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Transcript

How to Master AI Adoption in Commercial Real Estate · Industrial Advisors

• 0:00
  Give us some real world examples of what people are implementing
• 0:04
  in AI and how you, you know, help.
• 0:08
  Sure, no, thanks.
• 0:09
  I, I appreciate that, Matt.
• 0:10
  The, you know, really, right now, I think a lot of people are thinking
• 0:15
  of it strictly as a word processor.
• 0:17
  Somebody to help them get their emails done more quickly, more efficiently.
• 0:21
  We are all beginning to see those are easily identified as AI generated.
• 0:27
  Yeah.
• 0:27
  As opposed to thinking, what is my company's brand and what are our goals
• 0:32
  and how are we using AI to help us achieve those specific goals by stating those in
• 0:39
  a natural language processing standpoint.
• 0:57
  Well, welcome to our Industrial Advisors podcast.
• 1:00
  You have Bill Condon and Matt McGregor.
• 1:02
  We have a great guest today.
• 1:04
  Michelle Hamilton is joining us, uh, and I'll provide a quick intro on Michelle.
• 1:09
  So Michelle spent 30 years as a strategic leader in the commercial real estate and a
• 1:14
  EC sector before pivoting over to the ai.
• 1:18
  In investment and workplace adoption.
• 1:21
  She founded Spark AI strategy and AI class lab to help organizations
• 1:26
  turn AI tools into execution.
• 1:28
  So we'll talk a little bit about that, but you've also recently
• 1:31
  had a recent transition, right?
• 1:34
  Mm-hmm.
• 1:34
  So tell us about your, your recent transition and then what your
• 1:37
  day-to-day is now moving forward.
• 1:39
  Okay, bill.
• 1:39
  Thanks.
• 1:39
  I appreciate that.
• 1:41
  Yeah, actually I'm really excited.
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  I don't even start till March 16th, so that tells you how much
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  I'm gonna be able to tell you.
• 1:47
  But I was recruited by Answer Rocket.
• 1:50
  They are a global AI company.
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  They've been around since 2013.
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  They build out.
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  Predictive analytics and ag agentic workflows and have been deeply ingrained
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  in the AI space around the world.
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  And so I'll be leading up their change management and AI implementation division.
• 2:11
  That's great.
• 2:12
  And you are based in St. Louis and you'll stay in St. Louis, is that correct?
• 2:15
  I will.
• 2:16
  I will, I will, there's gonna be a lot of travel, but, uh, I'm
• 2:19
  really looking forward to it.
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  And like me, they really focus heavily on construction, commercial real
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  estate, really that A-E-C-C-R-E space.
• 2:29
  So it's gonna be a fantastic fit for us.
• 2:33
  That's great.
• 2:33
  Well, well, congratulations.
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  Let's kick it off by staying kind of in the commercial real
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  estate space and talking about.
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  AI and, and what you've seen, I mean, it's, it's developing so quickly, right?
• 2:45
  Mm-hmm.
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  So what have you seen that has really stood out to you here over
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  the last kind of 12 months as AI has transformed into the CRE space?
• 2:53
  I love that question because you are right.
• 2:55
  It is changing so quickly.
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  I tell everybody, today's the worst it's ever gonna be, and every 24
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  hours the news cycle is astonishing, even to those of us in the space.
• 3:06
  But really where I have seen some of the biggest impacts over the
• 3:09
  course of the last year especially, is companies were investing a
• 3:14
  lot of money in their AI tools.
• 3:17
  Trying to decide, you know, are we gonna be using copilot?
• 3:20
  Are we gonna use the Chachi PT enterprise?
• 3:23
  Whatever that might look like.
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  But what they were not thinking about was their human first AI adoption.
• 3:29
  They just brought the tools and assumed that everybody would use
• 3:33
  them or had their IT division.
• 3:36
  Maybe deliver slides and say, here's how you use ai.
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  You know, good luck.
• 3:41
  Mm-hmm.
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  And instead of really thinking about a human first approach, how do you
• 3:47
  meet teams where they're at and help them to meet their KPIs with specific
• 3:52
  AI first strategies based on the words and the processes that they're
• 3:58
  already using, and not overloading them with an entire new vocabulary.
• 4:02
  So the listeners to this podcast would be institutional investors,
• 4:06
  institutional landlords, capital market and leasing brokers, tenant brokers.
• 4:12
  Give us some real world examples of what people are implementing
• 4:17
  in AI and how you, you know, help.
• 4:21
  Sure, no, thanks.
• 4:22
  I, I appreciate that, Matt.
• 4:23
  The, you know, really, right now, I think a lot of people are thinking
• 4:28
  of it strictly as a word processor.
• 4:30
  Somebody to help them get their emails done more quickly, more efficiently.
• 4:34
  We are all beginning to see those are easily identified as AI generated.
• 4:40
  Yeah.
• 4:40
  As opposed to thinking, what is my company's brand and what are our goals
• 4:44
  and how are we using AI to help us achieve those specific goals by stating.
• 4:51
  Those in a natural language processing standpoint.
• 4:55
  Also, how are we leaning into generational data in order to predict
• 5:01
  how to move forward in the future, whether it is a downturn, an upturn.
• 5:06
  Depending on what you are trying to get your data to help talk to you
• 5:11
  about the fact that we can now take historic information from companies
• 5:17
  and turn that into actionable insights.
• 5:20
  Truly through conversational use cases is pretty astonishing.
• 5:24
  Previously technology really was for those in the technology space.
• 5:31
  Now really, anybody with the right training can take their job and their role
• 5:37
  and determine how are they going to be utilizing AI as their practice partner.
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  Not replacing them, not even necessarily just rinsing and repeating or copy
• 5:48
  pasting everything it says, but really truly using it as a research partner
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  and an opportunity to get back out into the field and close business
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  deals one-on-one as humans, as opposed to spending eight, 10 hours a day
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  behind a computer doing the research.
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  In those particular roles that you mentioned.
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  I'm even thinking about the ability to automate research, automate the ability
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  to come back and bring you reporting on a daily basis so that you understand
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  how your organization, or how, let's say your targets are needing to, uh,
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  work and help you to predict what to do next and how to move that ball forward.
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  How much red tape on like large companies, so for example, were with Collier, right?
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  And we're probably one of the less organizations with a lot of red tape,
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  but how do you work around them saying, gosh, you can't put this on your
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  server, or You can't put this software.
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  This has to be approved.
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  How do you work around that?
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  So for larger organizations, oftentimes they do have, uh, an
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  innovation team on the inside.
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  The innovation team generally includes key stakeholders as well as those
• 7:01
  from the IT department coming together to determine how can we move the
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  ball forward with AI in a safe way that is going to protect our data.
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  One of the things though that.
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  I am seeing again and again in that particular use case is because they
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  are taking such a measured approach, which is incredibly important to do,
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  and not teaching people how to use the products more than just some rudimentary,
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  like I said, you know, slides.
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  Here's how you do it.
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  Where they're putting themselves at.
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  The greatest risk is through what I would call shadow ai a fairly common
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  language, but it means if you are not showing somebody how to use the products
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  that you have either custom built for them or that you have invested in as a
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  firm, they are going to simply go around your governance and pick up their phone.
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  Open up their own private account with a chat, PT Gemini, Claude, and simply
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  put the information in there to get the results that they're looking for for sure.
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  So it is really the, the refined training so that people understand we have
• 8:08
  invested money, like I said, in either custom models, uh, or in an enterprise
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  version of a model at this type of scale.
• 8:16
  But we wanna invest in you to teach you how to do your job.
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  With this tool and there is a structured approach to that, and it is one that
• 8:27
  needs to be thought about very carefully.
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  I will tell you some of the largest organizations that I've worked with,
• 8:33
  um, have secretly brought me in the back door and have said, don't tell
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  the innovation team you're here.
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  We're gonna call you a marketing's expense.
• 8:41
  That's right.
• 8:42
  Now, show us how to use.
• 8:43
  I like it.
• 8:43
  Now show us how to use it.
• 8:46
  And so, you know, now we're getting, bill already knows what I'm thinking.
• 8:50
  Yeah, yeah.
• 8:52
  Well, and, but I will tell you the, the great thing is now we are beginning
• 8:56
  to get past that because I do think leaders are beginning to see, we've
• 9:01
  put hundreds of thousands, if not millions of dollars into these tools.
• 9:05
  And we're not seeing the ROI.
• 9:07
  Why is that?
• 9:08
  And where did we miss, let me go back.
• 9:12
  Uh, just to that, you know, kinda real world example, if you had
• 9:17
  mentioned, and I'm super sensitive to it, that your emails, you can
• 9:20
  tell and you can absolutely tell when they're produced by ai.
• 9:25
  I would say especially te right?
• 9:28
  I would say most people are using that.
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  So you can even tell the format, the language.
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  So let's say an average broker like myself or Bill in a 10 hour day is four to four
• 9:39
  and a half hours of that is responding to unexpected for the most part.
• 9:44
  Re emails.
• 9:45
  So your day is part research, right?
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  To answer those emails.
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  How does AI come in and you, you know, put you back out to
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  your unique ability, right?
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  And get you out of that reaction, or without looking AI and without,
• 10:01
  with really being productive.
• 10:03
  How does that happen?
• 10:05
  You know, I will tell you a great methodology for that.
• 10:09
  Now, you know, copilot is the most common corporate, uh, tool that people are using.
• 10:15
  It is beginning to catch up.
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  I will say predominantly, I haven't heard a lot of people super excited about it,
• 10:21
  but it is beginning to catch up with some of the, uh, other foundational models.
• 10:27
  But here's how you're gonna actually handle that within your.
• 10:31
  Uh, large language model within your chat bot itself, you're can actually
• 10:35
  move away from answering those emails directly in the email program itself.
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  You are going to begin building out project folders or consistent trained.
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  Conversations, if you will, where you have not only uploaded, you know, your
• 10:52
  company's brand standards and accepted language, and the language that really
• 10:57
  surrounds what has traditionally moved the ball forward, but also
• 11:01
  what are you as the communicator?
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  How have you communicated before ever, you know, AI ever came out?
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  What really made you great at what you already did, and you.
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  Basically build out a training document.
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  That training document forever lives in that conversation, and
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  the bot has been put on notice.
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  First of all, you follow my company's brand and you follow my
• 11:24
  personal brand and brand standards.
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  Second of all, there are no M dashes.
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  There are no emojis.
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  There's no hyperbole or overblown information here.
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  This is a conversation that I need to respond to in a way that is both clear and
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  reflective of who I am and who I work for.
• 11:42
  So you mentioned jobs earlier and whenever you go to a conference
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  and we, we talk ai, one of the biggest fears that people have is.
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  Is AI ultimately gonna take a bunch of jobs away?
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  Obviously jobs matter in the commercial real estate world, they do.
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  What's your take just overall when it comes to the AI sector
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  and in jobs and job growth?
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  Long term?
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  You're out.
• 12:03
  Bill.
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  She already says it is gonna eliminate you.
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  You're guy.
• 12:05
  Yeah.
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  Yeah.
• 12:06
  I'm not worried.
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  I'm not worried about me.
• 12:09
  Yeah.
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  Kids.
• 12:12
  Yeah.
• 12:12
  Yeah.
• 12:14
  That's what you missed in the last two minutes with the
• 12:15
  technical issue, your phone.
• 12:18
  Yeah.
• 12:18
  Um, I, I'll tell you, I get that question day in and day out, and for good reason.
• 12:23
  I mean, we are, we oftentimes lean into fear.
• 12:26
  AI is coming so fast and moving so fast, and we are seeing some
• 12:29
  mass layoffs within organizations.
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  Here's where those organizations almost across the board, have made a mistake.
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  They assumed that they could just move an AI product in-house and it would
• 12:40
  automatically replace the humans.
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  What they did not think about and what they did not reflect on is
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  why did they build their workforce to begin with, and why were those
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  individual contributors or groups?
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  Outstanding at what they already did.
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  And by taking that human first approach, organizations that perhaps were thinking
• 13:00
  they were going to be laying off are less likely to because they are going
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  to be looking as to those humans as.
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  Orchestrators and as the one that is working alongside the AI bot, can
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  I say it is not going to take jobs?
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  Absolutely not, because we are already seeing that it is.
• 13:19
  I guess the way I look at it is my job when I come into a company and I'm trying
• 13:23
  to reassure the employees, I'm not here.
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  So your company can take your job.
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  I am here so that you can become the very best AI user for your job, to give you
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  job security so that you will have a place within your organization as a leader and
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  as a voice to ensure that all of your tribal knowledge that you've carried with
• 13:45
  you throughout your career absolutely continues to move forward and becomes
• 13:51
  part of the collective solution on how that company is going to move forward.
• 13:55
  What jobs in CRE, in the CRE space do you think?
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  Will be eliminated or call it better than 50 or 60% eliminated.
• 14:06
  So I've got a couple of clients that are law firms.
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  I'm seeing law firms definitely experiencing some layoffs because
• 14:13
  of the cost to delivery model, and they are able to use AI to do a
• 14:19
  lot of the research now and build.
• 14:22
  Cases that actually are going to ultimately save their clients money.
• 14:28
  I'm also seeing more of the consultant roles, the roles where people are being
• 14:34
  paid to help you build out your strategy, help you be able to capture your, uh,
• 14:40
  next steps and how you are going to ultimately use, once again, your data or
• 14:46
  even your foundational knowledge from an owner that is getting ready to retire.
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  Perhaps, you know.
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  Bringing those consultants in.
• 14:54
  While I've got a lot of great friends in the consulting space, I do have
• 14:58
  some fear for their jobs and their, their positions because, um, a smart
• 15:03
  AI user within an organization can actually tap into whatever model
• 15:09
  they're using in order to achieve a lot of what was previously being paid.
• 15:15
  I was just gonna ask, as you say, you know, like you think
• 15:17
  about from a training standpoint.
• 15:20
  How to put yourself in the best position.
• 15:22
  How do you think, you know, we have, Matt's got one in college right now.
• 15:26
  I have one going into college next year and younger kids.
• 15:29
  And I'm curious, how do you think universities are going to
• 15:33
  implement the AI world into actual.
• 15:37
  You know, degrees and things like that, go forward.
• 15:39
  Mm-hmm.
• 15:40
  I love that question.
• 15:41
  But mostly because I came into the AI space when my son was a freshman in
• 15:46
  college and he just sent me this arbitrary text that said, Hey mom, I got this new
• 15:51
  thing called chat, GPT, but it's, and he got a very, very early version of it.
• 15:55
  He had a friend in Silicon Valley that sent him a, a, a, just a beta
• 15:59
  version about six months before anybody else had heard of it.
• 16:02
  And he said, I used it to write a paper and I got an a. And I was really mad.
• 16:10
  Wait, you're a freshman, right?
• 16:12
  Where do I even start?
• 16:13
  This is not how we raised you, but I will tell you.
• 16:16
  Super smart kid.
• 16:17
  And he said, no, I told you what the outcome was.
• 16:20
  I actually did not tell you how I used it as a contextual thinking partner.
• 16:24
  And we iterated back and forth on a specific topic and concept.
• 16:30
  I used it as both a research partner and a refinement partner.
• 16:34
  It was still my work.
• 16:35
  At the end of the day, I'm happen to be coaching a school district a, a
• 16:40
  local school district here, and we are talking a lot about AI for education,
• 16:46
  and I'm actually much more hopeful about the use of AI at the college level or
• 16:51
  even at the K through 12 level because previously we've all had very similar.
• 16:58
  Educational backgrounds.
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  Now we have this new tool, and if students are taught correctly to use it,
• 17:06
  they are actually going to be able to think bigger and broader than we've ever
• 17:11
  allowed human beings to think before.
• 17:13
  For sure.
• 17:14
  No longer can you say.
• 17:15
  Oh, you can't do that.
• 17:16
  You don't know anything about that.
• 17:18
  If you can dream it, you can actually begin to deeply investigate it.
• 17:23
  I'm seeing some university campuses as well, moving to where, as opposed
• 17:28
  to a singular professor being the be all, end all of knowledge
• 17:32
  at the front of the classroom.
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  Now they are really becoming more of a coach.
• 17:37
  I coached a large group of students at Washington University
• 17:40
  on this exact same topic.
• 17:42
  How are we making sure that.
• 17:45
  I am coaching you in the use cases, practices, and strategies that you
• 17:50
  should be implementing not only for your research, but your concept
• 17:54
  development, and then how are you forcing AI to provide full bibliographies and
• 17:59
  citations of all research and resources?
• 18:03
  I will say, and, and you know, we, we've all been to school.
• 18:07
  Very rarely did you get to ask your professor, oh yeah, cite your sources.
• 18:11
  Show me your bibliography.
• 18:12
  Now you get to utilize both the importance of being at a school and
• 18:19
  whether that's a trade school or whether that is a traditional college campus.
• 18:23
  Still having humans work together and understand how to work as a team, but now
• 18:29
  you've got this incredible tool to lean into in order to be able to extract and
• 18:35
  pull knowledge that normally would've taken months, even years, to document.
• 18:42
  And tell us, uh, we're gonna include your contact information, of course, um, uh,
• 18:47
  all of all of your contact information in the footnotes, but tell the listener,
• 18:51
  how do you engage you and your company?
• 18:54
  What does it look like?
• 18:55
  Is it a physically, do you come in with a big team?
• 18:58
  Are you virtual?
• 18:59
  How long does it take?
• 19:00
  What, what's the offering?
• 19:02
  I tell you what, and I love this about the way that we've sort of pulled
• 19:05
  together our package, we decided not to pursue the consultant role where
• 19:10
  you're just gonna pay me, you know, in perpetuity over the course of the months.
• 19:14
  This is an actual deliverable where I sit with the owners and key stakeholders.
• 19:19
  I understand what are your goals and what are you trying to achieve?
• 19:23
  And then we begin to break down phase deliverables.
• 19:27
  Generally, that is a company wide.
• 19:29
  AI 1 0 1, if you will, leveling everybody up at the same time so that
• 19:34
  everybody understands the approved tools and how you're going to use them.
• 19:39
  We can also help them build out their specific governance for their particular
• 19:44
  industry and use cases, and then from there we meet with departmental
• 19:49
  key stakeholders and understand what is your department responsible for
• 19:54
  delivering, and how can we now bring you.
• 19:58
  Actual hands-on training.
• 20:00
  I believe that our training should always be 20% theory, 80% hands-on.
• 20:06
  I want everybody in the room working alongside each other.
• 20:09
  This is not Mia talking head.
• 20:11
  This is all of us laptops.
• 20:12
  Open phones up.
• 20:14
  We are talking about how do you talk to ai?
• 20:17
  How do you get it to do what you are asking for at a much faster rate.
• 20:23
  Now that I'm gonna be working with Answer Rocket, I am going to have
• 20:26
  a much larger team to work with me.
• 20:28
  I'll be building a much larger team of me, if you will, so that
• 20:32
  we can be, uh, performing these tasks on a more global scale.
• 20:36
  Currently though, it is me and then I've got five individual additional
• 20:41
  trainers that come with me, a five.
• 20:43
  Got a much larger group because I want everyone to have a very
• 20:47
  hands-on experience so no one is sitting confused or frustrated.
• 20:53
  I want my team walking around the room answering questions and
• 20:57
  helping everybody to move the ball forward as a group and as a team.
• 21:01
  Then from there, they receive very specific deliverables.
• 21:06
  I believe in an IP based prompt library, something that captures the AI work
• 21:12
  that you have done as a team, and begins to capture those prompts into a
• 21:17
  library that builds out the intellectual property of the entire organization
• 21:22
  so that everyone can lean into that.
• 21:24
  And departmentally no longer is everybody just working siloed.
• 21:29
  They are seeing how is it used here and how can it move to me here?
• 21:33
  Excellent.
• 21:34
  Well, sounds easy.
• 21:34
  So just bring you guys in.
• 21:36
  Everybody becomes experts, and off we go.
• 21:40
  It's that easy.
• 21:42
  Yeah.
• 21:42
  Well, it's a really fascinating and important topic.
• 21:46
  Um, that's not going away.
• 21:47
  And so appreciate you sharing your expertise with us today.
• 21:51
  You know, although it's only been 20, 25 minutes, it's been really insightful
• 21:55
  and we will leave your information up in, in, in case folks wanna engage
• 21:59
  with you and your team moving forward.
• 22:01
  That sounds great.
• 22:01
  And you tell Colliers and I know where to find us.
• 22:04
  That's right.
• 22:04
  Thanks for joining us.
• 22:05
  We really appreciate it.
• 22:07
  Thank you so much, Michelle.
• 22:08
  All right guys.
• 22:09
  Thanks so much.
• 22:09
  Have a great day.
• 22:10
  You too.
• 22:11
  Great job.