How AI Can Resurrect “Dead Data” in the Workplace – Digital CxO Podcast EP105

Hello, and welcome to the digital CXO podcast. I'm Amanda Ani. I'm excited to be here today with the Work Leap, co-founder and chief Innovation Officer.

Gr, how are you doing today? I'm doing good. Uh, thanks for having me.

Can you share a little bit about Work Leap? What does your company provide? Yeah, that's for sure.

So, uh, work Leap. Uh, we are a, um, SaaS company. So basically we do software to help, uh, organization to, uh, build a better employee experience and to make work simpler.

So, uh, or software or services, uh, goes all around the employee experience. So from the first day someone is hired, we help them become productive from day one. And then we do, uh, everything around engagement and recognition.

So we, um, analyzing, um, employee engagement, making sure that people recognize themselves, performance management as well. So making sure that, uh, everyone stays, uh, on top of their game, uh, uh, all along the journey with the company. And, uh, we have various, uh, and we have the learning, uh, aspect too.

So helping every people to keep, uh, developing themselves during their, uh, lifecycle with the business. So it's, uh, it's large. We have a lot of different product and in place, but, uh, this is something that, um, we see as mostly small and medium mid market type of businesses are looking for.

And to be a good, let's say, uh, number, we, we could, we have to call ourself the number one, add, add-on, on top of your, let's say, basic HR system. So payroll benefits and stuff. So we are the, uh, the all-in-one for everything else to create a good employee experience.

Great. Thanks for sharing. Well, that means you're the person to talk to about our topic today, which is vanishing workplace data.

And, uh, HR is losing a lot of data about its employees and you feel that, uh, this is a problem 'cause there's a lot of great information there that could help companies. So can you share a little bit about that and what you're seeing? Yeah, for sure.

So, um, it's fun because, uh, most of the time I've meet, uh, I meet with HR people, so, uh, the, um, uh, people that, uh, that, that is strictly focused on people. And now, uh, I know on the pod is more on around IT professional. I have a dual background, so I am an it, well, well, an engineer, uh, um, from, uh, from my training.

And I've worked a lot, a lot in, in, in that field. But then from, for the last 10 year, I've been building stuff for HR people. So I have a, a specific view, specific view on the topic.

So the way I'm seeing it is that, uh, since hybrid work or remote work for some organization started a couple of years back. So, um, our digital footprint just grow, grew a year over year. So just, uh, as a, a key stat, let's say.

So we see that, uh, around 300 million terabytes of data is created every day, uh, in, um, in organization. So that's a lot of, um, of data. Uh, this thing or this context makes it really harder to manage your people to get a better sense of how they, they are doing online.

They are not always at the office. You don't have all of these small, small, uh, habits or small, uh, things that people does, uh, as a manager and, and presents with your team, let's say, and things like that. So the, the, the job of knowing your people, uh, managing your people, uh, managing performance of your people became really harder with all of these data.

So second revolution is obviously ai. I won't be the first talking about that in, uh, this year in the, in the, in the, in the upcoming episode. But AI is a good way to analyze a lot of data and get a sense out of it.

So what we do or what we are, uh, working on is how can we, um, analyze all of these, the digital footprint of every worker. Obviously we specialize with knowledge worker, so people working on the computer that don't, don't necessarily, it's not necessarily true for every type of worker, but let's say for knowledge worker, may you the audience right now. So people that is interested in that, like how can we look at all of these, uh, documents, this data and get a sense of what's going on in the business?

So from, uh, the employee from the first day someone came in to the last day, let's say, what happened, um, and, uh, and and so forth. So the goal there is to connect to all your, um, your, um, your internal system. And then with ai, we analyze everything.

We get some insights around people, like employees, their experience, how they collaborate with each other and everything. And then, uh, get some insights and take action on that. So for company leaders who want to implement AI to get these better data insights, what is step one?

Yeah, that's a, that's a great question and this is something that I've been working a lot, uh, internally as well at work. So, uh, I think that the, the good first step to really, um, leverage ai, uh, to, to get some insights for your, your teams is really, so I, I I will just explain what, what I'm currently doing, and I think it'll help people to understand. So basically the goal is to have like a clean, clean sources of data.

And we know it's not that easy for, uh, every business. So Wordle is a 18 year business. Uh, we grew, let's say we had, we had two acquisition the past two years.

Every time you, you change a business, you have new people, new systems coming in and things like that. So just a basic example. So we have let, let's say five CRMs inside the business as of today.

So if you have five CRMs, the data is a little bit scattered in every system, and it's really hard to, to to, to get a good sense of what's going on. And then ultimately the goal would be to automate stuff based on the these data. So, uh, the first step that, uh, that I'm, uh, that I always suggest is to clean up your mess or clean up your, uh, your data, data, uh, system or data architecture.

So consolidating your internal system, uh, making sure, uh, put, put some processes in place to make sure that the data is, uh, actually good because it's not, because you only have one system that that data is good. So making sure that you, you put some processes in place to, uh, to help having like a good quality of data. Uh, so setting up your business to have a good data quality and then, uh, implementing solution, uh, like work leap on top of that would be way better.

Obviously there is also some product that can help you, uh, do that, but, uh, this is definitely step one would be to, yeah, clean up your mess. But I, I see it in, uh, with a smile in the sense that it'll always, it will never be perfect, but at least having a, a clear guidelines on how you want to have your data set up. And then it'll be way more easier to, um, to set up some tech on top of that.

Okay. And so then once they've started this process and they're in and they're in that, um, integration phase, what are some things you've seen companies struggle with during that implementation phase? Yeah, I think, uh, like, like we just talked, uh, I think the data quality is definitely the, uh, the ultimate things.

We've been talking about it like for 10 years, 20 years, you probably had some podcasts on that subject, uh, a couple years back and things like that. But I think it's, uh, and this type of project is always hard to, to, to get funded in the sense that, okay, I have a big, uh, a million dollar project to clean up, like data and all of the system, things like that. So it's, it's really hard to, to get, uh, something.

But I think the, the yield, what we need to remember is before that it was mostly for, uh, analyzing data. So having some good report or, uh, operational report around like the data that you have. But now with ai, you'll actually be able to automate and take real decision and apply those decision live.

So let's say, let's say it's a million dollar investment, so you'll be able to, uh, automate stuff that will probably save you a million dollar in the future. And we see, we see this, uh, this trend, uh, coming in and a lot of specific, uh, vertical, let's say customer success is a good example of that. Uh, we see, uh, support center, um, starting to, um, to, to analyze, let's say support the tickets and everything and be able to, uh, answer the customer automatically having better response.

And so it's faster. So it's good for the customer, it's good for your business because you, you don't have, you don't need to do all that repetitive, uh, work. And I, I think it's also good for the customer in the sense that they, they have a, a, um, faster response.

And when you escalate the thing, so basically for people that is working behind the these AI thing, they can actually take the time to look at the solution and find like, okay, what's, what's going on there? So that's one vertical. Uh, on, on our side, we work mostly with the employees, but, uh, this is a good parallel to do, and this is type of thing that, uh, I think we'll see in various, um, type of worker departments in the upcoming years.

Mm-hmm. So for the future, AI is advancing rapidly. What do you see for HR and the workplace that you envision AI being used for employee, uh, data retention or anything?

Yep. Uh, so I think I will, I will start with the most obvious thing and one of my favorite topic as well. So performance reviews, performance management.

This is, uh, in my opinion, it's one example, but this is, I think the, the one that will speak to everyone here. So doing performance reviews, performance management, it's not that fun and it's pretty hard, uh, a couple of things around that subject. So, uh, when I say that it's, it's hard, it's like, okay, you need to take, you need to gather some information about, normally, uh, companies do the 360 feedback.

So basically they ask feedback for, um, the individual, the employee, the some peers and the manager. Then the manager takes all of that feedback, try to get a sense of it, ask follow up question, and try to, to make his mind on that. The manager does a review, the review if is valued by the organization in the big scheme of thing.

And then you meet your, with your employee to, to discuss those results. And, uh, and that, so this is a process where a lot of pieces of information and a lot of, uh, some bias as well, but a lot of, um, potential error, uh, can happen during the analysis. And obviously the ultimate, uh, outcome of that is most of the time a salary review for the employee, which is one or the most important things for every employee.

So the whole process designed to review a salary, it's, it's kinda critical to most our, our organization. So I believe AI and, um, having access to, uh, the, the, the full digital full footprint of each employee will help benefit this process in a lot of ways, let's say will be able to, uh, get facts around people rather than impressions. Uh, we be able to do, um, a year, let's say a 12 month history instead of, uh, uh, relying on your memory, uh, of let's say a couple of weeks, a month max.

So basically you'll be able to have a full picture of, uh, the timeframe of your, uh, performance management. You as a manager, when you manage 5, 10, 15 people, and then you have, let's say a hundred feedback to analyze and digest and things like that, it takes a couple of hours per person if you want to do the, the job properly. But with ai, uh, AI is very good at looking at the different point of view from employees to, uh, to peers, to manager find the problem, the difference, the difference with the, the rest of the team.

So, uh, being able to, to access like these insights instantly will really save the problem where will, will really save some time to the managers. And ultimately, and I think this is the most important part. So when you meet an employee with all the work that you did, I think the discussion, this is where you can have the most impact on your business, like having a, a good performance discussion at the end of the day and, and adding the backup and the tools and everything to support that discussion.

This is where like, uh, employees, uh, can, will either that go against the business, but either be mad or happy and move forward and help your business to, uh, to grow. So this is one example. I could reapply, like the same type of logic for employee engagement, recognition, uh, learning experience, things like that.

But this is one thing that, uh, I truly believe that can be completely change with AI and data. Wonderful. Well, if there was one key takeaway you could leave our audience with today, what would that be?

Yeah, that, that's a great question. Um, I would say that, um, I think that the business in the future will drive if you start like looking at the best, uh, the best way to change the way you operate and you, you do your business today. So I think it's, it's more like, I don't want to be that, that a, a bad profit, but like the, uh, I, I feel like business that will drive in the future are the one that are get, are getting started to really change how the work today.

So this is, uh, for me, it's, uh, this is what I'm building a set of work loop and actually believe that, uh, AI is about to really change our, let's say, uh, knowledge worker type of business or department, let's say, within business, uh, work and will be, uh, performing in the future. Absolutely. It will be interesting to see how the future unfolds.

Well, thank you so much for coming on our show and sharing your insights with us today. Yeah, thanks Amanda. All right.

And thanks to our audience, stay tuned. There's more.