GUEST SUBMISSION: AI has permeated almost all areas of our lives, from day-to-day personal tasks to our careers and businesses. Used the right way, it is a powerful tool that can increase productivity and make processes far more efficient.

Commercial real estate has not been immune, often with very positive results for how efficiently we serve clients.

But what is becoming ever more apparent in my practice is how clients themselves are now using AI for their real estate needs and in interactions with their broker.

More and more often, my clients are using AI to inform themselves about property-related issues before reaching out to me, during the search process, or during the negotiation. This is especially common among my younger clients, who are more AI-savvy and higher-volume users of AI in many aspects of their lives.

On the whole, this has been a positive development. The client is often much more prepared and informed about the market, property specifications and how the leasing or purchasing process will play out.

Even so, it’s caused me to reassess my approach and to evolve in the ways in which I add value for a client.

The aggregation problem

AI is great at taking broad data from multiple sources and summarizing or aggregating that data into an easily digestible response.

The problem with commercial real estate – and I imagine with other fields such as law and medicine, where AI use among clients and patients is also rising – is that there are so many nuances an aggregation of broad data does not capture.

For example, I’ve had clients use AI to get a sense of lease rates for industrial properties in a certain area. AI dutifully pulled data from various brokerage market reports and other publications to provide a number.

What wasn’t clear to the client in many cases was that those averages included a broad range of building sizes, ages, clear heights, shipping specs and sometimes locations.

For example, AI may give the client a response that the average lease rate in the area is $16 when, for the particular type of property the client is looking for, the typical market rate is around $18.

There have also been cases where AI has advised a client to be overly aggressive in their offer versus the reality in the market, and others where it was not aggressive enough.

That is because AI is scraping sources that may only trickle out information about specific deals, or that aggregate a broad range of transactions. In both cases it often misses what is actually happening on the ground.

The on-the-ground advantage

The specific details of real estate deals are not often made public, which means there is only so much depth to the data AI can scrape.

Even when a lease rate or sale price is published (in MLS or other sources), most of the other deal terms are not. On a lease these include the term, free-rent periods, rate escalations, tenant improvement allowances and landlord’s work, to name a few.

What’s more, by the time market sentiment and deal terms start to show up in the data, they are already stale.

I provide the commentary in Lennard’s quarterly GTA industrial market reports, which are frequently referenced by AI, and I’ve mentioned several times in those publications that the quarterly data being commented on is very much a lagging indicator of the state of the market.

Case in point: I stated in our Q3 2026 report that the data’s bullish indicators did not capture the mood on the ground among business owners, which was much more bearish and is only likely to show up in the next report several months down the line.

Brokers are doing deals all the time as well as continuously talking to other brokers, landlords, developers and business owners. That real-time intelligence does not show up in an AI response, and it’s the kind of value brokers should be leveraging for their clients.

For every client who is better prepared for a property search due to AI, which may make the broker’s life easier, there are just as many who have a skewed perspective because of it.

This causes the broker to put additional effort into explaining why some of the AI guidance should be taken with a grain of salt, relying on institutional and real-time knowledge more than ever.

There is real value in that. It will benefit the brokers who are not only doing deals but also actively engaging with their peers and with market players, allowing them to become true authorities in the market.

The relationship premium

Relationships have always been of supreme importance in commercial real estate, and none more so than the one between client and broker. It’s always been the case that a client will only allow themselves to be represented by a broker they respect, trust and like, and none of that changes in the age of AI.

In fact, as AI progressively commoditizes data and information once available only to a small slice of the industry, those brokers who excel in building and nurturing relationships will be the ones who prosper.

I’m more convinced than ever that the human element in brokerage can never be removed. In fact, I think it will carry a premium going forward, as clients crave the “analog” human side that can help them wade through the data and AI responses to uncover what’s real, what’s not and what’s missing.

Looking ahead

None of this is an argument against clients using AI. A client who arrives informed asks better questions, and that makes for a better process.

But AI can only work with what has been published, and in commercial real estate the most valuable information rarely is.

The brokers who thrive will be the ones who treat a client’s AI research as the start of the conversation, then add what it can’t supply: current deal terms, a read on where the market is heading and a relationship built on trust.

As the data becomes a commodity, that judgment and personal touch is what clients will rely on their broker for more than ever.