With AI fully dominating global dialogue, do people really understand what it means.
Artificial Intelligence (AI) stands out as the ‘buzzword’ for 2023. The overuse of the term has led to many people feeling AI-fatigue. After watching trends like the metaverse, Virtual Reality (VR), crypto, and web3, who can blame them?
Overwhelmed with the excitement around AI, we have managed to turn a complex concept into a surface-level term. This is because ‘AI’ is too broad to mean much on its own. With 2024 in full swing, it is time to start digging deeper into what “powered by AI” actually means.
Marketing teams for proptech software products will often claim to be “powered by AI,” to bolster the effectiveness and tech-savviness of their offering. Commercial real estate is no different. The surge of AI has led many proptech software companies to proudly label themselves as being ‘AI-driven’.
Generative AI (GenAI) based on Large Language Models (LLMs) are powerful tools. But, how do you break through the jargon and understand what these platforms and software do? As AI becomes a phrase employed in marketing, it’s worth pulling back to understand what we are really referring to when we say it.
What does AI actually mean?
Well, it’s quite vague. For those developing these innovative technologies, the word AI goes too far. By inflating expectations, the term can distract from the precise way that Machine Learning (ML) can help improve business operations.
Nearly every application or platform is built through Machine Learning, which falls under AI. The simple explanation: ML is used to make actionable predictions. These predictions then drive millions of operational decisions.
There are four parts to every ML process: gather data, feed it to the algorithm, validate the model, and use it to serve predictions. Technology powered by ML is everywhere. From suggested likes on social media platforms, medical diagnosis, to fraud detection.
A Large Language Model (LLM) is a ML model that is specifically trained to output text based on text input. In mainstream examples, we see this in GPT-3, GPT-3.5, GPT-4. Also LaMDA by Google, LLaMA by Meta and BloombergGPT. Some products that use LLM are using ChatGPT.
GenAI, the driving force for AI’s significant attention, is a type of automation that focuses on creating new content. GenAI is fundamentally built on LLMs, trained on significant datasets and able to generate human-like responses. Tying all of this together is a product, which makes it easier to work with the model and integrate it with other aspects of the platform.
The point of understanding the vast degree of differences and intricacies between subsets of AI is to appreciate that the term itself is too vague.
Is AI useful in commercial real estate?
The short answer is yes. AI as a concept itself is too broad, but the technologies it is referring to, LLMs, ML, and Natural Language Processing (NLP), are incredibly innovative for the field. Not only do these save time on business operations, but they also vastly expand our capabilities and insights.
Thanks to advanced pattern recognition, ML can improve decision-making. The advancements made to LLMs in the past year are behind the recent attention for AI. These improvements mean that models can take on various administrative tasks, freeing up commercial real estate professionals to focus on the relationship side of the business.
Utilising these tools has also replaced manual calculations with fast, accurate data processing. Asset managers benefit from AI in real estate when using tools to calculate factors like NOI and ROI, gaining insights for better deal assessments. Underwriters and lenders use AI to predict returns on contracts, enhancing investment security. AI algorithms analyse economic factors, market trends, and historical real estate data for risk assessment, aiding stakeholders in making informed decisions about potential acquisitions.
In real estate data management, AI efficiently extracts and manages data from various reports and documents, including offering memorandums and due diligence paperwork. It can notify individuals about updated records and report on trends, while also classifying, storing, and resurfacing documents, providing historical data for more informed decision-making in commercial real estate.
Why is it important to expand on how you are using AI?
There is a noticeable trend of companies using AI in their marketing efforts. But, with AI referring to such a broad set of technologies, it is important to be careful when this is used too loosely.
Much of what we see today is better referred to as data technology. When using the term “machine learning” or “artificial intelligence,” we imply that the computer has learned to model or predict based on data. The best way to differentiate true from misleading AI systems is to ask whether it is learning from patterns and features in the data it is set to analyse.
It’s important while choosing a technology provider to not be swayed by terms that are thrown around quite frequently. When sceptical about if technology is AI-powered, ask these key questions: Is this technology using data to improve its predictive capacity? Is this technology improving the effectiveness of a human operative?
Stonal leverages ML to address various challenges, including enhancing data management, streamlining ESG reporting, and optimising industry liquidity. The service analyses data from sensors and Internet of Things (IoT) devices, diagnostics or regulatory checks, to predict maintenance. Other features include data collection and verification, which helps with regulatory compliance. AI in real estate serves as the backbone, driving improvements in the background, rather than at the forefront.
The true measure of a company, platform, or solution lies in its outcomes, specifically in the practical applications of AI. Stonal emphasises the importance of making a tangible difference, highlighting that the significance of using AI in real estate ultimately rests on its ability to deliver impactful results.
