For decades, CIOs have chased incremental improvements in IT productivity. But what if the future of work isn’t about 10% or 20% gains, but about 10Xing human potential? At Cisco, we believe that the future is already here.
Since joining Cisco as CIO in 2022, our goal for Cisco IT has evolved from doubling throughput to an ambitious 10X increase in productivity, transforming us into an innovation engine. As CIO, I see AI not as an incremental gain, but as the engine to achieve this 10X human potential to empower our people and transform IT as well as our company culture. We’re accelerating this by combining machine learning (ML), generative AI (GenAI), and agentic AI to optimize IT operations and create proactive, personalized employee experiences. Our IT strategy is built on Agile, AI, and User Experience.
Fletcher Previn, CIO of Cisco, on AI, productivity, and the future of work
An IT framework built for AI
To stay true to our strategy and lean into our vision, we developed a tangible framework for an AI-ready workforce, applicable to any organization.
It started with a comprehensive, job-by-job analysis across our 19 IT job families, encompassing over 10,000 employees.
For each role, we systematically asked:
- What is the current headcount?
- What are the primary tasks?
- What is the AI potential?
- What specific AI tools are needed?
- What training and reskilling will uplevel our workforce?
- And critically, what are the key metrics to measure success?
This blueprint identified potential productivity gains, the necessary tooling, and the specific training and skills required for every role.
Creating delightful employee experiences and increasing productivity
This strategic approach is already yielding incredible results. In software development, AI-first engineering with tools like GitHub Copilot shows a 3X increase in output, shifting from human coding to human review. Across all IT functions, we’re seeing 30-50% productivity gains, projected at 36% overall, without sacrificing quality. These numbers are based on the current state of AI tooling—and they’re only going to improve.
“These also aren’t just efficiencies; they’re about creating delightful employee experiences by automating mundane tasks, allowing our people to focus on doing the best work of their lives.”
We are also using AI to enhance and ease the hardware updates for our employees by detecting a laptop’s memory, application performance and network telemetry to decipher between performance problems that can be fixed by the IT team versus when a device may fail and will need to be replaced. All of this is about enhancing the employee experience to cut down on friction and create a more positive, enabled working environment so employees can do the best work of their lives.
A deeper look at our methodology
With the rollout of AI-powered engineering tools—including our internal AI assistant—we’ve entered a new era of scale. And we’re just getting started.
Here’s how we’re thinking about it:
Developer Experience
- Upskilling with AI-first content and coaching
- Growing a strong AI engineering community
- Measuring what matters: Speed, Quality, Experience, and Impact
- Celebrating bold thinkers and early adopters
Agile, Accelerated
- Redefining teams: 2 expert AI first engineers + AI agents
- Understanding the business need, having the right well shaped work in the pipeline
- Prioritizing speed, iteration, and simplicity
- Automating testing, quality, and release pipelines
- Shifting from upfront design to AI-enabled experimentation
AI-First Engineering
- Unblocking access to AI platforms and tools
- Using our internal AI assistant for engineering, security, and ops
- Managing agent identity and access clearly
- Monitoring model usage and optimizing outcomes
We’re still early in this journey, but the results are real—and accelerating.
A cultural foundation built on our own AI-ready infrastructure
An AI-ready workforce (people and process) requires an AI-ready infrastructure (technology) to actually deliver on its potential. The blueprint tells you who needs AI, what tools, and what training, but those tools and trained people need a robust, high-performance, and secure foundation to operate effectively.
We’re not only leveraging AI to enhance the employee experience, but Cisco IT also partners with business units within Cisco to build the AI infrastructure that supercharges the employee experience across the enterprise.
“We are doing this by taking advantage of our own Cisco tech stack to build a robust AI infrastructure.”
It includes low-latency fabrics connecting compute, storage, and GPUs, managed by a control plane. This complete Cisco stack, often leveraging Nvidia GPUs, forms a shared AI fabric across the company, providing validated designs for our customers.
Ultimately, we know our success hinges on our people and our culture. Within Cisco IT, we believe how you get things done is as important as what you get done. At Cisco, we’re not just talking about AI; we’re building, securing, and making it work, every single day for ourselves, our customers and our partners.
