By Hoseb Dermanilian, Senior Director and Global Head of AI Sales and GTM, NetApp

As artificial intelligence (AI) continues to mature, its potential reaches far beyond mere automation. Today, AI has become a transformative force, redefining business models, sparking innovation, and creating new competitive advantages. For organizations ready to harness AI's potential, this moment presents an opportunity to unlock unprecedented growth and stay ahead of an ever-evolving competitive landscape. But realizing these benefits depends on more than enthusiasm for AI; it requires a robust, AI-ready infrastructure that can fuel these ambitious goals.

Organizations need to work at the intersection of data and AI, ensuring they have the tools and infrastructure needed to translate AI aspirations into measurable outcomes. Here's how businesses can leverage AI to drive growth, innovation, and long-term advantage.

AI as a Catalyst for Business Model Transformation

In recent years, AI has evolved from a tool for streamlining operations to a powerful engine for growth. It's no longer just about automation; it's about shaping new products, services, and even business models. AI is already transforming industries ranging from healthcare and finance to media and manufacturing.

For example, in healthcare, AI is shortening the time from drug discovery to market, accelerating treatments and improving patient outcomes. In finance, AI enables better risk assessment, fraud detection, and personalized customer service. Media companies use AI to create more tailored and engaging experiences. Across sectors, AI enhances business agility, enabling organizations to pivot more effectively in response to shifts in demand or market conditions.

But for companies to reach AI's full potential, they must move beyond isolated experiments and proofs of concept (POCs). They need to integrate AI deeply into their operations and decision-making processes, turning data into actionable insights. These companies also need the infrastructure that makes this transformation seamless-an infrastructure designed to support the high-performance demands of AI at scale.

Does your Infrastructure handle AI Workloads?

Despite widespread enthusiasm for AI, many organizations grapple with the misconception that they need a separate, siloed infrastructure to support AI workloads. However, creating isolated architectures often adds complexity rather than value. The real challenge lies in optimizing existing infrastructure to meet AI demands seamlessly.

Organizations are increasingly establishing AI Centers of Excellence (COEs) to scale AI efforts efficiently across departments. These COEs provide the expertise, tools, and frameworks necessary to harness AI's potential. However, success also depends on ensuring that the underlying infrastructure is designed to handle the scalability, speed, and security requirements of AI applications.

For industries with strict regulatory requirements, such as healthcare and finance, data governance and security must remain top priorities. Businesses need systems that safeguard proprietary information and ensure compliance while enabling the insights AI offers. Addressing these concerns is crucial for implementing AI responsibly and ethically.

Centralized Data Infrastructure: The Key to AI Success

As data proliferates, organizations are facing a "data tsunami." This surge of information requires a disciplined approach to data management. A centralized data platform-adhering to FAIR principles (Findable, Accessible, Interoperable, and Reusable)-allows businesses to extract maximum value from their data without being overwhelmed. With this structure, companies can consolidate data from multiple sources, making it easier to access and analyze across cloud and on-premises environments.

Reducing data silos and fostering interoperability accelerates AI insights and minimizes the friction that often accompanies data migration. A unified approach to data mobility ensures AI can leverage data wherever it resides, enhancing agility and reducing complexity.

Adopting Small Language Models (SLMs) for Enterprise AI

While Large Language Models (LLMs) have dominated recent discussions around AI, many businesses are finding greater value in Small Language Models (SLMs) that are more specialized and aligned with their specific needs. Rather than relying on general-purpose models, enterprises can fine-tune SLMs with proprietary data to address unique challenges in their industry.

For instance, healthcare organizations can develop smaller, targeted models to manage patient data more effectively, while financial firms can fine-tune models for improved risk assessment. This approach not only reduces complexity but also allows for AI to be more adaptable and precise in addressing industry-specific needs.

Intelligent data infrastructure plays a critical role here, supporting the ability to build, refine, and deploy these models effectively. By bringing AI models directly to their data directly, organizations can generate insights quickly and make informed, data-driven decisions.

Preparing for the Future: AI-Driven Opportunities and Competitive Differentiation

Companies that embrace AI swiftly and strategically will gain a lasting competitive edge. Early adopters unlock immediate benefits while building a foundation for sustained growth. As AI intersects with emerging technologies like quantum computing, its potential to drive innovation and agility will only grow. With the right infrastructure, businesses can overcome challenges and transform them into opportunities for innovation.

AI's Role in Driving Business Value

As AI becomes central to enterprise strategy, it brings a new era of opportunity. However, its success depends on the strength of the infrastructure that supports it. Beyond scalability and performance, organizations must prioritize security and governance. Protecting proprietary data, ensuring compliance with industry regulations, and maintaining ethical AI practices are critical to building trust and enabling long-term success.

Intelligent data infrastructure integrates advanced governance and security measures, empowering organizations to safely harness AI's potential without compromising compliance or data integrity. By investing in this robust foundation, businesses can transform operations, open new pathways for growth, and gain a competitive edge.

In an AI-driven future, companies with a clear vision and solid foundation will not just survive-they'll thrive. The time to prepare for that future is now.

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ABOUT THE AUTHOR

Hoseb Dermanilian is Sr. Director, Global Head of AI Sales & GTM at NetApp. Hoseb Dermanilian joined NetApp in 2014. In his current role, he is responsible for leading NetApp's global AI sales and go-to-market efforts. Hoseb heads a global team of sales specialists who are focused on helping customers build the right data platform for their AI and data-driven business strategies. He is also focused on developing and executing NetApp's AI go-to-market strategies across different functions within the company, and with various technology partners, such as NVIDIA. Hoseb comes from a technical background, having previously served as the technical lead for NetApp's Data Analytics business covering different geographies. He is the author of several publications in the domain of cryptography and machine learning at leading scientific journals.