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The 5 Artificial Intelligence-Based Technologies the Industry Can’t Ignore

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Artificial Intelligence is a broad term that means different things to different people. Some picture autonomous machinery executing a command; others imagine unseen intelligence up in the cloud or within a software suite. 

The lack of clarity around what exactly AI is and what its capabilities are may contribute to its slow uptake in construction. Fortunately, other prominent industries have already woven the technology into their operations to great effect, providing a road map construction firms can follow. Based on a recent McKinsey report, the following are five AI-related applications already used in other industries from which C&E firms could benefit.

1) Transportation – Route Optimization Algorithms

Much like construction firms, transportation companies deal with tight schedules and have little wiggle room for unexpected changes. Many have optimized their operations and improved traffic navigation using AI-based decision-making platforms and apps. This has helped them slash operating costs, reduce the number of miles driven, and identify the most efficient routes, all while easing the burden of planning and scheduling. Since operational efficiency is critical to the bottom line, smart technology that handles planning and scheduling could be helpful to construction companies. They would be able to make better decisions thanks to algorithms capable of evaluating multiple courses of action. What’s more, such algorithms are able to learn and improve continually. 

2) Pharmaceuticals – Outcomes Prediction 

The pharmaceutical industry has led the way in investing in predictive AI solutions, an investment that pays almost immediate dividends when it comes to research and development costs. By forecasting medical trial outcomes, pharma companies can better assess risk and plan where their R&D dollars can be spent more efficiently. Similarly, applying this predictive technology to construction, particularly on large-scale projects, firms are able to increase their ability to account for project risks, constructability, and the structural stability of various technical solutions, according to McKinsey. Companies can also use the technology for testing materials so that they can limit downtime for structures during the inspection process.

3) Retail – Supply Chain Optimization and Inventory Management

Retail and construction are both highly dependent on materials and supplies being in the right place at the right time. Largely out of necessity, retail has been able to reinvent itself by tightening up its supply chain with the use of AI. This has helped the industry cut costs, reduce logistical headaches and account for variability, McKinsey writes. As modularization and prefabrication gain popularity in construction, a steady supply of materials has become more critical than ever in ensuring uninterrupted project progress. Better coordinating the flow of materials and maintaining greater control of the supply chain are proving key aspects of managing project costs.

4) Robotics Industry – Modular Prefabrication and 3D Printing

Robotics, prefabrication and 3D printing have already made their respective marks on C&E, but the technology can be leveraged in other ways. McKinsey cites recent robotics industry research where robotic arms have been trained to operate and learn through simulations. Improving robots’ capabilities and versatility could help construction firms develop better prefabrication techniques. They could even be used in maintenance operations for construction projects in industrial sectors.

5) Healthcare – Image Recognition

The healthcare industry has been using machine learning for image recognition so that healthcare workers can better diagnose illnesses. In construction, image recognition could be used to give drone aircraft inspections a major overhaul: If a system knows what to look for, it could identify structural or aesthetic defects early on. This capability could help detect or prevent anything from a minor inconvenience to a future catastrophic failure. Machine learning allows image recognition systems to get better over time as they are trained to identify specific risks.

If you liked this article, here are a few eBookswebinars, and case studies you may enjoy:

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