NVIDIA CEO Jensen Huang has marked 2025-2026 as the initial inflection point at which AI has transitioned from a novel technology into a fundamental, utility-like resource, akin to water or electricity. In the first episode of the AI Intelligence Lab video series, Jeff Baxter, Vice President of Product Marketing at NetApp, sits down with Anne Hecht, Senior Director of Product Marketing at NVIDIA, to discuss how AI-driven transformation is already underway across industries, opportunities are just beginning to emerge from unstructured data, and how enterprises can adapt to harness its full potential.
AI’s Impact Across Industries
Organizations are leveraging artificial intelligence in a variety of ways to enhance productivity and accelerate progress. In healthcare, radiologists use AI to convert 2D x‑rays into 3D images, accelerating and improving diagnostics. Retailers deploy recommendation engines that personalize makeup and facial product suggestions. In manufacturing, AI and technologies such as NVIDIA Omniverse are used to design, simulate, and stand up new factories in the United States faster and more efficiently, leading to more productive facilities once they go live. Internally, NVIDIA uses AI for code generation to speed GPU development, enabling a new architecture every year that significantly outperforms the prior generation. These advancements across industries are furthering innovation faster than ever before.
The Unstructured Data Advantage
Enterprises are sitting on a goldmine of unstructured data. Most enterprise data is unstructured, but business analytics pipelines typically touch only 20 to 30 percent of the total data because they rely on structured sources such as databases and rows/columns. The remaining 70 to 80 percent live in PDFs, presentations, documents, and video assets like this very podcast with Anne and Jeff, which are where critical operational and customer insights have been effectively locked away.
NetApp and NVIDIA have developed an operating system for turning that data into AI-driven business value. With AI, organizations can now extract insights to provide strategy, HR, supply chain, product, and customer service teams with a far more complete and grounded view of the business.
Hybrid Cloud, Data Gravity, and Making Data AI‑Ready
NetApp enables enterprises to use their data “where it lives,” making it fast, AI‑ready, and usable without disruption. Because data readiness is foundational to AI success, NetApp brings one of the world’s largest collections of enterprise unstructured data under active management, spanning on‑premises, private cloud, and all major public cloud platforms.
In the video series episode, Anne and Jeff highlight how a deep integration between their two companies makes this possible. NVIDIA AI software and microservices are embedded directly into NetApp storage systems, allowing organizations to generate embeddings, build vector databases, and power GenAI applications without moving massive volumes of data. This lets customers apply AI consistently across fragmented data estates while enforcing guardrails and access controls at the storage layer.
Governance, Sovereignty, and the “AI Factory”
Security, governance, and sovereignty emerge as non‑negotiable requirements for enterprise AI adoption. As NetApp and NVIDIA unlock more data for AI, NetApp’s strengths in identity, role‑based access, and policy‑driven governance ensure that only the right people and models see the right data, in line with corporate and regulatory obligations. This matters especially as enterprises and governments stand up sovereign clouds, “neo clouds,” and dedicated “AI factories” that must comply with regional data residency and sovereignty requirements.
Anne explains the AI factory as a metaphorical production line where data enters, is processed by accelerated infrastructure (GPUs, high‑performance networking, and accelerated storage), and emerges as tokens. Tokens are the unit of value in generative AI workloads, measured and optimized as tokens per watt or tokens per dollar.
From Open Models to Enterprise‑Grade AI Platforms
To round out their discussion in the episode, Anne and Jeff connect open models, enterprise IP, and a jointly engineered data and compute platform. NVIDIA’s open Nemotron model family is an example where model weights, datasets, and “recipes” are shared so customers can post‑train with their own proprietary data, effectively turning the model into a highly skilled virtual employee steeped in 40 years of company knowledge.
That vision depends on a robust AI data platform: NVIDIA’s newly announced AI data platform for accelerating storage systems, and NetApp’s AFX and AI Data Engine, which are built on that platform and were jointly announced by Jensen Huang and George Kurian during NetApp INSIGHT in October 2025. Together, NetApp and NVIDIA present a blueprint for enterprises to harness their full unstructured data estate, build governed AI factories, and convert tokens into a durable competitive advantage. They emphasize how this partnership is deep, collaborative, and built on mutual trust and understanding of customer needs.
Explore More of the AI Intelligence Lab
While this first episode of the AI Intelligence Lab set the stage for a deeper exploration of how to build a robust AI infrastructure and drive meaningful business outcomes, the rest of the video series offers much more valuable insights into the critical intersection of AI, data, and innovation.
Listen to Episode 2 now, where the conversation continues about what AIDE does, how to get from pilot to production, and how NetApp brings AI to data (rather than the other way around).