by John Crowley | Aug 28, 2025 | Uncategorized
AI in Construction: Navigating Opportunities and Risks for SMACNA Contractors For SMACNA members, this technological shift presents both opportunities and challenges. As AI adoption accelerates across the industry, understanding its implications will be essential for growth and increasing margins, while keeping a firm grasp on the risks AI can present.The rapid emergence of AI technologies — from machine learning algorithms that optimize ductwork design to natural language processing systems that automate documentation, is beginning to fundamentally reshape how contractors bid, build and deliver projects. This transformation brings complexities that require thoughtful consideration and strategic planning.First, the OpportunitiesAI tools are accessible to anyone with a keyboard, and more and more people are getting used to using chatbots and other tools in their day to day. This fluency promises to spill over into the workplace, giving teams new tools and skillsets they can use to drive efficiency and control risk, as well as elevating the most critical skills by automating repetitive, time-wasting tasks.Here are some ways AI promises to improve the lives and work of contractors:Revolutionizing Document ManagementAI offers transformative benefits for document-heavy construction processes. AI technology can eliminate up to 90% of PO processing time while automatically identifying billing errors, which can save contractors 5% to 10% in material costs. For SMACNA contractors managing dozens, hundreds, even thousands of purchase orders, specifications, submittals and compliance documents, AI-powered systems can dramatically reduce administrative burden while improving accuracy.These systems go beyond simple automation. They can identify patterns in vendor pricing, flag unusual charges, and even predict potential supply chain disruptions based on historical data. Mitigating Field RisksAI’s predictive capabilities shine in field safety applications. Computer vision systems can monitor job sites 24/7, identifying safety violations that human supervisors might miss. These systems can detect workers without hard hats, identify improper ladder usage and even recognize fatigue patterns that precede accidents. Real-time alerts can enable immediate intervention, preventing injuries before they occur.Environmental monitoring represents another frontier. AI systems can continuously analyze air quality data during renovation projects, ensuring worker safety and regulatory compliance. They can predict when conditions might exceed exposure limits and automatically trigger ventilation adjustments or work stoppages.Productivity Gains That MatterConstruction professionals currently spend way too much of their time searching for information on projects. AI-powered search and retrieval systems can slash this waste, connecting workers with the exact specifications, drawings or procedures they need instantly. Natural language queries like “show me the rooftop unit specifications for the third floor” can instantly retrieve relevant documents from thousands of project files.Beyond search, AI enables entirely new workflows. Generative design tools can explore thousands of ductwork configurations overnight, optimizing for multiple parameters simultaneously, airflow efficiency, material usage, installation accessibility and cost. What once required weeks of manual iteration can now happen in hours, with AI presenting ranked options that meet all project constraints.Transforming RecruitmentAI is reshaping how contractors identify and attract talent in a tight labor market. Advanced algorithms can scan thousands of resumes to identify candidates with the specific certifications and experience SMACNA contractors need, from certified welders to TAB technicians. But the technology can go deeper, analyzing patterns to predict candidate success and cultural fit based on historical hiring data.Chatbots handle initial screening questions around the clock, ensuring no promising candidate is lost due to delayed response. They can answer questions about benefits, schedule preliminary interviews, and even conduct basic skills assessments. This frees HR teams to focus on high-value interactions with qualified candidates, improving both efficiency and candidate experience.Enabling Process InnovationPerhaps AI’s greatest promise lies in enabling entirely new ways of working. Machine-learning algorithms analyzing historical project data can identify patterns that predict delays or cost overruns with remarkable accuracy. By examining factors like weather patterns, material lead times, and crew productivity across hundreds of projects, AI can flag risks that human planners might miss.Integration with Building Information Modeling (BIM) opens even more possibilities. AI can automatically check designs for code compliance, identify clashes before installation begins, and even suggest optimal installation sequences based on site conditions and crew availability. Some contractors report 30% reductions in coordination time through AI-powered clash detection and resolution.Many of these opportunities will take some time, as managers become more comfortable with AI and begin to explore its capabilities. In fact, what we’ve seen already is that once construction leaders and field professionals get to a certain level of capability with AI, they start to find ways it can help both large and small companies. The true power of AI is giving teams the ability to adapt software to exactly what they need.The Double-Edged Sword: Risks AI PosesJust like any software, AI involves some risks. Here’s a good introduction to some of the bigger challenges:Data Access Policy VulnerabilitiesThe foundation of AI systems rests on data, documents and knowledge. For construction companies, this creates immediate concerns about data access and governance. AI systems require access to sensitive information, including infrastructure blueprints, cost estimates, client specifications and resource management plans, making them attractive targets for malicious actors. Without proper data access policies, contractors risk exposing proprietary fabrication methods, client details and competitive advantages that took decades to develop.The challenge extends beyond external threats. Internal data access policies must address how AI systems interact with different levels of sensitive information. What historical project information can be fed into third-party AI tools? Can subcontractor data be included in AI training sets? These questions require clear, enforceable policies that balance innovation with security.Consider a scenario where a well-meaning project manager uploads years of bid data to an AI tool for analysis. Without proper controls, this action could inadvertently expose pricing strategies, material sourcing relationships and proprietary installation methodologies to competitors if the AI platform experiences a breach or inappropriately uses the data for training public models.Accuracy Concerns and Meeting DocumentationAI’s promise of automated meeting transcription and documentation has captivated many contractors, but accuracy remains a persistent concern. When AI misinterprets technical discussions about HVAC specifications, refrigerant requirements or fabrication tolerances, the consequences can cascade through entire projects. The nuanced nature of construction conversations, with their mix of technical jargon, regional terminology and project-specific abbreviations, poses unique challenges for AI systems trained on general datasets. When a superintendent mentions “fishmouth” or “Pittsburgh lock,” will the AI accurately capture these industry-specific terms and their implications for the project?The Erosion of Critical SkillsPerhaps most concerning is AI’s potential to erode fundamental skills within the workforce. As contractors increasingly rely on AI for load calculations, psychrometric analysis and ductwork optimization, there’s a risk that essential technical expertise may atrophy. Young professionals entering the field might bypass the foundational learning that comes from manual calculations and hands-on problem-solving.This dependency creates vulnerabilities when AI systems fail or produce questionable results. Without the underlying knowledge to verify AI outputs, contractors may blindly follow flawed recommendations, potentially compromising project safety and quality. Imagine a scenario where an AI system recommends a duct size based on incomplete data, and no one on the team possesses the experience to recognize that the recommendation violates basic airflow principles.The risk extends to field personnel as well. As AI-powered tools handle increasingly complex tasks, from automated layout to robotic fabrication, the craftspeople who built their careers on these skills may find their expertise devalued. This could create a dangerous knowledge gap where the industry loses the very expertise needed to train and validate AI systems.Cybersecurity ThreatsThe cybersecurity landscape for construction AI presents unique challenges. AI systems face risks from attackers who “shift focus from stealing data to poisoning the AI models themselves.” For construction companies, this represents a new frontier of cyber risk. Compromised AI models could provide access to customer information, or lead to incorrect answers.The interconnected nature of modern construction projects amplifies these risks. IoT devices monitoring equipment performance, sensors tracking environmental conditions and integrated project management platforms all represent potential entry points for attackers. A breach in one system can cascade through integrated platforms, affecting everything from BIM models to financial systems.The “Bring Your Own AI” DilemmaAs AI tools proliferate, contractors face the challenge of employees using personal or unapproved AI applications for work tasks. This “shadow AI” phenomenon creates multiple risks: data leakage through consumer-grade tools, inconsistent outputs across teams and potential intellectual property violations. Without clear policies, sensitive project information could end up training public AI models, benefiting competitors.A foreman might use a free AI app to quickly generate a materials list, unknowingly uploading proprietary assembly details. An estimator could rely on an AI chatbot for code interpretations, receiving guidance that hasn’t been verified for local jurisdictions. These individual actions, while well-intentioned, can create liability exposure and competitive disadvantages that compound over time.Crafting Effective AI PoliciesEarly Stage: Foundation BuildingFor contractors just beginning their AI journey, success starts with solid foundations:Governance Structure: Create an AI steering committee combining IT, operations, field personnel and leadership perspectives. This group should meet regularly to evaluate tools, assess risks and guide implementation. Include both AI enthusiasts and skeptics to ensure balanced decision-making.Data Classification: Develop clear schemes identifying what information can be used with AI tools. Create categories like “Public” (marketing materials), “Internal” (general procedures), “Confidential” (project specifics) and “Restricted” (client data, pricing strategies). Each category should have clear handling requirements and approved use cases.Tool Approval Process: Establish a formal process for evaluating and approving AI tools. Consider factors like data security, vendor stability, integration capabilities and total cost of ownership. Start with pilot programs on non-critical projects before widespread deployment.Training Foundation: Implement basic training on AI capabilities and limitations. Every employee should understand what AI can and cannot do, recognizing both its potential and its pitfalls. Include real-world examples relevant to their daily work.Mid Stage: Systemic IntegrationAs AI adoption matures, policies must evolve to address more complex scenarios:Verification Protocols: Develop specific procedures for verifying AI outputs in critical applications. For example, any AI-generated load calculation must be spot-checked by a qualified engineer. Establish clear thresholds for when human verification is mandatory versus optional. A critical point here is to make sure someone “owns” every output from the AI system.Vendor Management: Create comprehensive assessment criteria for AI solution providers. Evaluate not just functionality but also data handling practices, model transparency and update procedures. Require vendors to provide regular reports on model performance and any identified biases or errors.Performance Metrics: Establish KPIs for AI initiatives. Track not just efficiency gains but also error rates, user adoption and impact on project outcomes. Regular reviews ensure AI investments deliver promised value while identifying areas for improvement.Conclusion: Balancing Innovation with PrudenceThe construction industry’s AI transformation is not a question of if, but how. SMACNA works to be one step ahead to ensure members have the resources and education to align with the growth curve. Success requires balancing aggressive innovation with prudent risk management.The path forward demands a thoughtful approach. Contractors must embrace AI’s potential while maintaining healthy skepticism about its limitations. They must invest in technology while preserving the human expertise that remains irreplaceable. Most critically, they must view AI as a tool to augment human capabilities rather than replace them.For SMACNA contractors, this means developing comprehensive strategies that address both opportunities and risks. It means creating policies that enable innovation while protecting against emerging threats. It means investing in both technology and training, ensuring teams can leverage AI effectively while maintaining the skills to work without it.The future belongs to those who can harness AI’s power while preserving the craftsmanship, judgment and expertise that define excellence in sheet metal and HVAC construction. By sharing experiences, best practices and lessons learned within the SMACNA community, contractors can collectively navigate this transformation, emerging stronger and more capable than ever before.As we stand at this technological crossroads, one truth remains clear: the contractors who thrive will be those who approach AI with both ambition and wisdom, leveraging its capabilities to enhance their work while never forgetting that construction remains, at its core, a human endeavor built on skill, experience and dedication to quality.
by John Crowley | Aug 28, 2025 | Uncategorized
Making AI Work: Tips & Tricks for Contractors From drafting proposals to managing complex Excel spreadsheets, AI can transform how contractors work, if they know how to use it effectively. For most modern AI users, this means understanding how to “talk” to AI, otherwise known as “prompting.” This guide will explain and demystify AI prompting and provide practical examples to help contractors harness these powerful tools while addressing common security concerns.Understanding Prompting: The Key to AI SuccessAt its core, prompting is simply the art of communicating with AI tools. Think of it as giving clear directions to a highly capable assistant who needs specific instructions to deliver the best results. Just as you wouldn’t tell a subcontractor to “build something nice,” you can’t expect AI to read your mind. The quality of your output directly leads to the quality of your input.In the beginning, when ChatGPT was introduced, most users were excited to just get plain English responses; this was a very new experience. As we’ve grown more comfortable with these tools, we want them to perform better and to actually be useful. And that’s where more careful prompting comes in. As you’ll see, many of the drawbacks that AI has can be handled, or at least minimized, by good prompts.For contractors, mastering this skill means the difference between generic responses and tailored solutions that actually save time and money. The good news is you don’t need a computer science degree to write good prompts, just a little understanding and some practice.Background: What Happens When You Enter a PromptModern AI is based on Large Language Models (LLMs), and these work by accepting everything you’ve entered, and based not just on what you’ve entered, but also where various items are in the paragraphs, and how you’ve marked things, creating a probability for what is the best response. This means AI is sensitive to everything you’ve included, as well as what’s in the beginning, how you end the prompt, whether you’ve been clear on outputs and more. Below we outline these details and suggest best practices. Anatomy of a Good PromptA well-crafted prompt contains several essential elements that work together to produce the results you want. Understanding these components will help you construct prompts that consistently deliver valuable outputs.Start with clear context and role: Provide context about your situation. For example: “I’m a general contractor preparing a bid for a commercial renovation project” gives the AI crucial information about your role and task.Tell it what you want done: Be clear and complete. Instead of “Help me with a proposal,” try “Create an executive summary for a commercial renovation proposal highlighting our 20 years of experience and focus on sustainable building practices.”Provide your desired output format: Specify how you want the information presented. Do you need bullet points, paragraphs, a formal letter or a table? The AI will follow your formatting preferences when clearly stated.Include constraints and requirements: Include limitations or specific requirements. For instance: “Keep it under 500 words” or “Use terminology appropriate for municipal government clients.”Provide examples if you can: For many requests, giving the AI an example of what you’re looking for can dramatically improve results. This is particularly useful when you have a specific style or format in mind, or if you have a form you want filled out (e.g., RFI).Example 1: Drafting Proposals and DocumentsOne of the most time-consuming tasks for contractors is creating professional proposals and documentation. AI can streamline this process when prompted correctly.Poor Prompt: “Write a proposal for construction work.”Effective Prompt: “I need to write a proposal for renovating a 10,000-square-foot office space. The client is a law firm that values professionalism and minimal disruption to their operations. Create a two-page executive summary that includes:Our company’s 15 years of commercial renovation experience.Commitment to completing work during off-hours.Our bonding capacity and insurance coverage.Timeline showing eight-week completion.Emphasis on our previous work with professional services firms. Please use formal business language appropriate for legal professionals.”This detailed prompt provides context, specific requirements and clear formatting instructions, resulting in a polished, professional document that requires minimal editing. You can reasonably save two to three hours per proposal using this approach, allowing you to bid on more projects without sacrificing quality.Example 2: Figuring Out Software and Technical IssuesConstruction software can be complex, and contractors often struggle with technical challenges. AI can serve as an on-demand tech support specialist when prompted properly.Scenario: You’re trying to create a Gantt chart in project management software but can’t figure out how to link task dependencies.Poor Prompt: “How do I use project software?”Effective Prompt: “I’m using Microsoft Project to schedule a residential construction project. I have all my tasks entered but need to link dependencies so that:Foundation must be complete before framing starts.Electrical rough-in happens after framing but before insulation.Drywall can’t start until electrical inspection passes. Please provide step-by-step instructions for creating these task dependencies, including which menu options to use and any keyboard shortcuts that might help.”This approach transforms AI into a personalized tutorial system, providing specific guidance for your exact situation rather than generic software advice. Many contractors find this more helpful than searching through lengthy user manuals or watching generic YouTube tutorials.Example 3: Customizing Excel for Construction ManagementExcel remains a cornerstone tool for contractors, but many only scratch the surface of its capabilities. AI can help you create custom spreadsheets, formulas and automation that specifically address construction industry needs.Real-World Application: A contractor needs to track material costs across multiple projects with automatic markup calculations and budget alerts.Effective Prompt: “Create an Excel formula system for a construction materials tracker that:Calculates 15% markup on all material costs.Flags any line item over $1,000 in red.Automatically sums materials by category (lumber, electrical, plumbing).Includes a dashboard showing total costs vs. budget for each project.Works with data starting in cell A2 with columns for: Item, Category, Cost, Project Name.Please explain each formula and where to place it.”This targeted approach helps contractors build powerful, customized tools without expensive software or consultants. The AI can provide not just the formulas but also explain how they work, enabling contractors to modify them as needs change.What if I Don’t Have Time to Write Long Prompts?One trick you can use is to write something high level, then tell the AI to ask you questions, one at a time, until it knows enough. This is great for things like agenda writing, where the prompt would be:“Create an agenda for tomorrow’s meeting. Ask me questions, one at a time, about the meeting until you have enough information to complete the task.” If the questioning goes on too long, you can always tell the AI “Please stop asking questions and draft the agenda.”Avoiding HallucinationsOne of the big drawbacks of LLMs is that, when they don’t know the answer, they’ll make something up. Even worse, because they’ve analyzed your request and come up with a high probability answer, incorrect answers often seem plausible. One way to avoid this is by ending your prompt with a clear statement of what to do if the AI doesn’t know the answer. Here’s a good example: “If you don’t know the answer, don’t make it up. It is OK to say you don’t know.”A second way to avoid hallucinations, especially when you’ve asked for something long, is to require that the AI provide a bulleted list of factual statements that are made in their response. Here’s an example:“After you have answered the question, add a bulleted list of all factual statements made or referred to in the answer, with links to the source of that information.”It turns out that by requiring the AI to list the facts it included, it will often correct itself. And you have an easy way to check each item.Data Security: Addressing the Elephant in the RoomOne of the biggest concerns contractors express about AI adoption is data security. The good news? When used properly, mainstream AI tools from reputable providers are generally safe for business use. However, understanding best practices is crucial.Key Security Considerations:Choose Reputable Providers: Stick to well-known AI platforms from established companies like OpenAI, Anthropic, Google or Microsoft. These organizations invest heavily in security infrastructure and compliance.Understand Data Handling: Most major AI providers don’t train their models on your individual conversations when using business accounts. However, always review the terms of service and privacy policies.Protect Sensitive Information: Never input client social security numbers, credit card information or highly confidential bid strategies. Treat AI conversations like email, professional but not for ultra-sensitive data.Use Business Accounts: Consider enterprise or business subscriptions that often include additional privacy protections and data handling guarantees.Best Practices for Contractors:Anonymize project data when possible (use “Client A” instead of actual names).Review your company’s AI usage policy or create one if it doesn’t exist.Train your team on appropriate AI use.Keep records of AI-assisted work for accountability.Regularly review and update security practices as technology evolves.The construction industry’s digital transformation is accelerating, and contractors who embrace AI tools gain significant competitive advantages. By mastering the art of prompting, you can transform these powerful tools from mysterious black boxes into practical assistants that save time, improve quality and help you win more business.The examples provided here are just the beginning. As you explore AI tools, you’ll discover countless ways to apply them to your specific needs — from safety planning to cost estimation, from client communication to regulatory compliance. The key is to start somewhere and build your skills through practice.
by John Crowley | Aug 28, 2025 | Uncategorized
A SMART Partnership We are lucky to have partners in SMART who share our core values. We aim to advance our industry, collaborate on projects that meet our customers’ needs and build a workforce of the highest quality. To meet these goals, it helps to have someone on the other side of the table who shares these values — and we have such a person in that position in SMART General President Mike Coleman.Many of you may know that Mike and I go back a while. He was a long-time member of the T.H. Martin team, working his way through the ranks of our company until he became foreman and leader within our company. Mike was what everyone looked for in both a leader and an employee. He is hard-working, always looking out for the folks under him, and is consistently a straight shooter. Mike is committed to bringing the best out of everyone around him. It was these attributes that have made him such a strong labor leader. Mike was tough but fair and never let minor disagreements override our joint vision of how we wanted our relationship to work. When it came to our partnership, we kept it productive by building a climate of trust, ensuring constant communication, and believing both of us had the best intentions. This foundation has led to collaborations on numerous critical matters, including efforts to achieve fair and amicable resolutions in collective bargaining, developing a robust talent development framework, and addressing shared legislative challenges. I can’t tell you how many local labor events, fundraisers and other get-togethers we have attended together during our time in Cleveland. We are both big-picture guys — balancing this critical relationship building while also serving the best interests of our core constituencies. To this end, we have also been partners in business matters. These include efforts to build enhanced market share and highlighting the value of signatory contractors as we work to reclaim market sectors that we may have lost to our non-union competitors. It is this commitment to the industry that made Mike such a phenomenal partner as we both built our careers in the Cleveland area. This focus continues to guide us in our current roles. When Mike became General President, I was excited to see my friend’s hard work and commitment be justly rewarded. However, in addition to that, SMACNA contractors now have someone on the labor side of the table committed to a collaborative approach that achieves results for all. During my service as a national leader, Mike has been more than advertised. As we have interacted throughout my term, Mike has not changed from the individual who worked his tail off inside the walls of T.H. Martin. I won’t kid you, though; Mike will fight for the best interests of his members and make sure that they are treated fairly and equitably. However, he remains a good listener who gathers all the perspectives around him and works toward solutions that benefit everyone. He understands very clearly that everyone prospers from working together. These are all the same traits I witnessed the first time I met my friend Mike Coleman. I am proud not only of our friendship but also of the fact that we can work together to advance our shared interests and promote prosperity for all.
by John Crowley | Aug 28, 2025 | Uncategorized
Progress, Partnerships and Possibility Since then, SMACNA has evolved into an association that is actively growing and changing to advance our renewed mission of serving our members effectively.MarketingThe Marketing & Communications Department, led by Susannah Forde, launched a new, user-focused website. We also introduced National Career in Trades Week, our first national public relations campaign aimed at raising awareness of job opportunities in the trades. The campaign received strong local and national media coverage, including a front-page feature in The Wall Street Journal. Next year, we will expand this initiative through industry partnerships. Our collaboration with SMART continues to advance shared priorities. We have also strengthened our social media and video content to showcase member achievements and thought leadership on key issues.Markets / AI / EducationThe market sector councils and task forces have also been busy under Linda Jennings’ direction. They are creating new programming at every opportunity. Some examples include the work being done by the industrial market sector task force on productivity and safety, the architectural task force’s efforts to leverage trade shows to increase industry awareness, and the residential group’s work to use new media tools to expand market footprint.In a similar vein, the AI Task Force, led by Travis Voss and Hugh Seaton, is working diligently to help our members navigate the rapidly evolving AI landscape. Through their informative webinars and other materials, we hope to answer the questions our members have on this complex topic. We recognize that traditional white papers may not be the most accessible format for our members. To address this, we have converted many of these resources into podcasts and infographics, and we will continue to develop on-demand videos for greater accessibility.Our education programming continues to evolve. SMACNA now offers 40 to 50 web-based courses annually and has expanded chapter education opportunities. Core programs such as PMI and BMU are scheduled more frequently and are available to interested chapters. Erykka Thompson is leading an education task force focused on expanding field leadership, supervisory and project management offerings.Ultimately, the FAB Forum exemplifies our programming. Fabrication remains at the heart of our industry, and it is more important than ever that we keep our members on the cutting edge of the technologies and best practices emerging in this arena. Linda’s team has done a tremendous job in building a program that gives contractors the tools and best practices they need to succeed. TechnicalOur technical standards remain one of the key assets produced by our association. Eli Howard and his team continue to consistently produce tools of maximum value to our members. We have produced several technical videos to increase the exposure and utility of these items. This is just the beginning of our push to make our standards as accessible as possible. We are working to translate select manuals into Spanish, ensuring that all contractors deliver work aligned with our top-of-the-industry guidelines.Government RelationsStan Kolbe and the rest of the Government and Political Affairs Group have been highly effective in advocating for our members and our industry in Washington. The 118th Congress witnessed Stan’s group skillfully lobbying for the enactment of key provisions to advance modernizing airport Infrastructure, Nuclear Power Financing-Permitting Reform and reforms for project labor agreements. As we work through the 119th Congress, we were already successful in enacting the extension of our top tax priority provisions and we will press our lawmakers on key items, including change order reform and the SAFER Banking Act. Labor RelationsLabor relations and local bargaining support have long been core services of SMACNA. Jason Watson has expanded the department’s work and is actively working with Chapters to create stronger relationships with labor. He has also held additional training programs and is building a new trustee training program for 2026.Our association exists to advance the industry and support our members. The team’s ongoing efforts reflect this commitment, and we remain vigilant in addressing emerging needs and trends. Aaron HilgerSMACNA CEO
by John Crowley | Aug 28, 2025 | Uncategorized
Artificial Intelligence: An Introduction Since late 2022, when ChatGPT was introduced, we’ve been hearing more and more about AI, usually with more hype than explanation. In this article, we’ll explain AI from the ground up, so you can be armed with a solid foundation of understanding as more and more products and pitches come your way. What is AI?First things first: what is AI? You’ve probably heard a few explanations, and they often trip over themselves trying to explain this or that model or algorithm. AI isn’t actually one technology. Think of it as a collection of approaches that seek to create machines that can think. We haven’t been able to do that completely, but along the way AI has enabled very useful tools, like recognizing email spam, understanding normal language, automating some tasks and more. AI is still just software though, not magic, not an ‘digital brain.’ A useful definition that’ll help you think about AI is: AI is software that can do unique and useful things because it learns from data. AI represents a fundamental shift in how software systems operate and make decisions. Unlike conventional software that follows predetermined rules, AI systems learn from data, adapt to new situations and improve their performance over time without explicit programming for every scenario.Today’s most advanced AI systems include Large Language Models (LLMs) like GPT o3, Claude and Gemini, which can understand context, engage in complex reasoning and even tackle multi-step problem-solving tasks.In a construction context, AI shows up in various forms: everyday chatbots that summarize documents, draft emails and can do research, and proprietary systems like computer vision systems that monitor job sites for safety violations, natural language processing tools that analyze contracts and specifications, predictive analytics that forecast project delays, and autonomous equipment that performs specific tasks with minimal human intervention. Since late 2024, we’ve seen more and more of a move to introduce “agents,” which are AI tools that automate workflows, especially the tedious day-to-day tasks that involve documents.HOW is it different to “normal” software? Traditional software operates on instructions, in the form of code. Developers write clear and complete instructions: if X happens, do Y, and the software does exactly that and only that. Every possible scenario must be anticipated, programmed and tested. This approach works well for predictable tasks but breaks down when dealing with the complexity and variability inherent in the real world. It is impossible to write enough rules to recognize a face, let alone millions of faces, yet AI does this all the time.AI makes software work in three main ways. First, AI systems learn from examples rather than following rigid rules. This is why we often hear so much about ‘data’ in the context of AI. It takes a lot of data for AI to learn anything useful, often hundreds of thousands of labeled, organized data points.Where a traditional software system might need thousands of lines of code to identify safety violations, an AI system can learn to recognize them from examples, generalizing to new scenarios it hasn’t seen before.Second, because of this learning from data vs. instructions, AI is much better at processing unstructured data: the messy, real-world information that makes up most of what we encounter daily. Things like images from job site cameras, handwritten notes, voice commands, architectural drawings and natural language in contracts all represent unstructured data. We call it “unstructured” because it isn’t neatly in rows and columns, like excel data or a database. These examples would be impossible to process at any scale with traditional programming. AI can extract meaning from these natural, messy sources, turning chaos into actionable insights. Third, and perhaps most importantly, AI systems improve over time through continuous learning. As they process more data and receive feedback on their predictions, their accuracy and capabilities expand. This adaptive quality means AI solutions can evolve with changing project conditions, regulations and industry practices without requiring manual reprogramming. A safety monitoring system, for instance, becomes better at identifying hazards as it analyzes more incidents and near-misses across multiple job sites.What is AI good at?We find in industry after industry, AI is good at things that make up for limitations that humans often have. Because AI can quickly recognize patterns, processing documents at lightning speed, modern AI systems can process millions of data points, from historical project records to real-time sensor readings, identifying trends and correlations that would take human analysts months to uncover. More realistically, AI can do things contractors just wouldn’t do because of the investment in time and money. Because it is software, the cost to do these things drops, and suddenly they become possible. In fact, that is the core of what we see AI changing in the near term: allowing contractors to control risk more completely because they can automate more of the tedious, time-consuming work that goes into analyzing, error checking and summarizing documentation that can so often be a huge source of risk. To expand on that, here’s a quick list of some of the things AI excels at: Natural language conversation: At the heart of the current AI wave is the ability for people who are not software developers to work directly with AI. We can ask it questions, get web searches summarized, get images created, and receive a growing list of everyday tasks that are now at our fingertips.Document processing and analysis: AI can analyze contracts, specifications, RFIs and change orders at superhuman speeds, automatically extracting key information, identifying conflicts and flagging potential issues. AI-powered systems can review thousands of pages of project documentation in minutes, ensuring nothing falls through the cracks. Safety monitoring: Computer vision systems powered by AI can continuously monitor job sites through existing cameras, detecting safety violations in real-time. This includes things like workers not wearing proper PPE, unsafe behaviors like working too close to moving equipment and so on. Predictive safety analysis, where AI analyzes historical safety data from previous accidents, near misses and safety observations, can identify patterns that predict future risks.Quality control: AI-powered image recognition can identify construction defects, measure installations against specifications and track progress with unprecedented accuracy. Drones equipped with AI can survey entire job sites, comparing actual construction against BIM models and identifying deviations before they become expensive problems.Automation of repetitive tasks:. AI can automatically generate quantity takeoffs from drawings, analyze and create optimal construction schedules considering countless variables, match invoices to purchase orders, and even draft routine correspondence. These time savings compound across projects, dramatically improving productivity.In all of the above instances, it is important to point out that no system is perfect, and we too often expect AI to be better than a human would or even be as good as an experience professional would be. In reality, AI systems are still just software, and they are good at automating parts of the work, supporting the professionals, but there are definite limits that users encounter quickly when they trust AI with too much, too fast.What is AI not good at?Despite its impressive capabilities, AI has serious limitations that contractors must understand to use it effectively. The core of these limitations is that AI doesn’t think like humans do, in fact under the hood, AI is nothing like the human mind. The fact that it can produce coherent, intelligent responses sometimes misleads users, but never forget it is software. Here’s a list of some of the limitations to watch out for:Made up information: Sometimes called “hallucinations,” AI will provide answers to questions even when it doesn’t know the answer. It does this with complete confidence, so it can be difficult to spot. This behavior is because AI, specifically LLMs like ChatGPT, are built from the ground up to provide answers. If they don’t know the answer, they’ll make one up that seems right. This can be almost entirely avoided by pointing the AI at real data, like a document or website, and asking better questions, but it is still a problem to be aware of.Understanding of the project: AI does not understand the world, so it will have no idea what certain things imply or what should be included or not. This is another place where AI being fundamentally different to human minds is important. You cannot ask AI a very high level question like, “Show me all the risks in this project manual” and get a good response, it’s just beyond the AI’s capability.Creative problem solving: While AI can generate variations on existing solutions or combine known approaches in new ways, it cannot match human creativity when facing new challenges. When a unique structural problem arises on a job site or when coordinating complex trades requires innovative sequencing, human expertise and creativity are essential.Complexity: AI can handle messiness much better than any other software, but it is nowhere near as capable as even an untrained human. This is why robotics is still limited to situations where the site has been cleared (like layout robots) or otherwise simplified. This is true also concerning what you ask AI to help with — involve too much complexity and you get a response that is not useful.What should contractors know?First and foremost, AI is a powerful tool, but it is not a replacement for human expertise. AI will not replace the fundamental need for a skilled workforce, expertise or lived experience. Successful implementation requires viewing AI as a partner that handles routine analysis and monitoring, while humans focus on complex decision-making, relationship management and creative problem-solving.For many applications, data quality serves as the foundation for AI effectiveness. These systems are only as good as the information they’re given. Start with areas where your documents and data are in good shape, then move from there. Where quality data is a struggle, start where the stakes of failure are lower and are likely to be caught, like marketing and sales, where AI can automate everything from proposal preparation to images and emails. The efficiency gains for these chronically understaffed functions are often significant.Implementation requires strategic planning and patience. Rather than attempting wholesale AI adoption across all operations, successful contractors start with pilot projects in specific areas. Common starting points include marketing, safety monitoring, document analysis or predictive maintenance for equipment. These focused implementations allow teams to learn the technology, demonstrate ROI and build confidence before expanding to other areas. While nearly all companies are investing in AI, only 1% of leaders call their companies “mature” on the deployment spectrum.Like all technology, training and change management will be crucial for AI adoption success. Address fears about job displacement directly and emphasize how AI augments rather than replaces human workers. The change management playbook is pretty well understood:Invest in training programs that build both technical skills and confidence. Create champions within your organization who can demonstrate AI’s benefits and support their colleagues.Align incentives to allow for time to adoptSecurity and privacy demand serious attention in the AI era. AI systems often require access to this data to function effectively. Contractors must understand how their data is being collected, stored, processed and protected. Key considerations include: Where is data stored? Who has access? How is it encrypted? What happens to data after project completion? Can competitors potentially access insights derived from your data?Vendor selection requires careful evaluation. The construction technology market is flooded with AI solutions, but not all deliver on their promises. Look for vendors with specific construction industry expertise, proven track records and verifiable case studies. Because of the hallucinations, and security issues mentioned above, require that vendors show how they evaluate their AI for accuracy and explain it in non-technical terms, it should not be rocket science. Similarly, require they explain data security, including if contractor team members leave.Future-proofing your AI strategy means staying informed about rapid technological advances while maintaining focus on fundamental business needs. In 2025, models will do more, and they will do it even better, with capabilities expanding monthly. However, avoid chasing every new feature or trending technology. Instead, maintain a clear vision of how AI serves your core business objectives: completing projects safely, on time and within budget.Finally, remember that AI adoption is a journey, not a destination. The technology continues evolving rapidly, and best practices are still emerging. Maintain a learning mindset, regularly reassess your AI strategy and be prepared to adjust as you gain experience. Connect with peers using AI in construction to share lessons learned and avoid common mistakes. The contractors who thrive in the AI era will be those who thoughtfully integrate technology while maintaining their focus on the fundamentals of good construction practices.
