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    Artificial Intelligence in Building Design & Marketing: It’s Here. What Now?

    A wave of change driven by mergers, new technology, and labor shortages has upended the architecture, engineering, and construction (A/E/C) industry over the past few decades. If you thought the waters were about to calm, hold on to your oars: here comes artificial intelligence.

    Artificial intelligence technologies, and most immediately one form called machine learning, promise to change not only how buildings are designed and constructed, but how A/E/C firms conduct their marketing and business operations.

    “I think we are on the precipice of a dramatic moment for machine learning in our industry,” says Alex Serriere, a principal and director of research at technology engineering firm TEECOM, “People are going to realize how much there is in A/E/C that can benefit from machine learning.”

    Henning Roedel, Western Region Innovation Leader at general contractor and construction management firm DPR, agrees. Construction firms like DPR are already using AI applications such as, which uses machine learning to “see” objects on video and recognize when things are out of place, improving safety and security on job sites.

    AI applications like this rely on a fairly straightforward set of data. Getting machine learning to the point where it can help out with the truly complex conditions involved in design and construction, however, will require large data sets that track a range of situations throughout time.

    “In order for AI to be successful, you need a data warehouse,” Roedel says, “A lot of effort, especially in leading contractors, is being placed in developing these warehouses.”

    The cutting-edge of machine learning is called “deep learning,” Serriere explains. Deep learning uses a very large data set to train a neural network (a neural network is a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns) to do interesting tasks for you.

    “What’s powerful about machine learning is that where previously you would have needed a programmer to figure out all the rules associated with your interesting task, with machine learning the machine figures out on its own what the rules are,” says Serriere, “That’s why the data source is incredibly important to machine learning.”

    Some data will always be proprietary to firms, but in order for A/E/C as a whole to have agency over how this technology will impact the industry, both Serriere and Roedel agree there needs to be more cross-organizational conversation about shared data warehouses.

    “I hope the industry will become better at sharing data,” says Roedel, “We’re having internal discussions about this [at DPR]. There are certain kinds of data the industry should come together and build for public use. Anything that improves value to the owner or increases safety to the workforce is something we should share.”

    One model for this type of public use is the data science website Kaggle, which allows companies to publish data sets in order to spur crowdsourced AI applications.

    “Companies can create a public data set and then teams and individuals can work on that data, and if they can get an AI that matches those requirements they get a cash prize,” explains Roedel, “The companies benefit from crowdsourcing what otherwise may be a complex problem.”

    Serriere started an online forum, Architecture ex Machina, to facilitate discussion about topics such as data sharing, data governance, and development of machine learning parameters. He is passionate about the need for A/E/C professionals to develop a strong voice in the upcoming technological shift.

    “We want not only to benefit from the tools, but to partake in the development of the tools,” he says, “We don’t want a tech giant to come along and say here’s the tool, go use it.”

    How can A/E/C marketers and business developers help guide their firms? What questions should we be asking our fellow firm leaders?

    Serriere suggests asking: “What are we doing to instrument our process so that we can gather better data about what we’re doing internally? What are we doing to capture project data or design data? How are we harnessing, capturing, and storing all that data so we can use it in machine learning applications in the future?”

    Roedel also suggests asking: “Is our organization working toward this data repository? Are we going to be able to use our data or have it in a place where a third-party AI company or tool can help us with it?”

    And if your company is in such a position, or working toward it, Roedel suggests there’s a marketing opportunity there: “If you’re situated in a good position, you can say, ‘We’re AI-ready.’”

    You can learn more about AI, machine learning, and data governance at the May 16 program, “Impacts of AI in A/E/C: What’s the Game Plan for Marketers and Business Developers,” featuring Alex Serriere and Henning Roedel, and moderated by Greg Gallimore, Digital Experience Design Director at Gensler. Register now.

    As Director of Marketing and Business Strategy at architecture firm Field Paoli, Traci Vogel supports the ongoing expansion of Field Paoli’s design consulting offerings. She leverages 10 years of experience for architectural and engineering firms in addition to a background in journalism, combining market knowledge with expertise in research. Connect with her at

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