Artificial intelligence (AI) has the potential to revolutionize the construction and design industries, reshaping project delivery and customer service methods. As an executive at a leading company, I’ve seen opportunities for AI to streamline processes, improve decision-making, and optimize project outcomes.
The impact of AI on project execution is sure to be profound and multifaceted. From the conception and planning stages, leveraging AI-based models such as Forma, Veras, Stable Diffusion and Copilot, teams can quickly generate design options with great creative freedom, laying the foundation for the entire lifecycle of the project This allows designers to make informed decisions, which ultimately lead to better outcomes for clients.
One of the biggest potential benefits of AI lies in its potential to improve budget accuracy and shorten delivery times, crucial advantages in an industry where time is money. By analyzing large amounts of data, AI algorithms could predict potential cost overruns and schedule delays, empowering project managers to proactively address issues before they escalate. This approach not only improves project quality, but also builds trust with clients who value the transparency and predictability that AI-based solutions provide.
However, unlocking the full transformative potential of AI is not without its challenges. It needs a thoughtful and strategic approach to leverage its engineering and construction power effectively. Despite the obstacles that come with adopting any new technology, the significant rewards make this journey too consequential to ignore. This requires a thoughtful and strategic approach.
From initial ideation to overcoming data challenges, we recognized the need to navigate the transformative power of AI while maintaining client confidentiality and industry standards.
To unlock the full potential of AI, we embarked on a strategic initiative to establish a strong governance structure. A dedicated team was formed to oversee the implementation of AI, partnering with external experts to develop an overall framework. Prioritizing education and upskilling our workforce was paramount as we recognized the symbiotic relationship between AI and the human experience.
One of our main goals was to redefine project lifecycle optimization through AI. We explored how AI could improve the various stages of project delivery, from scope definition, design generation and construction administration. Real-world examples showed the role of AI in early decision-making, energy analysis and site design optimization, ultimately improving project delivery. For example, AI allows us to perform site impact analysis of exposure to sun, wind and noise, as well as test multiple designs for a building (or multiple buildings placed on a site) to optimize the energy use strategy. In modernization projects that aim to integrate a new system into an existing facility, AI has the potential to replace the manual comparison of laser scanning with a 3D registration model to identify any deviations.
Accurate, real-time data is invaluable for making highly effective project plans and tracking information needs, decisions, actions, and deliverables, both internal and external. The ability to collect project data such as risks and lessons learned in a single repository and to organize and use meeting notes and design review feedback (including decisions, compromises and open issues) allows us to leverage this data to a more successful set of projects. results Archiving this data also optimizes the future success of the project.
The tangible benefits of AI for our customers and stakeholders became increasingly apparent. Automating everyday tasks, such as transcribing handwritten field notes, translating into multiple languages during meetings, and comparing presentations against design criteria to flag deviations, allows our professionals to focus on problem solving and innovation, driving long-term sustainability and contributing to customer success. . From streamlining the RFP response process to optimizing team composition to automatically evaluating laser scans with the design model, AI offers opportunities unprecedented efficiency and innovation.
While AI adoption is still in its infancy for many companies, the insights from our analysis underscore the importance of refining data quality. Disorganized data and resistance to change are common obstacles that need to be addressed. AI thrives on high-quality, well-organized data. To unlock its full potential, we prioritized data collection and management, identifying commonalities across projects to create a cohesive data strategy. This required technical solutions and a cultural change within our organization.
Examples of the data collection methods we are developing include RFI (requests for information) and project financial data. The RFIs had been stored in a portable document format (PDF) and organized by project. We are creating a structure to consistently record and categorize RFIs and use AI to identify trends that can be used for lessons learned to target future training needs. Having the data collected and organized in this way can also allow us to quickly consult previous RFIs as a cross-check on new designs to assess whether similar conditions existed and were clarified through RFIs on previous projects.
Historical financial data from previous projects can also be useful. We are trying to use AI to analyze project finances and identify the optimal staffing mix by staffing level (E1, E2, E3, D1, etc.) for performance against budget to allocate to new projects and improve our resource forecast. There are some special considerations when evaluating archived data, such as how to account for previous staff promotions and how old the data is. Because processes have changed over time, the impact of those changes is represented in our current financial performance metrics, but that impact diminishes as the data ages. There was also the risk that some projects were not ideal projects to emulate due to budget, schedule or quality considerations. These projects must be identified and excluded from the dataset used to train the AI.
We learned the importance of fostering an environment that embraces innovation and is willing to adapt to new technologies. Improving processes to collect accurate and consistent data was one of the first and most important lessons we learned and implemented. Ethics and a customer-centric approach remain key commitments for us as we embark on the implementation of AI. Technological innovation and dedication to industry leadership are fundamental considerations that guide our efforts to optimize AI for project delivery. However, we must recognize the challenges that accompany this transformation. Data privacy and security issues, labor transformation due to automation, and the need for substantial investment in technology and skills training are obstacles that must be proactively addressed to successfully navigate the AI transformation path.
Overcoming these challenges requires a focus on fostering a culture of innovation, continuous learning and continuous improvement. We’ve learned to prioritize educating teams about the benefits of AI, ensuring strong data management practices, and adopting a mindset of adaptation and resilience. The challenge of integrating AI into a large enterprise continues, and our governance team has rejected ideas and tools due to concerns with reliability, security, scalability, and accuracy.
The architecture, engineering and construction industries are at a crossroads with the opportunity to harness the potential of AI and shape the future. Through the proactive adoption and strategic integration of AI, we can redefine industry competitiveness and customer satisfaction, paving the way for a smarter, more sustainable tomorrow.
The journey ahead is both exhilarating and challenging, but the rewards are immense. By embracing the transformative power of AI, we can partner with the AI developer community to unlock new realms of possibility, driving innovation, efficiency and excellence in the AEC industry.
Brett Susany, Senior Vice President of AI Integration and PMO, SSOE Group has 30 years of experience in project execution, including project, program, account and division management, and most recently the successful launch of the complex project group of SSOE. Brett also serves on SSOE’s Board of Directors, setting the overall priorities and direction of the company. He is a graduate of Vanderbilt University with a degree in Mechanical Engineering.
