Autonomous AI review to accelerate first production

Wassim Ghadban, global SVP, AI & Digital Engineering at Kent, discusses the concept of autonomous AI-driven review, combined with human-in-the-loop validation, as a means to remove structural bottlenecks in project delivery, and how the convergence of AI capabilities across engineering and operations can enable earlier production, reduce capital inefficiencies, and redefine how projects are executed

In the delivery of energy projects, the industry has long focused on optimising engineering productivity, refining project controls, and improving procurement and construction strategies. Despite these efforts, delays remain a persistent challenge. These delays are often attributed to complexity, uncertainty, or resource constraints. However, a closer examination reveals that the underlying issue is more structural in nature.

Engineering outputs are developed rapidly, supported by sophisticated tools and experienced teams. Yet, the progression of these outputs through the project lifecycle is governed by validation processes that are inherently sequential and fragmented. Documents and models move through multiple layers of review, often across different disciplines, organisations, and geographies. Each step introduces latency, not necessarily because of the effort required, but because of coordination, alignment, and the need to reconcile inconsistencies.

In this context, the constraint is not the ability to produce engineering work, but the ability to validate and integrate it efficiently.

The nature of the bottleneck

As projects evolve from conceptual design to detailed engineering and construction, the volume and granularity of deliverables increase significantly. This expansion is accompanied by a corresponding increase in interfaces between disciplines. Mechanical systems must align with structural supports, electrical systems must integrate with control architectures, and all must comply with operational and safety requirements.

Traditional review processes address this complexity through iterative, discipline-specific validation. While effective in ensuring technical integrity, these processes are limited in their ability to scale. Reviews are conducted sequentially or in loosely coordinated parallel streams, and issues identified at later stages often require revisiting earlier decisions. The result is a cycle of iteration that extends project timelines and introduces inefficiencies.

This dynamic is further compounded by the fact that review cycles are not purely technical activities. They involve communication, coordination, and decision-making across multiple stakeholders. As such, the duration of a review cycle is often driven more by process than by technical effort.

The emergence of AI in engineering contexts

Advances in AI have reached a level of maturity that allows systems to interpret, analyse, and generate engineering content with increasing accuracy. AI models can process structured and unstructured data, identify relationships between elements, and detect patterns that may not be immediately visible to human reviewers.

In practical terms, AI can now extract information from engineering documents, generate drawings based on defined parameters, and evaluate the consistency of designs across multiple domains. It can compare specifications against standards, identify deviations, and highlight potential conflicts between systems.

These capabilities extend beyond isolated tasks. AI can operate across interconnected datasets, enabling a more integrated view of engineering systems. This creates the foundation for a new approach to validation, one that is not constrained by discipline boundaries or sequential workflows.

Autonomous review with human-in-the-loop validation

The concept of autonomous AI review builds on these capabilities by applying them to the validation process itself. Rather than relying on multiple rounds of manual review, AI systems can evaluate entire engineering packages in a single pass, checking for completeness, compliance, and cross-discipline consistency.

Crucially, this approach does not eliminate the role of the engineer. Instead, it redefines it. Engineers remain responsible for final validation and decision-making, but their role shifts from conducting detailed manual reviews to assessing and confirming the outputs generated by AI. This human-in-the-loop model ensures that accountability and professional judgment are preserved, while significantly reducing the time required for validation.

The impact of this shift is not limited to efficiency. By enabling rapid, comprehensive review, it reduces the likelihood of late-stage issue discovery. Inconsistencies and conflicts can be identified earlier, when they are less costly and easier to resolve.

Implications for project execution

The introduction of autonomous review has broader implications for how projects are executed. By reducing the duration and frequency of review cycles, it allows engineering activities to progress more continuously. Dependencies between disciplines become less restrictive, as validation can occur in parallel rather than in sequence.

This creates the conditions for a more dynamic and responsive execution model. Decisions can be made earlier, with greater confidence, and adjustments can be implemented without significant disruption.

The traditional concept of rigid stage gates begins to give way to a more fluid process, in which validation is embedded within the workflow rather than applied after the fact.
The cumulative effect is a compression of the project timeline. Engineering completion is achieved sooner, procurement can be initiated earlier, and construction activities can proceed with fewer interruptions.

Enabling earlier production

For owner-operators, the most significant consequence of this transformation is the potential to achieve first production earlier than originally planned. The value of this acceleration extends beyond cost savings. It directly impacts revenue generation and the overall economic performance of the asset.

By reducing delays in engineering and execution, autonomous review contributes to a more predictable and shorter path to operational readiness. This not only improves capital efficiency but also enhances the strategic flexibility of operators, allowing them to respond more effectively to market conditions.

In this sense, the primary value of AI in project delivery is not merely in reducing costs, but in unlocking time. Time, in turn, is directly linked to value creation in the form of earlier production and extended asset utilisation.

Extending AI into operations

The same principles that enable autonomous review can be extended into the operational phase. AI systems are increasingly capable of monitoring plant conditions, interpreting sensor data, and applying predefined operational philosophies to optimise performance.

In environments where safety, efficiency, and reliability are critical, AI can support or even execute operational decisions within defined boundaries. This reduces the reliance on manual intervention and enhances the consistency of operations.

In certain scenarios, AI can effectively operate a plant by following established control strategies and responding to real-time conditions. While human oversight remains essential, the role of operators evolves toward supervision and exception management.

Conclusion

Autonomous AI review, combined with human-in-the-loop validation, offers a practical pathway to addressing one of the most persistent challenges in project delivery. By transforming how engineering outputs are validated and integrated, it removes a key constraint that has historically limited project performance.

The resulting benefits extend beyond efficiency gains. They include earlier production, improved capital utilisation, and a more responsive and resilient execution model. When combined with AI-enabled operations, this approach forms the foundation of a new paradigm in which projects are not only digitally enabled but increasingly autonomous.

The future of project delivery will not be defined by how quickly engineering work can be produced, but by how effectively it can be validated, integrated, and executed. In this context, AI is not simply a tool for optimisation, but a catalyst for fundamental change.