Workday Podcast: How Citrix Is Closing the Skills Gap With Machine Learning

During times of disruption, it’s critical for businesses to have the right skill sets to be able to quickly respond and adapt. Trenton Cycholl, vice president of business technology at Citrix, shares how machine learning can help companies close the skills gap.

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This podcast was recorded before the global pandemic. While the world looks a lot different today, we believe this content remains valuable for helping organizations move forward and emerge stronger.

Emerging technology continues to outpace the skills of today’s workforce, making it harder than ever for organizations to find the right talent to fill jobs. According to Deloitte, 70%  of CEOs say that their employees don’t have the skills needed to adapt to the changes driven by digital technologies. Especially in times of uncertainty, it’s even more critical for businesses to have the right skill sets to be able to quickly respond and pivot. So, what can companies do to address the workforce skills gap? 

On this episode of the Workday Podcast, I talked with Trenton Cycholl, vice president of business technology at Citrix, about how the organization is using machine learning to close the skills gap. 

If you’re more of a reader, check out excerpts below from our conversation. 

Josh Krist: Before we get started, can you tell me a little bit about your background and your current role at Citrix?

Trenton Cycholl: Absolutely. I started my career working with structured data sources. I didn't know at the time that those data sources would later be the foundational pillars and centers of intelligence that companies would use to build future technology innovations, like AI.

I’m now the vice president of Business Technology at Citrix. I’m responsible for architecting and operating business solutions that help scale our business here at Citrix. My teams are focused on securing technology and delivering the tools, platforms, and solutions that make employees productive. This includes analytics and intelligence platforms that drive decision-making for the company.

Krist: Right, so you were a little bit ahead of your time when it came to working with data and understanding how it all flows and works together, which is great.

Cycholl: Absolutely. It’s just interesting to see how technology builds upon itself as we learn, and to find ways to adapt and generate new opportunities for companies to leverage.

Krist: In the tech industry today, what are the unique challenges when it comes to the new talents and skills that people need to take advantage of those new opportunities that are emerging?

Cycholl: When we look at these challenges, it’s important to recognize that people are the greatest asset a company has. At the same time, they represent the largest operating cost for most companies. For many enterprises, people account for up to 70 percent of operating costs. Another factor to consider is that we’re also seeing a talent crisis, not just in the tech industry but in every industry. As companies start to become more digital, technology skill sets are starting to blur into multiple industries. If you look at an example like Amazon, it started out as a bookstore and now it’s a digital company.

All of these factors create a talent war. There’s a new level of competition for people with the right skills, but to expand upon that, it’s also becoming more and more difficult for companies to find good people.

Krist: Right. So over the next five years, what are the skills gaps that you’re really looking to address?

Cycholl: When we look at the gaps in skills, and the skills that people need to build upon, we’re seeing that people increasingly need more creative and innovative skill sets. And we’re also seeing more and more of the redundant, mundane tasks—the busy-work type of items—going away. This means we’re hiring people that are quite honestly at a different level of intelligence, because now we’re going to exercise muscles that we hadn’t before in terms of intelligence and creativity.

Krist: How is Citrix staying ahead and addressing some of these challenges? What are you doing as a company?

Cycholl: One of the ways we’re addressing these challenges is taking a look at the intersection of IT and HR. For years, technology has been at the center of getting work done—and being part of the work getting done. HR focuses on making sure that employees have a great experience, that information is secure, and that business leaders have the insights they need, quickly. So we are looking at all those things because they are important to the people working at companies, and for companies in retaining talent.

And part of this is making sure that technology does not get in the way of people. Technology has to be somewhat invisible in how people get work done. And people don’t want to search for things anymore; they want technology to work a lot like it does in their personal lives. They expect that when they come into a company, their technology experience is going to be the same as when they get an Uber or Lyft, or when they order a coffee from a Starbucks. They expect simple point-and-click experiences. So we need to remove anything that gets in the way of people getting work done.

Krist: So, you mentioned HR and IT needing to work better together. How are you doing that at Citrix?

Cycholl: The relationship between IT and HR is an important one in employee experience and driving technology across the organization. At Citrix over the last three to five years, that relationship between IT and HR has changed drastically. Today, as HR makes people decisions, technology is just part of the discussion. Our HR platform is no longer looked at as just a side technology that gets implemented. We focus on questions like “How are we going to enable people to be productive using technologies? How are we going to enable people to lead and drive change within the organization?” Having that HR and IT partnership where we design solutions together has made a huge difference in Citrix as we strive for better employee engagement.

Krist: Citrix is a leader when it comes to leveraging AI and machine learning. So, how is the company currently using those technologies?

Cycholl: We see AI as a way to make processes more efficient. So we look for where we can apply technology and leverage AI to improve simplicity and to improve productivity. We’re at a point where technology has to be created to actually solve the problems that technology itself is introducing. We’re living in a world where there are so many connected applications and systems that people need AI to help navigate in a productive way across all these tools and solutions.

So we see productivity as the big win for artificial intelligence. We’re going to be able to actually have systems that learn how they’re connecting. So, for instance, as everyone does their annual performance reviews, they will receive recommendations for certain skill sets to work on. As an HR manager, why wouldn’t I just immediately have people registered for the particular training they need in my training system? Why would I wait for that conversation to come up again, where the manager or the employee says, “Oh, I forgot to take it because we had the conversation a year ago.” And so those are the kinds of productivity gaps we can close with AI, and that’s the kind of busy work we can move out of our company. We think that’s really important.

Krist: What you said there is really interesting—that there are so many tools and so much technology and so much data, that we need tools to literally help us figure out which tools we need—and when. And we need data-driven tools to help us decide what we need to know—and when. I mean, that’s really a fascinating thought.

Cycholl: Yes, absolutely. Statistics show that we use eight to nine applications per day in the enterprise, but the reality is that we’re probably an expert in only one of those. So why do we introduce all this complexity to people who are just trying to get their job done? Why do we expect them to take time away from their job to learn how to become experts in using each one of these tools and solutions?

Krist: So what type of insights do you get from machine learning that help you make better business decisions?

Cycholl: So, around business decision-making, we’re in the middle of a transformation that involves moving our 30-year-old business to a cloud-subscription model. For us, it’s valuable to know if a customer is going to renew their subscription or not. Anticipating any churn and identifying where we might need to apply either more customer service or attention to a customer—that’s a big part of some of the insights we’re starting to get from our intelligence system. So we’re driving a lot of machine-learning modeling and automated learning around our subscription model and renewals.

The other area that’s pretty important is anticipating security risks. AI and machine learning specifically are really getting good at understanding where there might be potential risk for security incidents. Knowing those risks obviously before they happen would give anyone an extreme competitive advantage and really help in better managing the challenges of security in today’s day and age.

Krist: Right. How do you think AI and machine learning will impact your organization when it comes to finding talent? And I’ll share something with you that I read—just to see what you think. There was a CIO who said, “In the future, especially within the tech organization, we’ll need our employees to not just know how to work with machine learning, but know how to teach the machines how to learn.” So we need somebody who can basically be a machine teacher, if that makes sense.

Cycholl: Yes, absolutely. I think there are a few things around talent, which is important when it comes to applying machine learning within the organization. And one is, how do we automate that training process and understand what those—for lack of a better term, robots—are going to need in order to understand how to do what they’re designed for?

Also, in general, there’s just a lot of busy work in the whole talent-acquisition process, and busy work is a great opportunity for AI and machine learning. This includes everything from finding skills and matches, to scheduling, to performing the actual candidate reviews and managing those reviews. There’s just a lot of busy work in that whole process. The hardest part of that whole recruiting process is getting the right candidates in front of that decision-maker. And this is a great opportunity where recruiters can automate all that busy work, find great candidates, and uplevel the skill sets that we’re looking for.

And more importantly, this is also a great opportunity to use AI and machine learning to find some of the skills that exist in organizations, especially the larger ones, because there are usually great matches within companies. This then creates great learning opportunities for existing employees and longer-term benefits for a company by retaining talent for a longer period of time.

Krist: The thing about the busy work is—just regarding time and money—there’s all that waste but also nobody comes home after a day of busy work saying, “You know, it’s great. I just spent all day doing something that really, we should automate.” Right? So, if this stuff is automated, then people can actually spend their time having a great conversation with maybe fewer candidates, but each one is more interesting and they can go deeper.

Cycholl: Yes, absolutely. And, you know, there’s also the cost of, quite honestly, hiring the wrong person. And with so much busy work, you’re going to make more mistakes as a human being. So it’s a great opportunity to not have to worry about all the time needed to match up all the skills and instead focus on other things like softer skills or understanding how adaptable someone might be from the interview—instead of, “Do they have experience XYZ?” That's a checkbox, and that’s a great opportunity to let machine learning do its thing, and let the robots partner with the humans to find the right candidate.

Krist: And as far as the softer skills are concerned, you’d mentioned earlier that people need to flex their creativity and their open-mindedness and their ability to make connections on the fly. Do you think those soft skills can be taught or encouraged, or are you born with them? This is just one of those things I ask myself a lot, and I have my own personal answer, but I’d be curious to hear what you think.

Cycholl: Some of them, I think, can be taught. And this might be a little bit of a flipped kind of answer because I think, in the grand scheme of things, the softer skills are something that you naturally get better at, if you have a natural tendency to do them. But I think over time, as you work around people with those skill sets, that experience is useful for learning.

The other truth to this is that some of those softer skills, you want to blend. When you’re creating a great team, you may not want everybody to look exactly alike. And some positions might be better off having multiple people with different views and lenses of perception. This is especially true if we’re going to focus on the people side of things, and on the more creative-thinking side, not just the scientific, binary decision-making that we can leverage machine learning for.

Krist: And how do you think machine learning will change how business is conducted over the next decade?

Cycholl: Like we just talked about a minute ago, there’s definitely a skills change from people who are programming the robots and telling them what to do, as opposed to actually executing the busy work. So, that busy work kind of goes away, and people will start to change their skill sets. You’re going to be able to sift through a lot of large sets of data quicker and not have to worry about spending as much time on them. So, that’s going to accelerate business growth, as well as how people aggregate and pull together new businesses.

And then, there’s a huge cultural change as part of this, very similar to this whole transition that we’ve seen in moving from the data center to the cloud. There’s been this whole sort of readiness activity that everyone went through, asking themselves, “Oh my gosh, I’m going to put my data in the cloud? What does this mean? How do I do this?” The transition we’re now seeing is, “Oh my gosh, there’s going to be a robot doing part of my job? What is this new job I now need to focus on? How do I do the things I need to do? And how do I go through this skills change so that I can be more creative in what I do?” All of this is going to cause a lot of people angst and present a lot of challenges. But in the next 10 years, that cultural shift is going to be something we look back on and ask, “How did we do it any other way?”

Krist: So what advice would you give to other companies looking to capitalize on AI and machine learning? We’re already going faster, and the acceleration that we’re seeing is going to continue.

Cycholl: The first thing is to get really laser-focused on those busy-work use cases. Most people know where they are—it’s just a matter of calling them out and making sure you’re focused on the use cases that are going to matter most. Because AI and machine learning are no longer just ideas. This is real stuff that can actually add a lot of value. So I think it’s important to find those use cases, and, more importantly, get to that human element of the change that people are going to go through. Get your organizations and people ready for the skills that they are going to need. Understand that change and what people are going to do with their careers—and how they’re going to manage all of this in a hybrid world that includes machine-learning robots and human beings at the same time. This is a big deal for people to go through, and people change—or changing how people work—is one of the hardest things companies do. So, it’s better to get started on that sooner rather than later.

Krist: All right. Great. Well, that’s all we have time for today. I want to thank my guest, Trenton Cycholl, for joining me, Josh Krist, on the Workday Podcast. Thank you, Trenton.

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