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The Future of the Built Environment: How AI is Powering Autonomous Buildings
Irina Koitz, Director of Data Strategy and Intelligence at Johnson Controls, recently participated in a thought-provoking panel discussion with leading experts from the commercial real estate AI sector, exploring the ongoing evolution from traditional Building Management Systems (BMS) to fully autonomous, AI-powered buildings.
During the session, Koitz emphasised a compelling vision for the future of the built environment. Her message was clear: the industry is progressing beyond the concept of smart buildings and entering a transformative new era of autonomous, self-driving infrastructure (where buildings can monitor, analyse and optimise their own performance with minimal human intervention). At the heart of this shift lies artificial intelligence (AI), which she described as the driving force behind this next-generation capability (enabling systems to learn, adapt and respond in real time to changing conditions and occupant needs).
This transition marks a significant milestone for property owners, developers and technology providers alike (particularly in the UK market, where sustainability targets and operational efficiency are increasingly shaping investment decisions). As AI continues to redefine how buildings are managed and experienced, Koitz’s insights offered a timely and strategic perspective on what lies ahead for the commercial real estate sector.
Artificial intelligence (AI) is deeply embedded across our OpenBlue digital portfolio in two key dimensions:
- Intelligent Capabilities: AI is driving a step-change in building performance, from forecasting occupancy patterns and energy consumption to enabling fully autonomous operations (where systems can self-adjust in real time to optimise efficiency and comfort). These capabilities are helping organisations achieve smarter, more sustainable outcomes.
- Enhanced User Experience; Generative AI (GenAI) is transforming how users interact with data by making insights more intuitive and accessible. It translates complex datasets into clear, actionable recommendations (even for those without technical expertise), empowering a broader range of stakeholders to make informed decisions.
Irina Koitz illustrated this impact with a compelling example from Stanford University, where OpenBlue technology delivered measurable results:
- £390,000 in annual energy savings (equivalent to approximately $500,000)
- 68% reduction in greenhouse gas emissions
- 15% reduction in domestic water usage
These outcomes demonstrate the tangible benefits of integrating AI into building operations (not only in terms of cost savings, but also in advancing environmental goals and improving resource efficiency). As UK organisations face increasing pressure to meet sustainability targets, solutions like OpenBlue offer a powerful blueprint for future-ready infrastructure.
In her closing remarks, Irina Koitz outlined a clear and practical roadmap for building owners and operators (particularly those navigating the UK’s evolving regulatory and sustainability landscape). Her guidance focused on how to harness AI incrementally to unlock long-term value and operational excellence:
- Begin with Anomaly Detection and Forecasting: By deploying early-stage AI tools, organisations can uncover inefficiencies and anticipate usage trends (providing a data-driven foundation for decision-making). This initial step helps pinpoint where energy savings and operational improvements can be achieved
- Implement Predictive Maintenance: AI can be used to monitor the health of building systems and detect early warning signs of potential failures (reducing unplanned downtime and extending the lifespan of critical assets). This approach not only improves reliability but also delivers significant cost savings over time
- Enhance the Occupant Experience: Intelligent systems can learn and adapt to individual preferences (such as lighting, temperature and space usage), creating more comfortable and responsive environments for tenants, staff and visitors. This personalisation contributes to higher satisfaction and productivity
- Adopt Closed-Loop Optimisation: AI can be enabled to make real-time adjustments within safe operating parameters (continuously fine-tuning systems for peak performance without the need for manual input). This ensures consistent efficiency and resilience across building operations
- Move Toward Interconnected Autonomy: The ultimate goal is to deploy systems that not only operate independently but also explain their decisions (building trust and reducing the need for constant human oversight). This represents true autonomy in action (where buildings become self-managing, transparent and aligned with organisational goals)