AI-powered solutions are reshaping the experience for deskless workers — those in retail, healthcare, manufacturing, and other essential industries who make up over 80% of the global workforce. Despite their vital roles, many deskless employees still lack access to basic digital tools, creating significant barriers to engagement, productivity, and safety. Jeremy Jacobs, CEO of Undesked, shares the root causes of these technology gaps, from outdated processes to siloed systems, and offers actionable strategies for overcoming them. Learn how organizations can implement mobile-first platforms, automated workflows, and contextual training to empower frontline teams and drive measurable business outcomes.
AI-powered solutions are reshaping the experience for deskless workers — those in retail, healthcare, manufacturing, and other essential industries who make up over 80% of the global workforce. Despite their vital roles, many deskless employees still lack access to basic digital tools, creating significant barriers to engagement, productivity, and safety.
Jeremy Jacobs, CEO of Undesked, shares the root causes of these technology gaps, from outdated processes to siloed systems, and offers actionable strategies for overcoming them. Learn how organizations can implement mobile-first platforms, automated workflows, and contextual training to empower frontline teams and drive measurable business outcomes.
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[00:00:00]
Alex: Welcome to the A IHI project. I'm Alex Alonzo shrm's. Chief Data and Analytics Officer. Thanks for joining us for this episode.
We're exploring how AI can address technology gaps for desk-less workers and their impact on engagement, productivity, and retention desk-less workers such as retail associates, delivery drivers, healthcare staff. And manufacturing employees make up over 80% of the global workforce, or approximately 2.7 billion people in this world today.
Yet many lack access to basic digital tools like email or pc. This leaves them at a significant disadvantage in today's AI driven workplace. [00:01:00] Our guest today is Jeremy Jacobs, CEO of Undesked. Whose work and presentation here at SHM 25 focuses on transforming the desk list experience through innovative solutions and ensuring frontline teams are no longer left behind.
Jeremy, welcome to the AI HI project.
Jeremy: Oh, great to be here, Alex, and great to be at shrm 25. It's been an excellent conference so far.
Alex: to kick us off here, I, I really appreciate the fact that you're here. Desk-less workers is something that my research team, I lead the thought leadership team at shrm. Uh, they, they are very en enthralled by this work.
They, they don't want that 80% of the workforce to be left behind. Right. And, and we see what a, uh, huge dynamic and what a multiplier that is. What inspired you to take on this work?
Jeremy: Well, I mean, it's simply, it's, it's one of the biggest markets. There is 80% of the workforce. And you know, if you walk the show floor out here on SHRM, uh, I would argue that the vast majority of the booths are focused on the other 20%.[00:02:00]
They're focused on the desk workers. And so no one was really tackling these challenges. And, you know, I walk into factories, I walk into construction sites, I walk into utility companies, healthcare. So on and so forth. They're, they just don't have the same tools that anyone else has. They haven't been given the same opportunities, and so they were just, they were overlooked and they were, you know, people were underdelivering to provide them resources and so I felt somebody needed to.
Alex: Why do you think they are overlooked? That's a, that's a great point. Why do you think they're overlooked?
Jeremy: Uh, you know, because the challenges, you know, there are a couple reasons. One, the challenges are extremely difficult. You know, a lot of times when people build tools, they build tools to make money, right? That's why you enter the software industry, the hardware industry.
You build tools to make money. So you're looking for easier problems to solve than a lot of people have. Uh, you know, in a, in a production environment, for example, it, it's hard to even understand what all those challenges are, and there's so integrated, there's so many different departments that touch each other, hr, safety, maintenance, you know, operations, so on and so forth.
So number ones are [00:03:00] overlooked. Number two, uh, you know, to put it bluntly. These workers aren't generally the best paid workers, and so you don't generally give your best resources to your lower wage workers so that they get, they get overlooked for that reason, you know, a whole lot. And then lastly, because you know, you walk into a lot of these environments.
Why do you do it that way? Well, because that's the way we've always done it. In the tech industry and in other industries where you have a lot of desk workers, that's not the mantra, you know, why do we do it this way? Because we've always done it that way. You're looking for change. But in these type of environments, you know, we've been running these hotels this way forever.
We've been running this hospital this way forever, this factory this way forever. So there's just not as much excitement to create change, I think is a big component of it.
Alex: Yeah. You know, I, in a prior life, so I'm an organizational psychologist by training and in a prior life I worked with a federal, uh, think tank that looked specifically at kind of programs that you could build in to prevent technology gaps, but also to. Mitigate the risks associated with what you're [00:04:00] describing, that people who are just left behind because of the context or the situational context of their workplace.
Right. And, uh, it, it strikes me, what kind of technology gaps are you seeing really arising with this dustless worker, uh, community? More, more than just the gaps. Which ones are the biggest ones and which ones are the smallest?
Jeremy: But you know, to, to kinda answer that question, I think you gotta break desk-less workers into two categories. And, and first off, you know, to, to kind of go back to the previous question, 'cause it ties into this one. If you look at a Google chart of the search terms for frontline or desk-less workforces, it was essentially non-existent just a few years ago.
This is, this is a new term, sort of like ai, you know, it's just become on the front line. So people are beginning to look at that. But I believe if you're gonna answer the question of, you know, what are these gaps? Which is the question, right? I think it's different for different classes or different tiers of desk-less workplaces.
So let me give you an example of what I mean. So if we look at hospitality, retail, and food service, uh, those environments are desk-less. You need to be able to give people [00:05:00] job assignments and things like that, but they're not unsafe environments. If you look at, you know, a different tier of deco's workforces like manufacturing,
construction
utility workers.
Not only are they desk-less, but now they're in extremely interesting environments where safety is also a massive concern. You know, a lineman can electrocute himself, person in the factory, they lose limbs and things like that. Not a lot of people working in McDonald's, I would say is lost a limb, right?
Or working in retail. And so there's different tiers. So these gaps are different really depending on which tier you're talking about. If we talk about the biggest, most dangerous tier, which is manufacturing, utility, places like that, the gaps are big. Uh, one of the biggest, biggest technology gaps is the fact that none of these systems are connected.
Safety is not connected to maintenance. Maintenance is not connected back to analytics to tell us what needs to be done to. Create a feedback loop in retail. It's, you know, in banks and, and hospitals and things like that. It's a little [00:06:00] different. Most of those gaps are communication related, less automation and more communication.
They just need to more effectively communicate with people.
Alex: walk me
through examples. Right. Talk to me a little bit about some examples where you're seeing that at, let's say
manufacturing Right.
Jeremy: yeah. Great, Great, '
example '
Alex: cause I, I, myself have been around. Yeah. I'm a favorite of it. Uh, I, I, it's one of my favorites too, at shrm it's the, the industry that we study more than any other.
And, and one of the things that strikes me is, uh, we look specifically at how it is that the experience might differ from one part of the manufacturing sphere all the way to the other. Right. I, I, I look at. Having worked with factories in a variety of enterprises, the kind that do corrugated card boxes or, or, or cardboard boxes or the kind that actually make the
machines to make those,
to make the
boxes,
to make the boxes like the Barry Way, Millers of the world as an example.
Right. And uh, it's, it's fascinating 'cause they're so tech rich in that manufacturing space, but in that same factory, you'll then have a a, [00:07:00] a machinist who doesn't even look. Anywhere near, uh, at, at, at a, some form of technology, right? They may work with a la they may work with 3D printing and those types of things, but they're not necessarily doing the kind of things that you would think are heavy tech.
And it always, I I, it always fan, I find it fascinating that you have in that same roof, under that same roof, you have people who are as tech heavy as
can
be
Jeremy: Engineers. Yep.
Alex: engineers, and then at the same time have people who are not and get almost
no
exposure to it.
Jeremy: We, and you got it. So you just separated it out. And manufacturing is the most interesting example of them.
So you got an engineering team that builds some of the most sophisticated machines to do some of the most sophisticated things. And then you have the
operators. Mm-hmm.
The, the engineers are desk workers, just so we're clear. Yeah. And the operators are desk-less. And so they suffer from three things usually.
Uh. First off is communication. You know, how do you communicate with a worker that doesn't have email? How do you give them job assignments? You know, how do you get feedback from them? How do you communicate with them? And so [00:08:00] one of the things we see that we've implemented is kiosks so that they can check a kiosk for job assignments, you can use text messaging, things like that.
The second major gap to categorize it is really driven around automation. And automation starts with getting rid of the analog stuff. So you walk in a factory, it's got a $3 million machine that makes whatever the widget is, but they've still got a paper checklist over here on the clipboard and somebody's signing off that they inspected the machine.
Well, that's a non-starter, right? We can't even begin to implement AI in this process until we get rid of analog. We can't even begin to implement automation. And so automation is lacking. I'll give you my favorite example. If, if someone's at a machine and there's a near miss, they almost get hurt. Well, nine times outta 10.
You would like maintenance to go out and inspect that. Like what happened was the guardrail loose? Is do we need to put a basket over this cutting thing before someone gets hurt? But if your analog, or even if you're not analog in your digital, generally those systems are silo. So they don't talk [00:09:00] to each other, they don't communicate.
Your near miss doesn't trigger ai, doesn't trigger a maintenance request to send someone out. So that's thing number two. So again, communication automation. And the third thing is resource sharing. You walk out on these factories with a $3 million machine. There's no tablet there in most cases, to tell them how to run this machine, how to change the blade, how to push a button to put a maintenance request in, hit another button for incident reporting, or I'm low on parts, I need inventory to keep my job going.
Let me hit this button. And you don't see that, that sort of technology as you do again, like you said, Alex, with the engineering staff, they've got all the best tech in the world, but the operators just don't have it.
Alex: So
walk me through some of the issues that you run through, but then talk to me about what you do at
est. What is it that, I mean, I know you focus on solutions like AI powered mobile connections and computing and communication. Talk me through some of those examples that might even apply in that manufacturing example.
Jeremy: Yeah. So, uh, I'll give you an example of a client of ours. You know, they have these machines, they have a lot of migrant workers.
That's been a [00:10:00] topic, uh, at, at SHRM a lot today. I've seen a lot of, and so you got the, and, and not only that, but if you talk specifically about manufacturing, 40% national turnover rate, you know, the person that you've got sticking on this machine today probably wasn't on the machine yesterday. So it starts right there, right?
So the first thing you've gotta be able to do is make sure somebody's qualified to run that machine. These are challenges that we see. So how do you do that? Well, you check into a machine. It needs to check a database. Have they seen the training video? Have they taken the training test? Have they signed off on all of those things?
So now we got that done. They're up, they're operational and unde can make those things happen. We have the technology to make it so you check in, it knows if you've been here, if you haven't, it makes you do these things to be sure you're gonna be safe. You're gonna operate the machine correctly. Uh, the the next thing is, you know, something's gonna
go
wrong.
It could be that, you know, like I said a minute ago, you're running outta parts to make your finished goods. Maybe that your bin becomes full and you can't even make another part. You don't have a place to put it. Somebody needs to take this thing away. Maybe the machine's not running right. Maybe the parts aren't passing qc.
So there's [00:11:00] a huge array of things that can go wrong, right? You need the ability to be able to reach out and answer that question immediately. And a lot of times, again, with operators, you just don't see access to those resources to do those sort of things.
Alex: So how are you communicating is, how are you pushing that out via ai? What's the, what's the nuts and
bolts of it?
Jeremy: Yeah, so at Ondes particularly, which
I think is your question. Yeah. Uh, so we make a interface that's mobilely responsive, can be put on any kind of device. Uh, and it's specific to its location. So if you're at one particular machine, there might be a tablet right there, and all the buttons that relate to that machine, I've had to run that machine or the parts inventory of that machine.
They're all right there. Uh, when you hit those buttons, you might be, it might be a form that you pulled up. And you fill out things on a form. It might be a video you watch, and depending on what that is, the end result might be in a job assignment. So I'll give you an example like I did earlier. Uh, someone puts a near miss in, well, I want to trigger an
assignment.
To the maintenance department to be due today to go overlook that. So we use AI to look for these [00:12:00] situations. You know, where there's a, there is an incident, there is a near miss. So that should trigger maintenance. There is, there is low inventory, could be off, a sensor could be off a button that triggers someone to go fill that bin up with parts.
And so those are the things we look for is what are these common workflows that are going on every day and how do we streamline and automate them?
Alex: do you see that, uh, with some of the, the, the, the folks that we're talking about in the Dustless workspace that they experience sort of the same thing that we've been experiencing in our own data at term.
Our recent research highlights the fact that ai, for example, and AI powered applications are actually driving greater efficiency.
89% of the workforce says they're doing things faster, quicker,
better
because of ai. Yeah.
Do you see the same thing happening with, with, with,
uh,
the
Dustless
community?
Jeremy: Oh, yeah. Like the example I just used, you're running low on parts. You hit a button, uh, and then AI can send the, the response to the correct person that handles the correct parts to send them to go do a job. And now you never left the machine so there's no [00:13:00] downtime.
Not only is there no downtime, but you again, didn't leave the machine and you didn't walk your way into the way of a tow motor and create a safety incident. So not only are you more efficient, but you're safety. And then we see, we see AI continually creating efficiency, continually creating safety, and it's measurable.
Like you can literally watch the implementation of ai. You can watch the number of incidents go down, you can watch the productivity per man per hour go up. And it's, it's super measurable and, and it needs to be, you know, AI has to have a business outcome. We don't, we don't need to just do AI to do AI because it's catchy, right?
Yeah. So there needs to be a business outcome and it needs to be measurable and you can see it.
Alex: So yesterday I was fortunate enough to be on a, a panel with folks from, uh, Oracle and folks from ServiceNow and Atlas Cloud. And one of, one of the things that stood out to me was, uh. We heard a lot about, and, and I sort of forced this question in there as the moderator of the panel, but I thought to myself, uh, what kind of stubbed toe moments have you seen people having with the desk-less community and in particular as it relates [00:14:00] to the integration of ai?
'cause nothing is ever perfect, right? Uh, there's always moments where you, you stub your toe. What kind of guidance would you offer HR leaders to avoid those kind of stuff? Tote moments
too.
Jeremy: Yeah,
I think, I think a lot of people, uh, and I don't think I'm speaking on a term here, I think that the term AI has just gotten so sensationalized. It's like we've gotta have AI and And it's, it's interesting, right?
We, we gotta have well for what? Right? So, you know, so what I would, What I would suggest to HR leaders is to first start when we're talking about frontline desk-less environments. Like why don't you go take a trip and talk to the desk list workers and see what they're having struggles and challenges is we don't need to just implement AI to implement AI because it sounds cool.
So what are they, what are they struggling with? Thing number one. Thing number two is like get contextual. Like go out there and watch the work being done. Don't just take, ask them what's going on, but don't just take their words for it. 'cause sometimes they'll say what they want to say to get what they want to get, [00:15:00] but go watch that work and get contextual.
Thing three, I would suggest if you know you're HR and you're wanting to implement, uh, ai, build out the workflows, like start watching what's going on, chart out those workflows. You're looking for redundancy, you're looking for opportunities for automation, you're looking for opportunities for efficiency.
But if you don't.
Document out those workflows and you don't personally go witness that work, then what do you know? What are we implementing AI for? Just because someone had something that sounded neat. So find the real problems in your business
and look for ai. Don't look for ai, and then go try to find a problem that it solves, look for your problems and work from the problem to the ai, not from the AI to the problem.
Alex: You know, I, I'm so glad you said that. I work quite a bit with the, uh, the National Academy of Sciences and, uh, or National Academies of Science, I should say.
And one of the things that they actually do, they have a board that's all on the human machine, inter uh, uh, interface. And [00:16:00] AI, as you might imagine, is the big thing that they've been studying over the last year. Uh, I work with
their
board
chair, uh, Emmanuel Robinson and a, and a buddy of mine, uh, Fred Oswald, who's the a, uh, the most down to earth person you've ever met.
From, uh, uc, Irvine, and, and formerly Rice University. Right. And he's been working in machine learning for the last 25 years. Uh, one of the things that they always highlight and that their work actually highlights is the need
for
that
context. The need to understand what people are actually using it or would use it for, right?
Because they actually have summarized and synthesized data across thir 300 different jobs and how people are using both
ai.
A human version of the, of the solution or just an AI alone version of the solution. And when they use AI Plus, hi, right? And one of the things that stands out is when they're using AI and hi
together,
it only works if [00:17:00] they've actually taken into account the context.
It's actually worse if you use worse than using AI
by
itself
or
hi, if you don't account
the
context right?
Jeremy: Yeah. 'cause, because if you're, if you're using both and, uh, you know, there's a dependency there, right.
Yeah,
a hundred percent. And, and so, uh, you know, again, the human interaction has to trigger something that's meaningful in
ai.
You know, so you're looking for the problem first, not the AI solution. You, you can't find the AI solution until you know what the actual real
world problem
Alex: Yeah. It's
almost like any good use case. Right. You gotta start with what the
problem
is, right?
Jeremy: Yeah.
But I think a lot of people, Alex, you know, and I think you're seeing this and everybody's seeing this, they're just so excited about, Hey, I, I was, I had a workshop earlier
today about desk-less workforces, right? And so I got to the workshop beforehand to, to prep for the, the session and all that. And there was an AI session. There's 85 chairs and there's probably 300 people there, right?
So it's a very interesting topic. So, you know, again, you [00:18:00] know, I, I encourage all HR leaders. Go find the problems and the problems you're looking for are you're looking for redundancy. Where are we doing the same thing repeatedly? Secondly, you're looking for efficiency through automation. Where is there a task that's being done that a human is having to push multiple buttons?
Literally the first button, the human interaction could have then triggered a bunch of. AI instead of a bunch more human interactions. And we see that. And so these are the things you're looking for. And then the last one you're looking for, which is, you know, sort of the classic definition of AI is where is there something that the amount of brain power that it would take?
To calculate this and sort this and decide to do this is just enormous. And then you want to implement ai. So that's, you know, my suggestion to HR is get inside of that environment, get down there with those frontline workers, look at that contextual work that's being done, and find those problems.
Alex: So
let's get back to HR leaders as a, as a kind of a group that we want to help, we wanna advise in some way, right? We've got. [00:19:00] 25,000 people here all in the HR industry in some way, shape or form between exhibitors and sponsors and, uh, folks who are, uh, attendees. One of the things that stands out to me is they're all looking for greater guidance on, I I I don't find people resisting AI anymore.
What I find people doing to your point is looking how to
adopt
it
and adopt it
well. Right. And that's a change management initiative in my mind. The new, the new imperative. I mean, I know we used to say change management. Change management in this industry, but it's really transformation, right? Is what we call, we
call it.
Yeah.
Jeremy: management.
Alex: How
do you guarantee that people are actually taking into account, or what do you advise HR leaders to make sure that this is a strategic people initiative or strategy as much as it is
a technology
one?
Jeremy: I said a minute ago, inclusivity,
like
start with the frontline worker. Like if you're not solving one of their problems that you know. How big of a buy-in do you think you're gonna get from 'em? Right? So [00:20:00] number one, start there. Uh, number two, if you're implementing ai, you're doing something new.
So let's not do a bunch of new things at once. Let's use a change management framework we're already comfortable with, just because AI is a new thing and it's a new kind of change, you're probably already using the change management framework. Don't redo it. Just because you're implementing AI, use a very comfortable framework that you've been used to and, and it's it tried and true and that your team is buying it.
Third is, is more so than in a lot of change management. These are gonna be, you know, cross-functional projects. This isn't just gonna be HR that's involved in, in an AI project, it's gonna touch other departments. And so more so. I think than ever is transparency and collaboration have gotta exist because you know, if you're gonna implement ai, it's not the same as we implement a new procedure and we got it wrong because we weren't collaborating, we weren't transparent about exactly what our goals were, and then we just go back out and we change that this [00:21:00] ai.
So there's a lot of programming that's gonna get involved. There's some pretty heavy engineering lifts that are related to this, and so this change management needs to be taken probably more seriously than a lot because the upfront investment. It's significant and undoing it is extremely hard. So a lot of transparency, like, you know, what are my challenges gonna be with this?
What are we gonna get out of this? And, and then, you know, lastly, our fourth. Uh, you gotta, you gotta coach up the buy-in. You know, you're gonna have to train everybody. You're gonna have to level set those expectations. If you're gonna get the adoption that you need, you can't just throw this thing out there and hope that it sticks.
I mean, some of these workers are concerned, AI's gonna take their job, right? So you need to explain to 'em how it's not the, the big, bad monster. You know, Hey, this is gonna empower you. To do more and be more efficient, and, and that efficiency's gonna secure your job. And so part of that training is on the ai, but part of that is also on the people side of the business about, hey, this is actually how this impacts your career here.[00:22:00]
And coach them through that. So, you know, I feel like if you follow that framework then you,
you'll be in
a
good,
spot as a leader.
Alex: you know it. I also think there's an important component to what you just said, which is really ensuring.
That
you're incentivizing people to come along on that journey. Right? Yeah. And the good, best leaders who are doing it in that change management framework are doing it with the aim of incentivizing people to change. Right, right. That's the, that's the whole purpose of it. Uh, I've heard recent examples across a variety of different, uh, communities where people are being incentivized and actually building in project work to incentivize people to learn.
AI to build it into their core work and be, and giving people the opportunity to be creative about how they build it into their work processes and their workflows. Um, this also makes me think a little bit about something that, that, uh, is sort of the hot topic of the day. The big, you know, two word phrase that we've been un unleashing here at SHM, uh, this year, at term 25, which is business accretive.
Right. And a lot of people you know, [00:23:00] want to know and want to talk about how much are we actually increasing the value, right? Accretive means you're increasing the value of your business. How much are we actually doing that? What do you see with the dustless workers and
teaching them how
to use ai?
Jeremy: Yeah. So I think it, to tie in a couple things that you said there, uh, and even to go back to the previous
question, like what is the business objective here?
Why are we doing this project? You know, what, what's going on here? Uh, and, and if we are successful, what is, what are our expected, measurable results? Right? And then to align performance incentives, you know, around those things for that adoption. As, as far as what I'm seeing in the desk-less workforce, uh, AI's new.
Like just the fact to even have an infrastructure that even is possible to build AI on. I wasn't kidding when I was talking about, I walk in these factories, these fast food restaurants, these hotels, you know, all these
places, they're still on pen and paper, Alex. Mm-hmm. Like the ideology of implementing ai, we're, we're [00:24:00] a long way from there.
We
can't
have a train until we lay down some tracks and paper is not the track. You can build ai and so. So, so what I'm seeing is I'm seeing a lot of these industries, you know, that have been resistant to change. Now they know they have to adopt change and they're miles behind. So right now they're going through a term, I guess it was you tell me how long ago digital transformation, when was that, the buzzword of the year?
Was that Five years ago?
Five years ago. So a lot of these places are just now going through digital transformation. In order to be able to even look at AI and, and it's again, those frontline environments because nobody has focused on those frontline environments the way they have those desk list or those desk environments.
And so right now we're just trying to build the infrastructure out and, and I won't say that's true for everybody. Like I can take you into a couple of facilities. Uh, you know, the, around the world of different things for, you know, production, manufacturing, production, that almost everything. You've been in a Tesla factory.
Well, [00:25:00] they're definitely not going through digital transformation over there, right? They're using robots and tons of ai. There's hardly any people in those facilities, but for the most part, you know what's stopping AI isn't even the humans itself. It's just the utter lack of infrastructure to even buildOn.
If it ain't, if there isn't data in a database, we can't sort it, sift it and act upon it
sitting
on a clipboard,
it's, you know, we're lost.
Alex: You're absolutely right. And obviously I data's near and dear to my heart as
firm's chief Data and analytics officer.
Jeremy: So you love paper then, right?
Alex: uh, well, listen, I, I'm the one that started a book, the first three
words
of
my
first
book.
I
hate
People.
But it was, second
sentence
was, I love numbers, right?
So that's, uh, de defines my love of data. But, uh, and, and I do not hate people. I just wanna offer, offer a disclaimer. 'cause my wife always gets mad when I say that. Uh, but one thing I will ask as a final kind of closing question for us
here, and, and I'm gonna throw you a little bit of a curve ball, right?
Because obviously
I love a good kind of conversation. Where [00:26:00] do
you
think
AI
is gonna
let us down?
Jeremy: I think it's our expectations of AI in general. You know, I, I believe people have, um, sold themselves that AI is gonna solve all the problems.
Right. And, and so we, you know, it's interesting that, um, you know, my thought of what AI would do is do my dishes and, robots in general and artificial intelligence of any sort would do my dishes and vacuum my house and mow my grass. So that I could make art, but what I see a lot of AI doing is making art while I'm mowing the grass and doing my dishes right?
And so I think the expectation of AI is set. Just super high that it's just gonna solve everything. And, so for me, that's where I think it's gonna let us down is it's not the AI that's letting us down, it's our expectations. Were unrealistic. And, and you know, it's really great at this point, at Redundant task.[00:27:00]
It's really great at being able to search a million websites on chat GTT for me in like 0.3 seconds. It would've took me a year to do that. Research and it's good in certain things and people are just have put themselves under this ether that it's just great at everything. 'cause it's artificial intelligence, but it's only as good as what we tell it to do.
And so our expectations that'll be not met are our own expectations, not the AI itself.
Alex: And with that in mind, I'm gonna give you an an opportunity to kind of give us a little bit of wisdom to HR leaders as a whole.
what?
guidance, what, you know, 32nd guidance would you offer them? What wisdom would you offer them so that they don't miss out and create a greater skills gap for dustless workers?
Jeremy: Yeah.
So,
um, it again goes back to, you know, what I was saying earlier is like, it's contextual. We talked about that. So what, what we, what we need to do is we need to figure out where there's the AI at. That's one thing that's, there's a practical use for it. And two, you, we had a question earlier about, and we talked about [00:28:00] training and getting the buy-in.
Uh. That's the other thing. They, they gotta be trained, right? So number one, the AI's gotta fit the, the thing that we're doing, it's gotta solve the problem. And number two, they just need to be effectively coached, trained, bought in, and, and then it's, and then the HI AI will work fine together. But if you don't make both of those investments, then it's, it's not gonna work out.
Alex: Jeremy, it was a real pleasure. Thank you so much for being here with us today. I really, I can't thank you enough for sharing the insights that you've had, uh, uh, in terms of EST and all the work that you do and you're bringing to the desk-less community.
I, I really am grateful for it.
Jeremy: Alex, I appreciate it's, it's, uh, great to be a part of CM 25 and to, you know, provide any guidance. I dunno how much wisdom you're gonna get out of me, but, uh, to provide anything I can to the HR community, its HR leaders.
Alex: I'll stop short of calling you Yoda, but you are by far Wise, my friend. And that's it for this week's episode. A big thank you to Jeremy Jacobs for sharing his experiences and insights with us.
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