Hybrid Work Challenges Building Management System Agility

Edge AI and real-time analytics keep responsiveness high and energy costs low

By Chris Penrose

While the COVID-19 pandemic is far from over, the stranglehold it placed on many companies to keep entire workforces away from the office has begun to loosen. New vaccines and continued use of personal protective equipment (PPE), such as masks, allow more employees to return to the office. But that doesn’t mean they’re returning to the status quo when it comes to building occupancy.Hybrid Work

Post-pandemic, hybrid work is the new normal, with 79% of executives adopting such models for employees to customize their work hours and perhaps come to the office only a few days a week. That’s causing heartburn for many facilities managers who—in matching their building management systems (BMS) to meet the fluctuating energy demands from these flexible schedules—are needing to evolve their systems to be much more agile and efficient.

The good news is that recent advances in BMS technologies are proving effective at delivering automation and responsiveness at the speed and scale necessary for this to happen. Thanks in particular to the growth of real-time analytics through AI-enabled edge computing, sometimes combined with low latency and high-bandwidth 5G networks, even the largest facilities networks can mobilize to improve energy efficiency and decrease operational costs in the face of hybrid work models.

Facilities Managers are Struggling with BMS Agility

The unfortunate reality today is that most BMS systems still rely on manual or pre-programmed adjustments to energy-hungry systems like HVAC, lighting, and other environmental controls. That may have been sufficient in a pre-COVID era of full capacity during predictable work hours. But as businesses now return their employees to the office in less predictable ways, these irregular occupancy loads are scrambling the equation and cutting into the bottom line with wasted utility costs and unnecessary wear and tear on systems.

The uncertainly extends beyond office cubicles and into warehouses, the energy costs for which can account for nearly 10% of a company’s annual revenue. Global supply chain disruptions from COVID-19 make it harder to predict inventory, and therefore harder to continually gauge how much warehouse space needs to be maintained with utility and environmental services to house and manage that inventory.

All of this not only cuts into the bottom line; it’s also a blow to sustainability, which ends up threatening both the environment and corporate compliance for companies trying to hit new greenhouse gas reduction targets for 2030 established this year by the federal government.

Hybrid WorkFortunately, facilities managers can leverage new advances in IoT sensors and actuators, together with BMS automation for more visibility and control through real-time analytics—dynamically shifting environmental conditions to match fast and unpredictable changes in occupancy and usage. This brings energy-saving agility in calibrating systems to adjust for office schedules, weather forecasts, hourly energy rates, and infrastructure status. The most advanced systems leverage edge AI to help do all this.

Meeting the Responsiveness Challenge with Edge AI

Edge AI happens when edge computing—in which data is processed at or near its source—is augmented with artificial intelligence (AI) and machine learning (ML) algorithms that shift the analytics workload locally. Especially as IoT devices and sensors proliferate in smart buildings, edge AI avoids the cost and latency that comes from collecting and transmitting all that data back and forth to offsite cloud services for processing. When done at scale, facilities managers can orchestrate control and cost savings across multiple buildings if necessary. Additional savings come from the ability to overlay edge AI capabilities onto existing systems without the costly need to rip and replace legacy components. These platforms can be further leveraged to deliver predictive insights. For example, data and analytics from high-frequency vibration sensors can detect irregularities in an HVAC fan that is likely to fail and then deliver an algorithmically prescribed fix—one that proactively avoids a costly breakdown and negative tenant experience.


The “new normal” of hybrid schedules for a post-pandemic workforce is prompting a much-needed revolution in BMS capabilities. Led by the real-time analytics power of edge AI and the networking gains in bandwidth and low latency from 5G, newly automated and scalable BMS systems are arming building managers with enhanced visibility and real-time agility to meet energy efficiency and cost-savings targets even in the most unpredictable hybrid work environments.


Penrose is the COO of FogHorn. He has responsibility for leading FogHorn’s go-to-market efforts across business development, technical sales, strategic partnerships, third party distribution, marketing, advertising, and public relations globally. Penrose also leads strategic planning for the firm. FogHorn is the leader in Edge AI and Machine Learning, and is the first edge-native AI and Analytics solution in the market. Penrose and his team create and deliver solutions all over the world to help customers across industry verticals achieve their desired business outcomes.


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