Unlocking the Potential of Generative AI: Challenges and Strategies for Organizations
Organizations are increasingly excited about the transformative power of generative AI (GenAI) in enhancing both business performance and workforce productivity. However, many are finding themselves hampered by a lack of strategic planning and a scarcity of skilled talent, preventing them from fully harnessing this technology’s capabilities.
A recent study conducted in early 2024 by Coleman Parkes Research, sponsored by data analytics powerhouse SAS, surveyed 300 decision makers in the United States involved in GenAI strategy and data analytics. The findings offer a revealing snapshot of investment trends and the obstacles businesses face.
According to Marinela Profi, a strategic AI advisor at SAS, it’s imperative for companies to realize that relying solely on large language models (LLMs) is not a panacea for their operational challenges. Instead, she emphasizes that GenAI should be viewed as a crucial player in hyper-automation efforts and the enhancement of existing processes, rather than just a tempting new tool that promises to fulfill all business objectives.
Key Implementation Challenges
The research highlighted four significant hurdles that organizations encounter in their journey towards effective GenAI integration:
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Building Trust and Ensuring Compliance: A mere 10% of businesses currently have dependable measures to assess bias and privacy risks associated with LLM usage. Alarmingly, 93% lack a comprehensive governance framework for GenAI, rendering many vulnerable to regulatory noncompliance.
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System Integration: Compatibility issues abound as organizations attempt to meld GenAI with their existing technological infrastructures.
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Talent Acquisition and Skill Gaps: There exists a stark deficiency of in-house GenAI expertise. HR faces difficulties in recruiting talent with the necessary qualifications, leading to concerns among leaders about their capability to extract value from GenAI investments.
- Managing Costs: Concerns about both direct and indirect costs related to LLMs are prevalent. While model creators provide token cost estimates, organizations often discover that expenses related to private knowledge preparation, training, and ModelOps management are far more complicated and substantial than expected.
Profi stresses the importance of focusing on real-world applications of GenAI that deliver sustainable and scalable benefits. “Identifying use cases that genuinely address human needs and yield high value is crucial,” she remarked.
A Roadmap to Resilience
The insights from this study were unveiled at SAS Innovate, a premier AI and analytics conference in Las Vegas aimed at industry leaders and technical experts. SAS remains committed to helping organizations navigate the complexities of AI technology, optimize their investments, and maintain resilience in a rapidly developing technological landscape.
Conclusion
As the potential of generative AI continues to unfold, organizations must prioritize creating robust strategies and investing in the necessary infrastructure and talent. By overcoming the current barriers to implementation, they can unlock the true value of GenAI, turning challenges into opportunities and ensuring long-term success in an increasingly competitive environment. Embracing a forward-thinking approach will be key for businesses aiming to leverage the ever-evolving world of AI technology.
