A report from MIT Technology Review Insights, in partnership with Snowflake, has revealed that poor data foundations are preventing four out of five businesses from fully capitalising on artificial intelligence (AI).
According to the report titled 'Data Strategies for AI Leaders', 78% of organisations are unable to maximise their AI investments due to inadequate data structures. The findings highlight that while there is a strong business drive toward generative AI—to increase efficiency, enhance market competitiveness, and foster product innovation—these ambitions are hindered by foundational data issues.
Baris Gultekin, Head of AI at Snowflake, commented on the research findings, stating: "Many of today's organisations have big ambitions for generative AI: they are looking to reshape how they operate and what they sell.
"Our joint research shows that as organisations feel increasing urgency to deploy AI applications, they are realising 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."
The report further showed that a mere 22% of business leaders feel 'very ready' to engage with AI, with 53% claiming to be 'somewhat ready'. Organisations that rate their readiness higher often encounter fewer challenges with computing power, data silos, integration issues, and data governance. Despite this, the majority of businesses are recognising that effective data management is essential to unlocking AI's value efficiently.
One critical challenge underscored by the research is the difficulty of deploying AI at scale. A significant 95% of survey respondents acknowledged facing obstacles in this area. The major challenges include data governance, security or privacy concerns (59%); data quality and timeliness (53%); and costs related to resources or investments (48%).
The report highlights that although the cost of generative AI is decreasing, primarily due to enterprises developing smaller yet capable large language models (LLMs), resource allocation remains a substantial barrier. These financial and resource decisions are essential, particularly when improving an enterprise's data foundations.
For organisations already advanced in their data strategies, the benefits of generative AI are becoming apparent as they leverage extensive data foundations. These businesses are realising advantages by integrating AI into well-structured data, allowing them to mitigate governance and security concerns as they exploit AI's potential.
In summary, the report calls for businesses to fortify their data foundations across a diverse range of processes, thus enabling them to fully harness the power of generative AI and its transformative capabilities. This approach not only promises improved AI utilisation but also addresses common data management concerns, paving the way for efficient and secure AI-driven advancements.
