ANALYTICAL STUDY OF STRATEGIC PRODUCT THINKING IN CONSTRUCTION AND HEAVY EQUIPMENT FOR MODERNIZING AI ADOPTION

Abstract

In many asset-heavy industries like construction and heavy equipment, the adoption of artificial intelligence (AI) continues to lag despite its clear benefits in safety, maintenance, and efficiency. This paper examines how ideas from strategic product thinking can make AI adoption more practical and sustainable in such environments. The research brings together recent academic studies (2020–2025) and lessons drawn from on-the-ground modernization projects, including data migrations and AI-enabled analytics in construction operations. This study used a comparative, qualitative approach, reviewing research findings alongside practical experiences to understand how AI adoption unfolds in real organizations. The findings suggest that most obstacles are organizational rather than technical. Challenges such as resistance to change, limited digital literacy, scattered data systems, and weak implementation planning often stand in the way. In several modernization efforts, projects slowed down not because of technology itself but because teams lacked ownership, training, or clear communication about the changes taking place. The analysis shows that AI adoption works better when treated as a gradual, people-focused process instead of a single technology rollout. When organizations use product-thinking practices: testing in small steps, learning from feedback, and refining through collaboration, they build stronger confidence and capability over time. These findings point toward a practical pathway for legacy industries to structure AI projects, prepare their workforce, and turn digital initiatives into measurable long-term value. By applying these principles, this study aims to bridge the gap between technical potential and organizational readiness in legacy sectors.