AI, or at least what’s being called or marketed as AI (more on that in a bit), is a big topic in almost every industry right now.
AI seems to be entering every element of life from robots in factories to coding assistants, from graphic ‘design’ (the graphic above was AI-generated for example) to architecture, and even in healthcare, security and banking.
Obviously Commercial Real Estate isn’t immune, or being left out.
Consultants are training up and deploying their learnings. Prompt-engineers are making names (and bank) for themselves. Courses are being built and podcast series produced.
We get asked about AI a lot too, from office building owners and flex space teams alike, and if we’re honest we are truly excited about the possibilities and opportunities this wave of technology will open for all operators of mixed-use and multi-tenant spaces.
But… we do feel that there’s a humungous brightly-colored elephant angrily stomping it’s feet in the middle of the table in every CRE boardroom or decision-making team, and it’s about time we address it.
Before we get to that though, here’s a quick overview of what tech this wave of AI is made up of and what it could mean for the office industry.
Prefer to jump right over to the elephant? Here you go.
But first, what exactly is this wave of AI?
The technology collectively being referred to and marketed as AI is actually a cluster of really cool and exciting technology, but there’s a lot of debate about whether it can actually be called Artificial Intelligence.
What you’re most probably hearing about, seeing or playing with can be broken down into the following:
Machine Learning (ML) – although quite complex, it is essentially the process of a computer trying tasks again, and again, and again (infinitely) until it gets to what are deemed successful states.
Large Language Model (LLM) – a system that takes in huge amounts of usually public data and creates a kind of ‘auto-complete’, in which is returns the most probable series of words based on what you ‘ask’ it.
Natural Language Processing (NLP) – is a technology that uses data around words and definitions to allow computers to convert text or voice into commands.
ChatGPT – is a proprietary technology from OpenAI that combines NLPs and LLMs that made submitting questions (called ‘prompts’) and getting responses easier than was previously available to the public. Since the viral launch of the technology, other large firms like Google, IBM, Meta and others have rolled out their own user-friendly input/output technology.
There’s a lot more other technology and terminology to go through, but that’s now why you’re here.
For more on these though, you can grab a free 1-on-1 call with our team or subscribe to our newsletter below to hear about when our team will be on stage IRL or virtually to dig into these details.
What does this mean for CRE?
In short, we’re seeing this technology being deployed in 3-4 use-cases already.
Generative – creating copy, artwork, interior design inspiration, architectural options, and more.
Summarization – taking large files (usually in PDFs or legacy formats) and summarizing them into digestable versions.
Procurement/efficiency – taking previous purchasing data, utilization data and other information and streamlining what is bought, turned on/of, or stored.
Operations – using past, live and external data to make operations more efficient, profitable or reactive to macro- and micro-market shifts.
Once again, there are more, and as each business is built differently so are the ways they could implement the technologies above to automate, streamline, optimize and out-compete.
🐘 Ok what about that darn elephant?
The elephant is data.
The questions it keeps shouting as it stomps its feet on the boardroom table are:
Where is your best data? Is it in one place? Spread across multiple systems? Is it somewhere you own or rent? If you had to move it, change it, enhance it – could you?
Who can access this data? Internally, who can get to it, and how? Externally, how can you protect this data? How much of it do you “give away” with every AI system you connect, use or share with?
Is it useful? How is it stored? Can it be transformed internally, or externally? Can it be queried, merged, calculated with, or enriched?
Is it fresh? If the data is live, how is it kept in sync with other systems? If it’s not live, how long until it can be assumed to be wrong or outdated?
No matter what tool or technology you’re looking to implement, the quality of the results they can return rely almost entirely on the quality of the data you can (and are willing) to put in.
Done right, you could give your teams the ability to do in minutes what used to take hours or even days.
Done wrong, you could burn out your team, acquire technical debt, deplete budgets, and potentially release troves of data you may not have wanted to.
Not sure where to start? Or just want a helping hand? Let’s schedule in a call to chat.
We don’t build AI systems, but we do help busy operating teams improve the way they use their data and technology across their businesses every day.