NetApp INSIGHT 2024 | Alessandra Yockelson, NetApp

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Alessandra Yockelson

Chief Human Resources Officer

NetApp

How AI-driven HR boosts employee growth and decision-making at NetApp

As companies face the challenges of digital transformation, AI-driven HR strategies, data management, artificial intelligence and employee development are emerging as critical pillars of success.Effective leadership is embracing technology through AI-driven HR strategies not just to streamline operations, but also to spark employee growth and enhance organizational agility. The key lies in using data to sharpen decision-making, boost productivity and ensure workforce development keeps pace with technological advancements, according to Alessandra Yockelson (pictured), chief human resources officer of NetApp Inc

play_circle_outline Maximizing Employee Development with AI and Data Quality in HR at NetApp: A Success Story

play_circle_outline Connecting skills to career opportunities, improving talent management with AI

play_circle_outline Trustworthy AI: Removing Bias and Ensuring Security in Internal Operations with Generative AI

play_circle_outline Categorizing jobs for productivity gain, supporting employees in evolving with AI.

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Alessandra Yockelson, NetApp

Alessandra Yockelson

Chief Human Resources Officer NetApp

NetApp Insight 2024, live coverage from Las Vegas, Nevada. Alessandra Yockelson, CHRO at NetApp, discusses embracing data leadership. Four pillars in data management, agile operating model, and talent development. AI to enhance employee feedback, nudging for improvement. In rewards, algorithm reduces bias in decision-making. Use of AI in HR to improve operations and employee experience.

Focus on skills-based workforce and personalized career planning. Technology brings equity and meaningful connections. Trust in AI through secure processes and removi... Read more

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What steps has NetApp taken to embrace data leadership in HR and drive success in the digital age? add

What new tool can HR leaders use to help manage the skills of their employees in a more effective way? add

What is the speaker discussing regarding the importance of separating the product side and internal operations, and the focus on generative AI within the tech industry? add

What steps has NetApp taken to analyze their workforce and determine necessary changes for future success? add

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Alessandra Yockelson, NetApp

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Rebecca Knight

>> Good afternoon, everyone. We are in the middle of day two of NetApp Insight 2024, theCUBE's live coverage. We are here in Las Vegas, Nevada. I'm your host, Rebecca Knight, alongside my co-host and co-analyst, Rob Strechay. Rob, we're going to talk about HR. One of my favorite topics, and I'm not even kidding.

Rob Strechay

>> I was going to say, but I love it because I learn every time you and I are up here talking down this path. I think people power companies, and I think that, to me how AI impacts that is a big thing, I think, in how it will play out.

Rebecca Knight

>> And very exciting.

Rob Strechay

>> Yes, very exciting.

Rebecca Knight

>> Exactly. Well, with that, I would like to introduce our next guest. She is Alessandra Yockelson, CHRO here at NetApp. Thank you so much for coming on the show.

Alessandra Yockelson

>> Thank you so much for having me. I'm really looking forward to this dialogue.

Rebecca Knight

>> So as Rob was actually just saying, in the digital age, HR plays such a key role in driving success on the individual employee level, but also organizational outcomes. How is NetApp embracing data leadership in this area?

Alessandra Yockelson

>> Yeah, I would say there are two sides to the coin. There is the HR side, how we are doing it in our own function, because if we want others to embrace, we need to be a role model organization function within NetApp. So in HR, I will talk about it a little later, but for the enterprise, we started with the fundamentals, like what George pretty much talked about at the keynote yesterday, right? So it's basically four pillars. Data is key, and usually our AI challenges reside on the data challenges that you have. So we really created centers of excellence for data management, data cleansing, all the good stuff that we know that are important, but sometimes we don't do. So we created better federation for the data, and also expanded the use cases in which we have data for. Because without the data, of course you can't answer business questions with AI. So, that was pillar number one. Then we also made our operating model more agile. We embedded the data and the insights that you get in the way we run our business, so that employees and leaders, they see that we are looking at what we're learning from the algorithms, and making business decisions with that. Otherwise, it can really become pet projects., and then you don't really benefit from the productivity or the revenue growth. So created the data lakes, created, expanded the scope of data, and also made sure that the data and insights are embedded more often in our operating model than it used to be. Two last pieces, the talent side. Early on, many companies I believe were afraid of what the risks of a large language models would do to their data, and I think it's a very important concern that companies need to have. But we partnered as a leadership team to really lean forward and say, "Okay, we are going to have," and this is now common sense, but at the beginning, we are going to have large language models within the network of the company. And then we are going to really encourage our employees to experiment, to be better at prompting, for instance, just as a silly example, but create that awareness that this is important. And then last but not least, we also expand, and this is a never-ending journey. The pillar, one of the pillars that George said was the data ecosystem. So, the data ecosystem being expanded, working with partners, with customers to make sure that we have more intelligence altogether, to really make NetApp the true intelligent data infrastructure company. So, that was at the enterprise level.

Rob Strechay

>> Yeah. How is it applying? You kind of mentioned it briefly, from an HR perspective.

Alessandra Yockelson

>> Yes, I do believe as a leader in the company that I have to role model the change that I want to see. So, we went through the same four pillars, and I would say for anybody watching us, if you are in this journey starting, don't forget the four pillars. It's the data quality, the data cleansing, the data scope, the governance, the agile operating model, the expansion of the data ecosystem, and making sure that you up-skill the workforce. So in HR was the same thing, but if I'm specific in terms of the learning for HR, HR in the past was not really known by being very data-driven, right? Maybe it's one of the stereotypes that we have is that we are the "people" people and we are, but to make the best use of the employee experience and give employees the best environment to do their best work, you really need to be much more data-driven in HR and everywhere. So, we created different use cases. We started to, everybody in HR usually starts with chatbots, and we started with that. So for years now we have, our Nibble is the name of our chatbot, but that became really common place. So we are now experimenting with a few things. So maybe I'll give three examples. One that is the one I'm most bullish about is, 360 degree feedback has been around for forever. But most companies, when we do it, we always struggled with how to help the employees learn from the feedback and what they can do to actually address the gaps. This is a universal need and until now, in most cases you'll get a coach, but it's a solution that is not scalable because cost-wise, it's intense, or it's your leader that sometimes is biased. So what we are doing now, and we are about to launch it, so the employees at NetApp know about this. At the service anniversary, every NetApper will have their 360, as in most companies this happens, but then they will be able to upload the data, and then have an LLM that is going to help them understand what the data is telling them, but more importantly, give nudges on what are the development actions that have been most effective for that specific gap? So then, the employees can actually be always learning from the LLM in terms of what they can do to grow in their careers, and close the gaps that they learned through 360. So that's one of them.

Rebecca Knight

>> So, that is fascinating. If someone got the feedback of, you are talking too much in meetings or you're interrupting too much, so the nudge would be, "Hey, maybe it would come up in your calendar, but before you have a next team meeting to make sure you're letting other people listen, or why don't you wait for four people to have talked before you say something?"

Alessandra Yockelson

>> Exactly. So, it's that dynamic like that. And it's also structured, because sometimes people do these things, but they don't necessarily take the actions that are the most effective for the feedback that they got. Because if you are not good at something, it's very hard for you to figure out how to be better at that very same thing. If you know how to ride a bicycle, you usually have the hardest time teaching other people how to do it by telling them. And then, if you know it so well, you intrinsically know. So there is that element of nudging and real time in the flow of work. We say nudging, but there is also the mentorship, the coach that comes from the LLM, we will know the answer. If I need to speak slower, what is the best way? And then the LLM will know better than I can figure out by myself. So, that's one of them. But the other use cases, it's another one that we are very fond of, is in rewards. We in companies, usually when we have data about the talent, then we are very siloed. It's not really a unified story. How the people processes, the data flows from one experience to another. So you hire someone, and then they have their evaluations, and then they have their development programs, and then it's time to do salary review. Usually the leader will have very rewards-related information. They'll not have information about when you are hired and what have you done. So, we are bringing out together. And then for managers, when they get to do salary planning, the tools are already pre-populated based on the entire story of that employee, what's the best. And of course, the leadership of the people will always have the final say, we are not going to lead based on AI models, but it augments the intelligence of the leader, and also reduces bias in these decisions because the algorithms we have, of course, are sanitized for that kind of risk.

Rob Strechay

>> Yeah, I think what's interesting is that you're drinking your own champagne, right?

Alessandra Yockelson

>> Yes.

Rob Strechay

>> You are an internal customer leaning in on the technology. And I think one of the things that I find super interesting about this whole thing, because again, being the geeky side of things, is that when you're going in there and leaning in, I used to be in the customer 360 for retail type stuff.

Alessandra Yockelson

>> Yeah, similar.

Rob Strechay

>> It would seem, it's very similar application that you want to have employee 360 or something like that.

Alessandra Yockelson

>> Like that. And then, that's only the beginning of the development process. But there are so many, I am so excited about this moment in HR, in human capital management at large because these are all things that we always wanted to do, but the technology was very limiting. And so for you to do things like this at scale and in the flow of work, you have to leverage AI, you have to leverage intelligence. And for other things we talk a lot about, and I'm sure Rebecca, you are fond of it as well, the skills-based workforce, that skills are the new currency, but there are millions of skills and we have more than 12,000 employees now, and each one of them may carry more than 1,000 skills. So, when you think about how to manage that talent at the skills level, not at the individual level, your challenges as an HR leader, they multiply exponentially. But with technology, you can now, and we have it in-house, we can harness the power of the skills that our employees have, and also the skills that all the jobs in the company require. And then with the algorithms, we match-make them so the employees are nudged for career opportunities that in many cases they would not consider. And I find that fascinating as well. It's something you always want to do, but without AI, it would never have been possible.

Rebecca Knight

>> I'm curious what you make of, if you spend any time on LinkedIn, you see tales of people who have applied for zillions of jobs not getting any callbacks, and they think that it's because their AI is filtering out their resumes, and in many cases they are correct.

Alessandra Yockelson

>> Yes.

Rebecca Knight

>> So how do you find the sweet spot? Because by all accounts, these people are talented people who've got good experience and it would be an asset to companies. So how do you make sure you're not filtering out, and using the robots to filter out potentially really great talent?

Alessandra Yockelson

>> Yes. So I think that there, I do believe that technology will actually bring more equity. I do believe that, and that's what we fight for every day at NetApp. There are biases in the way we operate now, because humans are biased. And the problem is that we are in many cases not aware. With the algorithm, you can bring it to the open and see that there are biases. So for us, for instance, we are very focused on the skills at the skill level, not at the resume level, at the college level or where the person is from. It's really bringing that down to, what are the skills that the jobs require and what are the skills that the talent has? I would not discourage people to continue to pursue their career dreams. I think what's going to happen is, the relationships are going to become more special. We come from an era in which there were masses of applicants to masses of roles with no screening. So, the operation of HR became very costly, especially acquisition and when we had the war for talent. And so, people did not get replies the same. And I'm sorry to admit that that happens, but it was because of the sheer volume. Now, we are in a transition period in which the bots are fighting each other. So the candidates use the ChatGPTs and LLMs to prepare their resumes. And then on the other hand, the algorithms are trying to filter through that. But we are moving, I do believe, and I can see a more meaningful connection. So as back to your original question, as a talent, you're going to have to be more clear about what skills you want to add to your future, which kind of companies have those skills to develop in you, the culture that they offer. Are you committed to the innovation that they bring? Do you want to work for an intelligent data infrastructure company, or for a commodity storage company? You have to make those fundamental decisions. And then when you do that, you are able to, through LinkedIn or events like this, make human connections. And with that, you are able to get ahead. It will come from a massive volume of manual work to a massive volume of digital work, to a very intentional, driven by the individual career planning. I am passionate about that, too.

Rob Strechay

>> And it sounds like, I think again, to your point, it's how do you develop that trust, with not only prospective employees and current employees. And when you say AI, a lot of times, and it's been all over the news, there's security, there's how effective it is. It sounds like you're using what we would term a small language model. It doesn't mean it's like parameters are small. It just means...

Alessandra Yockelson

>> No, it's not an LLM.

Rob Strechay

>> Yeah, you're not rebuilding ChatGPT.

Alessandra Yockelson

>> Makes no sense.

Rob Strechay

>> You have it very focused.

Alessandra Yockelson

>> Yes.

Rob Strechay

>> How do you impart that, or gain that trust and ensure to your customers that, "Hey, it's secure, it's trusted, here's what we're doing. We're removing biases." George was talking about yesterday, how do you go down that path with them?

Alessandra Yockelson

>> I really think it's important to split between the product side and the internal operations, because we want to drink our own champagne, but our products have to deliver just that, right? So internally, I say now it's about generative AI, but I try not to remember the name, and just say it's another massive change and we know how to lead massive changes. The tech industry is one of the most dynamic ones. So I would say it's the same processes, the same steps, but now it happens to be AI and gen AI. So on the product side, you heard a lot about what we are doing in intelligent data infrastructure, and Harv and team lead that very well. So maybe I will pivot and focus on the internal operations, because the same fear and the same risks apply, and in the beginning we created a specific COE, center of excellence for AI and gen AI. That's not very different. Most companies that are on this journey have it, but we also created a center of excellence for process and work, because internally you have to remember that AI and gen AI are in service of becoming more productive, more robust, more reliable, which also reduces operational risks. And then at the same time, initiated grassroots, fomented grassroots initiatives with the employees just for them to experiment. In addition to creating this positive peer pressure, a little bit of, who is prompting gen AI inside the company. That was phase one. Phase two, which is what we are going through right now, was to say, "Okay, we have the center of excellence for AI and then we have the center of excellence for process being matured. That is the least mature at this point, but we need to look at the work." We have more than 12,000 employees, so we partnered with an external organization to look at the work that we do at NetApp and then categorize them. What are the jobs that we don't even have that we need to have? What are the jobs that we have that will be fomented, like an AE, the sales consultant, or which are the jobs that can get substantial productivity gain, like folks in communications, in marketing and supply chain, or some of the entry-level job work that should be replaced, and we should upgrade those people to do more meaningful work? Now we are working those use cases, supporting the employees involved in those use cases on how to go about it. I think that that's the key. Leadership needs to show that we are vulnerable, that we don't know. You have to role model. You have to also shrink the problem and say, "Let's start small and then evolve," and tell the employees what I think we all feel strongly about, that this is about their employability. It's how to know how to type. It's going to get to a point where it's going to be commonplace, but just embrace it. There is no risk. The riskiest thing is not to try. I think that that's more risky than embracing it.

Rebecca Knight

>> That's great advice. Alessandra Yockelson, thank you so much for coming on theCUBE. It was a real pleasure.

Alessandra Yockelson

>> Thank you so much. It was nice to be here.

Rob Strechay

>> Yes.

Rebecca Knight

>> Keep it right here on theCUBE. We'll be back with more from NetApp Insight 2024. I'm Rebecca Knight. For Rob Strechay, you're watching theCUBE, the leader in enterprise tech news and analysis.