Rising energy costs and intensifying demand reshape how owners and operators evaluate building performance. Electrification increases load across markets, expanding data centers add constant demand, and climate adaptation raises expectations for comfort and resilience. Against that backdrop, New York Technology Innovation on December 10 sharpens a practical question for real estate and facilities leaders. Operational excellence increasingly depends on the ability to sense what is happening inside a building, translate signals into decisions, and execute improvements at scale across portfolios.

That operating reality anchors the perspective shared by Maud Demurge, Head of North America at Akila, in her New York Technology Innovation session titled Unlocking Smarter, More Sustainable and Efficient Buildings with AI. The core thesis centers on a specific mechanism. A unified real estate data platform paired with an AI digital twin changes buildings from isolated mechanical systems into managed performance systems, where energy, maintenance, security, and operations become measurable and optimizable within one shared model.

How is High-Cost Energy Driving Building Intelligence?

Energy cost volatility increases the value of control. When electricity prices surge and demand rises, small inefficiencies compound into material operating exposure. Real estate teams feel this pressure directly because HVAC, ventilation, and other building systems translate market conditions into monthly costs, tenant experience, and operational risk. A strategy focused only on procurement or retrofits leaves value trapped in daily execution, because many buildings underperform for reasons rooted in operations rather than capital plans.

Innovation in this context means improving the operational loop. Buildings need continuous visibility, fast diagnosis, and repeatable responses that do not rely on heroics from a few experienced engineers. That requirement pushes the conversation beyond dashboards and toward platforms that coordinate data, analysis, and action across systems and sites.

Why Are Buildings Considered Data Blind Spots?

Buildings consume meaningful energy while often operating with fragmented information. The source material for the session references research from the U.S. Department of Energy and Deloitte Real Estate to underline the same structural issue. Buildings generate large volumes of data, but that data frequently sits in separate systems that do not connect in a way that supports decision making.

A data gap inside a building becomes an execution gap. If teams cannot see system interactions clearly, they struggle to identify the true drivers of consumption, comfort issues, and maintenance risk. If they cannot compare performance consistently across sites, they struggle to replicate what works. These gaps explain why many organizations invest in sensors and software but still fail to achieve sustained operational improvement.

Digital Twins as an Operating System for Real Estate

Akila positions its platform as a unified real estate data layer supported by an AI digital twin. The platform centralizes building systems and connects operational domains that typically remain separate, including energy, maintenance, and security. Digital twinning matters because it creates a shared representation of building behavior rather than a collection of disconnected time series.

A unified model enables portfolio benchmarking, mobile operational tools, and an AI assistant within the same environment. Benchmarking supports comparisons that reveal outliers and best practices. Mobile operational tooling closes the gap between insight and on site execution. An assistant layer supports faster interpretation of complex conditions, especially when teams manage many buildings and cannot afford slow diagnosis.

How Do We Turn Monitoring into Portfolio Level Execution?

The session framing emphasizes an operational journey from data collection to action. Monitoring, tracking, and acting moves organizations from passive visibility to continuous oversight and corrective action planning. This stage matters because many programs stop at reporting, which improves awareness but does not change outcomes.

Execution, replication, and scaling then become the differentiator. When insights translate into practical actions that can be repeated across sites, organizations build a portfolio capability rather than a collection of isolated improvements. Standardized workflows, shared definitions, and consistent measurement allow a solution proven in one building to become a template for another.

AI for HVAC Control and Human Decision Support

The platform design uses multiple AI modalities that map to different operational needs. AI driven analytics translate raw data into insights intended to support decisive action. HVAC optimization uses AI HVAC agents and smart control models that interface with the building management system and optimize HVAC operation in real time.

Large language models support interpretation and interaction with complex operational environments, and the session materials reference NVIDIA NIM as the enabling layer. Visual language models add another dimension by connecting vision based signals to operational understanding, with NVIDIA DeepStream and NVIDIA Metropolis referenced as supporting technologies. Simulations powered by NVIDIA Omniverse expand the ability to test scenarios and evaluate changes before committing to physical modifications.

Proof of Value Across Asset Types

Claims of impact matter most when they show relevance across different building categories. The following data demonstrates the breadth of Akila's proven results across diverse sectors, signalling a robust portfolio logic.

What is the Strategic Trajectory for Smarter Buildings?

Innovation and intelligence become practical when they compress the distance between observation and outcome. The message from New York Technology Innovation is that smarter buildings emerge from a disciplined operational system, not from isolated technology deployments. Unified data enables consistent measurement. Digital twins enable shared context. AI enables optimization, faster interpretation, and scalable execution.

Real estate leaders increasingly manage portfolios where performance expectations rise faster than headcount. Platforms that turn data into actions help close that capacity gap by standardizing how teams diagnose issues, prioritize interventions, and sustain improvements. In that model, AI augments operational discipline rather than replacing it, and building performance becomes a managed capability that can improve with every cycle of measurement and response.