For years, construction has lagged behind other industries regarding technological advancement, disruption and digital automation. Finally, this is starting to change. The reducing cost of digital enablement, staffing challenges and the increasing complexity of business are forcing disruption upon the industry. Of all the digital technologies, automation holds out the most promise for achievable digital enablement. Automation can be defined as the use of machines and computers that perform tasks without needing human control. This approach provides an extraordinary capability to process and organize massive quantities of data in a fraction of time compared with manual execution while operating 24 hours per day.
Initial investment is required; however, benefits delivering quality and insights are realized in a short period of time. We have seen companies that invest in automation have:
- Reduced processing time by up to 98%
- Reduced front-line full-time employees by up to 64%
- Reduced back-office full-time employees by up to 47%
- Reduced employee time by up to 50%
- Reduced transaction turnaround time by up to 50%
- Reduced opex costs by up to 35%
- Reduced inventory levels by up to 10%
- Removed error rates to achieve 100% accuracy
- Identified thousands of duplicate invoices and suppliers in its enterprise resource planning
- Enabled clear accountabilities and data stewardships to sustain data integrity and trust
With these advancements come new challenges of turning an influx of information into action. Identifying the right processes and tasks that can be automated while executing proper change management is critical. One of the most cutting-edge forms of technology that offers a tactical solution is artificial intelligence (AI).
There are three key applications of AI throughout a construction project. The first is utilizing AI in planning — preparing for construction using design and production modeling. Following sequence in a project, AI monitors progress throughout the build phase and predicts risk before it occurs. Finally, operations utilize AI to map cross-functional processes in real time to produce insights for strategic decision-making.
AI leads the charge in construction
In generic terms, AI is a technology that responds to environmental stimuli and then adapts its processing to improve the actions taking place. AI models are trained on data in an iterative process by trial and error, making adjustments using rule-based mechanisms. These rules are designed to mirror characteristics associated with human behavior to effectively reason and self-correct — at an exponential level. For example, when reading an invoice, initially, the automated system cannot track or even recognize key pieces of information such as an address. However, over time, it learns by applying simple rules that categorize invoice information that can further be used for applications such as fraud detection or risk profiling.
Similar to how humans utilize different parts of the brain to solve problems, AI subsets have been developed that specialize in desired applications. The following section outlines the most commonly deployed core subsets of AI:
- Machine learning (ML) can perform a specific task without being explicitly programmed, instead relying on patterns and inferences. Large data sets are collected quickly and cleansed in structured steps using networks to determine relevancy and improve accuracy with each data point, providing additional layers of intelligence (see prior address example for a form of machine learning). The machine operates on diversified learning patterns (supervised, unsupervised or reinforcement) to accomplish a desired task most efficiently. Machine learning platforms have become foundational to real-time predictive analytics that allow users to better mitigate risks and monitor controls.
- Natural language processing (NLP) enables computers to structure, interpret and understand human language. NLP is typically used to collect and categorize content, and then analyze intent. This makes it possible for computers to extract keywords and phrases from speech or text, understand the content, and generate a response. It enables efficient mining of relevant information to perform tasks, such as compliance reviews, contract evaluations and chat-driven interfaces. An example is a chatbot, those creatures that pop up when visiting a website and ask you questions.
- Computer vision trains computers to interpret and understand the visual world. These machines accurately identify and locate objects through digital images. This technology is most commonly found in equipment, such as cameras and videos, that break down images into organized clusters of pixels through pattern recognition. Applications are diverse and can include surveillance, facial recognition, 3D modeling and automotive safety.
- Physical robots combine computer science and engineering to accomplish physical tasks typically performed by humans. Traditionally, robots can lift or move predetermined objects in a specified trajectory. When fitted with internet of things (IoT)-enabled sensors and AI, robots can track an object regardless of its location in the working space. Similarly, an AI-enabled robot can maneuver using pre-programmed maps to detect obstacles and pivot as needed. Applications, such as autonomous bulldozers and cranes, can significantly reduce time and labor requirements to streamline construction operations and improve safety on site.
- Robotic process automation (RPA) is used to replace repetitive and mundane tasks — typically, tasks related to project controls, finance, building operations and maintenance. Automation takes a current or modified process and enhances this via digital tools and AI, enabling computers to undertake the work previously executed by humans. It improves speed, accuracy, consistency and cost of executing these routine tasks.