80% of AI decision makers are worried about data privacy and security
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 realising 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 realising that large language models (LLMs) alone don’t solve business challenges.
“GenAI should be treated as an ideal contributor to hyper automation and the acceleration of existing processes and systems rather than the new shiny toy that will help organisations realise all their business aspirations. Time spent developing a progressive strategy and investing in technology that offers integration, governance and explainability of LLMs are crucial steps all organisations should take before jumping in with both feet and getting ‘locked in.’”
Organisations are hitting stumbling blocks in four key areas of implementation:
• Increasing trust in data usage and achieving compliance. Only one in 10 organisations has a reliable system in place to measure bias and privacy risk in LLMs. Moreover, 93% of U.S. businesses lack a comprehensive governance framework for GenAI, and the majority are at risk of noncompliance when it comes to regulation.
• Integrating GenAI into existing systems and processes. Organisations reveal they’re experiencing compatibility issues when trying to combine GenAI with their current systems.
• Talent and skills. In-house GenAI is lacking. As HR departments encounter a scarcity of suitable hires, organisational leaders worry they don’t have access to the necessary skills to make the most of their GenAI investment.
• Predicting costs. Leaders cite prohibitive direct and indirect costs associated with using LLMs. Model creators provide a token cost estimate (which organisations now realise is prohibitive). But the costs for private knowledge preparation, training and ModelOps management are lengthy and complex.
Profi added: “It’s going to come down to identifying real-world use cases that deliver the highest value and solve human needs in a sustainable and scalable manner.
“Through this study, we’re continuing our commitment to helping organisations stay relevant, invest their money wisely and remain resilient. In an era where AI technology evolves almost daily, competitive advantage is highly dependent on the ability to embrace the 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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