In recent years, artificial intelligence (AI) has evolved from being merely a buzzword to presenting definable roles and tangible benefits for smart buildings. The past three years have seen the emergence of genuinely innovative use cases for AI technologies within the IoT for smart buildings (BIoT). While the rise of generative AI, like ChatGPT (developed by Open AI), has brought unprecedented levels of attention and investment to the sector in the past six months. Our new study explores these advancements that have moved AI beyond the confines of research to begin developing real purpose in the smart building.
“The utility of AI will continue to improve with algorithmic advances, increased data availability, and significant growth in power and storage capabilities at a lower cost, and as AI technologies rapidly advance and push new frontiers of innovation, business adoption continues to grow across a wide range of use cases,” reads our latest BIoT research. “The pace of improvement in AI technology and algorithms has drastically increased over the last five years, so much so that increasingly, organizations are now grappling with the transformational impact that AI could have on industries and operating models.”
Our findings reveal that IT decision-makers are progressively acknowledging the merits of AI and machine learning (ML), placing emphasis on incorporating these technologies to optimize operations, forecast business outcomes, mitigate risks, recruit skilled personnel, and deepen insights into their businesses and clientele. Furthermore, financial support for AI-focused startups and research continues to grow, as a wide array of AI applications - from autonomous vehicles and industrial automation to climate predictions and disease diagnostics - continue to flourish and bring attention to the technology.
“One of the most significant impacts of AI for many enterprises could be in assisting the analysis of big data, as AI and ML functionalities are increasingly integrated into data analytics platforms,” our new study finds. “ML is particularly effective at making sense of unstructured data, identifying anomalies or unexpected patterns, and extracting actionable insights. AI can process vast volumes of data, far beyond human capabilities, and use historical patterns to predict future data quality outcomes and detect anomalies that might have otherwise gone unnoticed.”
“Generative AI” is a rising term in the industry, referring to unsupervised and semi-supervised ML that create new, original content and code for specific purposes —think ChatGPT for smart buildings. While the immediate applications of generative AI in smart buildings might not be as apparent as in other sectors, the potential to unlock and decipher vast volumes of untapped data presents a promising avenue for expansion.
The recent surge in chatbot popularity has also led to more funding in the area and a rapid acceleration in their capabilities as they feed off unprecedented levels of new data. Our new research suggests that these conversational interfaces could substantially influence how building owners, managers, and occupants engage with data generated by the BIoT. As the technology continues to mature, it is expected to further revolutionize interactions and data management in the smart building ecosystem.
“In the near future, we anticipate the development of BIoT solutions that seek to combine the power of large language models, AI, and the IoT to produce products that function as advanced personal assistants for users, for example,” our in-depth BIoT report explains. “These assistants (think of Alexa on steroids), would be much more adept at learning user preferences, providing recommendations, and making it easier for people to engage in a dialogue with their smart systems/devices and utilize technology more effectively.”
Indeed some developers have already begun to explore the potential for ChatGPT to augment the user experience of smart building owners. Memoori interviewed Bitpool’s CEO David Blanch in early March 2023 to explore how the smart buildings diagnostics firm is experimenting with using ChatGPT to create their own Chat Bot for Building Data. When the user initiates a chat with their platform the Bitpool API sends all of the visible time series and named data to ChatGPT for analysis and response, the user can then engage in dialogue with ChatGPT to gain more understanding and insights from the data.
Memoori has tracked large levels of investment into the development of smart building platforms aiming to be the “single-pane-of-glass” for all the building’s data analytics and reporting needs have been made by a plethora of firms in the industry. Such a shift towards natural language interfaces could ultimately disrupt the existing dashboard-based landscape of these solutions, but this seems unlikely in the short term, according to the report, which doesn’t see NLP as a direct replacement just yet.
“While NLP offers several benefits and improvements for data interaction, users who are already proficient with existing systems and UI’s may still prefer to interact with the data in the old ways. Dashboards and single-pane-of-glass solutions can excel at visually representing the data, enabling users to quickly understand trends, patterns, and anomalies. Chatbots, while efficient at processing language-based queries, are likely not as effective in providing the same level of visual insights that a well-designed dashboard can offer, for the time being at least.”
The emergence of AI in the smart buildings sector has gone from unavoidable buzzwords, through tangible use cases, and into early development in just a few years, and for every application imaginable. While the building industry has been through similar hype cycles for recent pivotal technologies like cloud computing, wireless communications, and the IoT itself, the speed and omnipresence demonstrated by AI in recent years feels different.