Driving Financial Insight and Decarbonization Action with Artificial Intelligence

Friday, October 23, 2026 10:15 AM to 11:15 AM · 1 hr. (US/Eastern)
Technology

Information

Building owners, developers, design teams, and sustainability leaders are under growing pressure to decarbonize assets, improve performance, and justify investments with measurable environmental and financial returns. Rising energy costs, capital constraints, disclosure requirements, and performance targets demand faster, data-driven decision making. At the same time, AI-enabled tools are rapidly reshaping how project teams analyze buildings, prioritize investments, and translate sustainability goals into high-impact, measurable results. This session explores how these challenges can be tackled together with real-world examples. Bringing together The Sobrato Organization’s VP of Real Estate Sustainability, Stok, and AI solution leaders from Audette and Tangible, this panel will unpack how artificial intelligence is rapidly enabling smarter decisions and real actions across energy management, decarbonization planning, and financial analysis at the asset and portfolio levels, enabling project teams to invest for impact. Starting with a review of the decarbonization software landscape based on analysis of 100 software products, panelists will then share real-world examples demonstrating how emerging agentic AI tools are being applied to identify cost saving opportunities, model decarbonization pathways, quantify embodied and operational carbon tradeoffs, inform capital planning, and connect sustainability metrics with financial performance. The discussion will also address where human expertise remains essential in AI-powered tool deployment and management. Attendees will gain practical guidance on how to evaluate, pilot, and scale AI-enabled decarbonization tools, align technology investments with climate and business goals, and turn building data into actionable strategies that deliver measurable impact across projects and portfolios.
Learning Level
Intermediate
GBCI Rating System Specific Credit
Does Not Apply
Program
Track Session
Track
Technology
Learning Objective #1
Evaluate how AI-enabled tools can be applied across building decarbonization workflows, including energy analysis, carbon modeling, materials assessment, and portfolio planning.
Learning Objective #2
Compare AI–enabled approaches for identifying and prioritizing high-impact decarbonization investments based on both carbon reduction potential and financial performance.
Learning Objective #3
Identify key data, governance, and quality control requirements needed to responsibly deploy AI-enabled tools.
Learning Objective #4
Develop a practical framework for piloting AI-enabled decarbonization tools within projects or portfolios that aligns sustainability goals with capital planning and measurable impact.