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According to the International Energy Agency, annual investment in new capital projects—which stood at just over $1.5 trillion before the COVID-19 pandemic—is set to reach nearly $2 trillion between 2025 and 2030. Capital projects in the energy industry typically involve billions of dollars in investment and millions of engineering and construction hours. Over the life cycle of a capital project, tens or even hundreds of thousands of documents are generated, reviewed, modified, tracked, executed, and handed over to the operations. Moreover, these documents are of diverse types, including engineering plans, regulatory permits, CAD drawings, instrumentation indexes, and construction details, among others. The use of enormous numbers of documents shared among hundreds of different parties—including engineering, procurement, construction (EPC) contractors, equipment vendors, suppliers, quality assurance and control parties, and government bodies—using dozens of different knowledge management systems (KMS) over many years of a project life cycle has the scale to deliver meaningful business outcomes. Generative artificial intelligence (AI) can drive such outcomes through savings in engineering and construction hours and schedule acceleration, helping avoid all too common project delays and cost overruns.
Generative AI differs from traditional AI in its ability to understand context, think critically, and generate original and realistic content. During the initiation and definition phase of a capital project, generative AI can promptly summarize and interpret massive amounts of documentation from the public domain—for instance, it can obtain federal, state, and local regulations and standards from reports and other outputs of various agencies—alongside research materials from closed sources. Using generative AI in this way can accelerate a feasibility study, which might ordinarily include such tasks as site analysis, regulatory compliance and environmental impact reporting, among others, to confirm that the project is legally, technically, and economically justified. Capital project teams can benefit in particular from using chatbots powered by Amazon Bedrock, the simplest way to build and scale generative AI applications with foundation models, with Retrieval Augmented Generation (RAG), a combination that helps deal with inquiries more responsibly and with higher relevance. The fully managed, RAG-based feature of Amazon Bedrock extends its large language model (LLM) capabilities so that project documents of varying formats, sizes, and types can be aggregated and made searchable promptly, reducing timeframes between conception to implementation. Amazon Bedrock–powered chatbots can provide answers based on both public and closed information, including rules on local zoning, permits, environment, mass transportation, traffic, parking, telecommunications, utilities, fire, and health and safety. Combining Amazon Bedrock with a ready knowledge base increases cost-effectiveness by removing the need to continuously train an LLM on constantly growing and changing project data. Chatbot responses can be further improved through fine-tuning and prompt engineering.