By Karuna Ammireddy
The construction industry is in the middle of a shift from an analog past to a digital future. Faced with stagnant productivity growth, increased competition and a chronic skilled labor shortage, players in the engineering and construction sector have been evaluating the use of machine learning and artificial intelligence (AI) applications as a way to transform their operations.
The Problem in Numbers
Despite a historical resistance to the adoption of new technology, construction leaders have realized the status quo must change in order to remain profitable. Using technology to boost productivity in the engineering and construction (E&C) sector could generate an estimated $1.6 trillion in market value, according to McKinsey estimates.
The construction tech market has seen tremendous growth in the past decade. Ambitious startups and software developers have realized the potential of developing technology for the traditionally underserved industry, releasing products aimed at streamlining workflows throughout the construction process.
In recent years, cloud-based applications and all-in-one construction management software platforms have proliferated. The next big trend in construction technology is the use of AI and machine learning. Despite a slow initial adoption rate, construction leaders are beginning to take a greater interest in the transformative potential of AI tech. During the next five years, expect an increasingly rapid pace for tech adoption as applications and products targeted for construction continue hitting the market.
As companies integrate AI technology into their workflows, they will need to address some common challenges — including the need to improve data infrastructure. AI and machine-learning applications depend on the quality and availability of data for successful outcomes.
That might not sound like an issue, as a typical construction company might generate hundreds of gigabytes of data for a single project alone. The problem is that data is fragmented. To leverage the transformative capabilities of AI, construction companies need to draw all of that data together into one place and one format. The industry as a whole is only beginning to tackle the issue by looking for ways to break down the institutional silos responsible for isolated data that prevent efficient analysis by AI and machine learning platforms.
Where AI Excels
Construction is a documentation- and reporting-heavy field. The traditionally manual reporting processes are cumbersome and time-consuming.
From RFPs to specifications to submittal logs and daily reports, an almost daily deluge of information is generated throughout the lifecycle of a construction project. Right now, project managers and construction supervisors don’t have effective tools to help them understand the information faster. Instead, they have to read through hundreds of documents and absorb every bit of information page-by-page. It takes a long time to digest and comprehend all the information in order to act.
AI and machine-learning applications are particularly suited for streamlining these kinds of workflows within the construction industry by collecting, analyzing, and summarizing data, allowing supervisors to quickly understand the information and make appropriate decisions more quickly.
Repetitive, manual reporting tasks can be automated, lessening the amount of time poured into performing mundane but necessary administration. By streamlining and reducing the administrative overhead, general contractors can reallocate the man hours that have been freed up to other jobs that contribute to faster project completion.
For example, submittal logs are used to track design team approvals for materials and final designs prior to any installations. They’re an important managerial mechanism to ensure proper completion and quality control for construction projects.
They’re also tedious and time-consuming. Traditionally, the logs would be manually created by drudging through hundreds of pages of a spec book and compiling a spreadsheet. For larger projects with thousands of pages, this can end up taking hundreds of hours over its lifecycle. Using AI to compile and create the registers reduces the time needed to a fraction of that.
The Importance of Getting It Right
Although the adoption of AI technology in the construction industry will continue increasing, there are factors that must be addressed to encourage it. The construction industry is human-driven. This makes the learning curve for AI applications crucial. The time available for a construction worker to learn how to use a software product is limited. The product has to work pretty seamlessly and intuitively to encourage adoption and use.
The challenge for developers is creating AI applications that pay as much attention to the customer experience as they do to performing workflows. If the product is anything less than perfect, it’s more difficult for construction workers to integrate into their day-to-day jobs, and more likely they’ll pass it over in favor of their familiar, manual processes.
As these issues are resolved and the final barriers to widespread adoption of AI technology fall, the construction industry is poised for even greater innovation and increased productivity. In five years, the use of AI and machine learning tech will be the rule in the industry rather than the exception.
Karuna Ammireddy is a co-founder and CTO of Pype, the construction industry’s first SaaS provider for submittal log management and project closeout.