From Tools to Decision Assurance
A cross-domain review of recent professional and
academic literature from 2023 to mid-2026
FEATURED PAPER
By Konstantin Lagutin
Tel Aviv, Israel
Abstract
This review maps professional and academic literature published from 1 January 2023 through mid-July 2026 on the applicability of artificial intelligence (AI) across the industrial project value chain – from front-end planning and design through construction, commissioning, operational readiness, and production. It draws on a curated, deduplicated corpus of 118 publications covering project management (PM), design and engineering (D&E), industrial construction, and mining. The selected platforms connect directly or indirectly to capital-intensive industrial projects and reflect the author’s professional experience and specialization. The evidence base is predominantly practice-led: 83 publications are practical or applied and 35 are theoretical or academic, a ratio of roughly 7:3.
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- PM writing concentrates on adoption, governance, decision support, project controls, and PMO transformation.
- D&E, despite its high value potential, remains the smallest stream, with early work on AI-BIM interaction, generative structural design, design-to-cost, and asset-life decisions.
- Construction emphasizes schedule risk, field evidence, safety, coordination, and automation.
- Mining remains the most research-intensive sectoral stream, addressing planning under geological uncertainty, geotechnical design, predictive maintenance, safety, and production optimization.
The review shows that substantive industrial applications were already emerging in 2023 and that governance, risk, assurance, and ethics have become the largest cross-domain topic. The literature is moving from day-to-day productivity tools toward governed decision infrastructure. Viewed along the industrial project value chain, AI creates its strongest value when earlier, better-evidenced intervention can prevent design, cost, schedule, safety, or production losses before commitments become difficult to reverse.
For sponsors, project managers, PMOs, project controls teams, and technical leaders, the findings provide a practical framework for selecting high-value use cases by project phase, defining governance requirements, assigning accountability, and measuring whether AI improves decisions across the industrial project value chain.
Keywords: artificial intelligence; industrial projects; project management; design and engineering; construction; mining; CAPEX; project controls; anticipatory assurance
- Introduction
AI has entered industrial project delivery through many routes, from generative assistants and forecasting tools to design optimization, computer vision, autonomous equipment, and mine-planning models. The label therefore covers applications with very different consequences. A chatbot that drafts a status report and an optimization engine that changes a major development sequence should not face the same value test or control regime.
This review is both a look back and a point-in-time map. It traces where AI can add value along the industrial project value chain, which uses deserve priority, and how those priorities differ across PM, D&E, construction, and mining. Its guiding idea is that AI applicability and assurance intensity should be assessed against the sequence of consequential decisions from front-end definition and design through construction, commissioning, operational readiness, and production, rather than folded into one generic category of “AI in project management.” Capital-intensive projects combine staged commitments, long lead times, technical interdependence, multiple delivery organizations, and decisions that are costly to reverse. Protecting value therefore depends on governed data, clear processes and decision rights, and human accountability, so that reliable evidence reaches the right decision-maker while there is still time to act.
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To read entire paper, click here
How to cite this paper: Lagutin, K. (2026). AI Across the Industrial Project Value Chain: From Tools to Decision Assurance; PM World Journal, Vol. XV, Issue IX, September. Available online at https://pmworldjournal.com/wp-content/uploads/2026/09/pmwj168-Sep2026-Lagutin-AI-Across-Industrial-Project-Delivery.pdf
About the Author
Konstantin Lagutin
Tel Aviv, Israel
Konstantin Lagutin is an international PMO and capital-project management leader with 25+ years of experience across mining, metals, chemicals, oil and gas, and industrial transformation programs in the GCC, EMEA, Russia, CIS, Europe, and the United States. He holds an MBA and an MSc in Data Business Analytics and is a certified Project Management and MIT Digital Transformation professional.
Konstantin has built PMOs and project management systems from scratch for major Mining & Metals companies, supporting capital-project portfolios from $1 billion to $7.5 billion. His work spans stage-gate governance, digital PMOs, lean construction, BIM-enabled, Agile, and 4D construction management. Most recently, he has focused on AI-supported PMO transformation, integrating schedule, cost, risk, document control, and executive dashboards to improve capital discipline and decision quality.
Konstantin can be contacted at lagutin.ki@gmail.com
