Artificial intelligence (AI) is no longer confined to futuristic speculation or isolated use cases in consumer tech. It has become a driving force across industries, and facility care is no exception. For organizations responsible for the operation and optimization of buildings, campuses, and large-scale facilities, AI offers a powerful set of tools to increase efficiency, cut costs, and improve the overall experience for occupants. Capturing these benefits takes more than algorithms alone. Success depends on a clear data strategy, integrated systems, and a commitment to continuous improvement.

Hear directly from Reshmi Chakraborty as she breaks down the phases of AI integration and the role of data governance in long-term success. For more AI insights, watch the full webinar: AI and the next era of facility care.  

ABM Contributor:
Reshmi Chakraborty
VP of Technology

A brief evolution of AI

To understand its role in facilities today, it helps to appreciate how far AI has come. The concept dates to the 1950s, when Alan Turing first suggested that machines might be capable of thought. Early programs in the 1960s solved basic math and logic problems, while the 1980s saw advances in neural networks held back by limited computing power. By the 2000s, however, breakthroughs in computing and access to massive datasets fueled the rise of deep learning. Today, AI underpins countless systems, from healthcare diagnostics to financial risk modeling, and is increasingly embedded in the tools facilities teams use every day.

At its core, AI enables machines to learn from data, recognize patterns, and make predictions. Core technologies include:

• Machine learning (ML): Systems that improve over time by analyzing historical data.

• Natural language processing (NLP): Tools that interpret and respond to human language, like voice assistants.

• Computer vision: Algorithms that analyze visual data, powering everything from self-driving cars to occupancy tracking.

• Predictive analytics: Models that forecast future outcomes, such as equipment failures.

• Generative AI: Emerging technologies that not only analyze but also create, i.e., generating text, images, code, and more.

This progression positions facility leaders at a unique moment: to move from reactive management toward predictive, even prescriptive, operations.

From data to decisions: Real-world facility applications

The true power of AI emerges when paired with robust data strategies. In facility operations, four groups play key roles:

• Data engineers and architects bring together information from sensors, systems, and documents, ensuring it’s accurate, consistent, and ready to use.

• Data scientists build models that help teams predict issues, plan maintenance efficiently, and automate repetitive work.

• AI engineers design and deploy intelligent systems, from conversational assistants to automated workflows that connect data-driven intelligence with everyday operations.

• Analytics teams evaluate performance data, identify trends, and translate insights into strategies that drive decision making.

When aligned, these functions enable a wide range of applications:

Predictive Maintenance

Instead of reacting to breakdowns or relying on rigid maintenance schedules, AI models forecast when equipment is likely to fail. IoT sensors capture performance data in real time, anomaly detection flags unusual behavior, and predictive analytics recommend proactive interventions. The result is less downtime, longer asset life, and significant cost savings.

Space Optimization

Facilities waste millions annually on underutilized or poorly allocated space. AI can change that. By analyzing IoT sensor data, access logs, meeting bookings, and employee feedback, algorithms identify usage patterns, highlight inefficiencies, and predict future demand. Computer vision applied to existing security camera footage can further refine insights. For facility managers, this means smarter layouts, reduced costs, and improved occupant experiences.

Enhanced Tenant and Workforce Experience

Generative AI offers new ways to create user-facing solutions, from automated assistants that streamline work orders to customized communications that guide occupant behavior. When tied to predictive models, these tools can make facility interactions seamless and personalized.

Best practices for AI success

While the potential is vast, effective implementation depends on careful planning and execution. Several best practices stand out:

1. Build for scalability. Design cloud-first or hybrid architectures that can accommodate growing data volumes and AI workloads. Establish consistent taxonomies to ensure interoperability across platforms.

2. Prioritize data readiness. Reliable inputs are non-negotiable. Faulty sensors or incomplete datasets can derail even the most sophisticated models. As the saying goes: AI is only as good as the data it is trained on.

3. Strengthen governance. Implement validation rules to maintain accuracy and consistency. Assign clear data ownership within business units to maintain accountability and hygiene.

4. Align AI with business goals. Success depends on defining specific, measurable problems before developing solutions. For example, rather than vaguely aiming to "improve customer satisfaction," focus on reducing response times for billing inquiries. Clarity drives impact.

5. Commit to continuous optimization. AI is not a one-and-done deployment. Models require ongoing refinement to adapt to new data, evolving systems, and shifting user needs.

Avoiding common pitfalls

With the rapid adoption of AI, organizations must also be mindful of the traps that can undermine progress. Over-reliance on untested sensors, deploying AI without clear ROI models, or neglecting governance structures can all lead to wasted investments and eroded trust in the technology. Likewise, treating AI as a static product instead of a living system overlooks the need for constant monitoring and adaptation.

Unlocking the next era of facility performance

The future of facilities lies in treating data as an interconnected ecosystem. By combining real-time inputs from IoT sensors, integrating them into scalable platforms, and applying predictive and generative AI, facility leaders can uncover hidden patterns and opportunities. This enables them to:

• Enhance operational efficiency

• Reduce costs

• Improve sustainability

• Elevate occupant experience

The shift is clear: facilities are no longer passive spaces to be maintained, but dynamic environments to be optimized. AI is not just a tool for incremental improvement; it is the foundation for a new era of performance.

Successful AI in facilities management is not about adopting the newest technology for its own sake. It is about building strong data foundations, aligning AI solutions with clear business goals, and continuously refining systems to deliver measurable value. The organizations that master these principles will be best positioned to thrive in an AI-powered future.

As facility leaders strengthen their data foundations, the next challenge is ensuring those systems remain secure, compliant, and trustworthy. Data governance isn’t only about accuracy—it’s about protection. Building on that foundation, Stacy Hughes explores how robust governance frameworks and cybersecurity practices enable organizations to deploy AI confidently and responsibly.

From data strategy to secure implementation, ABM partners with organizations to turn innovation into measurable performance—helping facilities evolve through intelligence, technology, and possibility. Speak with an expert to learn more.