Malaysia, 16 October 2024 – Artificial intelligence (AI) holds the promise of transforming businesses, yet a staggering 78% of organisations are struggling to maximise their AI investments due to weak data foundations, according to a new report by MIT Technology Review Insights, in partnership with Snowflake, the AI Data Cloud company.
The report, titled ‘Data Strategies for AI Leaders’, outlines the challenges businesses face in deploying AI and highlights the need for robust data strategies to unlock AI’s full potential.
AI’s Potential at Risk
Businesses are placing high hopes on AI, with 72% of respondents aiming to improve efficiency, 55% seeking competitive advantages, and 47% focusing on innovation. However, despite these ambitions, the report found that only 22% of business leaders feel ‘very ready’ to engage with AI, while 53% are ‘somewhat ready’.
According to Baris Gultekin, Head of AI at Snowflake, “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.”
Data Challenges Hindering AI Adoption
The lack of a strong data foundation is preventing businesses from unlocking AI’s full capabilities. Only a small percentage of businesses (22%) report being fully prepared to engage with AI, while 95% face significant challenges when implementing AI systems. These hurdles include data governance and security (59%), data quality and timeliness (53%), and cost-related issues (48%).
“Generative AI’s benefits are becoming visible to those companies farther along their data journey,” Gultekin adds. “A strong data foundation is at the core of generative AI capabilities, and business leaders need to move quickly to address concerns like data security and cost.”
The Importance of Data Foundations
A solid data foundation allows businesses to harness both structured and unstructured data—such as videos and images—to support AI applications. This foundational framework involves processes like data gathering, aggregation, storage, and accessibility.
While businesses are eager to deploy AI at scale, data silos, integration issues, and lack of scalable computing power continue to slow down AI adoption.
Also Read: Unveiling Leica’s Latest Innovation: An Exclusive Interview with Sunil Kaul and Chef Ranveer Brar on Photography, Storytelling, and Creativity
Investment in Data Foundations Is Key
The report concludes that businesses must prioritise investments in their data infrastructure to fully realise the potential of AI. As generative AI becomes more accessible, the cost of implementing AI models is decreasing, allowing companies to create smaller, equally capable large language models (LLMs).
The future of AI is promising, but businesses need to address foundational data challenges first. As Gultekin explains, “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.”
About Snowflake
Snowflake enables companies to easily, efficiently, and securely integrate AI into their operations. With the Snowflake AI Data Cloud, businesses worldwide harness AI’s potential to share data, build applications, and power AI-driven decisions. Learn more about Snowflake at snowflake.com.
Methodology
MIT Technology Review Insights surveyed 276 executives across various industries in May 2024. The research focused on their data strategies and ambitions regarding generative AI deployment. The respondents represented organisations across the globe, offering insights into the challenges of scaling AI applications.
