Organizations are excited about the potential of productive AI to increase the productivity of their businesses and people, but a lack of strategic planning and talent shortages are preventing them from realizing its true value.
That’s according to a study conducted in early 2024 by Coleman Parkes Research and funded by data analytics firm SAS, which surveyed 300 U.S. GenAI or data analytics strategy decision makers to pulse check important areas of investment and the obstacles they face. organizations face.
Marinela Profi, strategic AI consultant at SAS, said: “Organisations are realizing that large language models (LLM) alone do not solve business challenges.
“GenAI should be viewed as an ideal contributor to hyper-automation and acceleration of existing processes and systems rather than the shiny new toy that will help organizations realize all their business ambitions. Taking the time to develop a forward-thinking strategy and investing in technology that unifies, governs and explains LLMs are critical steps all organizations must take before jumping in with both feet and getting locked in.”
Organizations face barriers in four key application areas:
• Increase confidence in the use of data and achieve compliance. Only one in 10 institutions have a reliable system for measuring bias and privacy risk in LLMs. Additionally, 93% of US businesses lack a comprehensive governance framework for GenAI, and the majority are at risk of regulatory non-compliance.
• Integration of GenAI into existing systems and processes. Organizations are finding that they face compatibility issues when trying to combine GenAI with their current systems.
• Talent and skills. Internal GenAI is missing. As HR departments face a shortage of qualified hires, organizational leaders are concerned that they don’t have access to the skills necessary to get the most out of their GenAI investment.
• Cost forecasting. Leaders cite prohibitive direct and indirect costs associated with LLM use. Modelers provide a symbolic cost estimate (which organizations now realize is prohibitive). But the costs of preparing private knowledge, training and managing ModelOps are long and complex.
Profi added: “It will come down to identifying real-world use cases that deliver the highest value and solve human needs in a sustainable and scalable way.
“Through this study, we continue our commitment to helping organizations stay relevant, invest their money wisely and stay resilient. In an era where AI technology is evolving almost daily, competitive advantage depends heavily on the ability to embrace the rules of resilience.”
Details of the study were revealed today at SAS Innovate in Las Vegas, SAS Software’s AI and analytics conference for business, technical users and SAS collaborators.
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