Concerns over data security and privacy are shared by 80% of AI decision makers.

Organizations are excited about generative AI’s potential to boost business and people productivity. However, strategic planning gaps and talent shortages are hindering its full realization.

This insight comes from a study by Coleman Parkes Research, sponsored by data analytics firm SAS, which surveyed 300 US decision-makers in GenAI strategy or data analytics to assess major investment areas and challenges.

Marinela Profi, strategic AI advisor at SAS, emphasized, “Organizations are finding that large language models (LLMs) alone don't solve business challenges. GenAI should be seen as a catalyst for hyper automation and process acceleration rather than a panacea for all business goals. Developing a strategic approach and investing in technologies that provide integration, governance, and explainability for LLMs are essential steps before fully committing.”

The study identified four key implementation challenges:

Trust and Compliance: Only 10% of organizations have systems to measure bias and privacy risk in LLMs. Additionally, 93% of US businesses lack a comprehensive governance framework for GenAI, risking noncompliance with regulations.
Integration: Organizations report compatibility issues when incorporating GenAI into existing systems.
Talent and Skills: There is a shortage of in-house GenAI expertise. HR departments struggle to find suitable hires, causing concern among leaders about lacking the necessary skills to maximize their GenAI investments.
Cost Prediction: Leaders cite high direct and indirect costs associated with LLMs. Initial token cost estimates are proving prohibitive, and the expenses for private knowledge preparation, training, and ModelOps management are substantial and complex.

Profi added, “The focus should be on identifying real-world use cases that offer high value and address human needs sustainably and scalably. This study reaffirms our commitment to helping organizations remain relevant, invest wisely, and stay resilient. In a rapidly evolving AI landscape, competitive advantage depends on embracing resiliency principles.”

The study’s findings were revealed at SAS Innovate in Las Vegas, SAS Software’s AI and analytics conference for business leaders, technical users, and SAS partners.