Rebecca Warren 00:00
Welcome to the March Talent Table. I’m living in Chicago this month. We’ve got some great guests who are going to join us. But before I introduce them, mark your calendar for our following Talent Table in April, where we’ve got some great speakers and a great topic coming up as well. So I am so excited for us to talk about navigating the wild world of AI and HR tech. Before we get into that, I will have our speakers introduce themselves, Mark. We’ll start with you. Then we’ll go to Bharat, and then I have an opening question, which I know you’re all excited for.
Mark Stelzner 01:03
All right, happy Thursday, everybody. I’m Mark Stelzner. I’m the founder and Co-leader of IA. I founded IA about 19 years ago. That’s why I’m so young and fresh. I have about three decades of experience in people transformation and am based in Atlanta, Georgia and pleased to be with you all. Thank you.
Bharat Daga 01:23
Thanks a lot. Mark, hello, everybody. My name is Bharat Daga. Currently, I’m serving as global director of IT, leading the HR and Payroll technology team at Qualcomm, based out of San Diego. I manage operational and strategic aspects of all our people landscape, which is impacting over 45,000 employees 10,000 contingent workers across 40 different countries. One of our key goal is basically to enable and utilize advanced technology to help attract, retain talent, and at the same time enable Qualcomm’s diversification strategy. I’m very passionate about leveraging technology for enabling employee experience process improvements and driving innovation and business value.
Rebecca Warren 02:20
Amazing, all right, and I didn’t introduce myself before, but I am Rebecca Warren. I host this talent table series every month, and each month, and I work for Eightfold. Each month, we develop a question that helps us get to know our guests a little outside of the topic. So come up with a fun one today that I’m going to throw out there. If you all have thoughts on this, feel free to throw it in the chat or also send it to us on LinkedIn. So here is my opening question mark. Bharat, are you ready? Okay, maybe not. Okay. So would you be rather followed? Would you rather be followed by a bagpiping band wherever you go, or have Rosie Perez narrating your life? Real time. Who wants to start?
Mark Stelzner 03:19
I’m a big talker. So my team would say, bagpipes drown me out. Do not let me speak. It might actually make me more of an introvert if no one could hear me. So that would be a benefit for the world.
Rebecca Warren 03:34
Not how I expected that to go, like backpacking, yes, but to drown you out, no, okay, that’s passionate.
Bharat Daga 03:43
Yeah, it’s definitely a tough one, right? Followed by a bagpiping band. Like, I feel like I’m, like, basically leading a parade or something. I’ll go with Rosie Perez, because, of course, with her voice and with all her demeanor, she can she can make my some of the boring aspects of life sound exciting and maybe like an Oscar worthy performance while I’m taking out trash as well.
Rebecca Warren 04:14
You too. Those are some of the best answers that I could not have predicted. I absolutely love that. So thank you so much for jumping into the wacky question.
Bharat Daga 04:23
Very interesting question.
Rebecca Warren 04:27
Have you ever been asked a question like that before on a webinar? No, you win.
Mark Stelzner 04:31
Okay, winning.
Rebecca Warren 04:35
All right. Well, hey all I’m really glad that you’re here today. Super excited to talk with Mark and Bharat, so we are talking, as I mentioned before, that world of AI and HR tech. So, HR ops teams are carrying more weight than ever, and they are tasked with finding and implementing the right technology to keep everything running smoothly. Mark, you mentioned in a LinkedIn post that the unsung heroes of the HR tech world, which I think is a great, a great way to describe it. So with AI evolving at lightning speed, how do you even know what’s worth your time? So that’s what we’re going to talk about today. So we’re going to break down what to look for in AI products, what are the must have features, how to properly vet your vendors, and what role should HR ops have in making all of this happen? So by the goal is, by the end of this session, you’ll have a clear roadmap to make starter smarter, more strategic, AI tech decisions. So we ready to get into it. Y’all. All right, let’s do it. So I think as we set the stage and we think about what we’re actually talking about, what do organizations have to determine in terms of what matters to them before even thinking about tech, what do organizations have to do to set themselves up to be able to make sure that they’re making the right decision? So I’m not going to pick either one of you to start, but would love to have us just kick it off. What should organizations be doing first?
Bharat Daga 06:08
I can go right, of course, for a great, great question, right? Because anything new which you start, right? It’s always a moment of internal reflection on what exactly you would like to achieve out of anything, right? So, basically, yes, we all have to reflect, to identify our AI goals, and how does it translate into our business objectives? Right? Anything that HR does, right? Is it tied towards your bigger strategic goals? How is your business shaping? That’s the key, right. Apart from that, like having a clear expectation in terms of the matrix, what do you want to achieve? Right? Let’s say like we wanted to reduce our time to hire by n, number of days, right? We wanted to improve our employee experience by a certain percentage. Have a higher recognition platform adoption, those are the key metrics which will put you on the success path right now. While we are talking about what to do, I would also like to mention what not to do, right? AI is like moving at a pace which is like, means it’s tough to catch right. So don’t overthink, right, because you’re not going to solve everything internally, right? Be open. Have an agile mindset, making sure that, like, as you learn, you are taking an iterative approach for your decision making. Mark, anything that you’d like to add here?
Mark Stelzner 07:42
No, I think I’ll just compliment your point of view. We work on business cases for transformation, and our clients, our meeting client, is a 75,000 person enterprise in 70 or 80 countries. And as you know, broad just at Qualcomm, the pace of change is relentless, and in some respects, it’s unprecedented. The speed at which organizations need to change. Need to pivot. The number of external factors that we’re all living in the world right now are compounding. Anxiety is at an all time high, and honestly, it’s sometimes difficult for enterprises to tether themselves to a north star, to a singular strategic goal. But one thing I’ll say, just based on experience, is if we can tie directly between the goals and objectives of these particular opportunities to the goals and objectives of the people strategy, as well as our enterprise digital strategy, as well as our enterprise finance strategy. And that in turn, ties one to one to our global objectives for the organization that case will be approved almost every time. And the reason is that this is inextricably linked. One of the challenges right now is we have no shortage of tools and opportunities that are looking for problems to solve. And I would argue that we need to invert that conversation, and that where HR and people leaders can come into play is start with use cases. As you said, brought what are the metrics? What are the objectives, the KPIs, the OKRs, what’s our sense, almost a hypothesis of what we think can be achieved through these tools. And then Rebecca to the really the core of today’s conversation, then we have a purpose by which we can vet and attach the provider community into attaining those goals or serving it as an accelerant. Because I almost think of things in two categories. There is accelerants and distractions. There are no shortage of distractions. Ron, I have no idea your inbox got flooded with today. I counted this morning at 87 emails with AI in those topics in some form. So how do we separate signal from noise? Really step back from the day to day and be very intentional about what we’re trying to achieve with these capabilities.
Rebecca Warren 09:46
Yeah, and I think so those absolutely are right. And when we add on to that, making sure that we understand not just the what, but the why, right? So, right? You said tying it to those organizational goals and not just the metrics of reducing time to fill or adding, you know, X percentage to our NPS score. But why, right? Like, if we can tie, as you said, Mark, if we can tie everything to those organizational objectives and bring folks in early, right, I think that’s where a lot of times any kind of initiative fails, is when it gets stuck at one level, right? Somebody makes a decision here, the rest of the folks aren’t involved, and then they’re like, oh, no, I’m not doing that, right? So involving your stakeholders early and often and making sure that there’s clear communication throughout the organization either what you’re trying to accomplish or what you’re actually going to do, because I think that’s where sometimes there’s a disconnect where someone’s like, hey, we really want to do this, and folks are like, well, we don’t care about that at all. And there isn’t that alignment. So without that alignment, it’s really hard, no matter what decision you make, even if it’s the best decision ever for the business, if it’s not attached to the business goals, and folks aren’t aligned, it has very little chance of success, in my opinion.
Bharat Daga 11:00
So Rebecca wanted to add one more. Right, basically, we need to do some sort of current capability assessment as well, right? That okay, because it’s not that like you’re going to rip and replace everything out there that we use as an HR tech or enterprise tech, right, having a clear understanding of what your HR Tech has gaps, what are, what is the key points, and what are your processes which are broken, will really step up the game, right? And also like assessment in terms of skills as well, you need to identify your change agents, your active champions, 100% and your champions, yeah, so those are the some of the things which like organizations can really take a look into before reaching out to AI tech vendors.
Mark Stelzner 11:53
Well and with so many projects, programs and initiatives across these enterprises, prioritization is equally as important. So Brian, I think you know you, Rebecca, you’re touching on internal readiness, which I think is critical. But we also know that these resources are stretched quite then we worked with a global enterprise, one of the largest sporting brands in the world. When we arrived, they have an 835 HR projects, programs and initiatives in one single year, if you can get one done like well done, we move as we move the decimal points out, it becomes more and more difficult. So do we have fractional allocations of resources, or do we have people that are really waking up every day with a significant portion of their opportunity cost and mindset, and dare I say, investment attached to these particular projects, programs and initiatives, because it’s distraction center out there right now with the whipsaw of prioritization and changing needs of the business, you may come into the year with absolutely, you know, defined North Stars and goals and objectives, but I guarantee they’re going to change over the course of a relatively short period of time. That’s true.
Rebecca Warren 13:01
Yes, so, so let’s talk about that too. So that that idea of what’s going to hamstring your initiative or your project, right? So what are some of those roadblocks? And let me, let me clarify this too, because I know we’re talking about it from an HR ops perspective, but this really is coming from anyone who owns the tech right, whether it’s pure procurement, who’s making the decision, whether it’s it whatever group it is. I mean, we’re using the HR ops because we tend to work with folks in that space, but it could be from anyone who is making those technology decisions. So when I say HR ops, I am actually I’m talking about anybody who’s involved in the tech decisions. So just to broaden that a little bit, but let’s talk about some of those roadblocks, right? So we’ve said stakeholder buy in can be a challenge. We’ve said, Hey, not really understanding what your goals, not tying to those organizational objectives, what you know, what? About data quality, right? Like AI and trying to figure out like governance and and legacy systems, like, what are some of those roadblocks that might make it more challenging to get some new systems in, especially AI-driven ones.
Bharat Daga 14:16
So definitely, AI governance, or lack of AI governance, comes top of the mind, right? So I was reading about, like, what are like, the number of organizations which has any sort of AI governance, right? So, around 60%-67% of organizations today don’t have any sort of AI governance. Yeah, 27% has AI governance, but doesn’t have an HR component associated with it. Only 6% of the organizations have an AI governance with an HR body embedded in it. So I think like, or this is, like, one of the biggest roadblocks, and like Mark mentioned, right? Too many options out there. That’s a roadblock. Because, yes, it is good to have too many options, but if everybody is wearing an AI t shirt, it’s like it’s becoming bigger than an Amazon catalog right now, right? So you need to really have some guidance out there. Now, this might not be applicable to almost all industries, but like there are certain industries where which are regulatory in nature, like healthcare and law, which definitely have little bit of change resistance over there because of the nature of their business, So organizations or HR leaders should be working very proactively with regulatory and compliance team if they want to enable AI in those specific sectors. This is like few things, which three things, in my mind, AI governance, too many options out there, which has, like, basically people getting confused. And in case of resistance to change or the nature of their industry,
Mark Stelzner 16:07
well, and when you have highly distributed workforces like Qualcomm, does the hyper-local regulatory and statutory requirements are another complexity factor that can hinder your ability to deploy widely in an enterprise level. And because that’s changing, who or whom is able to keep their finger on the pulse. And let’s just talk about even payroll regulations or general Employee Relations requirements, let alone, as you said, the speed boat that is AI. So as these regulatory bodies are waking up and trying to figure out the best means of both empowering digitization and automation, while at the same time having responsible AI, which certainly is what your statistics are quoting, which is, we may have an office, but have we really thought about what it means for us relatively responsible in AI? And I’ll just say your call outs on some of the sub-components of the employee journey are critical, so we may feel compliance in recruiting and onboarding, but do we really understand benefits implications? Yeah, and the type of guidance we had an organization that was working through piloting some capabilities right now, and what about mental well being and the AI engine that they were using, they started to inquire around emotional support, emotional well being, and they were shocked by the responses. So when we talk about race driven support, you really do have to be very forensic and specific. And it takes an enterprise to really think about this. I would also say, if you’re in the season, we’re going to a conference, you’re in Davos, you’re at off site, you’re coming back with a notion of what you think AI is. But I would also say literacy is is a huge barrier. Things are accelerating so quickly, like do we really understand what the new Microsoft chip could achieve, for example, do we really understand the computational power and what’s changing? Do we really understand the difference between China’s advances versus our advances in the EU versus here in the US? Do we really understand the investments in these large data centers, and what that means, again, as an accelerant or impediment to globalization. But what’s happening, I think, at the enterprise level, is these requirements are getting pushed into people’s annual goals, and one of the challenges to readiness sometimes is without a clear interpretation of what does that mean. So everybody needs to have an AI project. Everybody needs to bring efficiency and effectiveness to AI, one of our large global pharma clients, this just happened to them, and then everyone’s sitting in a room, and this is literally even at the VP level, going, do we really know what that means? Do we really know how that applies like and how do we go back to your point about, do we have a governance strategy to sort of funnel swords and vet this? We clarify our own intent, or try to interpret it to make sure that that’s, in fact, a clarification of what we’re trying to achieve.
Bharat Daga 19:05
So it’s very different, because everybody wants to jump on a bandwagon, but don’t know what really we wanted to achieve, right?
Rebecca Warren 19:11
Well, so 100% the difference between AI literate and AI informed, right? Is a challenge, and so brought to your point, what if folks jump on this AI bandwagon and they’re like, we’re gonna add this process, or we’re gonna add this tool or this initiative, and it maybe isn’t the right one. So let’s talk a little bit just briefly about what are some of the consequences if you jump into it too quickly and you put in the wrong AI vendor or the wrong solution, then what does that do for your organization?
Bharat Daga 19:48
Yeah, so first of all, right, HR today is definitely looked upon as a strategic partner to business, right? Because it’s no longer like maintaining the employee data and doing transactions, right? CEOs definitely are looking to HR that okay, what they can help with achieving all these strategic goals, choosing a wrong vendor, it’s definitely like a missed strategic opportunity, right? Yes, we wanted to do the right thing for our business, but a wrong vendor can lead us to not only like financial loss, missing of the strategic initiative, like not meeting the strategic objective, and at the same time, loss of credibility, right because we are putting our foot forward to help achieve business goals and drive value. And one wrong move can basically take us, like, three to five years back, right? So that definitely comes in top of my mind, right? Of course, right? You don’t want to be with a vendor, or, like, you choose a wrong vendor, and it causes some sort of an operational disruption, right? Think about AR or, like, what Mark was mentioning about payroll, right? These are very critical, critical annual milestone projects. You don’t want it to disrupt those type of like processes, right?
Rebecca Warren 21:11
And thinking about too. But you just said, maybe the insights or the information that’s coming is incorrect because it’s based on the wrong data, or based on the wrong, you know, suppositions or something like that. So you could be making downstream decisions based on data that’s inaccurate because you didn’t have it aligned in the way that was the most successful for your organization. So, yeah, anything you want to add? Mark, yeah.
Mark Stelzner 21:37
I mean, I think right now, if you look across enterprises generally and specifically within HR, there’s already a perception of a tremendous amount of tech debt, meaning we’ve gone out, we’ve selected some of the best, both bespoke and wide-reaching enterprise solutions for employee journey, but we’re not absorbing the lease in the way that it’s intended. So these are SAS solutions. And as you know, whether it’s Eightfold or anyone else, you’re putting a tremendous amount of research and development, but sometimes we don’t have either the capacity or capability to absorb those releases. So we’re falling behind, some cases, already in the cloud, as much as we fell behind when we were in the old on-premise days. And that’s comparing it to these licensing structures, is that, you know, I’m absorbing these capabilities. I’m prioritizing the release notes. I’m socializing them. But sometimes we find that the operational readiness, the structure to actually do so, and with a level of cyclicality and speed, that engine isn’t necessarily running as efficiently or effectively as it can. So then we start to amplify the capabilities of AI, which is really amazing, and some of that is delivered functionality within certain tools, and some of it is an add on skew or an add on license. And what’s the return on investment? And do we have demonstrable outcomes? Is there a peer group that I can talk to that’s actually achieved, what the hypothesis is intended to derive? And if, in fact, it is someone who specializes in this category, the robust level of InfoSec review that needs to happen. We may have great alignment between IT and HR. We may have a great use case and business case, but then we have to go through, understandably, right in today’s environment, a very comprehensive information security review, which is where some of those attributes Rebecca brought that you’re talking about really start to slow, meaning we may not be able to move as quickly as possible, because vetting those providers in today’s ecosystem is quite complicated, and that’s where, again, governing bodies and offices can certainly help respond to that. And then again, you know the experience, right? The candidate experience, the employee experience, factoring these pilots, depending on the results, depending on the sentiment, which sentiment, in some cases, almost is almost more powerful in some organizations than the actual COVID, financial or operational results. If there’s too much noise in the system and you’re a very risk averse organization, then you might pump your brakes and say, This is not the right time, or this is not the right solution, which, again, we know this needs to be iterative. This isn’t something that auto-magically, you turn on and it just works. So how do we set expectations appropriately so that we don’t throw the reputation of even these providers into the wrong bucket. So lots of considerations there.
Rebecca Warren 24:24
Yeah, okay, so let’s, let’s flip this a little bit then. So we’ve talked about some of the risks, some of the things folks should be paying attention to. What needs to be aligned before something like this, taking on a new AI initiative vendor platform could be successful. So what key features or capabilities should leaders prioritize when they’re evaluating these AI solutions? So not just the pitfalls, but what things matter when you’re thinking about those, those AI tech opportunities? So is it integration? Is it data, accessibility, analytics, user friendly, experience, all those things. I’ll just throw that out there. But what Should folks be paying attention to when they’re thinking about evaluating AI solutions?
Bharat Daga 25:15
Yeah, so on top of like, what few things, which you mentioned? Rebecca, right? Given the nature of HR work, right? I wanted to add like one thing, which Mark mentioned earlier as well, right? About too many laws out there, right? So is your AI partner or like these models? Are they flexible enough where you can deploy them by geography or by demographics, right? Because at one side, technology is moving at 100 mile our pace, and on other side, new laws are coming up. So are these models? Are these organizations have flexibility and scalability, which basically can help you achieve the results. Right? You don’t want to be in a space where you are not able to utilize these like, what you can say, newer technology, just because they don’t have flexibility right now. Another thing, which basically is very important and close like, which we have paid attention, is that having a capability of auditing and logging, right, as any investigation comes, right? You are deploying chatbots. You are deploying, like a lot of these NLP-based case deflections and like resume coaches, right? Does the tool have the ability to give you what was the, what was the given answer to a question in a meaningful manner? Right? So those are on the outside where you can say features and user experience and other bells and whistles, which we are looking for to come stop. In my mind, yeah,
Mark Stelzner 27:05
yeah. And I would just add, you know, back to the integration callouts, you have to be able to play well with others. No, no solution can live in an independent ecosystem today. So when we look at the core investments that have been made across employee experience platforms, across general work technology, whatever you believe in relative to your preferred operating system or preferred work tech, for mail or for chat or what have you, when we think about HCM, payroll, other solutions, understanding The indicative data which solution or repository is truly the system of record, how information will flow, how you will interoperate with those systems, and understanding your role within those employee journeys is a huge architectural consideration that I think is absolutely critical, is almost a price of entry. Minimally, you have have somebody of experience, or some codification of saying, Yes, we can. And again, bi-directional APIs are optimal, et cetera, like we need to have real time flow to make this meaningful and timely. And then the flip side of this is the employee experience side.
Rebecca Warren 28:16
That’s what I was just thinking, Yes, this is candidates.
Mark Stelzner 28:19
This is employees brought, you know, with your I think you said, was it 10,000 contingent workers as well? Yes, when we look at total workforce management, because of the way that skills and talent are dynamically assembled to provision work each of those ecosystems, each of those personas, and there’s lots of variations within those personas, of course, will need to know where do I actually access this in the flow of my technical journey? So if we end up having 4700 apps that are individualized to control for these capabilities, it’s quite possible you’re just going to get lost trying to find that point of entry. But if it’s neatly threaded into journeys, or it can be exposed through the common work tech that I might use every single day. The likelihood of driving engagement getting utility is also going to be critically important. And then broad you touched on this as well, which is what level of localization and hyper-personalization are possible. So we know that, you know, theoretically, there’s endless permutations. And this is where the discoverability, the ability to sort of replicate these solutions, gets quite difficult from an audit perspective, because brought your unique individuals with unique attributes, you’re changing. I’m changing at all times. We’re not static at these So given the fact that this is so dynamic, and all those attributes are being used to present the actual responses to our questions, or the value proposition of these workflows. It’s really, really complex to try to even come up with a use case that replicates that in near real time.
Rebecca Warren 29:57
Yeah, okay, so I think one of those things that you both have touched on is, if it’s not cascaded appropriately, it’s going to fail. So one of the things that that we continue to hear too is that, you know, we talked about this too, that AI literate versus AI informed, and that is important at the evaluation level, but it’s also important for that user experience. And Mark you talked about that, right? Like, what does that look like for folks taking the fear out of it, right? We joke around here like we’re not Skynet, right? We have those logging trails, and it’s Explainable AI, but it still can be uncomfortable for folks if they don’t understand what it does, why it does it, how it does it. So I think there’s an education component as well that needs to come in at all levels of whoever is going to be using that tech or involved with it to say, here’s what it does, here’s what it doesn’t do, here’s why we’re instituting it. That education piece, I think is really helpful to drive that usage and adoption, no matter how easy user experience is, if folks are scared of it or don’t understand it, I think then it’s it’s much less likely to be adopted.
Mark Stelzner 31:19
Okay, so let’s talk about how we work with a lot of organizations that have huge hourly frontline populations and broad will probably sound familiar to you as well. The use cases that are presented by the providers in the community are meant to be enterprise. They’re meant to touch every employee, every persona, and that’s where the value generation and the ROI are theoretically calculated, but we have some basic tenets we’re seeing with just general digital accessibility among these populations, some of its device enablement, some of it is concerns around Wage and Hour compliance. So in other words, when does the workday begin and end? And therefore, if we infuse into these personal devices, this capability we have to pay our employees for this, the division between, again, contract, contingent, seasonal labor and full-time labor. So I say this to say we have some foundational dependencies that still haven’t been fixed, generally across work tech or HR tech. So then we then bring this rocket ship of AI, which brings advances and actually brings a means of actually communicating with those populations in new, very interesting ways. We are finding issues with trust, meaning that I don’t even use this enough, let’s say to trust you to install any security protocols on my personal device, because I don’t know what you’re looking at, and on the flip side, then compliance potentially saying, But wait a minute, is Does this meet local Wage and Hour compliance requirements? So what we’re finding, unfortunately, is huge swaths of these targeted populations aren’t even eligible, per se, to use some of these tools. Broad. I’m not sure if you’ve encountered that, but there’s just some foundational limitations to even getting access.
Bharat Daga 33:04
Yeah, yeah, definitely, right, yeah, we do face these type of situations, right, especially in the manufacturing side.
Rebecca Warren 33:14
Okay, so, so let’s flip a little bit here. I want to spend a minute or two, and I’m watching the time because, oh my gosh, it’s going so fast, and I have like, 8 million more things I want to talk about, so we might speed through some things, and we might take a little bit more time on others. But one of the things I wanted to call out, too, is AI is kind of that, I don’t know if we call it the buzzword, the new thing, like everyone’s talking about AI, but how can AI vendors differentiate themselves, right? Like, what are red flags that some folks should think about? Is it really AI? Is it algorithms? Is it large learning models? Like, what kind of things should folks who are evaluating this tech pay attention to when you’re thinking about true AI vendors, and if they’re not just, you know, calling a spade a spade,
Bharat Daga 34:08
I think, yeah, that, like you’ve mentioned, right? There are a lot of vendors out there, right? And one need to make sure that, like these claims, which everybody are making, right about their tech stack or about their value proposition is not vague, right? You have to basically make sure that you either read out their case studies, the projects which they have delivered, and at the same time like ask for customer references, right? Because that’s an eye opener, right? In most of the cases we we have been in many situations, some situations where customer references were definitely an eye opener for us right now. Another, which is like a kind of an overlooked thing, is that basically the support right, while the technology is top notch, right? Is this AI tech vendor? What is their support Proposition? How? What? Because it’s like love and care, right? What Mark was mentioning that there are new features which keeps coming, right, once you have built that partnership, right? You don’t want it to be in a place where the support is not lacking our needs. So those are the things which basically, definitely comes top of my mind when it comes to catching those some red flags, another one might be a hidden cost, right? It’s not only about basically licensing or implementation fee. Are there any recurring cost for these new SKUs, right? Is it like a platform which they are going to evolve and provide it to their customers as part of their your base queue or every bills and whistles will come with another carding model, and you have to subscribe to them at an additional cost.
Mark Stelzner 36:00
That’s a plus one to everything, with particular emphasis on that second “s.” That service component, I think, is really important. Where they draw the demarcation line between the employer’s responsibilities and the provider’s responsibilities that sometimes can catch you off guard. Another way to forensically uncover this is understanding the investors and the leadership. So when you look at who or whom is funding this enterprise, where their perspective is there and it’s all discoverable. I mean, whether you ask for it from the provider, or whether you do your own search using the internet, you can find out a lot about the intent and even the sustainability of an organization, meaning, are they, you know, three years into their investment hypothesis, and therefore we’re likely to see either an exit or some form of a business combination. Do their funders and who’s in their governing board, who’s in their advisory board? So the point about customer references, do we have credibility in the humans, right, that are absolutely driving the agenda, the development, the ethos of what these providers attest to be focused on just other variables that I think ran out the picture brought that you painted agree, right?
Bharat Daga 37:16
Because we have done like the financial stability check for like, majority of our vendors, right? Because you don’t want to be in a situation where, like, they’re burning the cash and you basically, like, run out of like, gas.
Rebecca Warren 37:34
And what a good call out the support piece, not just from getting the tech challenges fixed right from support tickets. But that that champion, right? You know, when I first came to Eightfold, I moved into customer success. I’ve been in ta leadership for a long time. Before that, I didn’t even know what customer success was, right? Like, I worked with some vendors who had it may be built into in the past, maybe built into the sales persons function, where then they just continue to monitor but coming to Eightfold and having a customer success organization built around championing for that customer once the implementation was over, was kind of a new thing for me. I hadn’t spent time in there, so helping to build out the customer success group here at Eightfold, I learned so much about all the things that matter, and remembering my my good and bad experiences as a practitioner, and then pulling that forward to say, how do I want to be treated right? If I were in their shoes, gets a whole different way of looking at things. Instead of just submit a ticket, done right? It’s having somebody who is proactively paying attention to your account, to saying, Hey, here’s something new that’s coming, or something to watch out for. Or we’d love to be able to, you know, have you be an early adopter to help us test this or try that? Right? You talk about the flexibility and and the can the capacity for change and for growth. Having that partner makes such a difference when you’re thinking about tech, that maybe in my day to day job, I don’t have the time to do all the research or all of the all of the vetting right after I’ve implemented a solution. So having that partner for life, not just to fix things, but also to make sure that they are, you know, keeping their customers on the edge of what they need to get done, understanding those objectives and then driving for that success as well. That’s such a great call out for that partnership.
Mark Stelzner 39:35
I love that, Rebecca, and also, I’d say what’s implied in that is understanding how they’re incentivized and measured and compensated, right? We find often that the account executive is there purely to upsell, cross sell. How many new features or capabilities can I get organization, a, b and c to buy, versus Am I measured on both objective and subjective measures, on customer satisfaction? We see so many, so many weak SLAs, I guess I would say relative to performance, where the organization ends up being a watermelon, meaning it’s all green on the outside, but it’s right in the middle. The sentiment is, we’re frustrated. We’re not getting a bunch of seeds Exactly, exactly they’re getting their way. So I just want to emphasize, I think that is critically important and a community of interest, right? So if we’re early adopters, and we’re all in this together with who or whom, is the company that I’m keeping, and therefore, are we, is there an environment of collaboration? Are we fostering a sense of community so bright you can come together with your peers and understand what they’re doing, because everyone’s innovating in different ways. And I think that knowledge sharing is just critical.
Bharat Daga 40:40
I read it somewhere right where it was, like, very fascinating that, like, the licensing model as this AI progresses, might change. Today it is based on head count. Today it is based on your demographic. Today it’s based on number of seats. In future, it might be based on the efficiency of these matrix which you bring to table that, hey, yes, we will deliver you X number of like or X percentage of business value, and that’s your licensing fee.
Mark Stelzner 41:11
There’s a risk sharing model. Basically, we have a stake in the outcome. Yeah, that’s fascinating.
Rebecca Warren 41:18
So before we switch to solutions and strategies, kind of rounding out the best practices section. Why are we talking about this right? Like AI coming in, we’ve talked a lot about pitfalls and the ways that it might make your life more challenging. There’s so many good things that come out of using AI in your day to day and let’s let’s be real. It’s embedded in so many things right now that people use that they probably don’t even realize their day to day tools, the things that they’re doing, the ways that they’re working. So I would love just to before we jump into, you know, solutions. How are leading organizations? So what you know of Mark, how is Qualcomm using it brought what? What are organizations doing to successfully leverage AI to improve efficiency, effectiveness, value, impact, all of those things. So I would love just to get a why are we even talking about this? Because, you know, what is this bringing to the table and and why are we prioritizing this? So I’d love just to hear your thoughts on why this matters.
Mark Stelzner 42:27
Our philosophy is process-led and tech-enabled, and we know the shortage of unbelievably complex, very unique, highly agile processes across the higher-to-retire life cycle, endless permutations. What sometimes we lack, though, is a global and unified point of view. So we establish, and this puts pressure on our COEs, on our expert functions, as it were, to come forward, we call it freedom in the frame, meaning come with a frame, come with a sense of yes and our point of view, but allow for the freedom for tools, technologies, populations, again, what I refer to as that hyper localization to drive the unique attributes of the application of that across the enterprise. But, but again, coming back to this notion of if we don’t have a point of view, if we haven’t established a sense of where we’re going, this North Star that we’re reaching for, and sure it can be a very circuitous route to get there. Then we start to infuse a I as a means of achieving that in a very different and in some respects, highly human way, because of the permutations that AI can derive. But from my standpoint, it all starts with establishing a really fixed in firm point of view. And as we said earlier, having those goals and objectives, there’s a lot of space in between for us to then solution and bring these opportunities to life.
Bharat Daga 43:52
Yeah? So some of the leading organization right with home, like, we are collaborating, and I wouldn’t say that, like, yeah, we are leading right. But basically the way to approach it is will be like what other organizations have done is like, think big, start small, and then scale right. What are your low hanging fruits? Right? Start with in terms of Team efficiency and collaborations, then eventually, like, moving on to operations, and then going on to like, experience and creativity, right? Yeah, we personally have enabled, like, some of the AI tech in terms of recruitment, in terms of employee engagement, in terms of like, recognition, and Now eventually, like, what, what is our overall candidate on boarding experience.
Rebecca Warren 44:46
Love it. Love it. And so I just a couple of things that I took notes on that I loved. I love freedom in the frame, right? That’s like an improv principle. Give you the guard rails and then let you do whatever is inside the guard rail. So freedom in the frame. I’m gonna have to see if that’s licensed or copyrighted, because I might just start in integrating that into my talk tracks with your provision. Mark and Brett, you’re absolutely right. We talk about however we want to say that you can’t boil the ocean. You eat the elephant one bite at a time, however we’re saying that for elephant. But as we’re saying those things, right? Think big, start small and then figure out how to scale. Is a super good way to think about it, whatever piece of AI tech that you’re trying to put in there, right? Whether it’s helping to just automate tasks, right, or if you’re thinking about workforce planning or enhancing that employee experience, you can do that in in ways that enable the organization, as opposed to scare them. So love that idea of Think big, start small, and then be able to scale. So let me, let me ask a question here, and I mentioned before, right? This doesn’t just have to be for HR ops. It could be for anybody who is thinking about integrating new technology. But there was a question that came in, which I think is really good question, is if it is making AI decisions for HR, and HR is not involved in those decisions. And I know this is a curve ball. We haven’t talked about this. Talked about this one, right? So, so what? What recommendations do you have to get HR involved in the conversation, especially when it affects the technology that HR is going to be tied to? Love to hear your thoughts on that.
Bharat Daga 46:38
Why don’t you want to take that one post. Yeah.
Mark Stelzner 46:41
I mean, what we’re finding in leading organizations is there’s dynamic teams that are highly designated, if not dedicated to these opportunities. So when we talk about sort of separating signal from noise, ensuring internal alignment, ensuring that one part of the organization isn’t advancing at a pace that the other parts, frankly, can’t keep up with. We have to have people that wake up every day and are thinking about these particular opportunities. Understandably, if you’re in the technology function, you’re you’re looking at solutions and advancements every single day. You’re also thinking about, you know, integration and capability and information security, everything that keeps you up at night. I don’t see anything wrong with it, originating opportunities for HR to validate, but to truly get HR to buy in. As we’ve said, I think a few different ways we really need high-value use cases. So if we have a tool that’s looking for a problem, it’s actually the problem that our people function intends to resolve, or equally, believes where the opportunity lies. If we don’t get that buy-in, we will get dissonance. We will see blockers as it continues to move up the executive chain, and this goes down to just general governance between the technology and HR functions, understanding clearly roles and responsibilities, decision, authority, et cetera. There should be no restrictions on innovation. Anyone who has a great idea and can bring that great idea forward, regardless of the source, should have a means by which they can bubble that up. But there needs to be a joint means by which we assess the viability, we determine the prioritization, and then we again quickly, you know, start to iterate and prove our points to outcomes. But I don’t, frankly, I don’t care, or have a preference where that idea comes from, as long as they’re strong and deep and meaningful collaboration. So it’s human conversation and interaction.
Bharat Daga 48:35
It’s a cohesive unit, right? And like, we can form an AI governance or like it can be an AI Center of Excellence, where, within organization, you have a technology partner, HR partner, finance partner, right? Who all are basically coming together and making decision which are best for our employees, right? So I do agree that, yes, an idea can originate from everywhere, but the value, the implication which it brings, right? HR has a big say in it.
Rebecca Warren 49:08
They should be knocking on the door, is what I hear you say with the idea comes from it. They need to be saying, hey, we need to be involved early and often in the conversation. So maybe it’s elevating it to your HR leadership to say, Hey, this is important to us. We want to have an understanding of what tech is coming down the road for us. So maybe it’s a proactive conversation with your HR leadership. Love the idea of an AI Council making sure that HR has, you know, a seat there asking the questions. It sounds like maybe is the is the right answer if they’re not being involved currently, and Rebecca, let’s not forget our business partnering community.
Mark Stelzner 49:46
I mean, they’re, they’ve got our ear to the business. So if we we need to have a fluid means, because if the business, if one facet of the business, intends to engage or desires to fast track a capability in service of that business as our strategic HR business partnering function. They also should be generating and bringing those ideas forward. That’s where a lot of pilots are born. Frankly, because we have a business unit or area that’s ready, we have a use case that can be derived. We have the technical enablement, and then we can bring the tooling to immediately add value to those particular stakeholders. So it’s it’s really love that.
Rebecca Warren 50:24
Okay, so we’ve been talking about innovation, talking about tech, talking about a lot of things. So when we think about broad, as you said, right? Like start, think big, start small, but also innovation, we don’t want to stifle that. So how can teams balance innovation with practicality when they’re updating their tech stacks? Like, how should they be thinking about that? And Mark, I think you called out one thing right away is like, what does that look like for a test and learn or a pilot approach? How do we find those early adopters pulling in a particular department to showcase that viability. But what are some of those things that HR leaders, or anyone who’s trying to implement new tech should be thinking about to not lose the innovation with maybe some of that limitations or practicality that the organization already has?
Bharat Daga 51:20
I’ll go quickly right on this, right? I think the first focus on user experience, right? Make sure that, like the user experience is good, right? And you are updating your text tech stack and piloting any program, right? Means, I cannot say more about it, right, that because you don’t want to be in situation where you have boil the ocean and it doesn’t, doesn’t work, right? Start with a smaller subset, and then basically scale it out, have a gradual rollout, and at the same time, leverage the data which is coming out of it to basically make the difference, right? Because that will lead away for a continuous improvement, where you will be able to review and analyze and identify areas where you have to make more improvements before you roll out the technology to the entire broader user base.
Mark Stelzner 52:17
And I agree that, you know, the strongest correlation we see is a really robust HR data and analytics function connected with these projects, programs and initiatives because there are so many different means of measuring or predicting behavior across the enterprise right now that will find prizes in unexpected areas. And it’s not necessarily the most super sexy or interesting experience where, in fact, we can start momentum. Sometimes, it’s hidden behind the scenes. Sometimes, it’s a workflow among and between processes that aren’t even employee-centered. But if we can prove that that frees up capacity and capability to focus on those higher value tasks, so I’d say work with your data and analytics team to deeply and steeply derive what the data is telling us our use cases potentially should be as well, and those are always great places.
Rebecca Warren 53:08
Yeah, and something that you all mentioned earlier too, about the flexibility of the organization that you’re bringing on board, like without your tech vendor. So what brought you, it said too, about doing those pilots or those test cases, right? We like to call that an early adopter program at Eightfold. So like, making sure that your vendor has a way if you want to be involved in some of that, that they’re continuing to innovate if they don’t have an early adopter program, like, are they really innovating? Like, what kind of things are they doing right? Are they adding new things or updating things to the platform or to what the solution is? So, looking evaluating, do they have an early adopter program? And if you can get the buy in, inside your organization, making sure you’re part of that, being able to co create, sometimes, these solutions that then go at scale. It’s pretty cool to be a part of. And I know when I was in CS, and we had some of our our folks doing that, it was super exciting to be able to co create those solutions with our with our customers. So another way to think about that, okay, sailing into the last few minutes. So I’m going to throw one more question out, and then we’re going to do our wrap up. So how should organizations think about building a strong AI foundation to make sure that they’re not going to continue to do these tech overhauls? You know, with that flexibility that we talked about, scalability, integration, what kind of things should we be thinking about to make sure that tech isn’t going to continue to be replaced on an annual or biannual basis.
Bharat Daga 54:51
First and foremost, right? I think I touched on this is that build an AI Center of Excellence, having a centralized team basically, which is overseeing all the AI initiatives across organization will help you make the right decision, right or informed decision around what what is happening with other part of the organizations. Then have clear objective and goals we already touched upon it. Data is the fuel for AI. Have a very robust data strategy, right choose your right partner, pilot and partner with the right AI expert, and monitor them to make sure that they are on the right track.
Mark Stelzner 55:32
And I’ll just, I’ll just add authoritative content. We often don’t have trusted and sustainable sources of information that both drive these processes and from which we would derive the insight to provide the value to our people, leaders, to our employees, to our contractors, our candidates. Because the content lives in a million different locations throughout the enterprise. Hasn’t been up in 15 years. There’s 14 different versions of it. So if we start to put AI capabilities against disassociated content repositories. We won’t be surprised when hallucination or misinformation is the answer. So foundationally, having a point of view on where our content will live, who will maintain that content, a robust content governance and maintenance plan. These are foundational opportunities with or without AI that many organizations are struggling with and now prioritizing, because there is such a dependency on that authoritative content to drive so many of these solutions.
Rebecca Warren 56:33
Love all of that, so I have been summarizing throughout our chat here so what they should be looking for, what folks should be looking for, flexibility, scalability, integration, potential, audit, governance, strong education and positive experience, personalization, building trust support from both a problem solving as well as strategy going forward. Love data strategy and the authoritative content is great, not just from inside your organization who’s driving that, but also from your vendor partners, right? That’s one of the reasons that the group that I’m in was created, is who’s doing the thought leadership from the vendor. What kind of things are they paying attention to, and how are we sharing that, right? So infusing that through customer success, going to the C suite saying, Hey, here’s some things to think about from a strategy level. Here are the things we’re going to work on from a, you know, the the key contacts inside of the organization. And here’s the things that we are going to, you know, make sure that work and function. So, lots to summarize there, but if you could, if folks could walk away from this webinar only doing one thing you have, like, 30 seconds a piece, if you only, if they only could do one thing. What do you want it to be? Mark, we’ll start with you, and then brought will have you wrap it may be surprising play.
Mark Stelzner 58:00
Go out and play, and because there are more employees using these tools than I think organizations give credit to. So start experimenting. Find curiosity, you know, find any generally available tool and start using it. And find the pros, cons and capabilities, and that’ll build your literacy and comfort and actually open your eyes to the breadth of capability that then you can apply to your enterprise. So I’d say, be curious and start in this, in this space, as just a person and as a consumer in any category, that will drive a tremendous amount of momentum to bringing that back to your
Bharat Daga 58:41
enterprise. Repeat what I said earlier. Was like, think big, start small and scale.
Rebecca Warren 58:47
Love it. Y’all. This has been such a great chat. I can’t believe how fast time has gone. I knew it was going to happen too halfway through, I’m like, Oh my gosh, there’s so much to cover. Great insights. Love the ideas, thoughts and expertise that you shared folks thanks so much for joining us. We’re excited to be able to come back with our talent table next month where we’re going to be talking about talent management for talent management leaders. So thanks so much and have a great day.
