Multifamily and affordable housing developer JPI is embracing construction technology and artificial intelligence (AI) to reduce time and costs.
“The connection between our technology platform and workforce/attainable housing is direct: The structural challenge of affordable housing is cost. If you can’t build more efficiently, the economics don’t work at lower rent levels,” says Mollie Fadule, chief financial and investment officer at JPI. “Our goal is to continue to drive more efficiency, which will translate into lower cost and therefore the ability to build more affordable/workforce housing units.”
According to Fadule, JPI has made progress compressing the development and construction lifecycle through standardization and digital enablement. Its first pilot project saw total delivery time cut from about 40 months to about 29 months, approximately 30% faster than traditional timelines. The four key factors driving this progress include a standardized design library that has halved design time per project, automated planning workflows built from 3D Revit models, Takt-based construction sequencing, and JPI’s Operations Support Center, which provides real-time tracking and issue prevention across each active site.
In the field, JPI uses drones, 360-degree cameras, and augmented reality glasses as core data collection tools to aid daily production validation, earlier detection of installation deviations, and stronger adherence to daily schedules. Fadule says the next step for its roadmap is using AI to match drone captures against model geometry that will automatically flag deviations.
JPI also utilizes built-in AI features across its core platforms, has implemented the Anthropic stack enterprise-wide to boost individual and team productivity, and has developed internal knowledge bases and agents modeled on its operating model.
One of JPI’s strongest AI applications is in project scheduling and production planning.
“Traditional multifamily schedules typically contain 3,000 to 4,000 activities; our Takt-based production model requires far greater detail, often 15,000 to 20,000 line items of detail,” Fadule shares. “Historically, building a schedule of that complexity took roughly three full-time employees working four weeks. By leveraging AI and machine learning, we’ve cut that effort to about one person over three to four days, freeing our teams to spend far more time validating and optimizing the plan rather than manually building it.”
Once construction begins, JPI uses AI to analyze thousands of daily production updates from the field to identify trends, measure production velocity, and forecast schedule performance.
“These insights give earlier visibility into potential bottlenecks, letting project teams proactively adjust sequencing and resources before delays compound,” she says. “At JPI, AI isn’t replacing planners; it enables better planning upfront and more predictable execution throughout the life of the project.”
JPI also is seeing measurable returns from its AI investments. In addition to cutting project delivery timelines by about 30% and reducing design time by half, the developer’s pilot implementation of Claude identified use cases that generated more than 2,000 hours of time savings per month. The company evaluates technology investments based on schedule, cost, quality, and risk improvements as it expands AI.
In addition, JPI’s roadmap reaches beyond the field with every business unit using AI in some form—from extracting and structuring content from contracts in procurement to automating parts of the comp and underwriting process.
“Our model is designed so that the technology lays the tracks and people drive the train,” Fadule notes. “AI provides better information and faster insights, but decisions around quality, safety, trade coordination, and field execution remain firmly in the hands of our superintendents, project managers, and Operations Support Center.”
She adds one of the benefits JPI has seen is AI empowering subject matter experts to become creators of technology.
“People with extensive construction experience, who may have never written code, can now use AI to build workflows, automations, and tools that solve real operational problems. Because these solutions come from the people closest to the work, they’re more practical, adopted faster, and help capture institutional knowledge that has traditionally lived only in individuals’ heads,” Fadule shares. “AI doesn’t replace human expertise; it amplifies it, helping our teams work smarter, innovate faster, and continuously improve how we deliver projects.”