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Monday, November 17, 2025

The Way forward for Work: How Cisco IT Leverages AI for Innovation and Worker Expertise


For many years, CIOs have chased incremental enhancements in IT productiveness. However what if the way forward for work isn’t about 10% or 20% positive factors, however about 10Xing human potential? At Cisco, we consider that the longer term is already right here. 

Since becoming a member of Cisco as CIO in 2022, our aim for Cisco IT has developed from doubling throughput to an formidable 10X improve in productiveness, remodeling us into an innovation engine. As CIO, I see AI not as an incremental achieve, however because the engine to attain this 10X human potential to empower our folks and remodel IT in addition to our firm tradition. We’re accelerating this by combining machine studying (ML), generative AI (GenAI), and agentic AI to optimize IT operations and create proactive, customized worker experiences. Our IT technique is constructed on Agile, AI, and Consumer Expertise. 

 

Fletcher Previn, CIO of Cisco, on AI, productiveness, and the way forward for work

An IT framework constructed for AI 

To remain true to our technique and lean into our imaginative and prescient, we developed a tangible framework for an AI-ready workforce, relevant to any group.  

It began with a complete, job-by-job evaluation throughout our 19 IT job households, encompassing over 10,000 staff.  

For every position, we systematically requested: 

  1. What’s the present headcount? 
  2. What are the first duties? 
  3. What’s the AI potential? 
  4. What particular AI instruments are wanted? 
  5. What coaching and reskilling will uplevel our workforce? 
  6. And critically, what are the important thing metrics to measure success?

This blueprint recognized potential productiveness positive factors, the required tooling, and the precise coaching and expertise required for each position.

Creating pleasant worker experiences and growing productiveness

This strategic strategy is already yielding unimaginable outcomes. In software program improvement, AI-first engineering with instruments like GitHub Copilot exhibits a 3X improve in output, shifting from human coding to human evaluation. Throughout all IT capabilities, we’re seeing 30-50% productiveness positive factors, projected at 36% total, with out sacrificing high quality. These numbers are based mostly on the present state of AI tooling—and they’re solely going to enhance. 

“These additionally aren’t simply efficiencies; they’re about creating pleasant worker experiences by automating mundane duties, permitting our folks to give attention to doing the most effective work of their lives.”

We’re additionally utilizing AI to reinforce and ease the {hardware} updates for our staff by detecting a laptop computer’s reminiscence, software efficiency and community telemetry to decipher between efficiency issues that may be mounted by the IT workforce versus when a tool could fail and can must be changed. All of that is about enhancing the worker expertise to chop down on friction and create a extra optimistic, enabled working atmosphere so staff can do the most effective work of their lives. 

A deeper take a look at our methodology

With the rollout of AI-powered engineering instruments—together with our inside AI assistant—we’ve entered a brand new period of scale. And we’re simply getting began. 

Right here’s how we’re serious about it: 

Developer Expertise

  • Upskilling with AI-first content material and training 
  • Rising a powerful AI engineering group 
  • Measuring what issues: Pace, High quality, Expertise, and Impression 
  • Celebrating daring thinkers and early adopters

Agile, Accelerated

  • Redefining groups: 2 professional AI first engineers + AI brokers 
  • Understanding the enterprise want, having the fitting nicely formed work within the pipeline 
  • Prioritizing velocity, iteration, and ease 
  • Automating testing, high quality, and launch pipelines 
  • Shifting from upfront design to AI-enabled experimentation

AI-First Engineering

  • Unblocking entry to AI platforms and instruments 
  • Utilizing our inside AI assistant for engineering, safety, and ops 
  • Managing agent identification and entry clearly 
  • Monitoring mannequin utilization and optimizing outcomes

We’re nonetheless early on this journey, however the outcomes are actual—and accelerating.

A cultural basis constructed on our personal AI-ready infrastructure

An AI-ready workforce (folks and course of) requires an AI-ready infrastructure (expertise) to truly ship on its potential. The blueprint tells you who wants AI, what instruments, and what coaching, however these instruments and skilled folks want a strong, high-performance, and safe basis to function successfully. 

We’re not solely leveraging AI to reinforce the worker expertise, however Cisco IT additionally companions with enterprise models inside Cisco to construct the AI infrastructure that supercharges the worker expertise throughout the enterprise.  

“We’re doing this by benefiting from our personal Cisco tech stack to construct a strong AI infrastructure.”

 It contains low-latency materials connecting compute, storage, and GPUs, managed by a management airplane. This entire Cisco stack, typically leveraging Nvidia GPUs, varieties a shared AI material throughout the corporate, offering validated designs for our clients. 

Finally, we all know our success hinges on our folks and our tradition. Inside Cisco IT, we consider the way you get issues executed is as necessary as what you get executed. At Cisco, we’re not simply speaking about AI; we’re constructing, securing, and making it work, each single day for ourselves, our clients and our companions.  

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