Organizations Enthusiastic About Generative AI’s Potential, But Also See Obstacles

A recent study shows US organizations are enthusiastic about generative AI’s potential for increasing their business and people productivity. But beneath surging enthusiasm, leaders see understanding gaps, a lack of strategic planning and the talent famine as obstacles to realizing and measuring the technology’s full value.

The study, conducted earlier this year by Coleman Parkes Research Ltd. and sponsored by SAS, surveyed 300 US GenAI strategy or data analytics decision makers to pulse check major areas of investment and the hurdles organizations are facing.

“Organizations are realizing that large language models alone don’t solve business challenges,” said Marinela Profi, Strategic AI Advisor at SAS. “GenAI should be treated as an ideal contributor to hyperautomation and the acceleration of existing processes and systems rather than the new shiny toy that will help organizations realize 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 organizations should take before jumping in with both feet and getting ‘locked in.'” 

Organizations are hitting stumbling blocks in four key areas of implementation:

  • Increasing trust in data usage and achieving compliance. Only one in 10 organizations has a reliable system in place to measure bias and privacy risk in LLMs. Moreover, 93% of US 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. Organizations 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, organizational 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 organizations now realize is prohibitive). But the costs for private knowledge preparation, training and ModelOps management are lengthy and complex.

“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,” Profi said. “Through this study, we’re continuing our commitment to helping organizations 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.

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