When we talk about “smart engineering,” it’s easy to focus on all the recent advances in technology. At its core, however, smart engineering is about data. While new technologies allow us to analyze and collect more data than ever before, it’s imperative that we use such data to make decisions about infrastructure that maximize the value to society.

Using data for infrastructure decision making has been my soapbox for some time (see iimag.link/UtKYw). With limited resources, data allow us to make better decisions at project and portfolio levels. This has very large implications when we consider the total cost of ownership for an infrastructure project across the entire lifecycle of an asset. Engineering and design costs can range from 10 percent to 40 percent of a project, while mistakes at this phase can significantly impact the remaining 60 percent to 90 percent of costs occurring in the operations and maintenance phases and beyond.

In 2004, the National Institute of Standards and Technology (NIST) noted that data issues impact project costs across design, cost, schedule and asset information (iimag.link/FipVL). Their premise was to use IT systems to help alleviate this issue by capturing and efficiently sharing data across the project teams. (How novel.)

Two decades (and counting) removed from that NIST report, and we still see cost overruns on projects of up to 79 percent and schedule delays of 52 percent (iimag.link/inHGC). McKinsey notes that engineering firms can maximize project value (and ensure cost and schedule accuracy) through a collaborative approach with project owners through “agility and the use of lean execution and industry leading digital tools in planning and design from day one.” The core challenge from 2004 still remains today: how can we most effectively leverage data? The answer is right in front of us: technology.

Why Does This Matter in 2026?

There are three critical constraints today that “smart engineering” needs to consider and make data-informed decisions about to maximize value: limited funding, supplies and staff. First, the money question. The American Society of Civil Engineers estimates that the United States needs $3.7 trillion to meet the $9.1 trillion required to bring infrastructure to a good level of repair nationally. This means decisions need to be made about which projects get funded and built, and which will be delayed. For a regional planning association, this might mean analyzing the tradeoffs between building toll roads to improve access to jobs vs. investing in mobility programs that give more children expanded access to schools.

Then there’s the issue of supply scarcity. Inflation that has impacted the United States during the last five years isn’t limited to households. Nonresidential construction costs are up more than 41 percent since the beginning of 2021, and a key driver for the cost increases are supply chain disruptions. These problems will only grow in the short term. One example of these shortages is real competition among infrastructure projects for key components for electrical systems and semiconductors. This means decisions will be made about whether to allocate silicon chips for a data center or to systems that need continuous operations and maintenance such as a hospital or utility station.

Finally, there’s our industry’s continued workforce shortage. The ACEC Research Institute last estimated that the workforce gap for engineering companies (those leaving the workforce minus those entering) was approximately 18,000. This shortage is having a direct impact on firms’ bottom lines. Approximately half of firms responding to the institute’s quarterly engineering business sentiment survey reported that they turned away profitable work because they didn’t have the staff to complete it. With such staffing constraints, the only option is to be more efficient in allocating employees to work projects and improving employee productivity.

With the challenges listed, I see two opportunities for technology to help advance “smart engineering”:

1. Better analysis of existing data with new tools. AI, in particular large language models (LLMs), will continue to further democratize data science. Engineering firms have terabytes of data they can now unlock to query and data mine for insights. This level of analysis wasn’t easily available a decade ago.

2. The ability to collect new data we hadn’t been able to gather previously. The proliferation of sensors and devices such as drones and robotics means we’re able to gather new information previously unavailable to us.

Firms that are successful in “smart engineering” will continue to invest in a data-driven workforce and culture. The firms that win the future won’t be the ones with the most data; they’ll be the ones that weave them into every process, decision and outcome. At the end of the day, the dividing line will be between firms that react vs. those that anticipate.