Core Viewpoint - The integration of "Artificial Intelligence+" into the engineering consulting industry is essential for responding to national strategies and adapting to industry changes, which is crucial for promoting high-quality development and new productive forces [1] Group 1: Importance of AI in Engineering Consulting - The engineering consulting industry is a key practical field for implementing the "Artificial Intelligence+" initiative, enabling the assetization and intelligent reuse of knowledge [2] - AI technologies are transforming knowledge discovery processes, facilitating the integration of interdisciplinary knowledge to enhance innovative problem-solving capabilities [2] - AI, along with technologies like BIM and digital twins, is reshaping the entire lifecycle of consulting services, transitioning the industry towards data-driven, traceable, and intelligent decision-making models [2] Group 2: Changes in Talent Structure and Industry Ecology - The integration of AI will significantly alter the skill composition and competitive logic of consulting professionals, emphasizing soft skills such as innovative thinking and ethical judgment [3] - AI technologies are redefining the competitive landscape of engineering consulting firms, allowing smaller firms to enter the market more easily and challenging traditional competition [3] - The industry is moving towards a collaborative ecosystem, integrating resources across design, construction, and cost management, promoting an open and intelligent industry ecosystem [3] Group 3: Challenges in AI Integration - The engineering consulting industry faces multidimensional cost pressures related to AI integration, including infrastructure, technology services, and organizational management [4] - There are significant challenges in data management, including data silos and lack of standardization, which hinder effective data utilization [4] - The supply of interdisciplinary talent with both domain knowledge and digital skills is lagging, creating an imbalance in demand and supply [4] - The application ecosystem for AI in engineering consulting is still underdeveloped, with a lack of standards and guidelines for specific fields [4] Group 4: Recommendations for Integration - A feasible "Artificial Intelligence+" integration strategy should be developed, focusing on collaborative innovation and resource sharing among institutions [6] - A robust data support system should be established, including unified data standards and governance frameworks to enhance data quality [6] - A mechanism for cultivating interdisciplinary talent should be strengthened, integrating AI and data science into engineering education [7] - A collaborative innovation application ecosystem should be built, encouraging partnerships between consulting firms and AI technology providers, while establishing relevant standards and guidelines [7]