The business landscape is undergoing a profound transformation, driven by the rapid convergence of data and artificial intelligence. In this era of data and intelligence, companies must prioritize effective data management for AI to remain competitive and relevant.

AI is no longer a distant possibility but a reality that requires a strong data management for AI strategy to truly unlock its potential. Organizations that excel in leveraging data are already seeing significant progress, particularly in regulated industries. However, challenges remain, including the need for better infrastructure and integrated solutions to ensure that AI initiatives succeed. As more companies seek to adopt AI, the importance of simplifying data management and building strong partnerships has never been clearer, according to George Kurian (pictured), chief executive officer of NetApp Inc.

“The algorithms are only as good as the data that you feed them, so it’s super important that you have a really good data strategy and a data management approach that helps the algorithms understand your business better so that you can use [them] to drive business advantage,” he said.

Kurian spoke with theCUBE’s Rob Strechay and Rebecca Knight at NetApp Insight, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed data management for AI and how NetApp is helping organizations harness the power of data and AI by improving data management strategies, integrating infrastructures and addressing industry-specific challenges to drive business innovation. (* Disclosure below.)

The intersection of AI and data strategy

AI technologies have made significant advancements in recent years, but their success depends largely on the quality of the data they process. Despite the progress in AI tools and algorithms, effective deployment remains tied to strong data management for AI, Kurian emphasized.

“Usually, in a large enterprise, about 85 to 90% of their data is unstructured, meaning documents, images and voice recordings, which were never integrated into business systems for analysis,” he said. “Now, with generative AI, you are able to do that. So, we call it the intersection of data and intelligence, and we’re super excited to be part of that journey.”

Many organizations struggle with AI implementation, often treating it as a siloed technology disconnected from broader data management and infrastructure processes. This separation creates challenges that hinder AI’s full potential, according to Kurian.

“AI has been implemented as a silo from an infrastructure, data and process perspective, which is why so many of the AI projects never make it to production,” Kurian said.

This is where NetApp’s expertise in data management comes into play. It’s critically important to bring AI and data infrastructures together to ensure they work harmoniously, much like the cloud, Kurian explained. By solving the data management challenge, NetApp aims to help businesses overcome these hurdles and scale their AI efforts successfully.

Data management for AI: Industry-specific transformation

Industries such as life sciences and pharmaceuticals are advancing rapidly with AI, particularly because of their need for structured data management in highly regulated environments. These sectors are well-positioned for AI success as a result of their strong data handling and privacy standards, according to Kurian.

“We see that those institutions that have organized their data and managed it well are well-positioned for success,” he stated.

Industries operating in stringent regulatory environments are often better equipped to adopt AI because they have already established rigorous procedures for managing data and ensuring privacy. This strong foundation allows them to integrate AI more seamlessly into their operations, making them more prepared for advanced technological adoption, according to Kurian.

“Regulated industries are actually growing faster with AI than unregulated ones,” Kurian added, underscoring the importance of well-structured data in driving AI adoption.

Kurian also highlighted use cases where AI is making a transformational impact, such as in manufacturing, life sciences and even the food and beverage industry. For instance, AI is being used to improve processes in auto manufacturing through digital twins and enhance beverage production quality.

“In industries where the data is well-established, you’re starting to see the transformative impact,” he said.

The ‘true’ impact of AI

Despite the optimism around AI, Kurian acknowledged that some business leaders remain skeptical about its potential, citing concerns about its long-term impact. However, he believes that while initial expectations may be overly ambitious, the true impact of AI will unfold over the coming years.

“Maybe people think that the transformation will happen in two years, but actually if you look out over the long term or the medium term over a decade, it’s probably more impactful in the out years than people expect today,” Kurian said.

NetApp’s focus on simplifying data management, fostering strong industry partnerships and addressing the unique challenges of different sectors positions it as a key player in helping organizations navigate the AI revolution. By streamlining access to data and offering tailored solutions, the company is enabling businesses to unlock the full potential of AI across diverse industries, Kurian explained.

“We believe that for AI to have the transformative impact that people talk about today, it can’t just be the largest organizations, but it has to be every organization, whether it’s educational institutions — whether it’s governments, whether it’s underdeveloped countries, all of them have to be able to access,” he said.

Stay tuned for the complete video interview, part of SiliconANGLE’s and theCUBE Research’s coverage of NetApp Insight.

(* Disclosure: TheCUBE is a paid media partner for NetApp Insight. Neither NetApp, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE