Data Privacy and Security: Major Concerns for 80% of AI Decision Makers
Rel=”nofollow” target=”_blank”>Duncan is an award-winning editor with more than 20 years experience in journalism. Having launched his tech journalism career as editor of Arabian Computer News in Dubai, he has since edited an array of tech and digital marketing publications, including Computer Business Review, TechWeekEurope, Figaro Digital, Digit and Marketing Gazette.
Organisations are enthusiastic about generative AI’s potential for increasing their business and people productivity, but lack of strategic planning and talent shortages are preventing them from realizing its true value.
This is according to a study conducted in early 2024 by Coleman Parkes Research and sponsored by data analytics firm SAS, which surveyed 300 US GenAI strategy or data analytics decision makers to pulse check major areas of investment and the hurdles organisations are facing.
Marinela Profi, strategic AI advisor at SAS, said: “Organisations are realizing that large language models (LLMs) alone don’t solve business challenges.
“GenAI should be seen as a perfect participant in hyper automation and accelerating existing processes and approaches, as opposed to the new appealing gadget that will aid companies in achieving all of their commercial goals. Creating a forward-thinking strategy and investing in tech that provides integration, regulation, and comprehensibility of LLMs are essential steps every organization must consider before enthusiastically diving in and getting ‘stuck’.”
Companies are encountering obstacles in four primary areas of implementation:
• Boosting confidence in data usage and compliance achievement. Only a single organization out of ten possesses a reliable system to examine bias and privacy risks in LLMs. In addition, a startling 93% of U.S. businesses lack a thorough governance structure for GenAI, and most are in danger of regulation noncompliance.
• Incorporating GenAI into pre-existing systems and procedures. Companies disclose that they’re confronting compatibility issues when trying to integrate GenAI with their existing systems.
• Talent and skills. There is a recognized in-house GenAI deficiency. Faced with a talent supply issue, top-level management expresses concern regarding the unavailability of the necessary expertise to harness their GenAI investment optimally.
• Unforeseen expenses. Executives highlight the deterring direct and indirect expenses tied to using LLMs. The model creators present an initial cost estimate, which organizations soon perceive as dramatically high. Nevertheless, the costs for proprietary data handling, training, and ModelOps governance are extensive and complicated.
Profi summed up: “Resolution will ultimately hinge on pinpointing pragmatic use cases that deliver superior value and cater to human needs in a sustainable and scalable fashion.
“By conducting this research, we’re furthering our dedication to helping entities maintain relevance, allocate their funds intelligently, and uphold resilience. In an age bracket where AI technology progresses almost every day, the competitive edge leans heavily on the capability to adopt resiliency rules.”
Details of the study were unveiled today at SAS Innovate in Las Vegas, SAS Software’s AI and analytics conference for business leaders, technical users and SAS partners.
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