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Fifty-three percent of enterprise technology leaders are worried they will run out of computing power in the next decade — one of several challenges hindering organizations as they look to scale up artificial intelligence initiatives, according to a new report by SambaNova Systems.
With AI and ML becoming ubiquitous across industries, it has the same potential to refactor the Fortune 500 as the internet has had over the past several decades. But as the AI revolution accelerates, there’s a burgeoning gulf between the haves and the have-nots. That is, a growing number of top companies have figured out how to deploy AI initiatives at scale, gaining a competitive edge against businesses that have yet to do so.
So, why are some enterprises reaping the benefits of AI, while others are at risk of being left behind?
The report’s findings show most people are hopeful about the potential of AI and ML technologies; two-thirds of technology leaders plan to significantly increase their AI and ML investments over the next five years. Furthermore, more than three-quarters (78%) say that AI and ML is critical for driving revenue.
But even as organizations look to AI to drive innovation and revenue, many remain in the early stages of implementing AI and ML initiatives — with plenty of barriers holding them back. More than half cite customizing AI models as their top challenge, while around one-third blame insufficient computing infrastructure (35%) or a lack of trained talent (28%).
In the years ahead, enterprises are tasked with untangling the complexities of scaling AI/ML to keep pace with competitors. AI will only continue to rapidly expand and evolve, leaving technology leaders to determine which use cases will drive revenue and innovation for their business, and identify how to deploy AI technologies at an enterprise level.
For this report, SambaNova surveyed 600 AI and ML, data, research, customer experience and cloud infrastructure leaders across six industries.
Read the full report by SambaNova.
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