not distinguish between different types of AI systems. Future research could follow organisations over time, compare countries, gather evidence from different employee groups, and use operational records to measure outcomes directly. Examining AI readiness, team skills, and changing business conditions could clarify when particular systems deliver the most value.

The paper offers evidence that AI adoption, decision quality, and digital leadership belong in the same conversation about sustainable innovation. Its findings do not directly demonstrate reductions in emissions, leaks, or waste, since the study measured reported organisational practices and innovation rather than those physical outcomes. For readers considering what makes AI useful in a demanding industry, the research places attention on the people and processes that turn information into decisions.