Who Lets AI Take Control? The Token Bill Is Coming AI Warned but Nobody Took Action Keeping Field Intelligence Alive
Who’s Conducting Energy’s AI Orchestra? ISSUE 71 | NOVEMBER 2026
CEO Adam Soroka Editor Mark Venables
The work that makes AI useful An AI application can identify a developing equipment problem long before an intervention is agreed. The prediction may be sound, yet an engineer still needs reliable asset information and sufficient evidence to recommend action within established operating procedures. If those connections are missing, faster analysis does little to shorten the time between warning and response. That concern runs through this edition of Future AI, which examines what it takes to turn digital capability into dependable operational practice. We have concentrated on the point where technology meets existing systems and established engineering responsibilities. AI can make relevant information easier to find and evaluate, but its outputs must reach the right people through workflows that can withstand the demands of a working asset.
Who Lets AI Take Control? The Token Bill Is Coming AI Warned but Nobody Took Action
Our lead feature considers the growing interest in agentic AI. Software that can investigate a problem, retrieve technical information and initiate a workflow has clear attractions for operators managing complex facilities. It also raises difficult questions about authority. An agent preparing a maintenance recommendation is performing a very different task from one permitted to alter a production system. The permissions and safeguards surrounding those activities deserve as much attention as the intelligence of the underlying model.
Keeping Field Intelligence Alive
Who’s Conducting Energy’s AI Orchestra? ISSUE 71 | NOVEMBER 2026
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Several of our contributors examine why that foundation remains so important. RIVE explores the case for greater control over AI processing capacity and sensitive industrial information, while TBH considers the persistent weaknesses in major project delivery that better forecasting cannot resolve. Its argument is particularly relevant to anyone who has seen warning signs recorded in a project report without prompting action. Rosenxt brings an engineering perspective to structural digital twins, examining the validation needed before monitoring data can support decisions about offshore assets. Atelic, meanwhile, considers how knowledge might be carried through the life of a field or well rather than repeatedly rediscovered by different teams. These are different
applications, but both depend on information retaining its meaning as circumstances change. In my accompanying opinion piece, I argue that too much discussion still centres on proving that AI works, when operators need to establish who will own it once the demonstration is over. Maintaining data relationships, managing model changes and embedding outputs into established responsibilities may be less exciting than a new application, but those are the conditions under which benefits become repeatable. Oil and gas has good reasons to be cautious about technologies that promise autonomy. Its facilities operate under physical constraints that software cannot wish away, and its engineers carry responsibilities that should not be obscured by impressive interfaces. The industry’s opportunity is to use AI to make their work better informed and timelier, while keeping a clear account of who decides what happens next.
Mark Venables Editor
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DELIVERING TOMORROW’S
CRITICAL INFRASTRUCTURE Global Advisors. From strategy to delivery
OUR EXPERTISE STRATEGIC ADVISORY PLANNING PROJECT CONTROLS DISPUTE RESOLUTION
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06 Oil and gas AI agents must earn the right to act AI agents could transform how oil and gas operators investigate problems and coordinate operational decisions, but the ability to act independently introduces risks that conventional AI applications do not face. Their successful deployment will depend on trusted industrial data, shared asset models and governance that keeps autonomous activity within clearly defined engineering and safety boundaries.
10 Oil and gas cannot keep paying for AI one token at a time As oil and gas companies look to scale AI across their operations, the cost of processing vast quantities of industrial information threatens to become a significant barrier. Building their own AI capacity could give operators greater control over costs, sensitive data and the intelligence needed to make better operational decisions.
14 AI can spot the warning signs, but it cannot make project teams act Artificial intelligence promises to transform how major oil and gas projects forecast delays and manage delivery risks, but better information cannot compensate for weak planning or poor decisions. Realising its potential demands stronger project controls, reliable performance data and a willingness to confront problems before they become expensive disputes.
20 Offshore digital twins must earn the engineer’s trust Offshore structural digital twins promise sharper assessments of ageing installations, but their usefulness depends on sound models, credible measurements and a clear engineering purpose. A developing international standard could bring greater discipline to their deployment while keeping responsibility for safety-critical decisions firmly with engineers. www. futureaiconference.com/middle-east-2026/ | Future AI Middle East 2026 03
26 Better decisions by design: Industrial intelligence for complex operations
42 From engineering data to actionable industrial AI
30 Closing the loop: Digital Twins for smarter infrastructure
42 The future of AI Is an orchestra and energy is its next stage
Pedro Alcântara Nunes Neto at Falkor examines how industrial intelligence can help energy operators build greater resilience by connecting operational data, engineering context and simulation before conditions become critical. He argues that the real value of digital twins and AI lies in improving the speed and quality of decisions, giving teams the context to understand change, test options and act with greater confidence.
Borja García de Soto, Associate Professor of Civil and Urban Engineering at NYU Abu Dhabi and Co-PI for Sand Hazards and Opportunities for Resilience, Energy, and Sustainability (SHORES), explains how the SHORES Center is connecting sensing, engineering models, artificial intelligence and robotics to develop Digital Twins that move infrastructure from monitoring toward prediction, decision support and intelligent action.
34 Governing intelligence: why ai governance will define the next industrial revolution As artificial intelligence moves beyond decision support towards increasingly autonomous industrial operations, effective governance is becoming essential to managing risk and maintaining accountability. Arvin Singh, Partner – Head of Digital Advisory at Wood, explores why trusted data, continuous assurance and clearly defined human oversight will determine whether organisations can deploy AI safely and at scale.
38 Agentic driven-upstream: keeping intelligence alive across the life of a field Pedro Alcântara Nunes Neto at Falkor examines how industrial intelligence can help energy operators build greater resilience by connecting operational data, engineering context and simulation before conditions become critical. He argues that the real value of digital twins and AI lies in improving the speed and quality of decisions, giving teams the context to understand change, test options and act with greater confidence. 04 Future AI Middle East 2026 | www. futureaiconference.com/middle-east-2026/
Derek Middlemas, CEO of DIGATEX, explains why industrial AI depends less on sophisticated models than on the quality, structure and traceability of the engineering data beneath them. He examines how turning fragmented drawings, asset records and material data into trusted information can reduce engineering effort, improve maintenance decisions and create a more reliable foundation for AI.
Wassim Ghadban, Global SVP, AI & Digital Engineering at Kent, argues that the future of AI in energy will depend on specialised agents working together across engineering, construction and operations rather than in isolated applications. He explores how shared data, orchestration and human oversight can connect intelligence across the asset lifecycle to improve engineering quality, operational resilience and decision-making.
42 Final Word: The oil and gas AI ecosystem must be built around work, not technology
Oil and gas companies can demonstrate the value of AI in individual applications yet struggle to embed those gains in everyday operations. Mark Venables, Technology Writer and Editor, argues that scaling AI depends less on buying better models than on building the data and organisational structures that make their outputs useful.
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Oil and gas AI agents must earn the right to act AI agents could transform how oil and gas operators investigate problems and coordinate operational decisions, but the ability to act independently introduces risks that conventional AI applications do not face. Their successful deployment will depend on trusted industrial data, shared asset models and governance that keeps autonomous activity within clearly defined engineering and safety boundaries. 06 Future AI Middle East 2026 | www. futureaiconference.com/middle-east-2026/
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n unexpected increase in vibration from a gas export compressor does not automatically justify a maintenance intervention, much less a change to operating conditions. The reading might indicate mechanical deterioration, a change in operating load or a faulty sensor. An engineer investigating the problem would need to examine equipment history and operating conditions, establish whether maintenance was already underway and determine which actions were technically justified. An AI agent could gather that information, investigate possible explanations and initiate the next stage of the workflow. Whether it should be permitted to act on its findings is a considerably more difficult question. If the software investigating the compressor alert can also raise a work order or change a maintenance schedule, an error in its reasoning may have consequences beyond an inaccurate technical assessment. Agentic AI makes these connected workflows possible by allowing models to retrieve information, select appropriate software tools and carry out successive tasks within defined permissions. For oil and gas operators, the challenge is ensuring that these systems work with accurate asset information, respect established engineering procedures and recognise when a decision requires human approval rather than further automated action.
The industrial context problem Oil and gas companies already possess much of the information required for sophisticated agentic applications, although it is rarely organised around the questions an agent needs to answer. Process historians record operating measurements, engineering repositories hold technical specifications and enterprise maintenance systems manage work orders. A compressor might have different identifiers across these environments, while individual measurements only become meaningful when their units, operating state, timestamps and quality are understood. Retrieving the correct vibration trend is of limited value if an agent cannot establish which machine it belongs to or whether that machine was operating when the measurements were recorded. This explains the growing importance of common information models and semantic interoperability. Rather than simply connecting applications, operators need consistent definitions of equipment and the relationships between physical assets and their associated information. Established industry initiatives provide useful foundations. The OSDU Data Platform addresses standardised access to energy data, particularly in subsurface workflows, while OPC UA supports information modelling across industrial automation. The OPC UA MDIS companion specification, for example, defines interfaces and common information structures for communication between subsea production control and topside systems. Knowledge graphs offer a further means of representing industrial context by connecting equipment, measurements, engineering documents and operational relationships. An agent investigating
a compressor fault could use these connections to identify the relevant sensor, retrieve the applicable maintenance history and establish which procedures relate to the equipment configuration. However, graph relationships must be verified and maintained as facilities change. A component replacement that has not been reflected in the information model can leave an agent working from an apparently coherent but incorrect representation of the plant. Source provenance and the validity of information over time are therefore essential. Large language models also require access to information that can be distinguished from instructions. Retrieval-augmented generation can supply relevant technical documentation, but retrieving a document does not establish that it is approved, current or appropriate for a particular operating condition. Agents must recognise access restrictions, conflicting sources and missing evidence rather than treating every retrieved statement as equally authoritative. Reliable industrial reasoning depends as much on the integrity of this surrounding information architecture as on the capabilities of the language model itself.
Defining the limits of autonomy Some of the most promising applications involve coordinating established workflows rather than intervening directly in industrial processes. An agent could investigate a condition-monitoring alert, determine whether an inspection is already scheduled and prepare a draft work order supported by relevant engineering evidence. It might identify unavailable spare parts or conflicting maintenance activities that require attention before work proceeds. Such applications could reduce the time engineers spend searching across disconnected systems while leaving technical diagnosis and approval with accountable personnel. The degree of autonomy granted to an agent should reflect the consequences of the task. Reading maintenance records, drafting a work request and changing an approved schedule carry different levels of risk. Organisations can assign narrowly defined permissions, require approval before consequential transactions and restrict agents to approved functions. Those restrictions need to be enforced by the applications and interfaces through which agents operate. Instructions within a language-model prompt cannot provide an adequate security boundary, particularly when the agent can invoke tools capable of modifying operational or commercial records. For safety-critical processes, more fundamental limitations apply. AI-assisted operational planning must remain distinct from the protection functions implemented through safety-instrumented systems. IEC 61511 establishes requirements for the lifecycle management of these systems in the process industries, and an agent’s recommendation cannot substitute for the independently engineered safeguards required by a particular installation. Connections between enterprise AI applications and operational
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technology must also respect established cybersecurity requirements, including the riskbased principles covered by ISA/IEC 62443. The ability to exchange information with a control environment does not imply permission to issue control instructions. Operators may therefore find greater value in progressively expanding narrowly bounded capabilities than in pursuing unrestricted autonomy. An initial implementation could operate in read-only mode before progressing to the preparation of transactions requiring human approval. Administrative actions with well-defined outcomes might subsequently be automated where appropriate validation and recovery mechanisms exist. The acceptable boundary will depend on the workflow, the consequences of failure and the ability to detect unexpected behaviour before it causes harm. Trust must be demonstrated The technical challenge extends beyond whether an agent can complete a task under normal conditions. Industrial systems must also establish how it behaves when information is missing, contradictory or deliberately manipulated. A credible evaluation programme should test
scenarios involving incorrect equipment identifiers, obsolete procedures, sensor failures and requests that exceed an agent’s authority. The correct response may be to stop and request clarification rather than produce a recommendation. Prompt injection presents another concern, particularly when instructions embedded in retrieved documents attempt to redirect an agent’s behaviour or exploit excessive access permissions. Transparent operation requires more than recording the final answer. Operators need evidence of which information an agent consulted, which tools it invoked, what permissions were applied and who authorised any resulting action. The US National Institute of Standards and Technology’s AI Risk Management Framework provides a voluntary foundation for managing AI risks, although industrial implementations must translate its principles into controls appropriate to operational consequences. Model updates, changes to connected systems and alterations in engineering information can all affect an agent’s behaviour, making continued validation necessary after deployment. These requirements also have direct commercial implications. An agent that produces technically plausible recommendations but regularly requires extensive manual correction may offer little improvement over existing processes. Performance should be assessed through the quality of the evidence supporting its conclusions, the time required to resolve operational problems and the reliability of its interactions with established workflows. Success depends on reducing the effort between identifying an issue and implementing an authorised response, without creating additional operational exposure. Agentic AI could ultimately allow engineers to work across industrial information systems with far less manual intervention, while supporting decisions informed by a more complete understanding of equipment condition and operational constraints. Achieving that capability will require the same engineering discipline applied to other technologies operating in high-consequence environments. Oil and gas operators already understand the importance of defining what equipment and personnel are authorised to do, under which conditions and with what safeguards. AI agents will need to satisfy equally rigorous expectations before they can be trusted to move from interpreting industrial information to acting upon it.
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Oil and gas cannot keep paying for AI one token at a time As oil and gas companies look to scale AI across their operations, the cost of processing vast quantities of industrial information threatens to become a significant barrier. Building their own AI capacity could give operators greater control over costs, sensitive data and the intelligence needed to make better operational decisions.
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t an oil and gas refinery in the Middle East, the challenge was not a lack of engineering information but the difficulty of connecting a large archive of documents to the equipment they described. Without those relationships, potentially valuable records remained difficult to retrieve and use in operational decisions. When industrial AI company RIVE carried out a document-to-tag matching test involving approximately 50,000 equipment tags, the automated matching process took about 30 minutes, before quality assurance and checking. For Arun Govind, co-founder and chief product officer of RIVE, the refinery project illustrates a wider challenge facing operators as they attempt to extend AI beyond experimental applications. Processing large volumes of engineering information demands substantial computing capacity, while dependence on externally hosted language models can make the cost of such work difficult to predict. “If you have one
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million documents and each document costs five dollars to digitise and extract the information, that’s already five million dollars just to digitise the equipment,” he explains, using an illustrative calculation. Such economics, he argues, can stop a project before its operational benefits are tested. His proposed answer is what RIVE calls a ‘token factory’: AI processing capability that an organisation invests in and operates under its own control. Tokens are the units of text and other content that language models process and generate; their consumption often determines what users pay external AI services. Rather than paying a provider for every increment of usage, a company could run suitable open models on computing infrastructure it controls, providing capacity for its own teams and workloads. Govind sees this as a strategic decision about how industrial companies acquire intelligence, rather than simply another software purchasing choice.
Capacity that can be planned The economic argument has parallels with earlier industrial transformations. Access to reliable, affordable energy enabled manufacturers to mechanise production, while advances in computing allowed businesses to process information and coordinate operations on an unprecedented scale. Govind believes AI could have a similarly profound impact on engineering and operational decision-making, but its widespread adoption will depend on organisations having access to sufficient processing capacity at a predictable and manageable cost. “When I go to any industrial organisation, their biggest concern at the moment is how to control the token cost and token budget,” Govind says. “People are worried about whether it is going to shoot up and whether it will really give them a return on investment.” An internally operated token factory would combine hardware, open language models and an inference environment in which those models process requests. Its value would depend on matching capacity to the demands of the business, from large initial document-processing programmes to everyday technical queries. Local processing would not eliminate costs: organisations would still need to fund equipment, power, maintenance and the expertise to operate
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the service. It could, however, replace some unpredictable usage charges with investment that management can plan, while reducing dependence on a provider’s pricing decisions or model availability. Govind points to RIVE’s experience developing software with internally available AI capacity. Engineers can test approaches and run workloads without making a fresh budget calculation for each interaction. He believes that freedom could matter even more inside an industrial business, where the greatest opportunities may be discovered
by the people working directly with engineering problems. “Once you have an abundance of tokens and compute, it enables people to think outside the box rather than taking the traditional approach,” he says. The proposition is not unlimited processing power, but sufficiently predictable access to make repeated experimentation practical.
Keeping industrial knowledge under control For oil and gas operators, the argument extends well beyond expenditure. Engineering drawings, equipment histories and operating procedures may
contain valuable intellectual property or information that cannot be shared freely outside the business. Sending this material to external AI services introduces questions about where it is processed, how access is controlled and what contractual protections are in place. Govind has encountered similar concerns during earlier transitions from onpremise systems to cloud computing. “Once the data has gone out of the system, it is very difficult for somebody to track what happens to that data,” Govind adds. “Maintaining an environment under the operator’s control can
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make those boundaries easier to define and enforce.” Yet running a language model internally does not, by itself, create a dependable industrial AI system. The model still needs access to the correct asset information, and that information is frequently dispersed across maintenance applications, document repositories and historical work orders. RIVE’s approach is to create what Govind calls ‘connected intelligence’, bringing related information together so applications and AI agents can work with a more complete view of the equipment concerned. In the refinery project, the critical task was establishing relationships between documents and equipment tags, turning an archive into information that could be retrieved in an operational context. New maintenance records and updated technical documents mean that this work cannot simply stop when the existing archive is processed; those relationships must remain useful as plant information changes. Governance becomes particularly important when AI moves from retrieving records to proposing or carrying out work. Conventional industrial software was designed around human users, while agents introduce another kind of participant that may be able to change information across connected systems. “If you just let the AI agent run on whatever thing, it will just go and delete some data. Who’s responsible? Who’s accountable? Who’s auditing this?” Govind asks. His answer is to apply user-like permissions to agents, with traceability and data lineage throughout the application, rather than relying on safeguards at the language-model level alone.
From records to maintenance decisions The next application Govind describes is maintenance planning. Preparing a plant-wide maintenance plan requires engineers to consider failure modes,
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historical maintenance records, dependencies between assets and the practical consequences of different maintenance schedules. He envisages teams using groups of AI agents to analyse those relationships and develop alternative plans that optimise availability, cost and timing without compromising safety. “You can deploy a crew of agents alongside a set of users and give it a goal: give me an optimal maintenance plan and strategy for this plant,” he says. The agents could compare options and present the evidence behind them, leaving engineers to judge which recommendations can be implemented. The distinction between generating a plausible answer and supporting a defensible decision is essential in a safety-critical industry. Govind’s proposal depends on industrial context, controls over what agents may do and a human decision-maker able to interrogate the output. RIVE’s refinery documentmatching example demonstrates the potential speed of one foundational task, rather than proving that an entire maintenance planning process can be automated. The longer-term promise lies in making complex information accessible enough for engineers to consider options that would otherwise demand extensive manual research. Govind believes oil and gas companies should therefore consider how they will provision AI before demand becomes too large to manage through ad hoc external services. Their choice need not exclude every cloud-based model, but it does require a clearer view of which capabilities should remain under their own control and how those capabilities will be governed. “If your organisation is planning to invest in AI, rather than depending on an external organisation, you need to start exploring a mechanism to have this compute capacity within your control,” he says. For an industry built around long-lived assets and carefully managed resources, AI capacity may soon deserve the same deliberate planning.
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AI can spot the warning signs, but it cannot make project teams act Artificial intelligence promises to transform how major oil and gas projects forecast delays and manage delivery risks, but better information cannot compensate for weak planning or poor decisions. Realising its potential demands stronger project controls, reliable performance data and a willingness to confront problems before they become expensive disputes.
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y the time a major oil and gas project formally acknowledges that it is falling behind schedule, the underlying problems may have been apparent for months. Progress reports have been prepared, schedules updated and management meetings held, yet the information that should have prompted intervention has either been overlooked or failed to influence decisions. The project continues against a programme that no longer reflects what is happening on site, while the opportunity to recover lost time becomes progressively smaller. For Christopher Nicolas, director at project advisory consultancy TBH, this is an uncomfortable reality
that increasingly sophisticated artificial intelligence will not necessarily change. Drawing on 30 years of experience in construction and major project delivery, he believes many of the industry’s persistent problems have less to do with the availability of information than with the quality of project preparation and the behaviour of those responsible for delivery. “A lot of the requirements set by the client end up requiring a project controls team to do so much reporting that they get wound up in the reporting cycle,” he explains. “They collect the data, but they don’t actually use it in any meaningful way to review the metrics and take timely action.”
The problems begin before construction Nicolas sees the initial development of a project as one of the most consequential stages in determining its eventual performance. Across the Middle East, where major energy and infrastructure investments frequently operate against ambitious delivery targets, commercial pressure to accelerate construction can encourage organisations to commit before essential preparatory work has been sufficiently developed. “Clients are so eager to get to market and start their projects,” Nicolas adds. “Invariably, projects suffer from design that hasn’t been fully developed,
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procurement and packaging strategies that aren’t complete, or documentation that hasn’t been thoroughly critiqued to understand scope gaps.” Nicolas argues that additional preparation can produce a better overall outcome. A decision to spend several more months resolving design uncertainties or establishing a credible procurement strategy might prevent considerably greater delays during execution. The objective is not to eliminate every uncertainty before construction begins, but to ensure that the programme reflects a realistic understanding of the work involved. This philosophy underpins TBH’s involvement across the project lifecycle. Working with engineering, procurement and construction
(EPC) contractors, the consultancy helps establish programmes that properly account for scope and can be updated against actual progress. When representing clients, its role involves independently scrutinising proposed schedules, identifying omissions and examining contractual or commercial exposure before programmes are accepted. “We want to commercially protect either our EPC contractor or our client by ensuring a realistic and robust programme schedule and duration is established from the outset,” Nicolas explains. “If you’re not doing that from the outset, and a lot of projects are agreeing to highly aggressive durations, you’re setting yourself up to fail immediately.” Even where a credible programme has been established, the relationship between project controls and the wider delivery team can undermine its effectiveness as construction progresses. Planning engineers and cost controllers may recognise deteriorating performance long before those concerns are fully acknowledged by senior management,
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particularly where there is reluctance to communicate unwelcome news. Disagreements over design responsibilities and technical requirements can further complicate delivery, with repeated requests for clarification and unresolved technical issues delaying procurement and construction activities. In these circumstances, collecting more information achieves little unless the organisations involved are prepared to address the problems it exposes.
Turning project data into useful intelligence AI offers considerable potential to improve this process, particularly by changing the speed at which project teams can evaluate emerging problems. Traditionally, planners have relied on specialist software and manual analysis to develop alternative scenarios, assess the consequences of delays and determine the implications for completion dates. “We used to go away and do modelling. We would use trends and forecasts to model outcomes,” Nicolas says. “We’ve got software now that gives us the ability to continually and actively model whatever data we have to identify scenarios.”
“The beauty will be that AI allows the common user on a project, not a project controller but any participant, to ask the AI model for the insights they need,” he explains. “The programme doesn’t have to remain a dark art retained by the planners.” TBH is also exploring how AI can support the preparation of more credible programmes. Its internally developed AI tool contains industry productivity data that project teams can reference when developing schedules. Nicolas explains that the information includes practical benchmarks for activities such as pipe installation, allowing planners to draw on established production rates rather than relying entirely on assumptions. However, the availability of better modelling tools does not guarantee that their conclusions are dependable. Forecasts still require accurate progress assessments and a properly constructed baseline. If site productivity is incorrectly recorded or essential activities are missing from the programme, AI may simply process those weaknesses more quickly. Technologies such as site cameras and visual monitoring systems can provide more accurate, real-time visibility of construction progress, reducing reliance on manual reporting and supporting more informed decision-making. Nevertheless, Nicolas considers the integrity of the underlying data fundamental to every subsequent stage of analysis.
The decisions technology cannot make Perhaps the greatest limitation of AI-enabled project controls lies outside the technology itself. Even where an intelligent system identifies a significant risk and presents compelling evidence, somebody must acknowledge the problem and decide what to do about it. Nicolas believes this remains one of the industry’s most difficult challenges. Construction projects bring together organisations with different commercial interests and contractual responsibilities. Information that might help the wider project can also expose a contractor to criticism or create concerns about liability. As a result, there can be considerable reluctance to disclose deteriorating
Nicolas is particularly interested in how AI could reduce the gap between information being recorded and decisions being made. On large programmes, reports can already be several weeks old when they reach the meetings intended to address their findings. More immediate access to current performance data would enable those discussions to focus on the circumstances confronting the project. On major oil and gas projects, schedules can contain thousands of interconnected activities, making it difficult for engineers and construction managers without specialist planning experience to understand how changes in one area might affect the wider programme. Questions about the consequences of delayed engineering deliverables or deteriorating productivity often require a planning specialist to navigate complex scheduling software and interpret the relevant dependencies. This reliance on a relatively small group of experts can slow the exchange of information between those responsible for maintaining the programme and the wider team making day-to-day delivery decisions.
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performance before recovery options have been exhausted. “It’s a highly adversarial industry,” Nicolas observes. “Someone puts in a commercial notice and people take it personally. Contracts are there to protect both the contractor and the client, but we still end up having disputes on projects.” The consequences extend beyond commercial disagreements. If warning signs are suppressed or softened to avoid difficult conversations, management loses the opportunity to intervene while corrective measures remain practical. AI might make those signals more visible, but it cannot independently establish the trust and accountability required to act upon them. Nicolas believes the attitude of senior management is therefore decisive. Project teams operating within organisations that encourage transparency are more likely to disclose emerging risks, including information that may initially be commercially uncomfortable. Conversely, pressure to maintain an optimistic picture can undermine even the most sophisticated controls. “If you’ve got really supportive senior management above you, you’re going to be more encouraged to share the information and utilise it,” he explains. “It really comes down to the behaviours and the intent of the team.” For TBH, the practical response is to combine disciplined project preparation with continuous monitoring and meaningful intervention. Establishing a credible baseline allows performance to be measured properly, while integrated project controls provide the evidence needed to assess deviations and investigate recovery options. The value comes when those findings influence decisions rather than becoming another addition to an already extensive reporting process. A project whose scope remains uncertain, whose programme is unrealistic or whose leadership is unwilling to confront bad news will remain vulnerable, however advanced its forecasting tools become. AI may give decision makers a clearer view of the consequences of their choices, but it is the quality of those choices, beginning long before construction starts, that will ultimately determine whether the project succeeds.
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Offshore digital twins must earn the engineer’s trust Offshore structural digital twins promise sharper assessments of ageing installations, but their usefulness depends on sound models, credible measurements and a clear engineering purpose. A developing international standard could bring greater discipline to their deployment while keeping responsibility for safety-critical decisions firmly with engineers. 20 Future AI Middle East 2026 | www. futureaiconference.com/middle-east-2026/
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hen severe seas slam waves against the underside of an offshore platform deck, the question facing structural engineers is not how convincingly they can reproduce the installation on a screen. They must establish whether the structure can continue to operate safely, what remedial work is justified and how much confidence can be placed in their assessment. A past project involving an existing platform in Malaysian waters demonstrated how digital twin technology could contribute to that judgement, by assessing a structural analysis model with measured behaviour rather than relying on theoretical assumptions alone. For Partha Dev, Venture Manager for Offshore Engineering Structures business area at Rosenxt, this is the practical purpose of an offshore structural digital twin (DT). In this setting, it is a physics-based numerical model that can be calibrated and refined using monitoring measurements, rather than simply a three-dimensional representation of a facility. The distinction is important when operators are evaluating older offshore facilities, where the actual real-time response of a structure may differ from predictions based on original design information and potentially outdated assumptions about its condition. That calibration work depends on established engineering practice, but there is still considerable uncertainty about how much monitoring and model refinement operators need to develop a useful digital twin. “The rate of adoption in Structural Health Monitoring (SHM) and digital twins has been slow and experienced mixed progress over the years,” Dev acknowledges. Without an agreed framework, organisations have developed different interpretations of what a twin should contain, what problems it should solve and how it belongs within structural integrity management. The result can be considerable technological activity without a sufficiently clear connection to the
engineering decision that prompted the investment.
Start with the engineering question For operators considering whether to extend an installation’s life, structural integrity management already relies on inspection findings, engineering calculations and assessments of the hazards to which an offshore facility is exposed. Rosenxt works to integrate monitoring measurements into that process, using structural responses recorded in service to refine analytical models rather than treating monitoring and modelling as siloed activities. The resulting digital twin can help engineers challenge assumptions about structural reliability and determine whether existing inspection arrangements remain sufficient or further investigation is justified. “Operators should first define a clear objective for developing DTs,” Dev argues. “That objective may be comparatively modest, such as obtaining a more defensible interpretation of routine inspection results, rather than installing extensive sensing equipment or developing a highly sophisticated model from the outset.” The appropriate level of investment will depend on the structural risk and the decisions operators need the model to support. On structures with limited redundancy, where the loss of a critical element offers fewer alternative paths for carrying loads, more advanced damage-detection capabilities may warrant a more riskinformed consideration.
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“An ideal stage of a project to monitor and develop DT is at an early engineering phase,” Dev explains. “On a new installation, sensors can be incorporated more cost-effectively during fabrication phase as against delaying its installation in more challenging in-service subsea environment. Thereby, benchmark measurements can be collected while a facility is still in its pristine condition before ageing in a marine environment changes the structure’s behaviour. Those early records provide a reference against which engineers can subsequently investigate deviations, helping them determine whether an unexpected response merits inspection or reflects the conditions under which measurements were taken.” Retrofitting monitoring equipment later can still be worthwhile, although the operator will have missed the opportunity to capture the structure in its original condition. Most existing offshore facilities were not built with such instrumentation in mind, but that does not exclude them from benefiting. Rosenxt advocates combining targeted monitoring with existing inspection and assessment programmes, using changes in measured response to trigger closer investigation. An unexpected change may justify a fresh structural analysis or inspection, even when the available information does not establish that damage has occurred especially in the substructure, which is below the sea level and not accessible to general visual inspections. Monitoring can therefore improve the timing and focus of integrity work without being treated as an automatic diagnosis. The Malaysian project provides a more demanding illustration. Engineers assessed a fixed offshore structure exposed to wave-in-deck loading, a potentially serious condition in which waves interact directly with the deck structure resulting in a significantly increased structural loading. By calibrating the analytical model against monitored structural behaviour, the assessment could represent the facility more realistically, thus unlocking any available design conservatism and leveraging that to support a more fitfor-service evaluation of its reliability. Dev says the approach helped demonstrate compliance while limiting remediation requirements. The underlying methodology and the findings are documented in a technical paper (OMAE2020-18300),
presented at the 2020 International Conference on Ocean, Offshore and Arctic Engineering, showing how a calibrated model could inform decisions about an existing asset rather than serving simply as a digital record.
A standard for consistent practice Individual examples demonstrate what structural digital twins can achieve, but widespread use requires confidence that their objectives and validation requirements are understood consistently. Dev is the Project Leader of the second edition of ISO 19901-9, the international standard concerned with structural integrity management of offshore structures. The edition, still under development, is intended to bring structural health monitoring and digital twins more explicitly into an established integrity management framework, rather than treating them as separate technological initiatives. The proposed framework addresses how objectives are set, the types and methods of monitoring available and different levels of digital twin capability, supported by examples and references. Its purpose is to give operators a common basis for deciding what level of analysis is appropriate and what evidence should be required before results are used in integrity assessments. The wider revision also covers risk-based inspection, comprehensive assessment and the management of assets approaching or exceeding their original design lives. “The Standard will provide an international benchmark and provide checks and balances for compliant use of DT technology,” Dev says. His warning about the consequences of uncritical adoption is direct: “Poor use of any technology soon becomes its own enemy.” The aim is not to prescribe identical systems for every platform, but to establish discipline around why a twin is developed, how it is maintained and when its conclusions are sufficiently reliable to influence risk-informed decisions. Validation is particularly consequential because an inaccurate model with poor quality foundational data can produce confident-looking results that misrepresent the structure’s condition. A false positive may prompt unnecessary inspection or intervention; a false negative may allow a
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potential structural damage to pass unnoticed. Problems can originate due to sensor data artifacts, in the interpretation of measurements or in the assumptions used when calibrating the structural model. Complex, redundant structures may also be difficult to interpret because several elements can influence a measured response, increasing the uncertainty around what a change actually means.
measurements without appropriate review. This is why Dev favours a ‘human-in-the-loop’ approach, with information assessed and verified before decisions are made about continued operation or remedial work. He draws a deliberate distinction between data-driven and data-informed decision-making in the developing ISO provisions: measurements and models should strengthen professional judgement, not displace it. For ageing offshore installations, the lasting value of a digital twin will be found in the additional confidence it brings to difficult integrity decisions, especially when the cost of unnecessary intervention must be weighed against the consequences of missing a genuine structural threat, which could have significant impact on life safety, environmental pollution and asset risks in a hazardous offshore setting.
Dev calls for safeguards that extend beyond simply accumulating more data. Sensor quality needs to be verified, unexpected readings investigated and model performance demonstrated for the decisions it is expected to support. His project team’s material for the revised standard also identifies practical validation considerations, including measurement noise, data outliers and evidence of fitness for purpose outputs. More sophisticated twins may help identify damage, but their additional complexity must be justified by improved confidence rather than by a presumption that greater sophistication is necessarily better.
Engineering authority remains essential The importance of validation becomes even greater as artificial intelligence is introduced into structural monitoring. Machine learning or Deep Learning tools may help identify unusual patterns within large volumes of sensor information, offering engineers earlier indications of behaviour that merits investigation. Dev sees the greatest value in this form of assisted anomaly detection, particularly when it improves the quality of information available for a subsequent structural assessment. He is considerably more cautious about transferring responsibility for the resulting decisions to automated systems. “I strongly believe that the highest value can be derived if we use AI for assisted anomaly detection to provide excellent quality data, but engineers should remain as the owners of datadriven decisions,” he says. “Accountability for safety critical decisions is paramount and should remain with humans.” The role of a structural digital twin is to supply evidence that specialists can examine alongside inspection findings and their understanding of failure mechanisms. An algorithm trained on previous observations cannot be assumed to recognise every future hazard or distinguish genuine structural changes from faulty
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Better decisions by design: Industrial intelligence for complex operations Pedro Alcântara Nunes Neto at Falkor examines how industrial intelligence can help energy operators build greater resilience by connecting operational data, engineering context and simulation before conditions become critical. He argues that the real value of digital twins and AI lies in improving the speed and quality of decisions, giving teams the context to understand change, test options and act with greater confidence.
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esilience has always been part of our job – we design our assets around uncertainty. We plan around equipment failure, changing production conditions, maintenance constraints, and unexpected events. That will not change. What is changing is the speed and complexity of the conditions that operators are being asked to manage. For energy producers in the Middle East, that challenge has particular relevance today. Recent events have again demonstrated how quickly the conditions around an operation can change. At the same time, operators are managing increasingly complex assets with growing volumes of data.
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use cases. It is also why the digital twin is evolving beyond visualization to include live operational data alongside engineering information, models, documentation and work processes. The journey usually starts with connecting and organizing asset data. Once that foundation is in place, it becomes possible to monitor the operation, test scenarios, optimize performance and, over time, automate more of it. The point is to give the organization a common operational picture from which it can act.
Expertise does not always need to be at the asset Falkor Surface brings contextualized asset information into a common operational environment.
is happening, evaluate their options and make the right decisions quickly enough? Digitalization is taking on a different role today.
Readiness, but also resilience Much of the industry’s digital transformation has understandably focused on efficiency. Digital twins have been deployed to improve production, maintenance, energy consumption and workforce productivity. Recent advances in AI are accelerating that journey. But something else is happening too. The capabilities we have built for efficiency are increasingly proving their value in making operations more adaptable. That shift is particularly visible in the Middle East. Siemens’ 2026 Middle East Infrastructure Transition Monitor, based on a survey of 400 senior executives and interviews with regional leaders, found that 62% expect AI to reshape infrastructure operations within three years, while 61% say industrial AI is already making critical infrastructure more resilient. The expectation is already translating into operational deployment. Aramco reports that in 2025 it recorded $2.6 billion in Technology Realized Value directly attributable to AI, across roughly 500 identified use cases.
Think about what a resilient operation requires: • Access to expertise – whether at the asset, or somewhere else • An understanding of consequences before committing a decision to the physical asset • Situational awareness across the operation, even when the relevant information sits in different systems These are increasingly digital capabilities. We are interested in the technology, of course. Ultimately, what matters is the quality and speed of the operational decisions it enables.
You cannot respond to what you cannot see As the old joke goes, operators hate two things: the way things are, and change. Most operators are well aware of the difficulties in bringing asset information together in a way that reflects the physical asset and the way people work. During normal operations, those difficulties can be managed. Someone finds the document, calls an experienced colleague or reconciles information between systems. When conditions change quickly, that fragmented context becomes an operational constraint. This helps explain why some of the region’s largest operators are now moving beyond isolated digital
That common picture also changes where work can happen. Some industrial work will always require people physically at the asset – digitalization does not remove that reality. But remote operations can allow specialists elsewhere in the organization to work from the same operational context, wherever they are. Being on site is no longer driven by the location of information. Digital twins can support remote monitoring and troubleshooting, while drones and robotics can extend inspection capabilities into areas where deploying people may be difficult or unnecessary. The expertise is still human, but technology changes how far it can reach.
Simulate before you act Perhaps the most important capability is the ability to ask: what happens if we do this? When operating conditions change, the exact combination of conditions may never have occurred before. This is where a physics-based digital twin allows teams to test scenarios against a virtual representation of the asset before changing the physical operation. Different operating parameters can be evaluated, constraints understood, and alternative courses of action compared. AI is extremely good at processing information and identifying patterns – but an industrial system cannot simply optimize toward an objective without understanding the physical constraints of
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the operation. As industrial AI continues to develop, this difference becomes more important. Combining contextualized industrial data with deterministic simulation creates a stronger basis for both human and increasingly AI-supported decisions. The principle is straightforward: expand the operating envelope on the model before testing it on the live plant.
What this looks like in practice: a live use case An independent 2026 assessment of a Falkor deployment by S&P Global Energy describes an integrated digital twin spanning the offshore field and processing facility, combining real-time information with continuous process simulation and operational applications. The implementation was not one large leap toward autonomy. It developed progressively, beginning with the integration and contextualization of asset data and moving toward optimization, predictive capabilities and increasingly sophisticated operational decision support. Optimization of monoethylene glycol injection increased gas 1.3%. Optimizing electricity consumption, primarily output by 1.3% around compressor operation, reduced electricity use by 1.8%. Maintenance tool time (wrench time) approximately 1.8% increased from around 45% to more than 80%.
There is significant value available before an agent controls anything: • A reliability engineer who gets an early indication of abnormal equipment behavior, the supporting history and relevant engineering context in one environment is already better equipped to make a decision. • A planner who can understand asset condition, work requirements and constraints without reconstructing the picture across disconnected systems is already working differently. • An operator who can simulate a change before implementing it has already reduced uncertainty. An industrial intelligence platform provides the contextual foundation underneath those decisions. Falkor Surface brings that context into the user’s operational environment, while products such as Falkor Maintain apply it to specific workflows such as maintenance. It is the same principle that comes up when we discuss where AI should and shouldn’t make decisions: improve the context and the workflow first, then increase automation where the evidence supports it.
Building resilience before you need it The mistake would be to think of operational resilience as something activated during a crisis. By then, much of the outcome has already been determined. The foundations of resilience are difficult to build in the moment: 1. You cannot suddenly contextualize years of fragmented industrial data.
Underneath these numbers, it is the capability we want to highlight: the digital twin uses real-time and historical information alongside continuous deterministic simulation. It can identify deviations from normal operating parameters, support whatif analysis and enable operators to test alternative strategies virtually before acting on the asset. The same environment supports remote monitoring and troubleshooting. The organization can understand what is happening, evaluate its options, and act faster, with the relevant operational context available to the people making the decision. This is what our industrial intelligence platform at Falkor is designed to provide: an industrial context layer that connects asset information and makes it available to the people, applications and AI systems that need it.
Industrial intelligence before autonomy There is understandably a great deal of discussion today about agentic AI and autonomous operations. Both will play an increasing role in our industry. But right now, autonomy should not become the measure of whether an industrial organization is digitally advanced.
2. You cannot capture decades of operating knowledge overnight. 3. You cannot build a trusted digital representation of an asset at the precise moment people need to rely on it. Those foundations must exist beforehand. That is what makes the direction of travel in the Middle East so interesting. Across the region, industrial AI is moving from individual use cases toward a broader operational capability: improving efficiency, yes, but also helping operations become more adaptable and resilient. For operators across the Middle East, the opportunity is therefore larger than the next AI application. It is to build operations that can see clearly when circumstances change, connect people to the expertise they need, simulate options before committing to them, and maintain the quality of decisions under pressure. Over time, more of those decisions will be supported, recommended, or even executed by AI. But resilience begins with an operation that understands itself well enough to adapt. Pedro Alcântara Nunes Neto, Falkor.
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Closing the loop: digital twins for smarter infrastructure Borja García de Soto, Associate Professor of Civil and Urban Engineering at NYU Abu Dhabi and Co-PI for Sand Hazards and Opportunities for Resilience, Energy, and Sustainability (SHORES), explains how the SHORES Center is connecting sensing, engineering models, artificial intelligence and robotics to develop Digital Twins that move infrastructure from monitoring toward prediction, decision support and intelligent action.
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igital Twins are increasingly used to describe everything from 3D models to connected cyber-physical systems. Their value lies not in visual fidelity alone, but in whether they can continuously connect physical infrastructure with data, engineering models and decision-making. At the SHORES Center at NYU Abu Dhabi, researchers are developing Digital Twins for structural infrastructure, geotechnical systems, construction and building operations, combining engineering, robotics, sensing, computational modeling and AI.
Borja, the term Digital Twin is now used very broadly. What actually makes something a Digital Twin? Borja García de Soto: A Digital Twin is more than a BIM model, a 3D visualization or a simulation. One useful distinction is between a digital model, a digital shadow and a Digital Twin. A digital model represents the physical asset, but information exchange may remain largely manual. A digital shadow adds an automated flow of information from the physical asset to its digital representation. A more complete
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Digital Twin introduces a bidirectional relationship between the physical and digital environments. That bidirectionality is central. Sensors, inspections and robotic systems inform the digital environment about the physical asset. Engineering models, simulations and AI interpret that information, generate predictions and evaluate responses that can then inform decisions or interventions in the physical world. I therefore see the Digital Twin as an evolving
The predictive level asks, What will happen? Historical and real-time data are combined with physics-based, statistical or machine-learning models to forecast future conditions, with model outputs supporting engineering judgment. The prescriptive level asks, What should we do? The Digital Twin can evaluate alternatives, recommend interventions and, where sufficiently validated, support automated actions through AIenabled decision support. Simulation cuts across these levels by allowing us
cyber-physical process that closes the loop between sensing, understanding, prediction and action. What can a Digital Twin do once that connection has been established? Borja García de Soto: We can think about Digital Twins as progressing through four levels of analytical and decision-making capability. At the descriptive, or monitoring, level, the Digital Twin answers, What is happening? It integrates current and historical data to provide an up-to-date picture of the asset, with decisions remaining primarily manual. The diagnostic level asks, Why is it happening? Statistical analysis, pattern recognition and engineering models help identify anomalies and possible causes, with the human in the loop.
to test what-if scenarios before acting. The progression also reflects bidirectionality: data flows into the digital environment, while digitally generated insights and decisions can influence the physical system.
How is SHORES applying this concept to existing infrastructure? Borja García de Soto: A good example is the Mussafah Bridge Digital Twin, developed within SHORES and led by Prof. Mostafa Mobasher. The bridge dates from the 1970s, so the challenge is transforming legacy infrastructure into a smart, connected asset. The project combines structural models with field measurements for real-time monitoring and predictive maintenance, including forward analysis, automated damage detection and estimation of load magnitude and vehicle speed.
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Monitoring alone is not enough. A sensor may tell us that something has changed, but the Digital
Why are Digital Twins particularly relevant to geotechnical engineering?
Twin should help us understand whether that
Borja García de Soto: Geotechnical systems are especially
change matters, what may have caused it and what
interesting because much of what we need to understand is below
could happen next. That is where we move from
the surface and cannot be observed directly.
collecting data toward interpreting infrastructure
Within SHORES, Prof. Tarek Abdoun and Dr. Waleed El-Sekelly are
behavior based on engineering principles.
advancing the geotechnical Digital Twin work, including applications
to reclaimed islands. Sensors such as piezometers, inclinometers and cone penetration testing instruments provide information on pore pressure, displacement, settlement and soil conditions. These measurements update models describing interactions between soil, foundations and the superstructure. This creates a clear closed loop. Information flows from the physical system into the digital environment; the Digital Twin analyzes and predicts behavior; and those predictions support decisions in the physical world. For reclamation and coastal development, this could allow engineers to identify emerging issues earlier and adapt decisions as conditions evolve.
How do you establish confidence in those predictions? Borja García de Soto: Validation is essential, especially when results may influence safety-critical decisions.
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NYU Abu Dhabi allows us to combine computation with physical experimentation. The Advanced Geotechnical Centrifuge Facility enables scaled modeling under controlled conditions, while high-performance computing supports numerical simulation and machine learning. Researchers can compare predictions with measured behavior and refine models before field application. With AI, this is particularly important. Data-driven predictions still need to be interpreted within the underlying physics and engineering context.
How does robotics fit into the Digital Twin ecosystem? Borja García de Soto: Robotics becomes important when we need a frequent, repeatable and efficient way to capture the evolving condition of physical assets and keep the digital environment aligned with reality.
Construction is a good example. A BIM model tells us what should exist, but not necessarily what has actually been built. Our construction Digital Twin research therefore uses automated reality-capture approaches such as SLAM-based 360-degree video processing, Neural Radiance Fields and Gaussian Splatting. These can generate high-fidelity representations, while automated recognition and labeling add semantic information with less manual intervention. Robots can serve as mobile and modular sensing platforms, carrying different sensors and repeatedly collecting data. As capabilities mature, the digital environment could also direct where a robot should inspect next, what information it should collect, or eventually what physical action may be required. This again reflects bidirectionality between the physical and digital environments.
maintaining a human in the decision loop. This is particularly relevant in Abu Dhabi, where extreme heat and dust create important interactions among indoor environmental quality, occupant wellbeing, energy use and building resilience. The twin allows us to examine these trade-offs together.
Will Digital Twins ultimately lead to autonomous infrastructure? Borja García de Soto: Increasing levels of autonomy are technically possible, but autonomy should not be the objective by itself. There is a progression from providing information, to recommending actions, to eventually allowing certain actions to be executed automatically. As we move along that progression, validation, uncertainty quantification, cybersecurity, traceability and human oversight become increasingly important. In safety-critical infrastructure, engineering knowledge remains fundamental. AI should augment engineering judgment, not replace it.
Where do you see this technology going next?
The larger objective is a lifecycle Digital Twin that evolves alongside the physical asset from construction through operation.
You are also using the NYU Abu Dhabi campus as a testbed. What are you investigating there? Borja García de Soto: We have developed an Indoor Environmental Quality, or IEQ, Digital Twin that combines IoT sensing, a spatial digital environment
and AI-based analytics. The platform monitors thermal comfort, acoustics, lighting and air quality, and illustrates the same analytical progression. At the descriptive level, it shows current conditions. At the diagnostic level, it identifies abnormal patterns. At the predictive level, deep-learning models forecast conditions such as indoor PM₂.₅ concentrations. At the prescriptive level, we are exploring how large language models can translate those analyses into understandable recommendations while
Borja García de Soto: Today, most Digital Twins focus on individual assets such as a bridge, a building or a geotechnical system. The longer-term vision at SHORES is to move toward interconnected infrastructure networks, or Twin Systems. Future systems could integrate bridges, tunnels, transportation infrastructure, buildings and coastal assets within larger digital environments supported by IoT, AI and cloud computing. Achieving that is not trivial and will require interoperability, reliable sensing, validated models, effective human-system interfaces and collaboration among academia, government and industry. The important question is not simply ‘How sophisticated is the Digital Twin?’ It is ‘What can the Digital Twin help us understand, predict and do that we could not do before?’ When information can move continuously from physical infrastructure into the digital environment, and analysis, simulation and decisions can influence the physical system in return, we close the loop. At that point, the Digital Twin becomes part of how infrastructure is understood, managed and ultimately made smarter.
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Governing intelligence: why ai governance will define the next industrial revolution As artificial intelligence moves beyond decision support towards increasingly autonomous industrial operations, effective governance is becoming essential to managing risk and maintaining accountability. Arvin Singh, Partner – Head of Digital Advisory at Wood, explores why trusted data, continuous assurance and clearly defined human oversight will determine whether organisations can deploy AI safely and at scale.
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rtificial Intelligence (AI) is rapidly becoming embedded within the structure of modern industry. Across engineering, operations and corporate functions, organisations are deploying AI to augment expertise, accelerate decisions and unlock higher levels of productivity. As AI evolves from an analytical tool into an autonomous actor capable of influencing decisions and initiating actions, a new reality is emerging and the greatest challenge is no longer building AI, it is governing it. The next phase of industrial transformation will not be defined by who develops the most sophisticated algorithms, but by who creates the trust, accountability and assurance required to deploy AI safely, responsibly and at scale.
The current landscape The pace of AI use and adoption across industrial sectors is unprecedented. What began as experimentation with machine learning models has evolved into enterprise-wide programmes focused on generative AI, copilots and increasingly autonomous agents. Companies across
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energy, mining, chemicals, manufacturing and infrastructure are exploring how AI can improve engineering productivity, optimise operations, enhance maintenance strategies and unlock greater value from organisational knowledge.
many organisations have established AI innovation programmes and experimentation environments, far fewer have developed structured approaches for governing autonomy, assuring AI performance or defining accountability for AI-enabled decisions.
The eagerness and enthusiasm surrounding AI is understandable; AI aims to reshape how information is consumed, decisions are made, and work is executed. Engineering teams can access expertise faster, operators can identify issues earlier, and organisations can automate increasingly complex activities. The potential gains in productivity and operational performance are significant.
As industries seek to scale AI beyond pilots and proofs of concept, this gap is becoming more and more evident. The challenge is no longer proving that AI can deliver value, it is creating the confidence required to deploy AI within environments in which reliability, transparency, compliance and accountability are critical.
At the same time, AI itself is evolving. Early implementations focused on giving insights and recommendations. The next generation of solutions is increasingly agentic, capable of interacting with systems, coordinating workflows and executing actions within defined boundaries. AI is therefore moving beyond decision support and becoming an active participant in business processes. However, governance maturity has not advanced at the same pace as technical capability. While
Why the governance challenge is different Many organisations initially assumed that existing IT, cybersecurity and data governance frameworks could simply be extended to encompass AI. While these disciplines remain important, AI introduces characteristics that make governance considerably more complex than traditional software systems. Conventional enterprise applications operate according to predefined business rules and generally produce expected results. Governance models were developed around this determinism. Risks could be assessed, tested and controlled
because system behaviour was largely understood in advance. AI systems operate differently; modern models infer, interpret and generate outcomes based on probabilities, context and data. Their behaviour is affected by factors that may change over time, making governance less about managing technology and more about managing decisionmaking. As AI becomes increasingly embedded within operational processes, organisations must determine what AI should be permitted to do, under what conditions it can act independently and where accountability ultimately resides. The emergence of agentic AI amplifies these challenges. Systems are capable of pursuing goals, interacting with applications and coordinating activities which introduce governance questions that many organisations have not previously encountered. The result is a shift away from technology governance and towards a wider discussion of authority, oversight and organisational accountability.
The emerging governance gap The gap between AI capability and governance maturity is becoming one of the defining challenges of the AI era. While businesses continue to invest heavily in platforms, models and use
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cases, comparatively less attention has been given to the governance structures needed to manage increasingly autonomous systems. One of the most significant dimensions of this gap concerns autonomy itself. Traditional automation executes predefined instructions according to established business rules. Autonomous systems evaluate information, determine priorities and make decisions within defined boundaries. As organisations move from automated workflows to AI-enabled decisionmaking, governance requirements increase significantly. However, many companies still lack structured and organised frameworks for classifying different levels of autonomy and aligning controls accordingly. Systems with very different risk profiles are often governed in similar ways despite the consequences of failure being dramatically different. A knowledge assistant, for example, presents a very different risk profile from an AI system capable of influencing maintenance strategies, engineering decisions or operational activities. A second challenge concerns accountability. Human-in-the-Loop (HITL) approaches have become a common response to AI risk, but meaningful oversight requires more than just a human approval step. Team members must have sufficient understanding, context and authority to challenge recommendations when necessary, and without these capabilities, accountability can become largely symbolic with responsibility remaining with humans, while practical authority shifts towards the machine. Governance is further complicated by the dynamic nature of AI systems. Data evolves, operating conditions change and models may behave differently over time. Governance therefore cannot be treated as a one-time approval process.
It must become a continuous discipline involving ongoing monitoring, validation, performance assurance and risk management throughout the AI lifecycle. Agentic AI brings an additional consideration. Organisations are beginning to deploy autonomous software entities capable of accessing systems, coordinating activities and executing tasks. These agents are rapidly becoming operational actors within the enterprise, creating governance requirements that increasingly resemble those applied to human users and critical assets. Questions around identity, permissions, monitoring and intervention become fundamental to maintaining control. The governance gap therefore extends well beyond technology. It encompasses risk management, operating models, cybersecurity, information governance and organisational accountability. Closing that gap will become one of the most important leadership challenges of the coming decade.
The shift from automation to autonomy For more than two decades, digital transformation initiatives focused primarily on automation. The objective was to execute established processes more efficiently through technology. AI is changing that paradigm. Rather than simply automating tasks, intelligent systems increasingly support, and in some cases shape, decisions by providing the right recommendations. AI can observe patterns, recommend actions and evaluate alternatives, while agentic systems are beginning to coordinate execution across multiple workflows. As industries move further along this spectrum, governance requirements become increasingly important. Not all forms of autonomy carry the same level of consequence, and governance frameworks have to reflect these differences. Just like industries classify physical systems according to risk and criticality, organisations will need structured approaches for governing different levels of AI autonomy.
Why human-in-the-loop (HITL) is not enough Human monitoring and oversight remain essential, but oversight alone is not governance. Simply inserting a human checkpoint into an AI-driven process does not automatically reduce risk. As AI systems become more capable, reviewers will find it difficult to maintain the right level of scrutiny required to validate increasingly complex recommendations, and within a viable timeframe. Over time, trust in the system can evolve into dependency on the system. Competent governance therefore requires purposeful design with HITL considerations; decision authorities, escalation criteria and intervention mechanisms must be clearly defined against a framework that can be auditable. Human involvement should be focused on areas where judgement, context, and accountability create genuine value, rather than using it as a procedural approval step. This also needs to be balanced for time-critical events, where HITL may not be the most viable approach and would need to be reflected in the governing strategy.
Governance begins with trusted information Many discussions around AI governance focus on algorithms and regulation, yet governance begins much earlier in the value chain. It begins with information. AI systems depend entirely on the quality, completeness as well as context of the data available to them. Organisations cannot expect trusted outcomes from systems operating on fragmented, inconsistent or poorly governed information. AI governance and data governance are therefore inseparable disciplines. Trusted data underpins every stage of the journey, from better decisions and effective automation through to increased autonomy. Companies which are seeking to scale AI successfully must invest not only in models and platforms, but also in the information foundations that support them. From human-in-the-loop to accountable autonomy The future of AI is unlikely to involve the complete removal of humans from decision-making. Equally, it is unrealistic
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to expect humans to manually review every recommendation generated by increasingly sophisticated systems. The future lies in accountable autonomy. AI will increasingly perform activities that machines are well suited to undertake, including analysing information, identifying patterns, monitoring compliance and coordinating routine actions. Humans will continue to provide expert judgment, industry knowledge, context, ethics and accountability. The organisations that succeed will be those that form clear boundaries between human responsibility and machine capability. Trust will not emerge from replacing people with technology, but from creating effective collaboration between them. The defining capability of the AI era Over the coming decade, industries will deploy increasingly capable models, more sophisticated agents and broader AI-enabled operating models. Technical capability will continue to advance rapidly. However, technical capability alone is unlikely to determine who succeeds. History demonstrates that transformative technologies create value only when businesses establish the structures needed to govern them effectively. AI will be no exception. While intelligence may become increasingly available, trust will remain difficult to earn. The organisations that achieve the greatest value from AI will not necessarily be those that adopt it first. They will be those that establish the confidence required to deploy AI safely, scale it repeatedly and integrate it into the core of their operations. The defining capability of the AI era will not be intelligence itself. It will be the ability to govern intelligence.
The next industrial revolution will be shaped by organisations that can trust AI, govern it and scale it with confidence. As a trusted Digital and AI partner, Wood helps clients establish the governance, assurance and information foundations required to transform AI from a promising technology into a trusted operational capability. By combining decades of experience in engineering, risk management and asset performance with leading digital and AI capabilities, we help companies create digital-native operations that deliver sustainable value throughout the asset lifecycle. See what’s possible: woodplc.com/digital
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Agentic driven-upstream: keeping intelligence alive across the life of a field Upstream operators generate vast quantities of data, but maintaining continuity of knowledge throughout the life of an asset remains a significant challenge. Simon Williams, Chief AI Officer at Atelic, explores how agentic AI built around the field and the well could preserve that knowledge from initial seismic surveys through to abandonment.
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sk any upstream team where value leaks and the answer is rarely a lack of data. Sensors, historians, daily drilling reports, mud logs, production databases and inspection records accumulate by the terabyte. The problem is what happens between them. Decisions are spread across vendors, systems and operational teams, and every handover, from subsurface to drilling and from completion to production, is a point where context is lost. The cost is familiar to anyone who has worked an asset through its life. Decision cycles slow while teams reconcile data from different sources. Knowledge disappears when a phase ends or a contractor demobilises. Execution varies from well to well, because the reasoning behind good decisions was never captured in a form the next team could use. Operators spend years and significant capital building an understanding
of their fields, and much of it stays locked in the people, spreadsheets and applications that produced it.
Beyond the dashboard The industry’s first answer was visualisation: bring the data together on one screen. Dashboards help people see what happened. They do far less to help them decide what to do next, and nothing to carry reasoning forward from one phase of an asset’s life to the next. Agentic AI changes that. Instead of presenting data for people to interpret alone, AI agents work alongside engineers and geoscientists: searching across sources, running simulations, detecting risk and recommending action, with a human in the loop at every step. This is the thinking behind Agentic Driven-Upstream™ (ADU), developed by Atelic with Dell Technologies: a single operational intelligence layer across the upstream lifecycle, built around two concepts every operator already understands, your field and your well.
Much of upstream’s most valuable knowledge is not in a database at all. It sits in daily drilling reports, mud logs and well files, P&IDs and engineering drawings, scanned PDFs and inspection records. Structured and live data, from SCADA and historians to production databases and spreadsheets, add another layer. The Knowledge System’s industrial knowledge graph connects to any of these sources. It parses complex documents, including scanned material through OCR, and resolves entities so that a well, a pump or a pipeline segment is recognised as the same asset wherever it appears. The result is a unified operational data model rather than a collection of files. On top of that sits the Knowledge Studio. Engineers can search across every source and receive answers cited back to the original document, with a confidence level on every answer. Deep research agents handle the questions that would otherwise take days, and alerts reach teams through email, Slack or Teams.
Start with the data: joining OT and IT
Two twins, one model
Agents are only as good as the context they work from, and in upstream that context lives in two worlds. The operational technology (OT) layer holds machine and process data: sensors, PLCs, historians, edge devices and IoT systems. The information technology (IT) layer holds business and operational data: ERP, maintenance (CMMS) and execution systems, databases and business applications.
ADU organises this knowledge around the field and the well. A FieldTwin brings together seismic and attributes, geo-horizons, structural models and reservoir constraints, supporting interpretation, reservoir simulation, prospect maturation and sweet-spot identification. That field-level intelligence flows down into well decisions, supporting better placement, higher productivity and improved long-term recovery.
In most operators these two worlds are owned by different teams, run on different systems and speak different languages. A pressure reading in a historian and a maintenance work order in the CMMS may describe the same pump, but nothing connects them. Until they are joined, no agent can reason across them, and no engineer can see the full picture without assembling it by hand.
A WellTwin is a continuously updating representation of each well, integrating wellbore configuration, real-time data, operational history, risk state, geology and integrity. It connects to the industry tools and simulation software engineers already use, and supports well design, real-time drilling assistance, a unified geology model and production optimisation.
Atelic’s Knowledge System sits between them, joining IT and OT into a single, contextualised view. It applies AI and analytics over a unified data model, creating the intelligence layer on which every agent in ADU depends.
Agents that follow the asset
Turning documents into knowledge
At the centre of ADU is the Agent Fabric, which handles agent reasoning, orchestration and supervision. It activates the right domain agents, from seismic interpreters and drilling optimisation agents to production lift-gas agents and integrity monitors, based on the current state of the well or field. As a well moves from evaluation through
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Atelic Studio combines data ingestion, industry-specific workflows, agentic system deployments and infrastructure architecture, connected by IT/OT integration, into one delivery engine.
drilling, completion, production, intervention and abandonment, agents switch on and off automatically, and the operational context is updated rather than rebuilt at each handover. Agent supervisors and human-in-the-loop controls keep engineers in charge of every decision. Everything surfaces in the Command Console, a single AI-assisted environment where engineers can ask questions in natural language, run simulations and act, instead of moving between a dozen applications. Consider a drilling engineer preparing a new well. Rather than requesting the latest interpretation, pulling offset well histories and checking integrity records in separate systems, they can ask the WellTwin directly. It already carries the geological context from the FieldTwin, the design constraints
and the history of comparable wells, with every answer cited to its source. When the well is handed to production, nothing is lost: the production team, and the agents that support them, inherit everything learned so far.
Single sign-on, per-document access control, promptinjection guardrails and a full audit trail are built in. Building on sovereign-grade infrastructure from the outset removes a barrier that has stalled many AI initiatives at proof-of-concept stage.
The same holds later in the well’s life. A production engineer weighing which wells to intervene on first can ask the system to rank candidates, see the reasoning and the source data behind each recommendation, and approve the plan. What is learned feeds back into the shared operational context, so each new well benefits from the last.
What changes for operators
Secure by design
ADU is designed to shorten decision loops, reduce non-productive time, support increased production and standardise operational excellence across teams and contractors. In practice that means drilling and production advisory, intervention prioritisation, pipeline integrity and reliability, engineering document search, and automated well and field reporting.
Where subsurface data lives, and who controls the models built on it, are strategic questions. ADU runs in the operator’s own cloud or on-premise, on Dell Technologies’ secure, sovereign AI accelerator platform.
Above all, it means tribal knowledge is captured once and retained. The drilling team inherits the reasoning of the reservoir team; a contractor
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Unstructured and live data feed the Atelic Knowledge System, where an industrial knowledge graph and Knowledge Studio power agentic deployments and upstream solutions, secure by design.
arriving mid-life works from the same context as the team that came before. The organisation stops relearning its own assets.
One delivery engine Agentic systems in upstream need deep domain knowledge, robust AI engineering and trusted infrastructure. Atelic brings the first two together through Atelic Studio, which combines four disciplines in a single delivery engine, all connected by IT/OT integration. Atelic pairs AI engineers with energy-sector domain specialists, so agents are designed around the way drilling, production and reservoir teams actually work rather than adapted from generic tools. That combination of domain expertise and agentic AI engineering, running on sovereigngrade infrastructure, is aligned from day one.
Data ingestion covers pre-processing, quality control, industry data mapping, connectors and document parsing. Industry-specific workflows encode how upstream work is done, using state machines and prompt and state logic tuned for throughput. Agentic system deployments provide agent reasoning, orchestration, supervisors and human-in-the-loop control. Infrastructure architecture, including an LLM foundry, an agent foundry, deployment infrastructure, security and governance with AI oversight, runs on Dell Technologies’ platform. ADU integrates with the tools operators already rely on, including NVIDIA, SLB (Petrel), IHS Kingdom, Waiw, Paleoscan, RokDoc and ESRI. With prebuilt solutions and an agent foundry, the cycle from concept to operational pilot shrinks from
months to weeks. The starting point is deliberately focused: a session with drilling, production, reservoir and digital teams to review specific workflows and select a well or field for an ADU workshop and pilot. Upstream companies will not win by collecting more data. They will win by keeping the intelligence they already have working for them, across every phase and every handover. Agentic systems built around the field and the well, and grounded in an operator’s own data, offer a practical way to get there.
About the author Simon Williams is Chief AI Officer at Atelic, an industrial AI company headquartered in Dubai, UAE, with strategic partners including NVIDIA, AWS, Microsoft Azure and IBM. www.atelic.ai
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From engineering data to actionable industrial AI Derek Middlemas, CEO of DIGATEX, explains why industrial AI depends less on sophisticated models than on the quality, structure and traceability of the engineering data beneath them. He examines how turning fragmented drawings, asset records and material data into trusted information can reduce engineering effort, improve maintenance decisions and create a more reliable foundation for AI.
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n 29 October I will speak at Future AI Middle East in Abu Dhabi. I want to use the time to talk less about AI and more about the engineering data underneath it, because that is where most industrial AI projects succeed or fail.
The calls we get Operators rarely come to us asking for AI. They come with an engineering problem. A shutdown is being planned, and nobody is sure which revision of a P&ID matches the plant. A maintenance team orders a part, and the material master returns three descriptions of the same item under different numbers. An EPC hands over a project as tens of thousands of PDFs, and the operator must rebuild the asset register by hand before anything can be loaded into SAP. Underneath, the problems are remarkably similar. • Drawings drift from the plant. Every modification, revision and handover widens the gap between what is documented and what is installed. On a petrochemical programme in Malaysia, four storage tanks were identical in
the drawings: one tank had been copied three times. On site, all four differed in piping, valves, flanges and tags. Found in a document check, that is a correction. Found in an emergency, it is a hazard. • Every contractor draws differently. On the same programme, isometrics came from more than ten contractors, each with its own templates, symbols and handwritten markups. The information was there; it just could not be compared. • The same asset has different names in different systems. A tag on a drawing, a datasheet, an equipment record and a sparepart number should describe the same thing. Often, they do not, and the result is duplicate stock, wrong parts and maintenance plans built on guesswork. • The knowledge sits with people who are leaving. In the UK, 41% of the oil and gas workforce is over 50. For years the fix for bad data has been someone who knows where to look. That fix is retiring.
Why AI does not solve this on its own Gartner expects organisations to abandon 60% of AI projects that are not supported by AI-ready data through 2026. In our industry, the hardest data to make ready sits in drawings. A P&ID is not a picture and not a page of text. It is a system of symbols, tags and connections whose meaning depends on line numbers, piping specs and continuations onto the next sheet. A generalpurpose model that reads it as an image misses the topology; one that reads it as text loses the structure. Worse, it will answer confidently from the wrong revision, and an engineer cannot act on an answer they cannot check.
What actually fixes it In our experience, most of these problems are solved by one thing: access to clean, structured data that engineers can trust. In practice that takes four steps. text 1. Extract the structure, not just the text. Symbols, tags, line numbers, connections and attributes go into a data model aligned with
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standards such as DEXPI and CFIHOS. documents A tag on a P&ID 2. Validate across documents. is checked against the datasheet, the isometric and the equipment record. Conflicts are flagged and resolved by engineers, not hidden.
What changes when the data is right On the Malaysian programme, the isometrics became one record per flanged joint, each linked to its source drawings. The project schedule fell by 70% and manual engineering effort by half. For another operator, digitising 120,000 engineering documents had been planned as a six-month project. It took six weeks, and 30 people went back to engineering work.
source Each attribute 3. Trace every value to its source. keeps a link to the exact sheet and region it came from, so anyone can verify it in seconds. 4. Publish once, use everywhere. The same validated record feeds SAP, AVEVA, Hexagon and the digital twin. This is what DIGATEX has done since 2017. Since December 2017 Azati has been our technology partner and R&D lab, building and running the computer vision, OCR and AI behind it. Across programmes we have processed more than 4.4 million engineering documents and 20 million drawing sheets.
The benefits reach the warehouse. In Thailand, linking drawings, bills of materials and material masters across 730,000 SKUs, 20 plants and 12 warehouses cut stock by 10%. Operators in the UK and Australia now share one register of 2.3 million spare parts. When a UK part failed its specification, the register found a matching part in Australia, and the turnaround went ahead. Only when the data is in that state does AI become useful. Our P&ID agent lets an engineer ask in plain language which pumps use a given bearing, or which drawings show a valve, and highlights the answer on the source sheet. It works because the data underneath has been structured and checked. Without that, it would be one more confident chatbot.
Bring a drawing that has caused you trouble In my session on 29 October, I will walk through these cases in more detail, including what went wrong along the way. The DIGATEX and Azati teams will be at Stand 11 throughout the event. If you have a drawing set, a register or a handover that has caused you problems, bring it. I would rather talk about your data than our slides.
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The future of AI Is an orchestra and energy is its next stage Wassim Ghadban, Global SVP, AI & Digital Engineering at Kent, argues that the future of AI in energy will depend on specialised agents working together across engineering, construction and operations rather than in isolated applications. He explores how shared data, orchestration and human oversight can connect intelligence across the asset lifecycle to improve engineering quality, operational resilience and decision-making. 46 Future AI Middle East 2026 | www. futureaiconference.com/middle-east-2026/
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n orchestra begins with a single instrument. Another joins, bringing a different sound. Then another. Gradually, individual contributions become a coordinated performance. Each musician has a specific role. Each follows a shared score, listens to the others and responds to direction. The result depends on both individual capability and collective coordination. This is how I see the future of artificial intelligence in the energy sector: an orchestra of specialised AI agents, working alongside people, data and systems across the entire asset lifecycle. We have spent years introducing digital tools into individual disciplines. Engineering has its design applications. Construction has its planning and progress systems. Operations has its monitoring and control platforms. Each brings value, but the connections between them often require significant human effort. The next opportunity is to make intelligence work across those boundaries. An engineering agent can generate a drawing. A review agent can assess it against requirements and connected information. A cybersecurity agent can identify gaps in the proposed control architecture. A construction agent can evaluate the implications for site execution. An operations agent can bring lessons from the field back into future designs. The real value emerges when these agents share context, exchange findings and coordinate their actions. Consider a change to a pump specification. Its consequences may extend to piping, electrical loads, procurement, installation, maintenance and operating procedures. An integrated AI ecosystem should help teams trace those dependencies, identify the affected information and route the necessary actions to the right people. That is the difference coordination can make: a local change becomes a managed lifecycle decision. For this orchestra to perform, it needs a common foundation. Reliable data provides the shared information. Engineering requirements and business objectives provide the score. Orchestration determines how tasks are assigned, how results are exchanged and when a decision needs human attention.
People provide purpose, judgment and accountability. In energy, this relationship is fundamental. Engineering quality, operational safety and asset integrity depend on decisions that are understood, traceable and made within appropriate controls. Greater autonomy must be supported by validated models, clear permissions and defined operating limits. This vision shapes Kent’s EnnovaAI: an integrated ecosystem
designed to connect specialised intelligence across how assets are designed, built and operated. Within engineering, EnnovaAI’s Autonomous Engineering capabilities support the generation of deliverables, intelligent drawings, P&IDs and 3D models. Autonomous Review connects documents, drawings and models with requirements and source information, helping teams identify inconsistencies earlier and reduce avoidable rework. OT Cybersecurity Assessment adds another perspective, evaluating design information against defined security requirements. During construction, the Project Control Center brings together information from CCTV, smart helmets, RFID, drones and robots to support safety monitoring and progress assessment. AI-powered robotic safety training creates opportunities for interactive learning, helping people practise how they recognise hazards and respond to site conditions. As projects move towards operations, Handover Validation checks information against owner requirements, helping identify missing records and inconsistencies before they become operational obstacles. In the operating asset, Integrated Operations Monitoring connects processes, data and systems. Industrial AI copilots support contextual questions and dynamic reporting. Soft Sensing, including virtual flow metering, uses available measurements and models to strengthen production insight and support well surveillance. Autonomous Operations extends this progression towards coordinated action within validated boundaries and human oversight. Physical AI connects the ecosystem to robots, humanoids and drones, bringing inspection findings and measurements from the field into the decision-making process. The AI-powered Digital Twin provides the connecting context: a digital thread linking engineering intent, construction records, operational behaviour and enterprise information. Together, these capabilities create a continuous exchange of intelligence. A field observation can inform maintenance. A maintenance finding can influence an engineering decision. A design improvement can become knowledge that supports the next project. The ambition is to make that exchange part of how the asset lifecycle works. For our industry, the measure of success will be the outcomes we deliver: safer work, better engineering, more efficient execution, stronger operational resilience and informed decisions throughout an asset’s life. The future of energy will be shaped by our ability to bring these capabilities together. Every agent has a part to play. People set the direction. And an integrated ecosystem allows the whole orchestra to perform.
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Final Word: The oil and gas AI ecosystem must be built around work, not technology Oil and gas companies can demonstrate the value of AI in individual applications yet struggle to embed those gains in everyday operations. Mark Venables, Technology Writer and Editor, argues that scaling AI depends less on buying better models than on building the data and organisational structures that make their outputs useful. After an oil and gas AI pilot succeeds, I want to know who will be responsible for it next year. The development team may demonstrate better seismic interpretation or a credible equipment-failure forecast, but someone else must maintain the underlying information, manage permissions and ensure the output reaches a person authorised to act. Too often, those obligations only become apparent once the pilot has been declared a success. I am increasingly sceptical of the industry’s appetite for isolated AI demonstrations. Oil and gas companies have spent years showing that algorithms can help interpret seismic information and recognise equipment anomalies. Yet an impressive result in a controlled environment tells us surprisingly little about what happens when the application meets legacy systems, competing operational priorities and the procedures that govern a working asset. Building the right AI ecosystem begins with identifying who will use an output and what they are expected to do differently. In exploration, that may mean getting AI-assisted interpretation into geoscientists’ established evaluation and review processes, with uncertainty and source data visible rather than hidden behind a confident prediction. In maintenance, it means connecting condition assessments to asset registers, work management and shutdown planning. In production, it means ensuring that recommendations are evaluated against operating limits and engineering knowledge before anyone changes a set point. These are different problems, and there is no reason to assume that a single platform will solve all of them. They do, however, share a dependency on accurate industrial context. A model needs to know which equipment a measurement belongs to, whether a sensor is reliable and which version of an engineering document is authoritative. Across joint ventures and acquired assets, even agreeing on asset identifiers and ownership of records can be difficult. Adding more computing power will not resolve those inconsistencies.
The answer is neither a grand programme to centralise every byte of data nor another collection of point-to-point integrations. Operators need common definitions where they matter, traceable sources and governed ways for applications to exchange information. Industry data standards and interoperable interfaces can help, but they require stewardship. Someone must be accountable for correcting erroneous records, maintaining the relationships between systems and ensuring that changes in the physical asset are reflected digitally. The ecosystem will only function if responsibility extends into the workflow, beyond the specialists who built the model. Too many pilots rely on enthusiasts who manually clean data, interpret the output and persuade colleagues to act. Once those individuals move on, the apparent capability deteriorates. An operational application needs a business owner, defined permissions and an escalation route when its evidence is inadequate. Where a recommendation could affect safety or production stability, established engineering approvals and protection systems remain non-negotiable. The way oil and gas businesses assess the commercial success of AI projects must also change. Teams often celebrate prediction accuracy, the number of models deployed or the speed at which a prototype was built. Those measures may be useful during development, but they do not establish value to an operator. Reduced unplanned downtime and less time spent reconciling inconsistent information are stronger tests, provided the organisation can demonstrate that AI contributed to the improvement rather than merely accompanying it. None of this scales unless businesses invest in work that rarely features in pilot demonstrations. Equipment hierarchies must be maintained, data access negotiated and models monitored as operating conditions change. That is particularly challenging in oil and gas, where operational technology is deliberately protected and older facilities may have limited connectivity. An AI ecosystem cannot be designed as though every asset were a new, fully instrumented installation. It must accommodate the working realities of existing facilities without weakening cybersecurity or process safety boundaries. I do not think the sector lacks ideas for using AI. It has too few dependable mechanisms for making those ideas repeatable when the original development team has left and the novelty has worn off. The companies that progress will be those prepared to fund ownership and integration with the same seriousness as model development. Until an insight reliably changes work, it remains an interesting technical result rather than an operational capability.
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Structural Integrity Management Breathing new life into ageing offshore facilities At Rosenxt, we look far beyond tomorrow. We are a niche, next-generation offshore structural integrity management (SIM) specialist service provider offering pragmatic engineering & consultancy services worldwide. As existing offshore facilities approach or exceed their original design life, traditional assessment methods built on outdated standards often struggle to cost-effectively demonstrate continued safety and integrity. We adopt a future ready, risk-informed approach towards fitness-for-service assessment, life extension and facilities reuse/repurposing projects. We offer cutting edge SIM services that are powered by an advanced assessment toolkit, powerful Risk-based Inspection (RBI) methodology and digital twin solutions. We at Rosenxt, deliver enhanced asset safety, tangible OPEX savings and the highest levels of technical assurance borne out of ex-operator experience. Backed by a lean team of integrity specialists, we eliminate waste without compromising rigor and deliver value to our customers.
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