Summary

This paper explores how artificial intelligence (AI) transforms work preparation in the Architecture, Engineering, and Construction (AEC) industry. It consolidates 20 cutting-edge future AI applications that improve planning accuracy, execution reliability, and cross-team coordination on construction projects. These AI solutions enhance critical areas such as scheduling, logistics, risk management, subcontractor collaboration, and site communication bringing a new level of automation, proactivity, and efficiency to construction operations. By embedding AI technologies into daily work preparation routines, companies can move from static, reactive planning toward dynamic, data-driven decision-making. This paper addresses AI's potential across tactical and strategic levels, helping engineers, planners, and decision-makers align project delivery with quality, time, and cost objectives.

Each AI solution is presented as a dedicated chapter with a standardized structure, allowing readers to quickly assess its benefits, requirements, maturity, and implementation roadmap.

Structure of Each Chapter

Each chapter in this paper follows the same structured format:

Brief Description

Explains what the AI application does, how it works, and which planning or coordination challenges it addresses in the context of construction.

Tangible Effects

Highlights measurable benefits such as time savings, productivity improvements, planning reliability, or risk reduction.

Implementation Requirements

Describes the data, systems, and integrations necessary to successfully deploy the AI solution on a project or company level.

Investment Needs

Outlines initial setup costs and annual operational expenses, giving organizations a clear idea of budget implications and scalability potential.

Obstacles

Identifies typical barriers that could hinder deployment, such as missing data, fragmented systems, or organizational resistance.

Challenges

Discusses technical, procedural, and cultural challenges that may arise during implementation and scaling of the AI solution.

Opportunities and Risks

Provides a balanced view of the strategic advantages and possible pitfalls, including the need for human validation and change management.

ROI (Return on Investment)

Estimates the timeline and factors contributing to return on investment, including labor savings, faster project execution, and better coordination.

Maturity Level

Indicates the readiness level of the technology using a visual cue (🟢 Market-ready, 🟡 Pilot-ready, 🔴 Experimental) and a short description of current industry adoption.

Time-to-Market

Provides realistic expectations for how quickly the solution can be piloted and fully rolled out, based on data availability and team readiness.

Future Outlook

Envisions how the AI solution will evolve by 2030, including its integration with other systems like BIM, digital twins, or procurement platforms.

The purpose of this paper is to equip engineers, planners, construction managers, and decision-makers in the AEC industry with a practical, forward-looking guide to how AI can be applied in work preparation. It aims to raise awareness of real, deployable AI use cases; support structured evaluation and implementation efforts; and promote a shift from experience-based, reactive planning toward digitally enabled, intelligent project management. By presenting each AI application with clear benefits, requirements, and strategic implications, the paper supports companies in making informed investment and innovation decisions.