Liran ZvibelCo-Founder & CEO, WEKA.
Artificial intelligence (AI) has ushered in a new era of smart innovation that is poised to transform every aspect of business and society. Speed in processing and managing data is paramount to ensure organizations take full advantage of the oncoming wave of AI opportunities and stay ahead of the curve.
The problem is that many organizations still rely on data infrastructure from the past, which includes ironclad storage components, data management models that don’t maximize numerous networking advances over the years, and a general lack of modernization. This leads to slow data movement, lower data quality and ultimately reduced competitiveness amid AI demands for unprecedented volumes of data at unprecedented speeds.
One of the most significant limitations of legacy infrastructure is its inability to scale to support the full potential of AI. Massive amounts of data are required to efficiently build and train models—requiring a modern, frictionless infrastructure based on data pipelines versus the old “hub and spoke” model of data storage and management.
As the future becomes present at an ever-increasing rate, AI requires accelerated data portability in extended petascale and even exascale environments that can move seamlessly across distributed edge, core, and cloud environments.
McKinsey described seven actions Data leaders need to consider maximizing their AI build, with the underlying data infrastructure being the key to unlocking the enormous value AI can bring to your business. Some of these actions touch on things that legacy architectures can’t do—including dealing with the evolving AI data lifecycle and how to improve it to ensure high data quality (an important element of effective AI modeling). , creating a data infrastructure that can handle new use cases as they arise, even using genetic AI itself to manage the data of your organization.
In addition, we assigned an August 2024 study of global trends in artificial intelligence conducted by S&P Global Market Intelligence, which surveyed more than 1,500 global AI decision makers. He found that many organizations fail to scale these projects to the enterprise level, and discovered a number of important findings about how we need to upgrade our infrastructure approaches to managing data in the age of artificial intelligence.
The findings of these studies highlight that organizations are still relying on outdated data architectures and approaches to meet the demands of next-generation workloads—with diminishing returns. To outperform your AI competition, you need a modern data foundation to address performance, scale and power requirements and extract maximum value.
Here are some of the key ways data leaders can capture the wide range of benefits of AI:
Don’t let “scale fail” derail your AI projects.
Although AI is proliferating rapidly in the enterprise, many leaders face an uphill battle to scale these projects beyond limited production deployments (and, in many cases, initial pilots). This can lead to a bottleneck of initiatives aimed at AI project teams – which, in turn, can be hampered by data challenges that limit their ability to scale deployments to full production capacity.
You should be able to analyze where lags are occurring and implement the right approaches to get your data moving faster so your AI teams are armed with the data they need to succeed.
Make sure your data accessibility game is strong.
Running AI model training and inference in production requires optimized access to quality data — and lots of it. Your project teams should be able to access this data at the speed of AI. Our study found that access to quality data was cited as the top barrier to effectively scaling AI initiatives (overcoming skills shortages and budget constraints), so you need to ensure your teams can quickly and easily access datasets to ensure your GPUs stay saturated with data and your AI projects are going full throttle.
Get your data house in order—fast.
If you want to implement AI at scale and haven’t taken a hard look at your data stack, reverse course immediately. AI requires frictionless data pipelines capable of moving data to your GPUs and AI models at breakneck speed and dynamically scaling up or down as needed.
Be mindful of your environment and audit every aspect to ensure your organization has what it needs to maximize AI without applying a Band-Aid every now and then to fix some issues. AI innovation will far outpace this approach and ultimately hinder your ability to keep up with the competition.
The emerging group of AI leaders who are successfully exploiting this rocket ship have sorted out their data and are now using AI to differentiate themselves from competitors stuck in the past.
Innovation and adoption of artificial intelligence is moving at a pace that surpasses what the world has seen ago, and you can bet this trend will continue to accelerate. Now is the time to act and respond to this pivotal moment with a modern, data-centric, native AI infrastructure that enables AI to scale so your organization can thrive.
Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Am I eligible?
