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AI is reshaping leadership, strategy, and the very role of HR. In each episode, the host Barb Bidan explores how AI drives innovation and leadership in HR with actionable insights for the future of work.
The Human Element is brought to you by Harper, Wisq’s always-on AI HR Generalist transforming how work gets done. Powered by deep HR intelligence, Harper delivers instant, accurate, and empathetic support—from policy questions to performance coaching—so your people get answers fast and your teams stay focused.
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Barb Bidan (00:00)
Welcome to The Human Element. Today I'm joined by Bradford Wilkins. Bradford is the VP of People and Organization at Cognite, an industrial AI company that's helping ⁓ energy manufacturing and infrastructure organizations use data and automation to work smarter and also safer. So with a background in people strategy, leadership development, and HR transformation, Bradford focuses on how technology and talent
can scale together. I am super excited to have you on the show today, Bradford. Thanks for joining me.
Brad (00:34)
Really excited to be here, Barb
Barb Bidan (00:37)
I'm going to dive right in because I know we're going to get into some good discussion here. So I want to start by talking a little bit about your role in the company that you're in. How is Cognite applying its industrial AI mindset internally ⁓ within the company?
Brad (00:55)
Absolutely. So, you know, I'm really blessed to be at a company where data and connecting different components is at the core of what we do for our customers. And obviously doing that for people is obviously integrated into what we do as well. So you think about Cognite at its core, what we do is take a bunch of disparate parts that normally don't talk to each other. So you think about an oil refinery, right, has 50 or 500,000 sensors that are disparate pieces of data. And what we essentially do is build a knowledge graph that connects all these different pieces and actually tells
a story and allows an agentic AI layer to get on top of that. Well, a lot of times people in AI, so people data doesn't necessarily connect together as well. So you think about historically, even really in the last couple of years, right, we may have had people data like an engagement survey or attrition data or performance data. ⁓ And, you know, even the most mature people analytics functions, we're looking at those in silos. And so one of the things Cognite has really been on the bleeding edge of is being able to look at that data.
Barb Bidan (01:35)
Mm-hmm.
Brad (01:55)
⁓
analytically and then connecting that in the same way through a knowledge graph. So looking at that engagement layer, engagement data layer through performance data that's maybe being fed from the Salesforce data or from with our professional development team, professional delivery team, you know, actual customer satisfaction rating. So let's say, for instance, we might have a really high engagement score on a particular function, but they're low performing in terms of their actual output, right? Is that a positive or an
negative or vice versa, right? Do we have high signals of burnout? And then we can go and pull metadata, for instance, and say like, ⁓ are managers having meaningful one-on-ones? And what is that signaling and where is that showing up? Is it showing up in other components around performance or speed to, let's say, a new hire, right? If a new hire isn't having meaningful structured one-on-ones with their managers in terms of their ability to accelerate, to ramp, to be productive, right?
impacting things or not, right, are some of these traditional signals that again we may have been looking at as a as a people function not just at Cognite but across human capital functions across the world as we've historically maybe looked at in silos just like how Cognite is building knowledge graphs Cognite is also starting to look at these from a human capital standpoint across the silos and connecting those dots to see what correlations and causations we might be able to identify and then obviously being able to build action plans
those into cloud and being able to build real-time actionable insights, ⁓ know, through scripts that are pre-built that can help signal for people business partners to connect with managers, provide real-time coaching that again is not necessarily anyone really lifting a finger, right? It's now these real-time conversations, real-time signals can trigger slacks. So a lot of really neat things are happening. And, you know, had you asked that question two weeks ago, you would have gotten even a fraction of that answer. Then had you asked that question three
months ago right so it's every day is an exciting answer.
Barb Bidan (03:57)
my gosh, completely right. I mean, you can actually go to the data with all of the sort of curious questions, right, around how things relate to one another. I with all of the sort of ⁓ points that you've mentioned, like items out of Salesforce related to people data. Do you have any good stories that you can share around how ⁓ having this sort of access and being able to leverage AI in this way has helped you to move more speedily ⁓ on something that you learned from the data?
Brad (04:25)
Yeah. So one of the groups I support is the professional delivery function, which is our professional services function. call value delivery, right? So we focus literally delivering value to the customer. And so what we can actually do is through a precursor, which is essentially the kind of parallel tools of Salesforce. So we can actually track kind of what employees, where they're spending their time, where managers are spending their time because they're, again, 95 % of this department is billable or at least funded, right?
so they're not necessarily billable. So we're tracking our time, so we're getting real meaningful data and we can see from, you know, where are they spending their time, where are they getting stuck, being able to correlate that and figure out again,
Where are they spending their time? And so for instance, we were able to find recently that some of our people managers were being pulled into, the sales organization, right? In fact, in very fresh off the presses, Almost 50 % of their time was being pulled into either pre-sales activities or again, whether it be actual sales calls or pre-sales, like creating kind of pre-sales work or like kind of production levels things.
Barb Bidan (05:36)
Mm-hmm.
Brad (05:36)
And one of the ways that this kind of got signaled in parallel was we had a new hire that kind of had their first 90 day survey hit in real time that said, Hey, I'm not getting the meaningful kind of connection with my manager that I was looking for. And so when I said, you know, let's kind of find the way that where the intersection of those two things hit. Uh, and again, it was really easy to say, great, why is this person not getting that connect the data? I said, great, where's the time being spent? Uh, and again, this was really, really
easy to do with AI didn't take all of 10 seconds to be like, oh, they're not being able to spend the meaningful time that's causing this disenchantment, slowing the ramp of the new hire because they're being correlated in time. It's something that frankly is not even part of their job responsibilities that they're kind of doing favorably, right? And so now it's allowing us in real time to go and say, great, whoa, whoa, whoa, should this be part of their responsibilities? All right. Let's actually look at job, respond, job role and responsibilities that we're now
going back to the job family descriptions, re-looking at that entire role and responsibility saying, should we carve that out in their role and responsibilities? And if so, what has to fall out? Right? Because again, someone only has a hundred percent of their time, again, using AI to able to look at that and say, great, you know, as we look at other companies, right, is this fall into this role and responsibility for this kind of EDF level role and responsibility. So being able to pull that into real time, look at roles and responsibilities. And again, this whole exercise that historically would have taken weeks
Barb Bidan (06:49)
Mm-hmm.
Brad (07:06)
and weeks and weeks was like.
Barb Bidan (07:07)
weeks.
Yes.
Brad (07:08)
was like a three hour exercise, right? And so we, again, we said, no, this doesn't necessarily fall into this roles and responsibilities. So then where does it fall, right? And now we can go reallocate this time. And again, it's not a light switch that we can say, great, stop doing it, right? But now we've said, hey, great, now we've got to figure out where this goes. We've addressed this. And so now new hires that fall back into this, again, hopefully won't have the same kind of drop off in terms of their ramp. And then we can now also from a financial implication say, and I can accelerate
ramp, right? What does that mean in terms of the actual cost per hire? I can actually put real quantifiable dollar value to this as well. So it's not only just like, oh, this, you know, a happy employee, which obviously, again, I kind of always talk about a tree, right? An employee has to be happy, but the roots of the tree has to be rooted in business value as well. So now we've got both happy employees, but also rooted in business value because I can accelerate ramp time and decrease the cost of hire.
Barb Bidan (08:06)
Yeah, I mean, in that one example, one piece of team member feedback led to at least, I counted two or three immediate things that could be addressed and fixed where prior to this, could that have instead looked like multiple pieces of...
Brad (08:16)
10%.
Barb Bidan (08:25)
new hire feedback across multiple new hires who weren't having a great experience, who maybe left the organization prematurely, et cetera. I'm not saying that happened with you, but that's what it could have looked like before. And instead, it nearly immediately turns into solving three things, right? That is amazing.
Brad (08:34)
No.
No, no.
your point.
Those 90 day onboardings, they pile up and, know, again, you're, you're not saying, Oh, I'll look at them at the end of the, you know, the quarter or the end of the year, maybe even I'll aggregate them, whatever, or they're, qualitative versus quantitative instead of being able to like real time trigger real time connect that to actual data orientation. Uh, no, it's, it's incredible, right? And so, and now to your point within a week's time, we'll have kind of everything at least on track to be resolved with some level of acceleration that again,
will mean that every new hire in this department thereafter will, you know, hopefully at least be back on track.
Barb Bidan (09:21)
That it's such a great story of shortening the time from signal to action to result, which I think is awesome. So I want to shift gears just a little bit, take you into the realm of more ⁓ HR operations. What are the areas within HR operations where you think there's the greatest opportunity more immediately for automation or augmentation through AI?
Brad (09:45)
Yeah, mean, honestly, there's infinite ones. mean, I think anything that's, well, let me first start with this filter. If all you're doing is putting AI as an augmentation on top of a bad process, you're just creating a compoundation of a bad process, right? So that's, think, the danger is I talk to friends in AI, talk to friends in HR that they're like, I'm just adding AI to the back of this bad process. Like, that's actually literally the worst thing you could do right now, right? ⁓
Barb Bidan (10:14)
Yep, start with the
work. We've said that a lot on the show. Start with the work first.
Brad (10:18)
Absolutely, right. ⁓ And we've taken the mindset here at Cognite, we talk about AI first, right? Our CPO actually, she wanted to stay AI native, but you can't be AI native if you actually aren't AI native. So we say AI first, right? And by that, mean that the first lens in which everything that we put through is like, why can't it be AI, right? Let's push it through that sieve, that cheesecloth, if you will. It has to really get through that. And then what's left on the other side, then you can figure out what can be human capital or what can be other process configurations
things like that. And so you think about anything that's kind of can be you know manual any kind of responsiveness anything around those lines like that's base layer stuff right I mean honestly if you're kind of sitting here in mid-April and anything around like employee queries or benefit queries or anything like that hopefully is any kind of ticketing system any of that if that's not automated by now hopefully your organization is getting behind you and you're able to get that going. The next layer that I'm seeing a lot
of really neat pieces is any kind of coaching. again, Claude is kind of the soup du jour, but you can layer it into your Slack, get some really neat insights. Even around if you're putting it into your interview, even if you use a Gemini to transcribe your interview, what I found is some really, really basic stuff like, evaluate this person's ability to do behavioral interviewing. And managers, in my experience, generally want to be better. Again, there's an outlier to that question, but
85, 90% of them want to be better. And it's really amazing when you can put in front of a manager like, hey, 90% of your questions were close ended questions, right? Or you were selling or, you know, in this meeting you talked 85% of the time, right? And so just even being able to use it for some really, really basic coaching layer stuff. ⁓ Again, it's funny what an objective outside view in terms of business partnering and helping from a coaching standpoint can do as well. ⁓ So there's that layer of things, right? ⁓
anything around.
Drafting I mean removing you know again whether it be adding empathy whether it be removing emotion in from things right? You know again finding your unique voice All that kind of stuff. I job descriptions Standardizing templates, know finding best practices and all that's again. That's got to be completely automated by this point You know internal comms and any kind of presentations like if you're putting up a presentation right now That doesn't look executive ready again zero excuse for that, right? mean
Barb Bidan (12:45)
Mm-hmm.
Brad (12:47)
like I'm riffing but like this is all I mean 50% of my job is gone at this point right and there are people who are resisting that because I'm like well what else do I do it's so exciting we're getting to do the really meaty strategic and empathetic work the stuff that frankly I don't think AI is going to be automating for a long long time ⁓ it's the really interesting work but if you've rested your laurels on this very basic manual stuff and you're afraid of letting go of that understandably because maybe that's how you
kind of, you know, cut your, cut your teeth for the last X number of years, right? If you don't let go of it, it's going to be pulled away from you really quickly. And so, ⁓ again, I, I could keep going, but I think you get the idea that there's just so...
Barb Bidan (13:29)
No, mean, I am,
I'm going to give you the opposite side of this same question because I bet you that what you're doing with all the time that you've saved is you're doing the work that used to form the 100 % to the 150 % of so many of our jobs in HR, right? So in with that extra time, what are the things that you are finding still require the human touch or
Brad (13:44)
Right.
Barb Bidan (13:54)
feeling to you like they're going to stay central to things that you want to be working on personally.
Brad (13:59)
Yeah, so the thing that's I think most
exciting still is really around team composition. And that's everything from kind of workforce planning to interconnective kind of how do people talk to each other? How do they work together? I just saw a really interesting study come out of the University of Queensland talking about the value of group intellect. So I think as people, we've always worked with skills matrices to understand how to build teams together or personalities to understand how you're building different personalities that interact well
together, but this really talked about actually the compounding layer of IQ together and how those kind of synapses can fire together. So was really interesting. And I think, again, the way you create teams together has always been really interesting to me. And I love the book, Multipliers. And I think, again, that
is going to continue to be just this abstract because it's human capital. The thing I've always loved about our craft is you're in trying to increase probability, knowing certainty is impossible because it's people still, right? There's things outside of our realm of control and AI is always trying to hit zeros and ones, right? Human capital will always kind of have an uncertainty component to it. And so any variation there of kind of coaching, I think empathy, right? Again, as we continue to be able to have
access to this data and these analytics and these tools, it still will require us to be able to look at it through the lens of the culture and the value system that each company is going to want, on the spectrum of do we want to be, you know, this kind of cold hearted, like top grading, really aggressive company? Do we want to be this super empathetic, right? Family come somewhere in between those two, right? And there's no wrong answer to any of those components, right? ⁓ And so again, each human capital practitioner
or has to be able to look at the outputs of these AI tools through that different lens and be able to make these judgment calls, whether that be about promoting, whether it be hiring, whether it be firing, right? So making those decisions around that as well. And then obviously again, AI is only as good as the input and the output and the different pieces around there. So again, the reality is, it's funny, I was talking to one of our operations leaders earlier,
today and talking about kind the acceleration that I'm seeing through AI and, you know, and we were talking about how some parts of the company are getting that and some are not getting it. And I was like, I don't understand why other people are not getting it. She's like, Brad, because some of your inputs are actually really thoughtful. And she's like, you're spending the time building the scripts. You're spending the time making sure your prompts are well thought through and you're, know, you're actually spending the front side of the work.
if you skip the front side of the work or the practitioner isn't there on the front side of the work, right, then your middle point of the work, the kind of the whatever you want to call that machinery, right, that still won't be there, right? And so there's still that element as well. ⁓ So again, I think.
Barb Bidan (16:56)
Yeah, so sounds like
spending some time getting yourself really good and adept ⁓ at the new AI tools that are in your toolkit, but also just spending some time thinking about these deeper topics like org design and teaming and how do you build a team that has, I loved the group intellect comment that you made, how do you build a team?
Brad (17:09)
Yes.
Barb Bidan (17:24)
whose collective IQ is off the charts, right? And those are just things that take some deep thinking. So, and because you brought up the topic of, you know, folks in other areas of the business, like, why is this taking off here and not taking off there? What steps are you finding are most effective to build trust among leaders or employees when you're trying to do this work of introducing AI into your workplace?
Brad (17:52)
Yeah, no, to your point, trust is a really important thing. I alluded to it in our own practice area where...
a lot of these areas, you're essentially telling people you're taking big parts of their job and chunking it away. Take, for instance, engineering, You're essentially, you know, in the next X number of months or years, right, you're saying you're no longer coding, right? Now you're going to focus on the left of the code and the right of the code. And what does that mean for your role and your profession? And I think it's really important that you help them to visualize ⁓ how you as a company are going to be part of that journey to upskilling them and
visualizing for them from a career path, a role in responsibilities, what that is going to look like, not just like, hey, stop, you're not going to code anymore, right? The code, making my number, 90 % of the code is going to be generated by AI now and you're going to focus on left to the code and the right of the code. Like it can't just be this, this is what's going to happen. It's here's how we're going to do it. Here's what your role is going to be in it. Here's why it's exciting for you. Here's what your future is going to look like. And again, will there be people
who don't buy into that, of course. But it's got to be done, and back to even your previous question around kind of what are we doing as HR practitioners?
Those are empathetic, sometimes one-on-one or small group conversations, even taking companies on these journeys in these small conversations. And it's interesting because, and I had this conversation a lot of times, it also is dramatically changing the hiring profile because no longer are we, in a lot of these cases, being able to hire someone with five years of experience. Now it's, we hiring even more so than even, again, I think this has been a trend for a while in lot of SaaS companies.
these growth mindset and fluid intelligence and all these big buzzwords, but this kind of curiosity and growth mindset and the ability to adapt and learn new things as technology is accelerating even quicker, that's more critical than ever. And so creating safe places, traditional learning and development and micro learning is now really about kind of show and tell and creating safe places where people can learn from each other and that type of enablement and kind of sandboxes.
and actually creating back to your point on space and time, right? It's not filling their brink with all this, know, hey, it's 40 hours of work. It's actually you have to create some space for them to be able to go and learn some of these new skills and share it with each other along the way as well. And so that's also part of the journey and making sure that they feel like they've got the space and time to be able to be successful as well.
Barb Bidan (20:29)
That you re-mentioned one of my favorite insights from a different episode of the show where it's like when you create or when you're when you create an opportunity for your teams to have space, let them use it for good. And some of this learning that you're talking about, like don't immediately like pour into it and and fill it up. Right. I think that's a big one to build trust. And I loved what you had to say about visualizing. Like they need to actually see it.
Brad (20:43)
with.
Barb Bidan (20:56)
they're not just going to take your word for it. Whether you're saying, is this don't don't worry, your job's not going to be lost, which is not a statement I agree with saying or, you know, like the sort of don't worry statements with no substance without actually helping them visualize. So I really love the way you talked about you actually said painting a picture like they need you need to take them on the journey with you so that they can see.
Brad (21:06)
Ready?
Barb Bidan (21:19)
what their job will look like after this and that they're excited about what that picture also looks like, I think.
Brad (21:27)
The other thing I just had a conversation, interestingly enough, and again, this is not super progressive, but it's again, it's funny. Some of these conversations, again, different people are kind of on different stages of their kind of journey. The, you know, as this is happening, obviously, you know, companies are growing and revenue per employee is going up theoretically at a lot of companies. But this idea that you're no longer going to necessarily be a manager of people always, right? That some of those boxes in the org chart.
Barb Bidan (21:40)
Totally.
Brad (21:57)
is
actually going to be agents, right? And so I was talking to someone who had a small team and he was saying, hey, how does my team grow? Right? They can't just have my job. And, you know, if we're going to be leveraging AI, then my team is not necessarily growing, which means there aren't management roles. I said, well, there might be management roles. They're just not management roles of people. And you watch the light switch go off for him. And it was really exciting to see, it's like, oh wow. So they could be managing a bunch of agents, right? And I'm like, yes, and they've got to be able to think about that.
Barb Bidan (21:59)
Mm-hmm.
Brad (22:27)
Again, different people will be very accepting of that. Some people will be very resistant to that. There's somewhere in between those two things for most people, right? But if they start thinking, and even as HR practitioners, ⁓ that our role is actually as we're designing these orgs, and I think a lot of, not a lot.
The early adopters of AI, a lot of chief people is adding AI to the end. It's chief people in AI are ⁓ officers because org charts are starting to include agents in the boxes, right? And you say you're an IC with six direct reports and they're all are agents, right? I think that's where a lot of these organizations are starting to go to or start the early adopters are starting to go to, there. Lots of early adopters. The early adopters.
Barb Bidan (23:06)
Yeah, right. is a spectrum. I love it,
right? I tried to do the same thing. I don't want to make anyone, like there are people that are way ahead and that we all look up to and get ideas from, but it's moving so fast that wherever people are on this spectrum of learning, don't panic. You're not too far behind or anything like that.
Brad (23:15)
Thank you.
Right.
No, no, exactly. So, I guess sometimes it's that
ultimately, like, aren't even letting ChatGPT in the organization yet, right? And I think that's...
Barb Bidan (23:31)
100%.
Brad (23:32)
of the thing is like you find like anything right I mean it just whether it be you know when we were doing progressive performance reviews right I mean anything it's just like find your early adopting department right who's like gonna be super excited by it figure out who's gonna be your adopter figure out your resistors right get your adopters to know the value find your middle point then they slowly come along your resistors eventually will be the last ones in and then they'll jump on board and it's just that's like any other initiative that we've done ⁓ it's just
Barb Bidan (24:00)
Yep. Just a really
big change
Brad (24:03)
Yeah, exactly, transformational law. Exactly.
Barb Bidan (24:03)
management project. Just big. But it is. It's change management, right? And it's the things that we need to do to kind of bring people along and bring them with us on the journey. I know Cognite helps clients with AI. And you're talking about the type of literacy that you're needing to build within teams. I would love to hear how you and your HR team are
playing a role in helping the company make that cultural shift and maybe even some of what you're doing to kind of skill build within the teams in your company.
Brad (24:37)
Yeah, no, it's interesting because Cognite, when we were just, I just had this conversation this morning. So IT sits today in finance as it does in a lot of companies, right? And then learning and development actually sits in our value delivery function because most of our learning is actually for customers and partners. And then that actually serves our internal groups as well. Although I actually, we're hiring a new leader for that function. And one of the things I told them is that their fourth customer
is going to end up being large language models as well. think learning and development functions are going to be the owners of training large language models of the future, but that's a whole other podcast probably.
And so, and then we've got us. And so, you know, was talking around, know, IT I think eventually is probably going to, and I've actually owned IT in my past lives as well, because I actually think it's going to start shifting back into the HR function because it's just so organically. So today we're trying to partner, right? So IT is owning all these, you know, Claude licenses and Gemini licenses and co-pilots and notebook LLMs it's like, today, these are tools, right? That they're out there kind of enabling.
We're here kind of talking about re-imagining the ways of work and running workshops and kind of org designs and things like that. And so, you know, there's still probably in a lot of cases, a bit of a chasm between those two, right? And so it's like, how do we push those even tighter together? But so I think where people is really pushing it is focused on kind of roles and responsibility, ways of work, org design. That's where we're making the largest piece. And then kind of calling over to the IT
department like, hey, in order to make this real, here's where we need the tools and the resources, which is where like, for instance, Claude wasn't on their roadmap, or at least wasn't, was accelerated on their roadmap ⁓ for us across all of the functions. And now it's like, I don't know how many licenses we just got procured in the last couple of months here, but it's just got accelerated along the way. And so that's really probably the first piece. And then like I mentioned earlier,
It's really about creating these kind of small...
I call them kind of learning labs, right? Because it's about environments where they're more workshops than kind of trainings. Like we're not doing a lot of like, come sit in a classroom type environment or take online classes, right? It's actually people who are finding our early adopters who are sharing what they're doing and then actually going and kind of clicking around with people and helping them set up their own pieces. And it's a lot of show and tell. And so that's the people practitioners, the people business partners.
actually being really tied to the ground, of burrowing down in the mud, figuring out who are those early adopters? Where are we getting the traction? again, using our own kind of data that we're attracting to see, hey, where are we seeing some acceleration of productivity again, whether that be from a productivity on the engineering side or delivery side or a sales side, where are we seeing unnatural acceleration of productivity or results? And that's giving us some signals very quickly of who's using it.
It's not an ⁓ accidental correlation there. And that's actually where then go drilling in like, ⁓ look, you, the SAP and PMO, you're using AI. can tell because your output is outpacing, you know, the other peers in your group pretty significantly. And then going and pulling that out. The other piece we're starting to do is in each of our business units right now. And again, whether this eventually gets centralized or not is a discussion we're having, but is circling and saying like, Hey, value delivery, this is your AI champion.
who's going to really own making sure that this kind of gets embodied and championed. So it's not just today and some functions, you know, it's kind of more organically happening, but actually saying waking up every morning thinking about this is something that needs to get done. ⁓ And so that's just now starting to happen, I think, across some of the non-obvious functions. So it was already like that already was a role within like engineering, for instance, but now it's happening in sales.
and now it's happening in the delivery organizations. It hasn't necessarily been a function in people because we kind of are eating our own dog food a little bit. ⁓ But I think again, in the finance organization, for instance, it's a role that we've talked about even hiring.
Barb Bidan (28:55)
Mm-hmm.
Brad (29:02)
saying, hey, who's going to wake up and go and deal. And maybe it's a G&A role, right? That can be holiday because maybe that doesn't need its own wake up every single morning and be AI. again, it's about intentionality, right? ⁓ It's not necessarily like, hope it happens, right? Or count on these kind of low hanging fruits to pop up.
Barb Bidan (29:12)
Mm-hmm.
Brad (29:23)
because they're just so much. ⁓ And again, if people then wake up thinking about kind of back to that cheesecloth analogy, right, AI first, right, ⁓ and there's someone pushing everything through the cheesecloth, then we all of sudden need someone there kind of on the front side.
Barb Bidan (29:23)
Right.
Yeah, and what I hear you saying there, I think, is that that person's waking up ⁓ being the champion, right? It's not that they own AI, right? Like everyone, like we all collectively need to own it. It's not giving a single owner and thinking that's going to solve your problem, but it's someone who is being paid to think about serving as a champion for driving AI initiatives forward in the organization. Am I understanding that distinction well? Yeah.
Brad (30:05)
Yeah, well,
I mean, I'll give you an example. Like even next week, I'm going to a workshop that originally was framed about like, how do we take work from ⁓ function X and how do we maybe move some of it to the partner organization or how to move some of it to India was the initial framing of the workshop, right? And again, what this champion was a lot said, Hey guys,
before we get to either of those two layers or those two roads, right? The first layer is how do we think about AI, right? What's removed out of that function with AI and then it goes down those two roads, right? And so it's someone who's just kind of...
essentially a filter for those people to kind of say, wait, are you thinking about AI, right? Because people are maybe some people are thinking about it naturally, some people are not. And now the whole front side of the workshop is great. Let's think about AI and where can we with intentionality put AI first as part of this kind of workflow.
Barb Bidan (31:00)
totally get it. So Bradford, this one went by so fast. I'm already going to lead us into the ⁓ lightning round here. So just a couple of quick hit questions for you to wrap us up. First one is, what is one misconception that HR leaders have about AI adoption?
Brad (31:05)
you
the entry level people don't have value. I would actually argue that they...
It's probably to me the four to 10 year people are the ones that are the most at risk right now. The people that are ironically coming out of university right now who have been using AI for the last three years, who organically are coming in and understand why AI is the only way to go are probably the most valuable. The kids right out of school right now probably have the most value in a lot of your organizations to come and superpower your more senior executives.
Barb Bidan (31:57)
could turn that one into a podcast in itself with you. have college-aged kids, and I do want to talk about this more after the show for sure. ⁓ I'm going to bring you to the next one.
Brad (32:04)
Thanks.
Barb Bidan (32:06)
If you had to consolidate to a single tech platform or go with best in breed tech solutions, maybe including an AI bot in there, which way would you go and why?
Brad (32:20)
best in breed. And probably just because I, and again, with the asterisk being maybe someone one day will be able to create a all in one, but they don't exist on the market today.
Barb Bidan (32:21)
Love it, me too.
And yes, you're making trade offs. You are, I am highly aligned with that answer. So as you were talking, Bradford, I always try to get some ⁓ nuggets of wisdom to kind of recap with the audience. So let's see how I did here. My first note was that there is magic in being able to connect disparate data faster than we ever have before. So this idea of moving from.
the signal or the having of the data to action to a result. And you shared some really great examples when we talked about that. Second, I'm going back to the visualization comment that you made when we talked about building trust. So build trust with your teams by helping them visualize where their jobs are headed, where you're headed with AI. Like they do need to see it to believe it and to trust you. And I think that's an excellent bit of advice on how to build trust.
And then the last one is, ⁓ I loved the concept of learning labs. So building capability via what you described as learning labs. So the idea of hands-on tools, right? Learn with people while doing. This is not something that is best learned. ⁓ you are taught information. You just take it in, and you know this is a hands-on thing.
Lots more in the show that I'm sure our listeners will get, but we always leave guests with the last word. So what is one piece of advice that you would give to HR leaders who are just at the start of their AI journey?
Brad (34:05)
⁓ I would go with one of my favorite Chinese proverbs, is the best time to plant a tree was 20 years ago. The second best time is now, which is again.
Barb Bidan (34:14)
I love it. I love it. It's true. And
it's not too late. That is a clear message of it is not too late. Just get going. ⁓ Thank you so much for joining me on the show today. It was a great conversation and so fun to talk to you.
Brad (34:28)
Always a pleasure. Thanks, Barb.
Barb Bidan (34:30)
Thanks.