Aligning the power of AI with the future we want to build – Intelligent CIO Middle East

AI For Business

César Cernuda, Global President of NetApp, delves into the transformative potential of AI, its heavy reliance on data, and the ethical considerations we must address.

Cesar Cernuda, Global President, NetApp

Today, Artificial Intelligence is perceived as a revolutionary force that is changing the rules of the game, or a juggernaut that is overrated by some and underrated by others. Personally, I believe that Artificial Intelligence is another milestone in innovation that has driven human progress in the same way as the Industrial and Scientific Revolutions, and that, like those two, it can be used for good or to create chaos.

The potential of AI in business is undeniable. Predictive AI is already being used to recognize patterns, achieve dramatic efficiency improvements, and solve business and societal problems faster and more effectively – for example, to predict how proteins will fold in medical research, or detect financial fraud to protect users and businesses. And its ability to process vast amounts of data is helping inform decision-making and transforming how we address the greatest threat to humanity: climate change.

What's more, Generative AI doesn't just recognize new patterns – it generates them, making software developers more productive, content creators able to deliver more immersive experiences to their audiences, and helping customers, employees, citizens, and students find the information they need much more easily. And all of this vast potential comes from one ingredient: data.

Yes, artificial intelligence is fueled by data. Therefore, data storage, security, and accessibility are crucial to the analytics that AI delivers. Renowned computer scientist Peter Norvig summed it up succinctly: “More data beats smart algorithms, but better data beats more data.” Thus, generative AI deployment projects are only as good as the quality of the data that feeds them. And as AI evolves and becomes more business-critical, the importance of an intelligent data infrastructure becomes crucial.

Data integration, performance and reliability needs

To “leverage” data, companies need to manage multiple versions of their models and keep them up to date with the latest datasets. This requires a free flow of data, whether it's an organization's own data or other relevant data used to improve AI systems. This isn't as simple as turning on a tap, because we're talking about massive amounts of constant data that is distributed, unstructured, and in need of protection.

Complex technology and organizational silos are the main obstacles to launching AI projects. Therefore, organizations that want to make progress in this field must adopt a modern, intelligent, integrated cloud data infrastructure that provides the most complete, powerful, and sustainable solutions without information silos.

Companies large and small that want to optimize their data engines to reap the benefits of generative AI must address four key issues: First, ensure that your data and AI organizations are aligned. Today, many companies have data analysts and engineers with a deep understanding of data, data scientists who can apply modern analytical tools, and business analysts who understand how to use data and AI recommendations to drive business outcomes. But these different functions within each organization need to work together as one team to accelerate the impact of AI.

The second fundamental aspect is the analysis and integration of unstructured data. For years, companies have invested in tools to extract value from structured data. But generative AI provides a powerful engine to extract value from unstructured data, the fastest growing part of data today. Therefore, each organization needs to have an up-to-date view of their unstructured data landscape and associated applications so that they can be used in AI applications.

To prepare for AI, businesses must also remember to consolidate workloads and data onto an intelligent, multicloud hybrid infrastructure. With the volume, variety and velocity of data growing relentlessly, there is a huge amount of data to process, so simplicity and integration are crucial.

Finally, strengthening data security and governance is also important. With great power comes great responsibility, and this is especially true with AI. Personal data is much more valuable with AI, but it can also be a source of error, bias, and inaccuracy in models, so it needs to be protected and governed.

Ultimately, when organizations invest in optimizing their data engines, they are building a solid foundation to unlock the full potential of artificial intelligence in a responsible, safe, and accessible way. Over the past year, AI has kept us IT market leaders up at night – not because of fear, but because AI has changed reality with the potential to reshape industries and redefine the human experience.

Despite our enthusiasm for the technology, we must face real challenges in this space, including bias, transparency, and oversight. And our focus going forward will go beyond pushing technical boundaries to reconcile the transformative power of AI with human ingenuity, ethical considerations, and a clear vision for the future we want to build.

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