More Than Buzz – The Real Potential of AI in the Construction Industry

Hello colleagues! Having worked in the world of architecture and construction for decades, I've seen trends come and go. However, the hype around Artificial Intelligence (AI) is different. It's not just another buzzword; it's a technological leap that can fundamentally reshape how we design, build, and manage projects. I know that as an SME leader or project manager, your time is precious, and dealing with future technological advancements is difficult amidst daily firefighting.

However, just as we at Toldy Construct [This reference was in the original full article, but not in the text you provided for translation, so it's omitted here] believe in precision, reliability, and continuous improvement, I think it's important to understand how AI can help us achieve these goals – without immediately spending fortunes or needing to become IT geniuses. This article isn't about pipe dreams but about the tangible first steps we can take today towards AI. Our target audience now is you, dear construction company leader, project manager, engineer, or site professional, who wants to understand how to profit from this technology without getting lost in the details.

Why Should an SME Even Bother with AI?

Before we get to the "how," let's clarify the "why." AI is not an end in itself. It can bring concrete business benefits, even on a smaller scale (as industry analyses also highlight [1]):

• Increased Efficiency: Automating repetitive administrative tasks, faster data analysis.

• Cost Reduction: More accurate cost estimations, minimizing waste, early risk detection (e.g., predicting delays, cost overruns).

• Better Decision-Making: Data-driven insights into project status, optimizing resource allocation.

• Quality Improvement and Safety: Recognizing patterns in defects, identifying potential safety risks based on data.

• Competitiveness: Companies that embrace new technologies can gain a long-term advantage.

Where to Start? The First Realistic Steps

The most important advice: don't try to do everything at once! Forget the self-thinking robots from Hollywood movies. Start small, where the pain is greatest or the victory is easiest.

1. Identify the Key Problem: Where do you lose the most time or money? With constant schedule slippages? Inaccurate cost estimates? Mountains of paperwork? Choose one specific area where you want to achieve improvement.

2. Look at Your Existing Tools: Surprisingly, many modern software applications already have built-in, "unnoticed" AI features. Are you already using any project management, budgeting, or even communication platforms? It's worth checking if they offer AI-based assistance.

3. Start with Data: The "fuel" for AI is data. Even if you don't implement AI software right away, start consciously collecting and organizing your project data in digital form. This is a valuable step in itself. The importance of data-driven operations is emphasized by numerous studies [2].

What Data Do We Need?

Start with what you already have, and strive for quality:

• Project Basics: Duration (planned vs. actual), costs (planned vs. actual), resources (people, machines).

• Communication and Documentation: Emails, meeting minutes, plan revisions, RFIs (Requests for Information), change orders.

• Site Data (if available): Daily reports, photos, drone footage (analyzing these involves more advanced AI).

• Safety Data: Incidents, near misses, inspections.

The most important thing: data should be digital, structured (as much as possible), and accurate. AI cannot draw good conclusions from bad data. Even digitalizing and standardizing data collection processes is a huge step!

Handling the Challenges: Cost, Data, Expertise

Let's be honest, implementing AI doesn't happen smoothly. But the challenges (which industry reports also address) can be managed:

• Cost:

  • Solution: Start with low-cost or freemium SaaS (Software as a Service) solutions. Focus on areas promising a quick Return on Investment (ROI). Running a pilot project in a single area carries less risk. Don't buy expensive software until you know exactly what you will use it for.

• Data (Quality and Quantity):

  • Solution: Start digital data collection now! Invest in data quality – it pays off in the long run. You don't need "big data" immediately; start with the most important existing data. The first step might be implementing a better data management system, not necessarily an AI tool directly.

• Lack of Expertise:

  • Solution: Look for user-friendly tools that don't require programming knowledge. Train existing staff on how to use specific software. Often, AI doesn't replace but complements and enhances existing expertise (e.g., AI data is even more valuable in the hands of an experienced project manager). If necessary, an external consultant can help with the initial steps and strategy development, but the goal is to build internal knowledge.

• Resistance to Change:

  • Solution: Clearly communicate the benefits (not job elimination, but easier, more effective work). Involve the team in the selection and implementation process. A successful pilot project is more convincing than any presentation.

AI is Not Magic, But a Tool

Like a good plan or a reliable machine, artificial intelligence can be a tool in our hands. For construction SMEs, the key is not chasing the latest, most expensive technology, but rather a gradual, thoughtful implementation that solves specific problems and creates real value. Start small, focus on data, and choose tools that support our existing processes and expertise.

AI is not a distant promise of the future – we can take the first steps towards it today.

How to Proceed?

1. Think it over: Which single area in your company's operations has the greatest need for increased efficiency or reduced errors?

2. Look into it: Are you already using software that might have hidden AI capabilities? What KKV-friendly project management or data analysis tools are available on the market?

3. Talk to your team: How do they see the potential of technological development? Where do they feel the greatest need for assistance?

Digital transformation is a journey, not a single leap. Let's embark on it thoughtfully and use AI for what it's meant for: to build better, smarter, and more efficiently.

Sources:

(Please note that the availability of links may change over time.)

1. McKinsey & Company: "AI in construction: Paving the way for a smarter future" (October 18, 2023) - Comprehensive article on the potential of AI in construction, including impacts on SMEs. https://www.mckinsey.com/capabilities/operations/our-insights/ai-in-construction-paving-the-way-for-a-smarter-future (Accessed: May 28, 2024)

2. Deloitte: "Future of Construction: Building a digital tomorrow" - Although a more general report on digitalization, it emphasizes the importance of data-driven decision-making in the industry. https://www2.deloitte.com/xe/en/insights/industry/engineering-and-construction/future-of-construction-industry.html (Accessed: May 28, 2024)

3. Associated Builders and Contractors (ABC): "Tech Report: Overcoming Challenges to Technology Adoption" (2023) - While US-focused, it identifies relevant challenges (e.g., cost, training, integration) for technology adoption in the construction industry. https://www.abc.org/Portals/1/CE/Docs/2023%20Tech%20Report%20FINAL.pdf (Accessed: May 28, 2024)