January 2022 | Vol. 27 No.1

by Keith Kirkpatrick, Artificial Intelligence & Intelligent Automation, Omdia

With the increasing use of artificial intelligence (AI), building operators and vendors have more advanced ways to improve building operations while incorporating a vast array of datasets from internal and external sources.

Data from Omdia, a global research firm, indicates that revenue from sales of connected equipment in commercial buildings reached $2.28 billion in 2020, reflecting an increasing desire to leverage the data being collected and generated by and within buildings.

Once composed solely of mechanical and electrical parts, buildings have become complex systems that combine hardware, sensors, data storage, microprocessors, software, and connectivity in multiple ways. Additionally, the development of smart devices, wireless networks, and data platforms forces vendors to consider how value is created and captured. They also must think about how the growing volume of newly generated data is utilized and managed and how they can refine relationships with traditional business partners and competitors.

Artificial Intelligence—an umbrella term for enabling technologies that allow machines to mimic human-like decision-making—is becoming an integral part of the product itself and is driving new changes in the way smart building products are offered to customers. Sensors, processors, software, and connectivity embedded inside devices, combined with on-premises, cloud, edge, and hybrid data models, are ushering in dramatic product functionality and performance improvements and creating new questions about data use and storage ownership, and privacy rights.

Driven by Data

Using AI within smart buildings is the culmination of a digital transformation that began with sensors. Over time, it has matured to include the development of Internet of Things (IoT) networks to connect these sensors, resulting in the creation of digital twins or virtualized models of physical systems. AI takes these physical models further, enabling machine learning and deep learning beyond basic analysis. It allows future predictions of performance, wear, and behavior.

Data ultimately drives smart buildings, and a comprehensive data strategy is required to implement traditional analytics models and more advanced AI-based algorithms. This strategy must encompass a wide range of technological and operational issues and consider regulatory, legal, and governance concerns likely to bolster or hinder the overall smart buildings market over the next several years.

Organizations will need to assess the level of functionality required or desired for each use case and balance that against the costs of deploying more advanced AI algorithms and applications. Whether they use IoT, digital twins, or AI, efficient building operators must ensure that building systems have sufficient data. They must also adopt policies and procedures to ensure data is clean, secure, distributable where needed, and protected against cybercriminals or data breaches.

Data collection within a smart building can also bring about data privacy issues. Much of the information collected by vendors and operators is machine-generated. Even so, sensors that capture information about people can be subject to specific data privacy laws, such as the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA).

That is why building operators need to make sure that specific data, such as a person’s image, records of their comings and goings, and other attributes that would be considered personal or private, are adequately anonymized, obfuscated, or destroyed. That way, it ensures those people are not subject to personal privacy data regulations’ data storage and handling requirements.

Benefits of Connected Products

Smart, connected products—whether powered by analytics, AI, or both—can have four main functions: monitoring, control, optimization, or autonomy. Each area builds on the preceding one and can provide exponentially more significant benefits. The following chart details each category, example use cases, and how to derive additional benefits.

Survey: Smart Technologies Popular in Buildings

Not surprisingly, the percentage of end-users that currently have smart building technologies or applications within all or part of their organizations is relatively high at more than 70 percent, according to Omdia’s 2020 Smart Building End-Users Survey. The findings from the survey cover a wide range of topics and issues, but the following highlights indicate a growing propensity for using AI within smart buildings.

• Commercial buildings, which comprise privately owned office buildings and financial institutions, typically represent the largest end-user industry for Building Management Systems (BMS) platforms and connected equipment worldwide, accounting for more than 35 percent of the smart buildings market.
• When collected and analyzed, end-users indicated that they use data primarily to provide visibility of equipment performance (49 percent of end-users are analyzing data).
• End users want a single platform for their facilities (44 percent). Still, it is unachievable today primarily because of legacy equipment and infrastructure and equipment that continues to communicate through proprietary protocols.  

Create a Data Culture

For building operators, managers, and product vendors, smart building technology can be a complex market to address and operate. There are wide-ranging issues related to data collection, sharing, storage, and use. Further, the ecosystem has changed dramatically, with former rivals partnering together to win business and old guard building operators (and their technology providers) venturing further into software and networking than ever deemed possible or necessary.

Whether or not they use AI algorithms, it is essential for building operators and vendors to create a data culture. A data-driven culture is one where all stakeholders have access to data and analytics and the knowledge to utilize those insights to manage the business. While each organization will have a unique method and approach to achieving this culture, all analytics and AI activities’ success depends upon solid data capture, handling, processing, and storage policies and procedures.

While the technical frameworks and standards for achieving an efficient data ecosystem are important, enabling a business ecosystem that protects valuable IP, trade secrets, and competition is paramount to the success of any data-led initiative. Organizations need to work together to create mutually beneficial partnerships that protect IP, promote more significant innovation, and ensure that each market participant is assured a viable commercial path forward.

“The issue isn’t whether vendors should turn their arm’s-length relationships with cloud service providers or rivals into close partnerships, but how,” says Steve Griffith, Senior Industry Director with NEMA. “Given the interrelated nature of building data captured and utilized across the entire smart building value chain, close-knit partnerships should be formed to allow all stakeholders to continuously learn, improve, and prosper along with their clients.”

Both the vendor and end-user segments of the smart buildings industry stand ready to incorporate AI technology. The key to doing so effectively revolves around the ability for all those involved to work in tandem to address the benefits, risks, and strategies required to store, manage, and process data safely and efficiently. Additional insights into the drivers, technologies, use cases, regulatory issues, and deployment strategies used by manufacturers can be found in the NEMA white paper, AI in the Smart Buildings Sector, available on the NEMA website.