Majority of businesses are unable to capitalize on AI due to poor data foundations – MIT Technology Review Insights & Snowflake report
Business leaders have high hopes that Artificial Intelligence (AI) investments can drive market-changing innovations to transform everything from customer satisfaction to product innovation. However, a poor data foundation is holding 78% of organisations back from achieving these goals.
A new report from MIT Technology Review Insights, in partnership with Snowflake, titled ‘Data Strategies for AI Leaders’, found that while businesses have big ambitions for generative AI — with 72% looking to increase efficiency or productivity, 55% betting on increased market competitiveness, and 47% aiming to see more innovation in products and services — the foundational data strategy needs to be improved to maximize AI’s potential.
Businesses need strong data foundations, powered by modern cloud data platforms, to enable them to harness their own stores of data, and also vast volumes of previously inaccessible data, largely from unstructured data such as videos and images. According to the report, just 22% of business leaders say they are ‘very ready’ to engage with AI, while 53% are ‘somewhat ready’. Higher readiness correlates with fewer challenges related to accessing scalable computing power, data silos and integration issues, and data governance. Despite many business leaders’ confidence in the results AI can deliver, they are realizing that data is key to how quickly and effectively they can unlock AI’s value.
Another challenge facing organizations is deploying AI at scale. 95% of those surveyed reported facing hurdles when implementing AI. 59% of respondents cited data governance, security, or privacy as their most prevalent challenge, followed by data quality and timeliness (53%), and costs of resources or investment (48%). Spending and resourcing decisions, including those needed to improve data foundations, are a challenge when it comes to any technology investment. But the cost of generative AI itself is decreasing, with enterprises having begun to build smaller large language models (LLMs) that remain equally capable, and less expensive.
“Many of today’s organizations have big ambitions for generative AI: they are looking to reshape how they operate and what they sell,” said Baris Gultekin, Head of AI, Snowflake. “Our joint research shows that as organizations feel increasing urgency to deploy AI applications, they are realizing that their data can help them deliver insights from previously untapped sources of information. A strong data foundation is at the core of generative AI capabilities, and business leaders need to move quickly to deal with concerns such as data security and cost, and establish the foundation they need to deliver on the promise of AI.”
Generative AI’s benefits are becoming visible to those companies farther along their data journey, because they invested heavily in data foundations and are now being rewarded by bringing AI onto that data. For any business looking to capitalize on AI, they must first establish a robust data foundation, which covers a broad collection of processes and assets involved in the gathering, aggregation, storage, and accessibility of organizational data. Investment in data foundations across an organization will enable much more powerful generative AI users, while also reducing governance and security concerns.
