Market Data 
- Francine Katsoudas, Cisco’s people, policy, and purpose chief, is on BI’s Workforce Innovation board.
- Katsoudas says AI adoption should start on the day-to-day level and be focused on skills training.
- This article is part of “Workforce Innovation,” a series exploring the forces shaping enterprise transformation.
Francine Katsoudas, Cisco’s chief people, policy, and purpose officer and one of its executive vice presidents, is no stranger to leading organizations through large-scale transformations. During her 27 years at the company, her remit has spanned social impact, government affairs and policy, and digital acceleration.
As leaders across industries grapple with how to stay ahead of technological change, Katsoudas says her strategy for keeping a pulse on artificial intelligence and its effect on the workforce is staying engaged with her peers and listening to people in her organization whose roles could be affected most.
In April, Cisco — along with other leading organizations, including Google, Intel, and Microsoft — launched a workforce consortium to focus on reskilling and upskilling roles most likely to be influenced by AI.
“I think we have to be so much more engaged with our peers, which is why having roundtables where we’re learning is important,” Katsoudas said.
“The AI-consortium work would have never happened two years ago,” she added. “There’s no way we would have come together and shared our views on roles and tasks and skills. But what we recognize now is that some of this is moving so quickly that you have to do the work together.”
The following interview has been edited for length and clarity.
What AI transformations are taking place at Cisco and among your workforce?
We have been talking about AI for years now. The biggest shift over the last maybe nine to 12 months is there’s a lot more clarity around real use cases that can be leveraged across an enterprise — at Cisco but also so many of our peers.
The other thing I’m seeing more broadly is an understanding around the significant impact on people. Initially, we talked about it from a “jobs are going away, sky is falling” perspective. What we feel now is AI will change jobs, and there’s a little bit of architecture that we have to create to help our people navigate that.
There’s an accountability that we have, and the speed is not going to work in our favor. It’s going to force us to kind of move quickly into concepts like skill jumping.
From a technology perspective, at Cisco there have always been three different motions that are going on. The first motion is how we help other companies build out the infrastructure to manage the workloads that come from AI.
The second area is the intersection of AI with the existing portfolio that we have. I think that’ll be the case for every company. But in particular, because we are a security company, this recognition of security for AI is top of mind.
The third element is, how does the technology intersect all of the different functions within the company.
We initially saw a top-down approach to AI. But what seems to be emerging at some organizations is the importance of power users — people who are showing use cases on the day-to-day level and showing others how AI makes their jobs easier. Are you seeing something similar?
Completely. We did a body of work, I think it was last November, where we reached out to organizations around the world about AI readiness. Of those, 98% of CEOs, CIOs, and IT professionals said they feel tremendous pressure, but only 14% had a plan.
To me, that reinforces the top-down element. So what we have done — because we’ve always felt like an element of our culture that is unique is that we believe strongly in the bottoms up. You need to have that to innovate and to be inclusive.
To give you an example, we asked people to participate in a pilot where we would provide them with training on how to leverage gen AI. In fact, they got Green Belt certification, and then we asked them to take those learnings and apply them to their day job and capture ways that their job could evolve based on leveraging technology.
I was hoping to get 300 people. And in the end, we had close to 800. What came out of that was like 282 ideas, and then a before and after on efficiency and productivity. Confidence and comfort in the technology and the willingness to think about how to apply it went up pretty dramatically. It’s a beautiful example of bottoms up.
I think the way to ensure that you do this right is to let the people that are closest to the work guide how it is leveraged moving forward.
The only caveat is you have to make sure that you have a responsible AI framework so that people know what’s appropriate and how to leverage data.
What do C-suite leaders need to do, and also leave behind, in order to drive innovation?
One of the hardest things that needs to happen to the C-suite is we need to pivot away from “roles” in jobs to “skills.”
Within Cisco, but also more broadly, the skills conversation never took off the way we hoped it would have. And now you’re going to be forced to play here, because it’s going to drive the level of agility that you need as a company.
Leaders also have to be comfortable allowing their teams to tell them how the technology should be used. All of us need to be thinking about, with the level of productivity and change we expect to see, what issues from an overall workforce perspective are we trying to solve?
Training will be on a much faster cadence than it is today. It should be bite-sized so that we can continue to make it part of our weekly to-do list.
As leaders, we’re going to have a level of workforce planning insights that we’ve never had before because AI is going to help us with this. We need to set expectations with people that were never done from a learning and change perspective.
How do you see talent acquisition changing in the next 12 months?
There were really positive shifts that we’ve been trying to achieve for years, and technology can make some of those shifts a little bit lighter for us. From a talent-acquisition perspective, I think we can use AI to reduce the bias in the system, and I think that’s something we have to work hard on.
As an example, many years ago, we experimented with blind hiring, so we remove the names off of resumes, even the universities, and really focus on the skills.
In technology roles, it was kind of easy because you could just send out a quick coding exercise and understand who had the capabilities. The beautiful thing about that was you found people that didn’t have college degrees. You found people that were doing roles that we would have thought were out of scope.
We need to leverage the technology to help us distill capability, and in doing so, we’ll have a more inclusive approach and more diverse talent.
