Human ingenuity has always been at the center of great engineering. Throughout the centuries, the accumulated knowledge of design, engineering, and construction experts has led to an impressive array of projects—from canals and viaducts to bridges and modern high-rise buildings.
But suddenly the ground is shifting. The rapid advance of artificial intelligence (AI), including generative AI (GenAI) tools that incorporate large language models, is redefining what it means to be an engineer. Although technology is nothing new to the profession—CAD and BIM tools have automated numerous processes, for example—AI is now extending the scope and boundaries of engineering.
The opportunities are substantial. AI can help firms streamline and improve processes, increase efficiency, understand costs, assist with design functions, predict failures, and identify maintenance requirements for projects. It also can aid in decisionmaking and unlock innovation at scale—and even generate ideas that humans have never considered.
“Engineers exist to solve problems, and we use data, software, and analytics to do so. For many of us, data also comes in the form of experience,” observes Mike Lawless, vice president of innovation at IMEG. “AI has the powerful ability to provide engineers with the right data at the right time.”
To be sure, AI is rapidly emerging as a potent force in the engineering field. “With AI and machine learning, the transition to a more predictive and prescriptive business framework is taking place,” says Bret Tushaus, vice president of product management for software firm Deltek.
The enormous value AI can bring to firms shouldn’t be ignored. Deltek’s 2024 Clarity: Architecture & Engineering Industry Study found that 62 percent of firms believe that AI will improve operational efficiency. In addition, over half (52 percent) think it will improve project timelines and delivery, while 35 percent say it will reduce overhead costs.
“There are changing dynamics in our industry,” says Raj Arora, chair of the ACEC Technology Committee and CEO of Jensen Hughes. “For several reasons, there’s an imbalance of supply and demand. Many firms have more than enough work. The challenge is sourcing quality talent and getting projects completed in a timely manner. AI could help us do more with less, work faster, and eliminate many mundane tasks, allowing engineering teams to focus on higher-value work.”
Engineering firms benefit from four key areas of AI, says Hank Tran, director of AI and analytics at software firm BST Global. The technology can improve design, production, operations, and productivity. Yet he also emphasizes that any project must begin with the recognition that AI is built on data. “Having the right data architecture, data quality, and data guardrails are critical factors that are often overlooked,” he says.
AI-fueled automation can simplify tasks such as data entry, invoice matching, and timesheet auditing. It can draft documents and pen letters to clients. It can also spot anomalies and exceptions in everything from project designs to financial systems. And it can serve as a digital assistant or natural language chatbot that replaces manual searches through documents, emails, spreadsheets, and other files.
“It’s possible to get answers faster, understand trends better, and make more informed decisions,” Arora says. For instance, Jensen Hughes is now exploring an AI tool that finds the most qualified team members for a project. “If we’re building an airport in Barcelona, for instance, who has the knowledge, experience, and language skills to lead the project?” he says.
GenAI’s ability to uncover ideas and designs that engineers haven’t even considered can prove transformational. After a set of parameters are fed into a GenAI system that has been trained on past projects, it can churn out hundreds or even thousands of drawings, renditions, and design concepts. “The architect can then refine these to create the best and final solution,” Tran explains.
“Our mindset—which we communicate to our staff—is that engineers are important to our firm’s future, and AI will be there to augment them, not replace them.”
MIKE LAWLESSVICE PRESIDENT OF INNOVATION, IMEG
Andrea Springer, vice president and director of digital engineering solutions at CDM Smith, adds, “We’re seeing improvements in processes and workflows, along with insights that didn’t surface previously.”
Employee retention could be a benefit of working more efficiently: According to the Deltek report, 72 percent of firms believe AI will improve their staff’s job satisfaction.
Engineering firms are beginning to recognize the value of AI. CDM Smith has incorporated machine learning and other AI capabilities into its practice. This includes areas such as computer vision, fuzzy logic, and machine learning (see sidebar below). Now the firm is pushing into natural language processing and other forms of GenAI. Springer says that AI has already aided in collaboration and enabled more creative and effective ways of working. “We believe it will be used to solve increasingly complex problems,” she says.
At IMEG, AI now helps engineers explore project scenarios. It presents ideas that “are often broader than we might have developed on our own,” Lawless says.
“It’s possible to get answers faster, understand trends better, and make more informed decisions.”
RAJ ARORACHAIR, ACEC TECHNOLOGY COMMITTEECEO, JENSEN HUGHES
As a result, teams are able to determine the best outcome more quickly. The firm has developed a chatbot named Meg that connects engineers with a vast array of content, including past projects. This helps teams sort through complex technical options and apply lessons learned. It also allows employees to get answers to general questions, such as which days constitute company holidays or how a particular program works.
“One of the benefits of our chatbot is that it can educate and enable our younger staff to perform closer to the level of our more experienced engineers,” Lawless says. “Meg will never replace human mentorship, which is an integral part of our firm. But chatbots don’t judge. They provide a safe place to start learning.”
The company is now looking for ways to use AI to promote document creation, utilizing all the information from past projects and models.
Shawn Weekly, digital transformation architect area lead for digital solutions at POWER Engineers, says that GenAI helps the firm prepare documents faster and at a lower cost point. “It is expensive to have designers and engineers digging through folders full of documentation to find specific equipment that’s recommended by the industry for a given set of variables,” he explains.
Instead, engineers can now access a GenAI tool that answers questions and finds relevant information in “a fraction of the time.”
POWER Engineers is now developing an enterprise-wide AI assistant that will contain large chunks of the company’s engineering and design data. “By leveraging this assistant, we expect to see a significant reduction in many of the tedious tasks that we grind through today in all our projects,” Weekly explains.
So far, a test environment has yielded productivity gains hovering around 50 percent. POWER Engineers is exploring ways to build machine learning models that tap visual and image recognition to spot errors earlier.
“Perhaps AI will help our industry finally catch up on the enormous backlog of engineering work that exists.”
NATHAN BINGHAMCHIEF DIGITAL OFFICERPOWER ENGINEERS
Despite its remarkable capabilities, AI comes with a few caveats. One common problem is that GenAI systems are prone to hallucinations and data biases—meaning that they sometimes invent facts or distort information. And without the right oversight and controls in place, incorrect information can be presented as fact.
To address such concerns, POWER Engineers has published AI usage guidelines on regulatory compliance, data privacy, and security; prohibitions on the use of customer data in AI tools; data retention and destruction policies; and incident reporting and response. In addition, the firm emphasizes that responsibility for the accuracy of content falls on those using AI, and standard checking procedures should be followed, says Chief Digital Officer Nathan Bingham.
When adopting the tech at a firm, Tran believes it’s wise to “start small and understand how you will measure the outcome of AI models.”
It’s also vital to ensure that sufficient guardrails are in place to avoid misuse or abuse and to address cultural issues. Many workers, for example, fear that the technology could push people out of jobs—but that’s unlikely in today’s environment. “Ongoing talent shortages in the engineering field make this possibility remote—at least for the foreseeable future,” Arora says.
Others agree. At IMEG, “Our mindset—which we communicate to our staff—is that engineers are important to our firm’s future, and AI will be there to augment them, not replace them,” Lawless says.
The focus is on upping productivity and improving results, not replacing people. “This technology can have a huge impact on outcomes for the people and communities our projects serve,” he adds. “AI also gives back some time to our engineers and helps them achieve better work-life balance.”
A firm can accelerate AI adoption with tech-savvy employees who are willing to serve as early adopters and experiment with use cases, Arora says. Yet it’s also crucial that managers and senior executives are on board with a program and don’t serve as an impediment. “Otherwise, everything falls apart,” he says.
To be sure, in the coming years, AI will alter business models and the way projects unfold. It will usher in new ways to work and new ways to design and build projects. “AI will soon be embedded in almost every type of software,” Bingham says. “Perhaps AI will help our industry finally catch up on the enormous backlog of engineering work that exists.”
The rapid advancement of AI is proving to be transformative—and it’s only going to get more prevalent.
“Artificial intelligence is here to stay,” Tran concludes. “It isn’t a destination; it’s a journey.”
Samuel Greengard is a technology and business writer based in West Linn, Oregon. He has contributed to Entrepreneur, Information Week and Wired.
ACEC’s Technology Committee is focused on tackling these and other emerging challenges. Join and get involved here: www.acec.org/committee/technology-committee/
AI biasA problem that occurs when AI algorithms reflect social biases and prejudices, typically due to issues with underlying training data.
ChatbotA computer program designed to simulate conversation with human users through text input or voice commands.
Computer visionA field of AI that focuses on interpreting and understanding visual data like images and videos.
Fuzzy logicAn AI technique that mimics human-like reasoning to handle ambiguous or imprecise information (versus traditional binary logic, which operates in a strictly true/false manner).
Generative AI (GenAI)This branch of AI generates new content, such as text, images, video, and code. It is increasingly used to summarize vast amounts of text, operate chatbots, and find new ways to design things.
HallucinationsA response from an AI system that is misleading, incorrect, or patently false. These problems occur as a result of flawed training data or algorithmic limitations.
Large Language Model (LLM)A type of AI specifically designed to understand and generate human language. Trained on vast amounts of content, it can summarize text, write code, and translate languages.
Machine LearningThese systems use algorithms to improve over time as additional data accumulates.
Natural Language ProcessingA field of AI that focuses on enabling computers to understand, interpret, and generate human language, allowing for more natural and intuitive interaction between humans and machines.