By Dr. Tom BartleyThe construction industry has always been driven by innovation, from advancements in materials to new building techniques. Now, artificial intelligence (AI) is making its way into construction, promising increased efficiency, cost savings, and enhanced safety. However, separating hype from reality can be challenging. In this blog, we’ll explore AI’s real-world applications in construction and how businesses can successfully integrate AI into their workflows.
Understanding the AI Hype Cycle
New technologies often go through a predictable cycle known as the hype cycle, a framework developed by Gartner to illustrate the path from initial excitement to mainstream adoption. Many technologies spend years in development before a breakthrough makes them widely known. Then, expectations skyrocket, often exceeding realistic capabilities. This is followed by the trough of disillusionment, where many projects fail due to overpromising and underdelivering. Only those that prove their practicality make it to the plateau of productivity, where they become standard tools in an industry.
AI is currently in various stages of this cycle, depending on the technology. Quantum computing, for example, is still in its early research phase, while generative AI (such as ChatGPT) has recently seen immense popularity but is now facing practical limitations, with its use in many critical business contexts being questioned. Some AI technologies, like computer vision for automated site monitoring, have already been at maturity for years, such as with automatic number plate recognition.
For construction companies, the key is to invest in AI solutions that are approaching or have reached the plateau of productivity—technologies that deliver measurable value rather than just riding the hype wave.
What is AI and How Does It Work?
At Mind Foundry, our definition of AI is “A machine that performs tasks which had previously been possible exclusively by human”.
At the core of any AI system is a “model”. A model is a series of algorithms that turns an input into a prediction or decision. Unlike traditional computing, where the output is determined by its programming logic, AI systems make their own inference as to the correct output based on their training and rules.
For AI to be effective, it must perform a task as well as or better than a human. If it can complete the task faster, cheaper, more accurately, or more consistently, then it becomes a viable business tool.
AI in Construction: Real-World Applications
AI is already proving its value in construction across several key areas:
- Computer Vision & Site Monitoring: AI-powered cameras can track workers and machinery on-site, ensuring safety compliance and preventing unauthorized access.
- Predictive Maintenance: AI can analyze equipment data to predict when maintenance is needed, reducing downtime and preventing costly breakdowns.
- Construction Robotics: Automated systems can assist with repetitive tasks such as bricklaying, welding, and 3D printing of structures.
- AI-Enabled Design & Planning: AI assists with automated building information modeling (BIM), helping engineers and architects optimize designs for cost and efficiency.
- Supply Chain Optimization: AI algorithms can predict material shortages and optimize delivery schedules to keep projects on track.
Why AI Projects Fail (and How to Succeed)
A staggering 87% of AI projects never make it into full production[i]. This failure often results from unclear goals, lack of proper integration, or an underestimation of the challenges involved. To improve the success rate, AI adoption must follow a structured approach.
The AI Lifecycle: A Step-by-Step Approach
At Mind Foundry, we follow a four-stage AI lifecycle to ensure successful AI implementation.
1. Design the Use Case
Before developing an AI solution, companies must define:
- The problem they want to solve (e.g., reducing project delays, improving safety, or cutting costs).
- The phase of the project lifecycle it will impact (planning, design, construction, operations, or maintenance).
- The success metrics (cost savings, efficiency improvements, quality enhancements, etc.).
2. Develop the AI Model
Once the use case is defined, the next step is building the AI model. This involves:
- Collecting and cleaning relevant data.
- Selecting appropriate algorithms.
- Training the model on past project data.
3. Integrate the AI Solution
AI must seamlessly fit into existing workflows. Whether it’s a standalone app or part of a company’s software ecosystem, integration ensures the AI solution works effectively alongside human teams.
4. Maintain and Improve AI Models
AI models must evolve over time. Construction environments change, and models need regular updates to remain accurate. This includes monitoring AI predictions, retraining with new data, and ensuring security and compliance.
Choosing the Right AI Strategy: Build, Buy, or Partner?
When adopting AI, businesses have three main strategies:
- Buy: Many off-the-shelf AI tools exist for standard tasks like project management, safety monitoring, and scheduling. This is the quickest and simplest option.
- Build: If a company has unique needs, developing a custom AI model can provide a competitive advantage but requires significant investment in data science expertise.
- Partner: Working with an AI specialist like Mind Foundry allows companies to leverage AI expertise without developing solutions from scratch.
Balancing AI and Human Expertise
AI should not replace humans but enhance their capabilities. There are three primary ways AI can be integrated into workflows:
- AI as a Tool: The human operator uses AI to assist in their work (e.g., using ChatGPT to draft reports).
- AI in the Loop: AI continuously runs in the background, providing alerts and recommendations, but humans make final decisions (e.g., AI-driven safety monitoring systems).
- AI as an Autonomous System: AI takes over certain operations with human oversight for safety (e.g., robotic equipment performing repetitive construction tasks).
The Future of AI in Construction
AI adoption in construction is increasing, but companies must be strategic in their approach. The key is to focus on proven, practical AI applications rather than chasing the latest trends. By carefully selecting AI projects, integrating them properly, and maintaining long-term oversight, companies can unlock AI’s true potential and transform their operations.
This blog is also available as webinar. For those looking to explore AI further, Mind Foundry’s white paper, “AI for Civil Engineers: What You Need to Know to Build the Future,” provides deeper insights into AI’s role in construction. Download it today and start leveraging AI for a smarter, safer, and more efficient construction industry.
Written by Dr. Tom Bartley
Director of Civil Infrastructure
Mind Foundry