Alessandra Yockelson, NetApp | NetApp Insight 2024

Thank you so much for having me. I'm really looking forward to this dialogue. As Rob was actually just saying, in the digital age, HR plays... There are two sides to the coin: the HR side, how we are doing it in our own function, because if we want others to embrace it, we need to be a role model organization and function within NetApp. 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. So basically, four pillars: data is key, and usually your 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 are important but sometimes we don't do. So we created a better federation for the data and also expanded the use cases in which we have data for, because they see that we are looking at what we are 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 we created the data lakes, 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 pieces: the talent side. Early on, many companies I believe were afraid of what the risks of large language models would do to their data. 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. Then last but not least, we also expand, and this is a never-ending journey, the pillar of the data ecosystem. So the data ecosystem being expanded, working with partners and 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.

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: the data quality, the data cleansing... HR is not really known by being very data driven, right? Maybe it's one of the stereotypes that we have, that we are the people people. 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 with chatbots, and we started with that. For years now we have our NEO, the name of our chatbot, but that became like really commonplace. So we are now experimenting with a few things. I will give three examples. One that I'm most bullish about is 360-degree feedback. It has been around for a while. In many cases, you 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 NetApp employee 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, and 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 learn through 360. So that's one of them.

The one I'm very fond of is in rewards. In companies, we usually have data about the talent that is very siloed. It's not really a unified story how the people processes and data flows from one experience to another. You hire someone, then they have their evaluations, 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 will not have information about when they were hired and what they have done. So we are bringing it all together, and then for managers, when they get to do salary planning, the tools are already prepopulated based on the entire story of that employee. Of course, they are sanitized for that kind of risk.

Yes. For other things, we talk a lot about 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 a thousand 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 multiply exponentially. But with technology, you can now, and we have it in house, harness the power of the skills that our employees have and also the skills that all the jobs in the company require. Then with the algorithms, we matchmake them.

Yes, so I think there are... 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 be intentional about 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. The operation of HR became very costly, especially in acquisition when we had the war for talent. People did not get replies, 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. The candidates are 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 massive volume of manual work to a massive volume of digital work to a very intentional, driven by the individual career planning.

I really think it's important to split between the product side and the internal operations. Because for internal ops, 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 GenAI. On the product side, you heard a lot about what we are doing with intelligent data infrastructure, and Herve and team lead that very well. So maybe I pivot and focus on the internal operations because the same fear and the same risks apply. In the beginning, we created a specific center of excellence for AI and GenAI. That's not very different; most companies 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 GenAI are in service of becoming more efficient. In addition to creating this positive peer pressure of who is prompting GenAI 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 augmented like a user of AI, a sales consultant, or which are the jobs that can get substantial automation? In those use cases, on how to go about it, I think that's the key. Leadership needs to show that we are vulnerable, that we don't know, because you have to role model. You also have to shrink the problem and say let's start small and then evolve. Tell the employees what I think we all feel strongly about: that this is about their employability. It's like knowing how to type. It's going to get to a point where it's going to be commonplace, so just embrace it. There is no risk. The riskiest thing is not to try. I think that's more risky than embracing it.

Thank you so much. Nice to be here.