ETR Survey Highlights Urgent Need for Expertise in Leveraging Generative AI on Unstructured Data

Enterprise leaders recognize the potential of unstructured data to boost operational efficiency, but expertise gap hinders progress

Qlik^®, a global leader in data integration, analytics, and artificial intelligence (AI), today announced new research revealing that while enterprises recognize the significant potential of unstructured data to enhance operational efficiency and drive meaningful insights, many are struggling to effectively leverage this resource. The survey shows that a lack of expertise and insufficient tools are major barriers, with only a small percentage of enterprises dedicating more than a quarter of their AI budget to unstructured data initiatives.

“With many sources citing that unstructured data makes up to 80% of the world’s data, it is no surprise that enterprise leaders want more real value from this untapped source,” said Brendan Grady, General Manager of Qlik’s Analytics Business Unit. “Yet, our survey highlights that nearly 70% agree their organization is not well equipped to understand how GenAI can be leveraged on their unstructured data.”

“Companies are looking for solutions that enable GenAI adoption without requiring them to overhaul their existing skillsets and technology stack. The opportunity is finding ways to integrate AI seamlessly into current analytics environments, allowing organizations to extract the right answers from unstructured data and drive meaningful business outcomes.”

The survey reveals insightful data on how leaders feel and what they are doing to address the opportunity that unstructured data and GenAI enable:

• Data privacy and compliance concerns dominate: 59% of respondents are very concerned about data privacy and 47% about regulatory compliance, significantly outweighing concerns about ROI (19%).
• Integration and cost are top priorities when evaluating vendors: When evaluating vendors, system integration (55%), cost (50%), and governance features (49%) are top priorities, whereas vendor reputation is a low priority (16%). Respondents expect modest financial gains from using unstructured data, with 45% anticipating a 10%-20% improvement in their top or bottom lines.
• Interest in GenAI is high, but significant investment is lacking: Among those interested in using GenAI for unstructured data, two out of three respondents plan to invest in an GenAI tool for unstructured data. Despite widespread interest, only 22% of all respondants indicate they are making “significant” investments in AI technologies.
• Unstructured data is seen as a key driver for efficiency: A clear majority (62%) see the opportunity in unstructured data to improve operational efficiency, while only 31% believe it can drive innovation. Nearly half (45%) describe a use-case involving better search and query tools to dig into internal documents.
• Traditional search tools fall short for unstructured data: There is strong agreement that traditional enterprise search tools are insufficient for maximizing the value of vast document libraries. Only 16% have already purchased a tool designed to deliver insights from unstructured data, and most efforts remain in early or pilot stages.

“The findings from our survey underscore a critical challenge facing enterprises today: the gap in expertise needed to harness the full potential of generative AI for unstructured data,” said Erik Bradley, Chief Strategist & Director of Research at Enterprise Technology Research. “While the appetite for leveraging unstructured data is high, the lack of specialized skills and appropriate tools is a significant barrier. To truly capitalize on the opportunities presented by GenAI, organizations must invest in bridging this knowledge gap and integrating advanced AI capabilities seamlessly into their existing analytics frameworks.”

The “Unstructured Data and GenAI Survey,” executed in April 2024 by Enterprise Technology Research (ETR) on behalf of Qlik, surveyed 200 enterprise technology decision makers across multiple industries.