Biotech Bytes: Conversations with Biotechnology / Pharmaceutical IT Leaders

AI Empowers HR to a Positive Future with Stela Lupushor

• Steve Swan • Episode 16

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Being on the cusp of technological advancements, businesses are rethinking how they manage people and processes. AI is not just a futuristic concept; it’s now reshaping human resources to optimize workplaces and enhance employee experiences.

I'm joined by Stela Lupushor, Chief-Reframer at Reframe.Work Inc. and co-author of Humans at Work and Humanizing Human Capital. Stela and I discuss optimizing work environments by matching workers to tasks and creating the most effective workplace setting. We focus on the integration of HR and IT and how their collaboration can solve organizational issues. 

Stela also shares her insights on the role of data analytics in improving employee experiences and business outcomes. We touch on the impact of AI on talent acquisition and the importance of ensuring that AI algorithms align with corporate values.

Listen to this episode to gain a deeper understanding of how technology can enhance employee experiences and business outcomes.

Specifically, this episode highlights the following themes:

  • The integration of HR and IT for solving organizational issues
  • Utilizing data analytics to optimize employee experience
  • AI's role in improving the talent acquisition process

Links from this episode:

Stela Lupushor [00:00:00]:
I think the analogous mindset is thinking about the customer experience. Right. How many market leaders were created? There were technology first, organizations that brought up customer centricity to identify what are the friction points for the customers and little by little, eliminate them in order to get to the final outcome, which is that purchasing decision. The same type of thinking needs to come to the world of work for the workers. If I as an employee, do not know what good experience looks like, how can I deliver that to my customers? The way HR typically looks at the world of experience is through the lens of HR processes.

Steve Swan [00:00:41]:
Welcome to Biotech Bytes, where we speak with technology leaders about their thoughts around current technologies and how they're going to affect our industry. I'm your host, Steve Swan, and today I have the pleasure of being joined by Stela Lupushor. She is the founder of Reframeworks. Thanks for joining me, Stela.

Stela Lupushor [00:00:57]:
Thrilled to be here, Steve.

Steve Swan [00:00:59]:
Yeah, this is great. I'm really excited about this. You know, Stela focuses mainly on, you know, works with HR departments in all different size organizations and helps them to understand artificial intelligence and where AI can help them, how to sort of, you know, based on an inver company, how to reframe it in their, in their thoughts and their strategies. So, but before we get into that, Stela, real quick, just like to hear about, you know, sort of who you are and how you got to where you are, you know, and got to this reframe work.

Stela Lupushor [00:01:28]:
Absolutely. So as many of the individuals you interact with and work with, I come from it background, right. I started in custom system development, math and computer science, and life took me on a series of pivots, a journey with multiple pivots at the time. When I graduated, the Soviet Union just fell apart. So I grew up in Moldova, the Republic of Moldova and the Soviet Union fell apart. And USAID funded a lot of the country transformation, or in general, in the region. And I ended up working on a project to privatize all the state owned enterprises and build the capital markets infrastructure for the country. And it opened up the world for me, opened up a lot of opportunities.

Stela Lupushor [00:02:18]:
From Moldova, we moved to Ukraine to do similar type of work, then to Russia to do similar type of work. And throughout the journey, I moved from custom system development into ERP implementation, then eventually moved into process improvement, service delivery transformation. I ended up in HR by chance. I had zero interest in HR, zero training, but I was sent to learn SAP HR because there was a plan to implement it at one of the customers. And that's how my world completely pivoted into the world of HR processes and I could so relate to everything. I finally realized that I found my space because I could see how technology can make a really positive impact or negative if, if you don't implement it correctly in the employee experience. And of course from there I continued on the HR consulting world mostly through the lens of technology. And meanwhile I started with price order house at that time in Moldova.

Stela Lupushor [00:03:29]:
Then I moved into price order house Coopers when they merged with Coopers and Librand. Then I ended up in IBM through the acquisition of PwC Consulting. And I was continuing to be a management consultant up until my kids were crying on my suitcases every Sunday night because I was on the road all the time. So that's when I moved to internal HRT, IBM and I continued to be working on consulting projects. Except that this time big blue was the big client, right. And I did a lot of HR IT strategy, business development, work service delivery, transformation for IBM. And closer to my part, latter part of my career at IBM, I ended up in future of work strategy and working with research labs on coming up with new innovative analytics solutions. So that's where kind of the pivot back into the analytics and math and computer science came full circle.

Stela Lupushor [00:04:22]:
And after the IBM, I ended up running people analytics function of a couple of financial institutions. So I've always been in this kind of strange space where there's no precise industry or sector description, right. It was at the intersection of technology, of analytics and employee experience. So I ended up through pivots. But I still feel that this is such a critical place that it's underserved and where technology can have a huge, huge impact in a positive way on how we as employees feel at work.

Steve Swan [00:04:59]:
So you have a big passion there. So that's what drove you into this and from analytics, right. Because when I talk to a lot of folks about AI, right, analytics or big data was sort of the precursor that got them into AI. So tell me about your pivot from analytics or big data, however you want to frame. You've been saying analytics, totally fine, into AI and how that's changed, a, how you think about it and B, where we are and where we're going. You know, I mean, that's a big.

Stela Lupushor [00:05:26]:
Question, I know, but analytics, big data, AI, you know, building models is part of the analytics world. So it wasn't something that was new and foreign that have emerged. It was part of our vocabulary for a long time. I think the difference that I see at the moment is emergence of generative AI and how that is changing a lot of fundamental activities at work, from how we describe what work is, from how we enable our workforce to use it, to consume it and be more productive, from how we need to rethink the workplace environment and what we define as productivity, or what we define as workforce composition, and the type of workers we engage and employ, and how we orchestrate work. So a lot of the traditional ways of operating from HR perspective will have to change, will also have to rethink work tech, and that there is a difference between HR tech and work tech, and to differentiate or to showcase the distinction between the two. Right. HR tech is really about automating HR processes and helping HR do its work better, faster, easier, more efficiently. Worktech is really helping people to do their work, whatever tools they spend most of the time in order to perform what they need to do.

Stela Lupushor [00:06:53]:
That's part of the worktech. And it's not just the work tools, but also the collaboration tool, the project management tools, the infrastructure that we put in place for people to be able to do what they need to do. So analytics and a lot of the work that previously was focused on helping HR make better decisions have naturally progressed into this worktech space. How can we look at the whole worker experience and identify different friction points through technologies, through data, through understanding of what they do, and then try to reimagine or reframe how that work gets done? Because at the end of the day, people ultimately, right, they're not intentionally or maliciously don't want to be effective or efficient. Right. It's the infrastructure and the context in which they work that gets in the way. And sometimes our technology is at the core of our issues, right. You may have devices that have not been upgraded, you have tools that do not integrate and do not talk to each other.

Stela Lupushor [00:07:59]:
You have processes that are designed to be followed, and then you have checkers upon checkers to make sure that that process gets executed, not that the outcome gets accomplished. So there's a lot of opportunities to use technology and use analytics and now use some of the AI informed decisions to identify where those changes can occur. And how will you measure the improvements over time? To me, AI is such a fascinating space. I have also a 22 year old son who is knee deep into learning linear algebra and trying to get ready for the world where AGI is a reality. So I can see on my children the impact it can have on their career, their interest in the type of work that they want to do. I have an oldest daughter who builds conversational aih. So the topic of AI is a dinner, dinner table conversation. De to me, bringing it back into the work environment and into the HR vocabulary is really, really important and timely.

Steve Swan [00:09:14]:
So let's just make this scenario so me as a chief HR human resources officer, my interest in this would be to create a holistic, analytical view of the folks in my organization. Understand, like you using your term, the friction points, right? From a technology perspective, from a process perspective, maybe even from an interaction perspective, right. From a manager or employees, that's probably a little harder to measure, but, you know, so that me as a chief human resources officer can now look at all this data, can look at these reports, somehow make sense to them and say, okay, these are some of the points. This is a common friction point for, I don't know, these 300 folks or 100 folks or 50 folks. We need to tweak this or adjust this. This is a high point. So we got to push this or do more of this. Am I thinking about that right?

Stela Lupushor [00:10:07]:
Absolutely, you're right on. I think the analogous mindset is thinking about the customer experience. How many leaders, market leaders, were created. There were technology first, organizations that brought up customer centricity to identify what are the friction points for the customers, and little by little eliminate them in order to get to the final outcome, which is the purchasing decision ultimately right for people to buy the product and build loyalty over time and continue to stay with the brand. The same type of thinking needs to come to the world of work for the workers. If I as an employee do not know what good experience looks like, how can I deliver that to my customers? The way HR typically looks at the world of experience is through the lens of HR processes. We look at recruitment process, we look at onboarding, we look at benefits and payrolls. We look at time management, performance management, you name it.

Stela Lupushor [00:11:12]:
People don't want to spend time in those processes. They want to spend as little time as possible in HR processes such that they can focus on the work. So can we look at what a journey of a worker is from the point where they do not know anything about your company until they discover it, until they decide that they want to come and bring their talent to your organization, until they go through the application process and get the job offer or nothing. All of those little touch points leave an impression, even when they become an employee. And then they move through onboarding, payrolling, benefiting, getting the work done, getting promoted, getting developed, or not getting to leave the company. Even after they leave the company, they still preserve a relationship. They may continue to be a shareholder, they may recommend their children to come work or not. All of that shapes the impression and the relationship between the individual and the organization as an employment brand.

Stela Lupushor [00:12:23]:
And we now have access to a lot more data to make decisions and observe what the journey looks like. And it's relatively simple from analytics perspective, as conceptual framework, as an organization, we can start with the outcomes that matter. What is it that we want to attain? A higher revenue growth, great relationship with our customers, external employment, reputation, brand, lower risks of regulatory, whatever it is that we want to accomplish as a company. Can we look at the entire lifecycle of our employees and understand where's the earliest intervention point to make a positive impact? Let me give an example. Right? When we think about attrition, it matters because it's the not cost, it's reputation, it's talent that leaves the door. But attrition doesn't happen overnight, right? It happens in little paper cuts. People quit in paper cuts. When is the earliest one that happens? Can we look at the data and say, where do we see the relationship between, I don't know, the number of managers and the likelihood of people quitting? Can we see a relationship between the number of training hours a person gets and their ability to deliver on the customer satisfaction? There are a lot of little signals that you can start now detecting in that massive amount of data and link those outcomes to the business outcomes that matters.

Stela Lupushor [00:13:51]:
And then from HR perspective, you'll have a very different conversation with your finance or it, or whatever business leaders that you want to talk to build that case for change and investment in really meaningful interventions.

Steve Swan [00:14:04]:
Yeah. And I was around the office and around my house. One of my big saying is, what's our downside? Right? So when you look at this, you say to yourself, what's my downsides? Or on the flip side of that, what's my roi? And you just said right there, I mean, your return is individualized, right, per company. I mean, like you said, I may want brand recognition, I may want longevity in employees, I may want customer experience, I may want brand loyalty. You know, I don't know what my product is. Right? So it's just, it's going to be different for each person or each company, rather, just like it would for each person. And the return is going to be based on whatever metric they want to be their measurable metric that they can say, this is what we want. You know, I can see another one, though, and I'm sure maybe this has come up, maybe this has.

Steve Swan [00:14:50]:
My head went to this as we were talking. Let's just say I'm the person responsible for bring your own device. I'm making this up, right? And the big friction point in my company in this data is BYOD. Bring your own device. Now all of a sudden I'm on the hot seat because I don't know, you know, and am I trying to hit the lead on some of this data? I mean, what am I going to do? You know, I guess I'm in the hot seat and it's for a reason, right? That's, that's why we did this. But I don't know that you have an answer for that. But that was just where my head went when I was thinking about all this. You know, it's going to, it's going to smoke out some the weaknesses, which it's supposed to do, but if it's a person, as opposed to a process, that's a tough one.

Stela Lupushor [00:15:31]:
You know, as somebody who grew up in it, I look at everything in the world as a line of code. Every problem can be solved through a line of code, right? We may look at some of these technical issues as a risk, but there are all this technical solutions to a lot of this, right? It's not that, you know, we don't have the right behavior in the company. Maybe we don't have the right type of security infrastructure put in place, or we may not have the right type of reward structures to motivate the right type of behavior. And back to my earlier point, right, people don't intentionally. Most of the people do not have a malicious intent or I, they are just naturally reacting to the context in which they work. If we're motivating certain behavior, that's what they're going to do. So how might we look at this experience as a system and look at it holistically? Like, what are the triggers, the rewards, the risks, the punishments that we have in place that will shape the behavior of people in a certain way? I remember one of the projects that we've done was the customer support center. And you would see significant attrition.

Stela Lupushor [00:16:46]:
You would see people leaving the company before or people becoming managers before they were ready, just because it was such a huge attrition that perpetuated the attrition of everybody else who had come behind them, that impacted the customer satisfaction ultimately and led to revenue impact. And when you start looking through the lens of the workers and following them from the moment when they get up until they get stuck in traffic, until they come to work, until they have a fixed spot and they cannot move around physically, they have fixed schedule, and they cannot go to the restroom to have their breaks when they need, but only when they're scheduled. A lot of that stuff starts making sense. Like, of course, of course you need to give them headphones so they can move around, don't tether them to a desk. Leave the manager to make decision on how to reallocate the schedule. A lot of those little, small, simple interventions can have a lasting impact on that retention, on the ability for people to feel that somebody cares about their day to day basic stuff. So a lot of times we miss the opportunity to do that examination and look and follow people around and understand what it's like to be in your shoes. And how might we make small tweaks to that experience? Because a lot of these may not necessarily be too expensive.

Stela Lupushor [00:18:18]:
And sometimes they do not require a big transformation project. They just make little, small changes to your day to day workflow that will completely change how you feel about the organization and how productive you will be.

Steve Swan [00:18:33]:
It's amazing how what you're doing is you're taking an art, which is hr, and you're applying your science, which is AI. Line of code can solve each problem and putting them together. And it's. To me, I don't know, I get excited about it. It's just because in my business, I do think that everything can be boiled down and analyzed on a micro level to see where things were missed or where they were hit. You know, I'm pretty meticulous. And when I go through my whole interview process of both company and individuals to make sure that I'm, you got to get those drivers right. You got to, you know, we.

Steve Swan [00:19:11]:
There's so many different color cars and so many different options because different things mean different things to people, right? Whether it's. Whether it's lunch, whether it's the color of the tile and the place, whether it's a. The color of my desk or the size of the phone, I don't know. You know what I mean? It's all sorts of different things. So to boil all those little things down means a ton. And the fact that you're adding that science to this, this art is just awesome. It's got to be difficult with all that data, though, coming in. And what's the good data and what's the bad data, right?

Stela Lupushor [00:19:45]:
Well, there are couple of ways to think about it, right? There are data that has to be precise. You know, data where you make decisions about people pay, where you make decisions about people's career. Those matter, to be precise. And then there's tons of data where you can have good accuracy, decent accuracy, but it don't have to be hundred percent right. That will give you directionally correct answers and will help you make informed decisions, not just based on the gut feel. So differentiating between those two is important. The other part is we put a lot of the measurements in one big bucket without necessarily kind of thinking, why do we even need some of these? And are they necessary? Are they, do they have the right type of database to make the right type of answers? Or is it just an effort? And just because we have the data, we're going to measure something, but it's not meaningful in the grand scheme of things in the running business. So the way I try to differentiate the types of metrics that organization pursue is in three big buckets.

Stela Lupushor [00:21:04]:
One is business outcomes that we talked earlier. What is it that the company cares about and matters to the, to the organizational success experience measurements. And those are the data across the employee lifecycle understanding, what are those moments that matter, be that big moments or little ones, that make a big impact? Paper cuts. And then what are the program HR program measurements to make sure that those interventions to improve the employee experience yield the right type of business outcomes. So now you have a trifecta of the types of data that allow you to continuously have this sense and response system because you say, okay, we see a spike in attrition. Well, let's look at the life cycle. Where can we make an intervention and then what type of program we need to put in place to execute on that intervention? It's relatively simple.

Steve Swan [00:22:05]:
Now, do most companies have all this data?

Stela Lupushor [00:22:07]:
I mean, increasingly, I think a lot of large companies invest in their data and infrastructure, and they've been on a journey of adopting people analytics for quite some time. So I think, yeah, a lot of the maturity is there. I think smaller companies may not necessarily have the HR doesn't have big data in those organizations. They have biggest data problems. They don't have a big data access.

Steve Swan [00:22:34]:
Jeff, I think it's great because, you know, I look at resumes all day long, right? And the stints at some of these companies is getting shorter and shorter. You know, back in the day, which, you know, I remember, I mean, my wife's been in her company 37 years. You know, you don't see that anymore, right? You know, two, three years tops. You know, I have a, I have a 22 year old and I have a 24 year old, you know, and a couple years in, I mean, all their friends are jumping around. I'm like, really? Wow. That's just the way it goes. But there's something there, right? So maybe it's a. It's solvable, you know?

Stela Lupushor [00:23:07]:
So my favorite question is, how might we, or what if we can make an assumption that people are not going to stay for a long time? Why is that a problem? Is it a problem that our processes are designed to make people productive in a very long time? It takes two years to bring somebody to productive level. How might we change the processes such that people don't need two years to be able to integrate and be performing at the top level? Right. So sometimes asking yourself that question can open up a whole new world of opportunities, and it's like, well, doesn't matter. You can stay or you can leave, because we've designed the process in a way that allows us to not miss a bit and allows you to be productive from day one.

Steve Swan [00:24:00]:
Right? And earlier you said not going off a gut feeling, but going off the data. This whole thing just, I watch sports, baseball in particular. My 21 year old, who's doing data science and a lot of the statistics, likes baseball as well. She likes for the statistics and everything. But, you know, with all the analytics in these sports, it's the same thing. They're not going off gut feel anymore either. It's really the same, almost the same mindset. Right.

Steve Swan [00:24:25]:
You know, if, if on a Tuesday night game facing a lefty, you know, that throws 93 miles an hour, swan Batten righty, he does well that night. So put him in there, you know, and you know what I mean? It's just looking at all those numbers, you're not, you don't have to go off or you're not going off gut feel. You're setting yourself up for. For more success. But then again, we go back to whatever your metric is for success that's different for each organization or boss or whatever you want to call it, right? And I can only imagine this just getting bigger and bigger and bigger, what you're doing.

Stela Lupushor [00:24:59]:
So one other layer on top of that. So what you're describing is the moneyball, right? We all read, or, and that was something that inspired a lot of the people, analytics people, too, on how can you quantify a lot of things that are intangible or difficult to quantify? A lot of stuff HR does. Is that right? There are a lot of intangible stuff. The other interesting thing that you were just kind of alluding to is how different the outcomes are depending on what the company is and the stage and the type of impact it wants to make similarly for the employees, what they expect from the work environment is different. Their expectation for employment value that they get for trading off their time and their talent and their interest for spending time with the company differs. And it's driven by the life stage you are at, is driven by the upbringing and the skill. You want to develop an ambition and kind of aspiration. You may want to come to work for attaining that title or, I know, getting fairly paid.

Stela Lupushor [00:26:12]:
Some of the fundamental things that are very consistent across everybody. You know, we want to work for a company that treats us well, that is ethical and complies with the law, and has accessible work environment. But then the more we go up the employment values pyramid, the more complicated it gets. Some people want to just come and do their work and leave at 05:00 so they can have other pursuits that they want to accomplish. Some people want to leave a legacy. Some people want to just have fun. Some people want a taste of transcendence, right? It gets very complex on the individual basis too, to figure out what is it that I want from this relationship with the organization. And then more importantly, for the organization to figure out what to give.

Stela Lupushor [00:27:01]:
And the challenge is really in deciding on what is the company for? What is it going to accomplish on behalf of its employee, what is going to be included. Employment value proposition. Make very deliberate choices and saying, okay, you may not get top salary here or you may not expect to have a lifelong career here, but whatever you do, you'll be the best prepared for your next job and the job after next. It can be just one simple development investment strategy that the company decides on. And then all the other decisions about processes that they put in place, reward structures, tools they give, everything is aligned to that employment value. That makes it very simple then to communicate that message and attract the people who are compelled by that employment value. So it's really thinking really deliberately about the employment brand that you want to position and then figuring out how you can embed that into everything you do such that you are attracting the type of talent that wants to be part of that.

Steve Swan [00:28:15]:
Right. Well, you know, that goes to, again, I'm throwing me back into the equation here, but I'm thinking about what you do, how you do it, and what we're talking about. And then I got to kind of put on my own lens, like, you know, because that's what I know, right? I'm my own data set, right? And so when I think about, you know, like I mentioned earlier, you know, looking at companies, drivers and individuals, drivers and getting them together. When I start working with an organization, you know, when I'm working with your company, make it up right. You're a thousand person company, and you're doing some hiring. Right? I get to know that organization. I get to know the personality. I get to know what their organizational drivers are.

Steve Swan [00:28:57]:
I've worked with several companies where I placed 15. One company, I placed 35. Another one, I've placed 70 folks at, you know, including their heads of it and all this stuff. Right. But I've gotten to know the organization, so I know, you know, that's. That's half, three quarters of the battle. You know, I can. I can interview somebody on the phone three minutes into it.

Steve Swan [00:29:15]:
This company would not going to work. This company would be great for you, you know, or vice versa. And I don't want to set anybody up for failure. Right. So, you know, understanding that what they stand for, whether they know it or not. I'll be honest with you, Stela, some of them don't know it. You know, that this is the kind of individual that they need or want or covet. I'm not quite sure how you want to frame that, but just the ones that actually will be successful in that organization is this kind of person, you know, and they won't do well at this company.

Steve Swan [00:29:43]:
So I get to know the personality. But I guess, you know, for them to look in the mirror and understand who. Who they are and what they want and what they're. What they're. What they consider, take it all the way to the end zone for you. What they consider ROI on the investment to Stela is different for everybody. And sometimes they might not even know it. So that's got to be a tough one for you.

Stela Lupushor [00:30:03]:
You know, Steve, I love what you just said because it points to. It brings us back to the AI conversation, right? You have a lot of tools that now are being used to identify and source candidates and then select them and then assess their fit to the company. And a lot of that is trained by vast amount of data tailored to the company's culture. Looking at what the successful profile would be for that company, what you're describing is human intelligence. It's not the artificial one. So you've used all of your experience, and while you may think about it as an art, it's an intuition, it's based on a lot of evidence. In your experience, you have a lifelong experience of understanding the best match between the individual and the culture of the company and what's going to work. So while you may think this is a gut feeling you have a lot of data at your disposal to make that decision and make that recommendation.

Stela Lupushor [00:31:10]:
I think the problem with AI doing the same is, and while it increases a lot of efficiency, especially for large companies that have a lot of applicants, have a lot of openings that they need to sort through the complexity is what type of data have you used to train that algorithm to make sure that, back to your point, you introduce certain mutations, because sometimes you need the different type of profile to create that little steer into the different direction that traditional, from the traditional direction. You need sometimes to examine whether the world you're going into is representative of the world that this past data describes. Sometimes you may need very different type of skill and change what success looks like or successful profile in that future would look like. You may need to eliminate the bias because the past decisions that you've made may have been very biased towards certain underprivileged groups. So the risk of using a lot of the AI in the context of employment decision making is significantly higher. And we all know, with some of the positions coming from Washington, DC, or from EEOC or from other regulatory bodies, is that the tool that you're using is not going to be necessarily held accountable, is the organization that is using that tool will be held accountable. So you really need to have the right type of knowledge, the right type of questions. You need to prepare your HR to be part of that conversation in order to make the right type of decisions and question some of these tools and selection algorithms that you're deploying to choose whoever is going to be the next talent.

Steve Swan [00:32:53]:
Well, one of my CIO's that I spoke to on a previous episode, one of the things he talks about, whether he got into it specifically on that episode or not, I think he did definitely on the side. When he and I have spoken, he mentioned to talk to me about how, you know, he was using chatbots specifically. But it goes into this, you know, it is an employee. So as you're going through and doing your validation, your testing of that particular system, use the same checks and balances that you would for an employee. You know, if an employee would say this, then that's fine, and that's okay with you. If an employee wouldn't say this, or they would get fired for that, you're on the hook. It's chat GPT, or whoever it is, a chatbot. And you've got, you know, weed that out.

Steve Swan [00:33:35]:
Same in the selection process, right? You know, you've got to figure out which direction it's going on something, and you got to tweak your algorithm. It's going to take a long, long time to do all that, because data is the gasoline that fuels that engine. You can build the cool, greatest AI, you can build the most awesome, sexiest tool. But if that data and the tweaking of that algorithm isn't correct or in line with your corporate values and such, it's going to be. It's going to come back to you, you know?

Stela Lupushor [00:34:04]:
Absolutely. Absolutely.

Steve Swan [00:34:06]:
So anyway, well, good. So I think we covered quite a bit there. Is there anything more that you'd want to get into as far as AI and HR that we didn't hit on? Anything more that you think we should get into or cover?

Stela Lupushor [00:34:24]:
I would love to. Since the audience of this podcast, it's quite technical. I would love to encourage or share a framework, a way to reframe the opportunity for us as technologists, we typically think through the world of jobs. And then I, we have a job description that was, you know, I don't know, for the past 1015 years, been updated with a few more technologies sprinkled on top of it, but usually it's a very old construct, and then we use that as a way to filter people out, as opposed to filter them in. So the question that I would like to pose to is, how might we change this mindset that we can open up opportunities for a lot more type of talent? We diversified the talent pools we tap into and we lowered the cost, ultimately, because you may not need to pay a lot of money for unicorns that would match those job descriptions. Right. And one way technology can enable this new type of thinking is to look at it as an optimization problem. Right.

Stela Lupushor [00:35:34]:
If we don't look at the job as a job description, instead we look at it as work. What is the work that needs to get done? What are the characteristics? Does it have a, you know, physical component? It needs to be done in person. Does it have a certain price point in the market? It's really a data, you know, problem. You deconstruct a lot of the workflow activities into units that you can then quantify and describe. Then you look at the worker mix that can get that work done, and that could be your regular employees, that could be your contingent workers, that can be a team of people that can get the work done, that can be a mix of human and digital workers that can do the work. And you then do the adequate matching of that work unit to the mix of workers who can do the work in the most optimal way, then you can start thinking about the work environment, the workplace, and not looking at it through the lens of, well, this is a physical office and this is your digital tech stack. Think of it as a workplace that can be the office, that can be your home, work environment that can be a coffee shop, that can be in a virtual reality. What is the most optimal workplace to get that work done by those mix of workers? And then the last piece is, what is the worth, the value exchange that motivates this work being done by those workers in this workplace? How can we set up the right type of rewards and incentives for that work to be done in the most optimal way? So at the core, you have these four elements, work, workforce, workplace, and worth, that you can optimize the mix and match of those elements in a way that delivers on the business outcomes.

Stela Lupushor [00:37:30]:
I know this is a little bit of a very different way of thinking about the work environment, but if there's a group that would have the highest likelihood of solving this problem, it people are it. Because really, it's a data, it's a technology, it's a different mindset. And I think this is where the next opportunity for organizations to really unleash a more value and unleash their talent to solve bigger problems.

Steve Swan [00:37:57]:
I had one, and I don't need you to comment on this, I just need to throw it up because you just kind of started going there. I had one of my CIO's offline who was talking to me about, in his opinion, HR, and it should kind of be one, you know, because they can, they can solve each other's issues, you know, because they're always kind of, I don't want to say pointing fingers, but, you know, kind of going back and forth, you know, and if they were one, like you just said, you know, it folks can solve these problems for HR and make the whole organization a better place. And I'm sure that's where he's coming from because he was talking about elm employee lifecycle management and so on and so forth, you know, all that stuff. So he was of the same belief, or, I don't know if, I didn't mean to put that word in your mouth, he was echoing the same thing. I'm sorry.

Stela Lupushor [00:38:44]:
It's complicated to put the two of them together, but they have to work to together in a lot tighter fashion. I found from analytics perspective, a lot more success working with IT leaders on one side, they have the technology, they have the investments, they have the need, and they have the technical expertise to solve a lot of issues. So they usually would lead the pack in terms of innovation when it comes to data utilization. So my experience of working with IT leaders was all, these are the people I want to hang out with.

Steve Swan [00:39:17]:
Well, that's where you came from, right? So you get it.

Stela Lupushor [00:39:19]:
That's where I came from. That's true. But they need to work a lot closer with HR. Of course, certain areas of HR are quite sensitive and the data impact is a lot more significant than risks and exposure and compliance and retention. There's a lot of it elements to that. I think there are certain nuances of IT management that continue to veer into the world of HR. So we've seen a lot of agile adoption. Right.

Stela Lupushor [00:39:48]:
Necessarily do the projects in waterfall fashion. Right. They're embracing this HR methodology, they're embracing design thinking, which is part of a lot of the IT development. They're embracing the data. So we could already see some of this spillover happening. It was interesting. One of the case studies I looked at was Vistaprint. That's where they had a lot of the HR transformation work done.

Stela Lupushor [00:40:11]:
It was hosted or reported into it. And there will still be HR recruiters, there will still be compensation people. But the people who would help transform the processes and change that experience, they report it into the it because it had both the budget, the skills, and the capabilities and technology to make those interventions in a meaningful way. So I think that's a perfect model that could have a significant impact on the employee experience in general.

Steve Swan [00:40:40]:
Interesting. Wow. Okay. So some folks are starting to go there or at least rethink the whole paradigm. Interesting. That's pretty cool stuff. I just love it, you know, the intersection of HR and it and data and analytics. Right.

Steve Swan [00:40:54]:
You know?

Stela Lupushor [00:40:55]:
Yep, that's my sweet spot.

Steve Swan [00:40:56]:
That is your sweet spot. I know, I know. So that's great. Well, good. Awesome. Well, so thanks for being with us today on Biotech Bytes. If you watched any of my episodes, I always throw out one last question to all my guests, and you may have seen them. I don't know if you have or have not, but I like asking everybody about music, live music, if you've ever, I don't know if you knew this was coming or not, but favorite live band, if you've ever seen one, if you haven't, that's fine too, you know, whatever.

Steve Swan [00:41:27]:
But anything you can point to that you could say, hey, you know, yeah, that was my favorite live concert, or that's my favorite I band or live band.

Stela Lupushor [00:41:36]:
So I love live performances and I am a big fan of U two. So I've been to several of their concerts, but the one that really struck me was 2016 Desert Trip, which was a big performance in the same place where Coachella takes place. And it was a weekend long, uh, experience, you know, Paul McCartney and, you know, you name it. So that was like a big lineup of incredible performers, and it was an incredible also experience for anyone who attended because you, you just were part of. Of that common living and breathing and.

Steve Swan [00:42:20]:
Yeah, yeah.

Stela Lupushor [00:42:22]:
Organism of. Of people who enjoy music, live music. So that was quite a memorable experience. Yes.

Steve Swan [00:42:29]:
Did you make it to the sphere to see you two?

Stela Lupushor [00:42:32]:
No, I didn't. Unfortunately, I missed that one.

Steve Swan [00:42:36]:
I didn't see you two in the sphere, but the sphere is pretty cool stuff, so. Yeah.

Stela Lupushor [00:42:40]:
Oh, my gosh. That's as a whole talk about technology impacting our experience. Those immersive, bringing people together in such a experiential way. It's incredible.

Steve Swan [00:42:54]:
Well, that's it, right? Everybody's having. Seeing the same experience right inside the sphere or Coachella or whatever, right? But everybody has a different interpretation. Just like employees, right, with their employee lifecycle and all that stuff, you know, how are they interpreting this and what data gives this outcome or that, you know, whatever.

Stela Lupushor [00:43:14]:
So anyway, brilliant, brilliant way to bring it back together.

Steve Swan [00:43:18]:
Cool. Well, thank you very much. I appreciate your time. Wonderful having you on. Maybe we'll have you on at another point too. Okay.

Stela Lupushor [00:43:26]:
Would love to. Thank you so much for the opportunity.

Steve Swan [00:43:28]:
Thank you.