AI Grassroots innovation into strategic transformation 

When I look back at how our AI journey began, it wasn’t part of a grand strategy; it started with a series of grassroots efforts. Individual teams were experimenting with artificial intelligence in isolated pockets, pursuing ideas that seemed promising but lacked a larger purpose. We had passion, but not direction.  
  
That changed when our leadership recognized that AI had to move from experimentation to execution. We needed a structured, strategic approach that guided people through every step, from identifying opportunities and developing use cases to measuring impact. That realization led to the creation of AI for Internal Operations (AI4IO), a company-wide program designed to turn curiosity into measurable business value. 

Our first step was outreach. We spoke with leaders across NetApp’s business functions to determine where AI could have the greatest impact. The response was overwhelming. Teams submitted dozens of ideas, and through a careful prioritization process, we narrowed those down to a focused portfolio of 29 initial use cases.  
  
To ensure accountability, we built a governance model that requires each function to own its projects and regularly report progress to the AI4IO Transformation Office, which consolidates updates for review by NetApp’s CEO, George Kurian, and the C-staff. This cadence keeps AI work visible at the highest levels of the company and reinforces the idea that this is not just an innovation experiment but a transformation effort.  
  
That top-down commitment has been one of the keys to our momentum. When everyone understands that AI success is measured, monitored, and supported by leadership, it drives the proper behavior across the organization. 

Early on, we realized that success couldn’t be defined by how many AI projects we launched, but rather by the value those projects created. To make that measurable, we built a framework that tracks value through three progressive layers:  
  

Adoption: Are people using the solution?  
  

Performance Indicators: Are those solutions improving their daily work?  
  

Business Impact: Are we seeing greater efficiency, reduced costs, or increased revenue?  
  
One example that illustrates this progression is our Dynamic Deal Scoring (DDS) capability. It helps our sales teams optimize pricing and margins in real time. We track adoption by the percentage of deals using DDS, performance by user sentiment and engagement, and business impact through improved sales yield when recommendations lead to successful deals.  
  
This structured measurement ensures that every AI initiative delivers tangible results rather than becoming another “cool idea” that falls short of follow-through. 

While focus is essential, we also didn’t want to stifle creativity. Even as we pursued the initial set of use cases, new ideas kept surfacing, some with enormous potential. To balance discipline with innovation, we introduced a quarterly review cycle to reassess our priorities and adjust to new opportunities.  
  
We also launched Abacus, our internal AI platform, to give teams a safe environment for experimentation. Abacus lets developers build, test, and iterate on ideas without impacting production environments. When something shows real value, it can be refined and published for broader use through NetAIChat.  
  
This approach allows innovation to thrive within a governance framework. Teams have freedom to explore, but their work still aligns with organizational goals. 

As the program matures, I see AI4IO evolving into something even more natural, seamlessly integrated into how we operate rather than a separate initiative. I envision a future where AI is woven into our annual planning cycles, where proposing AI-driven enhancements is as routine as any other business improvement.  
  
I also expect more of our enterprise applications to come equipped with built-in AI capabilities. Vendors like Salesforce and Oracle are embedding intelligence directly into their platforms, and we’ll increasingly leverage those capabilities rather than reinventing them ourselves.  
  
At the same time, I’m particularly excited about the growing role of AI agents, autonomous systems that can reason about a user’s intent and act on their behalf. With proper guardrails in place, these agents could handle complex tasks instantly, accelerating our operations in ways previously impossible.  

The more capable our AI systems become, the more critical it is to ensure they operate responsibly. Expanding autonomy means expanding risk, both from unintended actions and from potential external misuse.  
  
That’s why we’re taking what I’d describe as a fast-follower approach. We move quickly enough to stay competitive but deliberately sufficient to ensure proper review and security. It can feel frustrating at times, especially when new public tools appear daily, but it’s a necessary balance.  
  
We’re committed to innovation that’s responsible, sustainable, and secure. Our goal isn’t just to adopt AI; it’s to do so the right way, protecting our people, our data, and our customers.  

What I find most rewarding about AI4IO is that it represents more than a technology initiative; it is a cultural shift. We’ve created a model that encourages experimentation while demanding accountability, a structure that blends grassroots creativity with executive alignment.  
  
When teams across a company as large and complex as ours share a unified framework for using, measuring, and scaling AI, progress compounds quickly, and that’s what’s happening now.  
  
AI4IO has become the bridge between innovation and impact, turning bold ideas into business results and ensuring that every advancement strengthens the foundation of how we work. 

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