Enterprise AI
NetApp Reports Positive P&L Returns From 13 of 400 AI Initiatives
NetApp CEO George Kurian shared details at a Goldman Sachs conference on how the company reduced 400 AI projects to 13 that positively impact its profit and loss through a rigorous selection process involving proof of concepts and pipelined projects.
NetApp's enterprise AI initiative management is the bottom-up generation and funneling of projects to select those that deliver positive contributions to the profit and loss statement.
Executive Summary
NetApp, operating in the data management sector, has achieved a concrete AI win by identifying 13 initiatives out of roughly 400 that are contributing positively to its profit and loss. The company deployed AI in areas including product development, customer lifecycle management, customer support, and demand and supply forecasting at the sub-component level. This result came after starting about 400 bottom-up AI projects internally and narrowing them through a selection process to 140 real proof of concepts then 40 pipelined projects.
The quantified business outcome includes 13 projects already in the black, providing measurable returns according to disclosures at the Goldman Sachs conference. This approach demonstrates how enterprises can use AI to augment operations in coding, manufacturing and supply chains. The details were presented by CEO George Kurian at the Goldman Sachs conference where broader enterprise AI adoption was discussed.
NetApp established a center of excellence for AI platform governance. It uses open source and open weight models to control token costs. These elements combined to enable the positive returns from the selected projects as reported by Investing.com.
Background and Context
The discussion took place at the Goldman Sachs Communacopia + Technology Conference. NetApp shared its experiences with internal AI adoption alongside other tech firms. The company has been using AI in its business aggressively according to the CEO remarks cited in conference coverage.
According to reports from the conference, NetApp described how it is using AI inside the company. The main uses are product development, customer lifecycle management and customer support. Demand forecasting is also a key area mentioned in the disclosures.
This internal deployment is part of broader enterprise AI adoption across various sectors. The funnel process helps in identifying viable projects that can impact the bottom line as detailed in the Investing.com report on the event.
What's New in Detail
NetApp started about 400 bottom-up AI projects internally from employee ideas across the organization. These projects were evaluated and narrowed to 140 real proof of concepts based on their potential applicability.
From the proof of concepts, 40 projects were advanced into a pipelined stage for further development and testing. Thirteen projects are already contributing positive returns to the P&L according to the company statement at the conference.
The process reflects a methodical reduction that focuses resources on initiatives with demonstrated value. Related conference reporting noted similar AI deployments by other firms in code generation, product development, manufacturing automation, customer support, supply chain planning, pricing, quoting, and sales lead generation.
Technical Specifics
The main internal uses include product development where AI supports various tasks. Customer lifecycle management benefits from AI applications in tracking and optimizing customer interactions over time.
Customer support is enhanced through AI tools that assist with queries and issue handling. Demand and supply forecasting at sub-component level aids in precise planning for manufacturing and supply chain operations.
The company uses open source and open weight models to manage the costs associated with token usage in AI applications. This choice supports scalability without excessive expenses as part of the governance framework.
Market and Stakeholder Implications
For other enterprises, the NetApp example shows the value of a structured funnel for AI projects. It emphasizes starting with many ideas but rigorously selecting the ones with potential for financial contribution.
Stakeholders in the tech sector can see how internal AI use can lead to efficiency gains in operations. The positive P&L impact provides a model for similar companies seeking measurable outcomes from their AI efforts.
The approach aligns with broader trends where AI is applied to coding, product development, manufacturing automation, customer support, supply chain planning, pricing, quoting, and sales lead generation as noted in related reports from the Goldman Sachs event.
Expert Reactions
The disclosure from NetApp at the conference provides insight into how tech firms are achieving measurable profits from AI. The CEO's remarks highlight the aggressive yet selective use of the technology.
This case illustrates the potential for AI to drive returns when properly managed through governance structures and a clear selection funnel.
We’ve been using AI in our business aggressively. I think from a broad basis, we had about 400 bottoms-up projects. We narrowed that down to 140 real proof of concepts to 40 projects that have been pipelined to 13 that are contributing to positive returns in the P&L.George Kurian, CEO, NetApp
What's Next
NetApp and similar companies are likely to continue refining their AI project pipelines based on the success of the current 13 projects. The success may lead to scaling those initiatives further in coming periods.
Peers in the industry should consider implementing similar bottom-up to funnel processes to identify their own AI wins. The use of open source models can be a strategy for cost management in AI deployments.
The center of excellence model offers a way to maintain oversight as AI adoption grows. Future developments may include more projects moving from the pipeline to positive returns according to the patterns described.
| Stage | Number of Projects | Description |
|---|---|---|
| Initial Projects | Approximately 400 | Bottom-up AI projects started internally |
| Proof of Concepts | 140 | Real proof-of-concepts validated |
| Pipelined Projects | 40 | Projects advanced to pipeline |
| P&L Contributing | 13 | Initiatives delivering positive returns |
- Initiate a large number of bottom-up AI projects from across the organization.
- Evaluate and narrow to viable proof of concepts through validation.
- Advance selected projects to a pipelined development stage.
- Monitor and scale those that demonstrate positive P&L contributions.
- Establish governance structures like centers of excellence to oversee AI use.
Frequently asked
How many AI projects did NetApp start internally?
NetApp started about 400 bottom-up AI projects internally before narrowing them through evaluation stages.
What is the outcome for the 13 AI initiatives at NetApp?
Thirteen projects are already contributing positive returns to the profit and loss statement after being pipelined.
Where did NetApp CEO George Kurian share these details?
CEO George Kurian shared the details at the Goldman Sachs Communacopia + Technology Conference.
Sources
- Investing.com — NetApp reported that 13 of roughly 400 AI initiatives are already contributing positively to its P&L with main uses in product development, customer lifecycle management and customer support.
- Investing.com — NetApp disclosed that 13 of roughly 400 AI initiatives evaluated now contribute positively to the company’s profit and loss statement across product development, customer support, and supply chain management.
- Seeking Alpha — NetApp, Inc. (NTAP) Presents at Goldman Sachs Communacopia + Technology Conference 2026 Transcript