AEC Tech Journeys with Mayur Mistry

Jonathon’s journey to Developer Advocate at Speckle

#aectech

Tech adoption in Construction with Bhragan Paramanantham, AECOM

#aectech #innovation

Journey of building BIMLOGiQ with Ali Najmi

#ai #aectech

Journey from Architecture to Front End Developer with Daniel Boba

#ai #aec

Journey of automating floor plans at laiout with Cristiano Coretti

#ai #floorplan #startup

Journey of building AI Hub with Amir Noori

#ai #aectech

AI in Architecture Research with Karla Ochoa

#ai #aec

Tech Adoption in AEC: Lessons from Erin Khan Consulting

From Code to Strategy: Petr Mitev on Evolving AEC Design Solutions

Petr's career highlights include working at Kieran Timberlake, NBBJ, and currently at Chaos (after a merger with Andscape), where he has contributed to both architectural projects and software development.
At Kieran Timberlake, he worked on a University of Seattle student housing project that won an AIA award, where zoning challenges led him to develop his first automation tools.
At NBBJ, he helped develop the Applied Science Toolkit, collaborating with experts from various fields including psychology to create better healthcare spaces through data-driven design.
A significant project at NBBJ was the Amazon Spheres in Seattle, where he developed automation tools to optimize the glass facade's unique pieces for cost efficiency while maintaining the design vision.
Early in his career, he was inspired by pioneers like Andrew Heumann and Brian Ringley, who were among the first to integrate technology into architectural design processes.
He emphasizes that rule-based aspects of architecture (like building codes and ADA compliance) are prime candidates for automation, potentially reducing human error and improving efficiency.
Regarding AI in architecture, he believes the technology hasn't yet reached its full potential, comparing it to the BIM revolution where tools eventually became mission-critical rather than optional.
He identifies a significant challenge in AI adoption: the lack of AEC-specific training data due to IP and client ownership issues, suggesting that industry organizations like AIA could help address this through contract modifications.
On accessibility of technology, he stresses the importance of democratizing tools for smaller firms, noting that the average architecture firm has just two employees and limited resources for advanced technology.
Looking ahead, he sees AI as augmenting rather than replacing creativity, similar to how Adobe's tools enhance rather than replace artistic capability.

High Density AEC Automation Ideas with Bill Carney, Technology Leader DLR Group

Bill transitioned from being an architect to a technology leader, making the crucial decision to stop designing and focus on teaching and implementing design tools after gaining early expertise in Revit (2004).
At DLR Group, he evaluates technology investments using three key criteria: improving efficiency, enhancing quality, and enabling better design capabilities - projects must meet all three to be prioritized.
The firm has a unique approach to R&D investment, with dedicated budgets and teams, unlike many AEC firms that invest minimally in innovation and technology development.
His team follows a "20% automation rule" - aiming to automate 20% of routine work year-over-year, focusing on tasks like file archiving and project setup to free up time for more valuable activities.
DLR Group uses a structured forum system to identify and prioritize technology needs, including sector-specific forums (like K-12, Cultural) and discipline forums (Architecture, Engineering), ensuring comprehensive input.
Bill emphasizes gradual technology adoption using a "balloon analogy" - change is more effective when driven from within the organization rather than pushed from outside.
The firm actively pursues partnerships with other companies to share technology development costs, as demonstrated by their collaboration on the space program module with Imaginit.
Rather than focusing solely on computational design, DLR Group emphasizes machine learning and AI, with a strategy centered on collecting and grooming data for future predictive design capabilities.
For smaller firms with limited resources, Bill recommends partnering with other firms to maximize technology investments and leverage shared development costs.
A major challenge in implementing AI in the AEC industry is the complexity of data and the rare combination of data science and AEC expertise needed to effectively utilize it.