Practical AI Applications in the Built Environment Summit

Description: This opening keynote sets the stage for the AI Summit by exploring the evolving role of artificial intelligence in shaping the earliest stages of building design. As the architecture and planning industries move beyond theory into practice, AI is being deployed not just for efficiency—but to inspire new ways of thinking, modeling, and optimizing sustainable performance.

Delivered by a leading voice at the intersection of AI and the built environment, this talk will examine how designers are using machine learning, generative tools, and intelligent simulations to test ideas, balance constraints, and unlock value earlier than ever before. The keynote will also preview the downstream impacts of early-stage decisions—from cost control to embodied carbon—setting the tone for deeper, real-world case studies in the session that follows.

Learning Objectives:

1. Identify the core opportunities AI brings to early-stage design workflows in architecture.

2. Understand the relationship between early design decisions, sustainability outcomes, and project cost implications.

3. Explore how AI can support conceptual exploration, design iteration, and stakeholder alignment.

4. Prepare to connect visionary frameworks with practical tools presented in the session that follows.

This expert-level session gathers architectural leaders actively using AI-driven tools to assess, evaluate, and optimize cost-saving strategies and sustainability outcomes in real-world projects. Panelists Michael Hodge (TVS Design), Nick Kramer (LPA), and Wesley Reynolds (OPN) will share their technical insights on how AI enhances efficiency, automates design compliance, and lowers project costs—even when sustainability is not a top priority. Moderated by Charlie Cichetti, LEED Fellow, this session will emphasize practical, expert-led knowledge exchange.

Through detailed case studies and interactive analysis, panelists will break down AI-powered automation's role in reducing inefficiencies, improving green compliance, and justifying ROI. The discussion will also explore AI’s impact on decarbonization strategies, such as optimizing passive design, minimizing embodied carbon, and supporting regenerative material selection Panelists will compare and contrast multiple AI solutions—including Skema, Autodesk AI, TestFit, and Cove.Tool—to highlight measurable workflow impacts on the design process.

Unlike traditional lectures, this highly interactive session will incorporate live project breakdowns, decision-making frameworks, and audience exercises to ensure deep engagement. Attendees will categorize AI-driven workflows, analyze cost implications, and diagram sustainability integration strategies. Panelists will offer direct feedback on audience challenges, helping participants develop actionable plans for implementing AI-driven architecture solutions in their own firms.

By the end, participants will have mastered key strategies for prioritizing AI adoption, evaluating cost vs. sustainability trade-offs, and validating AI’s real impact on architectural design.

Learning Objectives:

1. Analyze real-world AI-driven design automation impacts on cost savings and efficiency.

2. Evaluate how AI tools streamline sustainability compliance, optimize energy performance, and support decarbonization efforts within complex design workflows.

3. Compare and contrast AI-powered solutions, assessing their strengths and limitations.

4. Assess decision-making frameworks that architects use to integrate AI while ensuring ROI and workflow improvements.

The construction industry is undergoing a quiet revolution—powered by artificial intelligence tools that promise not only to increase productivity, but also to make building safer, cleaner, and more efficient. This session brings together innovators from across the construction lifecycle to spotlight the tangible benefits and operational impact of AI integration on today’s jobsites.

Through lightning talks and facilitated roundtable discussions, participants will explore five high-impact areas where AI is actively being deployed:

• Computer-Vision Safety Alerts: Learn how AI-powered cameras and analytics are helping contractors identify safety risks in real time, automatically flag PPE violations, monitor fall hazards, and improve site compliance—creating safer environments for workers and reducing liability.
• AI-Driven Logistics & Scheduling: Discover how predictive modeling and intelligent sequencing tools are being used to streamline materials delivery, avoid costly delays, and optimize labor deployment with minimal disruption to timelines or budgets.
• Planning for Circularity with AI: Understand how emerging tools can help design for deconstruction, reuse, and recycling by analyzing material flows and flagging opportunities to reduce lifecycle waste—supporting both ESG targets and LEED credits.
• Robotics and Automation in Prefabrication: Explore real-world use cases of robotic systems and machine learning tools that are accelerating modular construction, reducing rework, and improving precision—from layout automation to drone site mapping.
• Embodied Carbon Calculations: Examine how AI is being used to forecast and benchmark embodied carbon in real time, helping teams make low-impact material decisions early in the process and align with whole-life carbon strategies.

In the second half of the session, participants will engage directly with topic experts in small-group roundtable discussions. Each table will guide attendees through key questions, real-world constraints, and creative solutions to help spark actionable strategies for AI integration in their own projects. Whether you’re a builder, architect, owner, or tech provider, this session offers practical insights to drive smarter, more sustainable construction.

Learning Objectives:

1. Identify current and emerging AI applications across construction use cases (safety, scheduling, materials, logistics).
2. Evaluate the potential sustainability and ROI outcomes of AI in construction workflows.
3. Distinguish between automation tools suitable for near-term piloting vs. longer-term innovation.
4. Collaborate with peers to explore strategic entry points for AI adoption in their organizations.

Many organizations are taking action to reduce their carbon emissions. This session presents the actions taken by one company to accelerate the decarbonization of its large real estate portfolio.

The session will begin with context on the legislative, reputational, financial, and market drivers of decarbonization for real estate. The presenters will then provide an overview of different tools available for portfolio decarbonization, highlighting the tradeoffs between using conventional engineering approaches - such as energy audits and building physics models - compared with newer AI and machine learning analytics.

This session will include a case study of how Amazon is scaling decarbonization retrofits across its building portfolio using Carbon Signal - a platform leveraging the complementary benefits of AI with the accuracy of building energy models to provide information comparable to an energy audit in a matter of minutes. The presentation will utilize and share candid real-world examples from Amazon in its decarbonization journey thus far, and Amazon's next steps as it moves forward on its carbon commitments.

By the end of the session, participants will leave with a strong understanding of the importance of decarbonization in the real estate sector and be able to compare the different tools and approaches available for decarbonization of large building portfolios - including use of AI - by examining scalability, efficiency, and accuracy.

Learning Objectives:

1. Identify the regulatory and market drivers of decarbonization in the built environment
2. Describe the challenges with conventional methods in developing decarbonization strategies and provide an overview of how AI and machine learning approaches can scale decarbonization for large building portfolios
3. Compare the tradeoffs between different tools and approaches to decarbonization, including AI, in terms of scalability, efficiency, and accuracy
4. Understand possible next steps after using a decarbonization analytics platform

Building on the Amazon case study, this interactive session invites participants to examine how AI-powered operational intelligence is being deployed across diverse building portfolios to justify upgrades, reduce emissions, and align with ESG and policy mandates.

Attendees will explore a range of AI applications—from digital twins and fault detection to predictive analytics and carbon forecasting—and assess where each solution fits across different scales, sectors, and data maturities.

Through small-group roundtable discussions, participants will work through real-world prompts such as:

• “What’s your biggest roadblock to using AI for operational decision-making?”
• “How would you demonstrate value or compliance through AI-powered insights?”

Facilitators from across the public, private, and institutional sectors will guide discussions, and each group will identify actionable strategies and gaps. The session will conclude with a brief report-out of shared insights and key transformation levers across ownership types.

Learning Objectives:

1. Translate case study insights into actionable strategies for your own operational or portfolio context.
2. Evaluate common barriers to operational AI adoption, including data readiness, compliance alignment, and ROI justification.
3. Distinguish between various AI tools—by function, scalability, and policy relevance.
4. Collaborate with peers to chart next steps for decarbonization, investment planning, and performance optimization.

As the capstone of the AI Summit, this session moves beyond inspiration and exploration into action. Featuring a framing talk on AI-assisted smart building operations—such as intelligent lighting, HVAC, and controls—the session will spotlight practical pathways for accelerating AI implementation at scale. Speakers will explore how integrated platforms (e.g., Lutron’s smart building systems) enable operational optimization and emissions reductions in real time.

Following the talk, participants will dive into a facilitated Action Lab: a multi-stakeholder exercise designed to synthesize takeaways across the summit. Tables will be organized by stakeholder lens—Owner/Operator, Designer, Tech Provider, Policy, and Academic—and prompted to co-create implementation roadmaps. Participants will define near-term goals (1–2 years), long-term targets (5–10 years), governance needs, and collaboration models.

The session concludes with a rapid group summation, distilling high-level strategies and calls to action from each group. Participants will leave equipped with sector-specific priorities and a shared blueprint for advancing AI responsibly in the built environment.

Learning Objectives:

1. Understand how AI-driven building control systems (e.g., lighting, HVAC) reduce operational inefficiencies and emissions.
2. Identify priority actions, barriers, and leadership roles across AECO+ stakeholders for responsible AI adoption.
3. Co-develop cross-sector strategies for aligning AI implementation with ESG, decarbonization, and equity goals.
4. Translate summit insights into concrete steps for deployment, governance, and stakeholder engagement.