The imperative for AI in engineering, procurement, construction and commissioning
Artificial Intelligence (AI) stands as a transformative force, prepared to redefine the operational landscape of the Engineering, Procurement, Construction and Commissioning (EPCC) Companies in Oil and Gas, Energy, Marine and Petrochemical sectors.
AI offers the analytical power necessary to navigate the complexities of modern large-scale projects, from optimizing design and procurement to forecasting risks. In SEAWING, we intend to research key AI applications which might improve the performance of project management activities.
This article serves as a strategic guide for executive decision-makers, offering an actionable framework to navigate the complexities of AI integration. It gives a theoretical roadmap for developing, deploying, and scaling AI capabilities that deliver tangible business value.
Understanding the AI adoption landscape in EPCC
A successful AI strategy must be grounded in a clear-eyed assessment of the specific obstacles inherent to the EPCC industry. By categorizing these hurdles, we can develop a structured understanding of the landscape, enabling leaders to anticipate and mitigate risks more effectively.
The core challenges to AI adoption in the EPCC sector can be summarized as follows:
- Business challenges: Without a quantifiable business value, securing the necessary investment for AI initiatives remains a significant barrier.
- Architectural challenges: Integrating AI solutions into Large EPCC companies (multinational entities, complex IT environments) is a considerable technical challenge.
- Process challenges: Inconsistent data collection methods and a widespread lack of data-driven workflows make it difficult to prepare the necessary datasets for deploying effective AI models.
- Organizational challenges: Sourcing and retaining AI talent with a deep understanding of the company’s specific business context is a major hurdle.
Analysis of core AI implementation strategies
EPCC companies have several distinct paths for acquiring and implementing AI capabilities. Each approach;
- Building internally,
- Collaborating with specialists, or
- Buying ready-made solutions
Involves significant trade-offs regarding cost, control, speed, and long-term capabilities.
Strategy 1: In-house development
| Category | Analysis |
| Strengths | Full control over Intellectual Property (IP); development of tailor-made solutions; accumulation of internal know-how |
| Weaknesses | High cost of resources; long development schedule; unpredictable performance for first-of-a-kind projects |
| Opportunities | Development of new business models; potential for future scale-up |
| Threats | High investment with uncertain ROI; potential for project delays |
Strategy 2: Collaboration with third-party platform providers
| Category | Analysis |
| Strengths | Access to established infrastructure; faster development and deployment schedule; opportunity for knowledge sharing |
| Weaknesses | Dependency on the provider; potential for high long-term costs (licensing) |
| Opportunities | Leverage provider’s expertise to accelerate innovation; focus internal resources on core business activities |
| Threats | Risk of provider changing their technology or business model; data security concerns |
Strategy 3: Utilizing off-the-shelf and outsourced solutions
| Category | Analysis |
| Strengths | Fastest implementation time; predictable cost and schedule; requires minimal internal resources |
| Weaknesses | No IP ownership; generic solutions may not fit specific needs; continuous dependency on the service provider |
| Opportunities | Quickly solve non-core business problems; test AI applications with low initial investment |
| Threats | Service providers may discontinue the product; data security and privacy risks |
The optimal choice among these strategies is not universal; it must be guided by a structured evaluation of specific business priorities.
A decision framework for selecting the optimal path
Choosing an AI implementation path without a structured methodology is a recipe for misallocated capital and strategic misalignment. This section provides a practical decision-making framework, providing analysis to help EPCC leaders select the optimal integration path by weighing a set of critical business factors.
| Strategies | The strategic decision path | |||
| Is the proposed AI application a core business differentiator? | Does the application address a highly specialized need where technology readiness is low? | Is rapid deployment a critical business requirement? | Are there significant data security or legal constraints? | |
| In-house development | YES | YES | NO | YES |
| Collaboration with third-party | – | YES | YES | – |
| Off-the-shelf / Outsourced | NO | – | YES | – |
Evidence of value: AI applications in EPCC project execution
The true measure of any technology strategy is its tangible impact on business operations. This section moves from framework to fact, showcasing two powerful, real-world case studies from the EPCC sector.
- Case study: Automating quality control in engineering works
An AI-based solution was developed using a deep learning model trained to automatically recognize both correct and incorrect design patterns on P&IDs. The model was trained in thousands of drawings to capture the rules and standards of engineering.
The AI model successfully identified nearly 100% of targeted design error patterns, demonstrating a level of accuracy and speed unattainable through purely manual review.
- Case study: Enhancing cost estimation and procurement
An AI solution consists of a deep learning model that uses computer vision to recognize symbols and text on engineering drawings to generate an MTO, and a machine learning model that forecasts material quantities by analyzing historical data from past projects.
The regression model demonstrated strong predictive power, achieving an accuracy range of 67.3% to 93% on test data when forecasting key material quantities.
These proven applications underscore the immense value AI can deliver when strategically applied to core EPCC challenges, reinforcing the importance of the strategic guidance that follows.
Strategic recommendations for EPCC leadership
This article is designed to help EPCC executives initiate or accelerate their organization’s AI journey in a deliberate, value-focused manner.
- Prioritize AI initiatives with a clear business case. As demonstrated by the case studies above, such applications build crucial momentum, and more ambitious AI adoption.
- Select your implementation path deliberately. There is no one-size-fits-all approach to AI implementation. Use the provided decision framework to consciously choose a model that aligns with your strategic goals.
- Establish data governance as a foundational prerequisite. Recognize that all successful AI is built on a foundation of high-quality, accessible data.
In the evolving global EPCC industry, a deliberate, strategic, and value-focused approach to AI integration will be a key differentiator for companies like SEAWING. The companies that successfully embed this technology into their core operations will build a more sustainable and resilient foundation for future growth.
Reference:
AI for Enhancing Project Execution in Engineering and Construction
Rimma Dzhusupova
Eindhoven University of Technology
https://research.tue.nl/en/publications/ai-for-enhancing-project-execution-in-engineering-and-constructio
This article was developed by specialist Osman KARAÇORLU and published as part of the seventh edition of Inspenet Brief February 2026, dedicated to technical content in the energy and industrial sector.