Enterprise conversations about AI are everywhere, yet many initiatives still stall long before they reach real production value. From years of working alongside customers, the issue is rarely a lack of ambition, talent, or access to advanced models. AI efforts rarely fail because organizations lack vision. They slow down when teams encounter friction moving, managing, and governing the data AI depends on. Increasingly, those realities shape what enterprises expect from their cloud partners.
Most organizations start with clear intent. They want to use AI to extract more value from the data they already have. As initiatives move beyond pilots, practical constraints begin to surface. Data lives across multiple environments. Moving it can be expensive. Managing it introduces operational and security concerns. Rearchitecting applications or duplicating data adds complexity that few teams planned for early on.
Enterprises consistently tell us that while their AI ambitions are real, moving and managing data across environments remains complex, slow, and costly. Those constraints often appear precisely at the moment when initiatives are meant to scale.
Where Progress Slows Inside the Organization
From the customer’s point of view, this is the moment where AI momentum becomes harder to sustain.
As initiatives mature, internal conversations shift. Teams move from asking what is possible to questioning what is practical. Stakeholders across IT, security, and the business begin to weigh risk more carefully. They ask whether existing applications will need to change, whether performance will remain consistent as workloads move, and whether using AI services will require copying sensitive data into new environments.
When those questions remain unresolved, progress slows. Decisions get revisited. Deployment timelines extend. What began as a forward‑looking AI initiative becomes an internal alignment challenge, and not because the use case is unclear, but because the data foundation underneath it is not yet mature enough to support confidence at scale.
That customer experience, where strong AI intent is constrained by data friction, is exactly what recent announcements at Google Cloud Next ’26 were designed to address.
Fixing the Problem at the Right Layer
At Google Cloud Next ’26, NetApp and Google Cloud shared updates focused on reducing one of the most persistent obstacles customers face: activating enterprise data for AI without introducing disruption. With Google Cloud NetApp Volumes, customers can move their enterprise block and file data into Google Cloud and then use Google Cloud services, including AI, directly on that data. There is no need to duplicate information or rearchitect applications, which removes a common point of friction as AI initiatives expand.
For customers, this approach shifts the conversation. Instead of asking how much change is required to adopt AI, teams can focus on how quickly they can move forward using the data and systems they already trust.
Why Unified Architectures Matter for AI at Scale
Across industries, enterprise leaders consistently emphasize the need for simplicity. They want to run established enterprise workloads and emerging AI workloads side by side, without fragmenting environments or introducing operational complexity that grows over time.
The availability of unified file and block storage services in Google Cloud reflects that requirement. A single storage pool that supports databases, AI workloads, and high‑performance enterprise applications across regions, without application changes, removes one of the structural hurdles that often delays AI adoption.
From a business standpoint, fewer architectural tradeoffs translate into clearer decisions. Organizational alignment happens faster. Teams spend less time navigating constraints and more time applying AI to real business needs.
Turning AI Readiness into Measurable Progress
The enterprises making meaningful progress with AI are not waiting for ideal conditions. Instead, they are investing in data foundations that allow them to move with confidence. Their data is accessible, protected, and usable wherever the business needs it next.
That customer focus was recently recognized when NetApp was named Google Cloud’s 2026 Infrastructure Modernization Partner of the Year for Storage, reflecting the progress customers are making when data friction is removed.
NetApp’s role as the Intelligent Data Infrastructure company is rooted in enabling exactly that kind of progress, helping customers activate their data for innovation, resilience, and growth across every cloud and environment.
When AI initiatives succeed, it is rarely because a breakthrough model appeared overnight. More often, it is because organizations addressed the underlying constraints that slowed them down in the first place.
That is where AI initiatives regain momentum, and where real business value begins.
In his current role at NetApp, Di Blasio is instrumental in advancing the company's Intelligent Data Infrastructure narrative, focusing on AI innovation, data security, and cloud transformation. His leadership is pivotal in positioning NetApp as the storage and data infrastructure vendor of choice in the AI era.
Prior to joining NetApp, Di Blasio served as Chief Revenue Officer at Commvault, overseeing all revenue-related activities. His career also includes significant GTM leadership roles at VMware and EMC, where he was responsible for driving strategic growth and market expansion.
In addition to his corporate roles, Di Blasio is a partner at Cortical Ventures, a Bay Area venture capital firm specializing in AI technologies. He has also contributed to the NASA iTech steering committee, reflecting his commitment to fostering innovation and technological advancement.
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