Organisations are enthusiastic about generative AI’s potential to improve business and people productivity, but a lack of strategic planning and talent shortages are preventing them from realising its true value.
This is according to a study conducted by Coleman Parks Research in early 2024 with support from data analytics firm SAS, which surveyed 300 U.S. GenAI strategy or data analytics decision makers to identify key investment areas and challenges facing their organizations.
“Organizations are realizing that large language models (LLMs) alone cannot solve their business challenges,” said Marinela Profi, strategic AI advisor at SAS.
“GenAI should not be treated as a new toy to help organizations realize all their business goals, but rather as an ideal contributor to hyper-automation and the acceleration of existing processes and systems. Taking the time to develop an innovative strategy and invest in technologies that provide integration, governance and explainability for LLMs is a critical step that all organizations should take before jumping in with both feet and 'locking in'.”
Organizations face obstacles in four key areas of implementation.
• Increase trust in data use and achieve compliance: Only 1 in 10 organizations have a reliable system in place to measure bias and privacy risks in LLM. Additionally, 93% of US companies lack a comprehensive governance framework for GenAI, putting the majority at risk of non-compliance with regulations.
• Integrating GenAI into existing systems and processes. Organizations have revealed compatibility issues when trying to combine GenAI with their current systems.
• Talent and skills. There is a lack of GenAI in-house. When HR departments face a shortage of the right talent, organizational leaders worry they won't have access to the skills they need to get the most out of their GenAI investments.
• Cost projections. Leaders note that the direct and indirect costs associated with using LLMs are prohibitive. Modelers provide token cost estimates (which organizations find to be prohibitive), but the costs of private data preparation, training, and ModelOps management are long and complex.
Profi added: “It will be important to identify real-world use cases that provide the most value and solve a human need in a sustainable and scalable way.”
“Through this survey, we continue our commitment to help organizations stay relevant, invest wisely, and remain resilient. In an era when AI technologies evolve almost daily, competitive advantage depends heavily on the ability to embrace the rules of resilience.”
Details of the survey were released today at SAS Innovate in Las Vegas, SAS Software's AI and analytics conference for business leaders, technology users and business leaders. Scandinavian Airlines partner.
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