5 Takeaways from the First AI/ML Benchmarking Survey

BuiltWorlds unveiled their new AI/ML Preparedness Track to address the pressing challenges and opportunities posed by AI and ML in the built world. At the Americas Summit on September 21st BuiltWorlds will host an insightful session that will unveil the track's scope, participants, and shared commitment to pioneering joint research efforts. This research track will be championed by BuiltWorlds Board Member, Rosemarie Lipman and facilitated by BuiltWorlds Research Analyst, Alexis Adams. This piece offers an overview of the track, along with an exclusive glimpse into some of the insights derived from our AI/Machine Learning Track Survey findings.

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AI/ML Prepardness Track Overview

This track is the latest expansion to the BuiltWorlds Benchmarking Research Program, stemming from last April’s CEO Forum discussion around emergent AI/Machine Learning technologies and their applications for AEC.

  • The goals of the track are to help members understand the applications of AI/Machine Learning for AEC. More specifically, we want to help our member companies avoid the risks associated with emergent AI/Machine Learning tech and to harness their potential opportunities. We also intend to explore the role of startups, cloud computing and tech companies, academic, and industry.

Adoption & Utilization

  • Approximately 59% of respondents have adopted AI/ML software and are using it in some capacity. Another 23.5% have plans to begin using this tech in the next 12 months. Only about 18% of respondents reported that their company is not currently prepared to begin integrating AI/ML software.
  • While adoption rates are high for the emergent tech, implementation rates are much lower. 37.5% of respondents reported that their company currently uses its solution on a few projects (10 - 29% of projects); 12.5% use it on every project; about 6% use it on most projects (60 - 90% of projects); and 12.5% are currently piloting their AI/ML solution. Additionally, about 31% of respondents reported that they are unsure how frequently their solution is being used across projects.
  • Adoption rates will likely continue to rise as AI/ML gains more traction in the space. Implementation rates are unlikely to keep pace with adoption during this early phase of trial and error. We can expect to see higher and steadier implementation rates as industry players identify their preferred, dedicated solution.

Priorities for AI/Machine Learning in AEC

  • We asked two open-ended questions about the best applications and priorities for AI/Machine Learning in AEC. Respondents most commonly mentioned that they were interested in leveraging AI/Machine Learning for workplace assistance. Respondents are interested in using these solutions for streamlining, digitizing, and refining processes. They are excited about AI/ML’s potential for reducing risk, errors, and tedious work.