As artificial intelligence reshapes industries around the globe, contractors have a unique opportunity to tap into this technology to revolutionize their operations. While AI might initially seem complex, its benefits in construction are tangible and impactful, offering solutions from project management to data insights that drive efficiency, cost savings and growth.

In an industry rooted in traditional practices, it’s understandable that many contractors might view AI with skepticism. However, as AI technology continues to advance, its potential benefits for construction firms are becoming too significant to ignore. Many companies are still held back by an outdated perception of AI as a costly or overly complex tool suited only for high-tech industries. Yet, the reality is that AI can drive efficiencies, cut costs, and open new avenues for profitability and growth in construction like never before.

AI adoption offers contractors a powerful advantage, transforming every phase of a project, from planning to execution. Below are ways all small and medium-sized construction businesses can harness the potent capabilities of AI, for those willing to embrace this emerging technology.

Natural Language Processing

When it comes to natural language processing, each business has unique requirements that must be thoughtfully considered to get the most out of their data. While uses for NLP vary greatly, some examples of highly useful applications include the following:

• Sentiment Analysis: These tools can help you understand the emotions and opinions expressed in text data. Understanding the sentiment in data can be used to monitor and analyze customer feedback, as well as to understand social media trends.
• Document Processing: These tools can analyze text, provide semantic search or semantic reasoning, and automate the tedious and time-consuming tasks of manual data entry and processing. Document-processing tools can extract information from various document types, including PDFs, images and scanned documents, like invoices, financial statements and compliance reports.
• Chatbots and Virtual Assistants: These tools can interact with your customers and provide them with the necessary information. Chatbots and virtual assistants can be used to handle customer queries, solve support issues, provide product recommendations and much more.
• Text Classification: Text classification and topic modeling solutions automatically classify and organize your text data. This involves the process of automatically assigning texts to one or more predefined categories and can be used to sort emails, articles, customer reviews and other types of text data.
• Entity Recognition and Extraction: Use these tools to identify and extract entities from text data. Entity recognition is a process of identifying and classifying named entities in text and can be used for advanced text extraction and text mining tasks.
• Natural Language Generation: These solutions can automatically generate text from data and can be used to create reports and provide text summaries, descriptions and other types of text data.

Predictive Analytics

Predictive analytics is a field of data science that uses historical data and machine-learning algorithms to make predictions about future events and unlock the value in data. This technology is used across industries for various applications, such as identifying trends, predicting demand, and making decisions about pricing and marketing strategies.

Demand Prediction: Predictive models for demand prediction can help you forecast future demand for your products or services, optimize inventory levels, and make better decisions about pricing and marketing strategies.

Churn Prediction: Custom predictive models can help you identify which customers are at risk of canceling their subscription, product or service. With this information, you can target at-risk customers with personalized offers and benefits to make them stay.

Predictive Maintenance: Predictive maintenance solutions that are driven by machine learning can detect potential equipment failures before they happen. By using predictive analytics, you can avoid costly downtime and improve the efficiency of your operations.

Predictive Manufacturing: Predictive models for manufacturing can optimize production processes, predict yield and identify quality issues. With predictive analytics solutions, you can improve the efficiency of your manufacturing operations and reduce waste.

Computer Vision

Custom image and video analysis solutions can be tailor-built to your business’ unique needs. Sophisticated algorithms and data-processing techniques can help you gain deeper insights into your data and optimize your processes for maximum efficiency. Some examples of computer-vision applications include:

• Image Analysis and Segmentation: Algorithms for image analysis and segmentation can help automatically detect and classify entities in images, extract specific features from images and develop biometrics systems for facial recognition.
• Object Detection, Tracking and Labeling: Custom computer-vision solutions can automatically detect, track and label objects in images and videos. Object tracking can help businesses identify and analyze the movement of specific targets over time to aid their surveillance capabilities, build intelligent systems for activity recognition or develop traffic-monitoring systems.
• Visual Search: Visual search capabilities can power recommendation engines, search engines and product catalogs. Using deep-learning techniques, image recognition models can be trained to accurately identify unique images or even search through large databases to find similar objects based on visual similarity.
• Intelligent Text Recognition: Intelligent character recognition solutions can automatically recognize text or handwritten characters in images and videos. This can be used to develop systems for intelligent document processing automation tools, build optical character recognition engines for scanned documents or even search for specific text in videos.
• Image Generation with GANs: Using Generative Adversarial Networks to develop custom-image generators is helpful for producing unique images or even realistic-looking video sequences. Various applications include data augmentation, art generation, marketing automation and more.

Recommendation Systems

Tailored recommendation systems can help you better understand customer preferences and suggest the best products or services for them. Based on deep-learning algorithms and by employing natural language processing, image recognition and other machine-learning techniques can deliver highly accurate recommendations for you to gain actionable insights into customer behavior and create targeted campaigns to boost customer engagement and satisfaction.

Recruitment: Intelligent recruitment and job-pairing solutions can help employers find the best candidate for a job. The system works by analyzing the profile of the job and the profile and resumes of potential candidates, then uses machine-learning algorithms to identify the best potential matches for the job, based on experience and qualifications. With a powerful recommendation system, employers can easily identify the most qualified candidates for a job.

News and Information Delivery: Recommendation system frameworks can apply data from previous user engagements to learn each visitor’s preferences and interests, as well as tailor engagement opportunities to meet the preferences of each unique user. Viewers can be best enabled to quickly find relevant and engaging content curated specifically to their profile. Additionally, systems can provide insights into user behavior, preferences and trends so that entities can better target their audience.

Online Shopping: Improve customer experience by providing personalized and tailored recommendations based on past user activities and preferences. The system can be integrated into existing online-shopping systems, and it uses data mining and machine-learning algorithms to track and analyze customer behavior and generate product, service and content recommendations. The system can also be used to display personalized offers and promotions to customers, boosting customer engagement and increasing sales.

Data Management and Analytics

Data has become the biggest asset for any business and, when processed properly, it can be greatly monetized. Some of a company’s most pressing and strategic questions can often be answered with the data itself. A robust data strategy and road map includes rapid prototyping and tool evaluation, data acquisition planning, data quality measurement and an understanding of data compliance needs.

Data Transformation and Connectivity: Considerations include data-scraping ad extraction, data matching across systems, scalable data processing and storage, as well as extract-transform-load functions. When the latest technologies, such as AI, ML and deep learning, are applied on data available from internal or external sources, a powerhouse for strategic decision-making is created.

Data Analytics Strategy: The approach is often multifaceted, with components including platform selection, AI/ML model application, comprehensive dashboard delivery for decision-making and product development considerations, like cloud data/AI platforms, as well as automated data integration. Artificial intelligence and analytics play a vital role in making organizations more efficient, bringing visibility and accelerating growth. With it, organizations can gain an informational edge by using data and actionable insights.

As construction businesses face increasing competition and pressure to improve efficiency, AI offers a way forward. Contractors who embrace this technology now are positioning themselves at the forefront of the industry, ready to tackle future challenges with agility. AI isn’t just for tech giants—it’s a powerful tool for contractors of all sizes to enhance productivity, boost profitability and secure a competitive edge. The sooner contractors overcome AI apathy, the sooner they can begin reaping the benefits that this transformative technology has to offer.

With construction becoming more data-driven, AI adoption is crucial for those who want to remain leaders in the industry. Contractors that embrace AI will find themselves better equipped to handle complex projects, meet client expectations and outperform competitors. For those still on the fence, now is the time to reconsider the potential of AI and explore how even small-scale integrations can yield measurable results.

Digital transformation is top-of-mind for all businesses, from startups to Fortune 500 groups. Technology continues to advance at a breakneck pace, as innovations like the block chain, artificial intelligence, machine learning, deep learning, advanced analytics, internet of things, talk technology, virtual reality and augmented reality continuously make new waves. Thus, it is critical to define the technology road map that meets your business goals—one that improves operational productivity through workflow digitization, process automation and enterprise integration; one that helps you understand disruptive technology trends and recommends opportunities to help you gain that elusive competitive edge.

AI is not a trend; it’s a shift in how construction contractors can operate smarter, safer and more efficiently. By harnessing its power, construction businesses can pave the way for growth, innovation and lasting success.