Cisco VP Reframes AI As Operational Amplifier To Scale Human Expertise

While headlines often focus on AI's role in drastic workforce decisions, a more pragmatic conversation is happening inside large enterprises that focuses less on human replacement and more on operational amplification. From this vantage point, AI becomes a strategic opportunity rather than a workforce threat. By moving employees and managers away from reactive reporting and manual coordination, leaders position their teams for proactive risk management, scaled human expertise, and predictive decision-making.

It's a concept Jason Andrews understands how to apply at scale. As a Vice President at Cisco, he leads strategy in a division of more than 22,000 employees contributing to $36 billion in annual revenue. Drawing on more than two decades of experience driving technology transformation, he said the first step isn't figuring out which processes to automate. It's deciding if they're necessary to begin with.

"You don’t start with automation to chase every task. First, you map the value stream, ask which processes still matter, and then apply AI to amplify the work that truly drives outcomes." This approach is a direct response to the low-value work that often bogs down large organizations. Andrews said employees often consume valuable time reporting on problems, which can prevent them from actively focusing on impactful work. "Imagine you have five days in a week and on the fifth day, you're supposed to present to a team. It takes four of those days just to compile the information. You haven't actually solved a single problem. All you've done is gotten all the problems on a piece of paper, and that's not hugely effective."

• Beyond the status report: In Andrews’ view, the path forward involves automating the planning and reporting tasks that can eat up so much of a team's time. "The goal is to enable proactive risk identification and mitigation by transitioning teams from data aggregation to actually solving problems. For example, an AI assistant can look through all the tickets to build a summary. From there, it can perform dependency collision detection or even enable portfolio simulations, which allows us to spend more time on solutions, not just status updates."

• From hours to minutes: Andrews said this approach leads to massive efficiency gains, ultimately allowing his organization to achieve the same outcomes with fewer resources. "I talked to a PM who told me he spent ten hours a week compiling a single report. Now, he's got that down to fifteen minutes. We were able to repurpose and scale him up to the point where we've seen almost a 40 to 50% reduction in headcount over the last five years just through automation and integration."

Andrews' playbook for building an AI-ready organization prioritizes creating interconnected ecosystems built on common data models and emerging agent workflows. The focus on preparation of data, systems, and people is what allows an organization to capitalize on the next wave of innovation by developing AI proficiency now. "The specific AI tools we're building in-house today likely won't be the same ones we use in three years. The real goal is to get your organization AI-ready by setting up the data framework and enabling your people. Then, when a superior commercial tool arrives, your organization can pivot and be effective on day one, not in year three."

• The college grad test: Beyond immediate efficiency gains and future-proofing, this approach also addresses how to best leverage the next generation of talent. "If you can give a portion of a job to someone fresh out of college with no prior experience, then you should evaluate whether AI is a candidate to do it," Andrews advised.

• The readiness ROI: As for the notion that AI will eliminate entry-level positions, Andrews pushes back, noting that such a move would be dangerously shortsighted. "There is a flawed theory that with AI, companies will only need to hire senior architects. That is a long way from reality. You cannot become a senior architect until you have gone through the foundational phases of your career." He noted that facilitating this will require organizations to prioritize education, helping more junior team members learn to use AI to scale.

For leaders who are still playing on the sidelines of AI transformation, Andrews offered a word of caution. "During the move to cloud, I saw people get left behind because they were dismissive of the technology. The ones who doubled down and experimented with the cloud became vastly more productive than those who stuck their heads in the sand." Based on the results he's seen thus far, he foresees a similar path for AI. "I believe automation got us to 40 to 50% resource improvements over the last five years. With AI, I think we have a chance to achieve another 100% improvement over the next three to five years. Once you truly enable AI-native systems and outcomes, that's when you'll end up seeing 4X and 5X returns."