New research from SAS has revealed 80% of business leaders are anxious about data privacy and security.

Additionally, many acknowledge a lack of governance framework in their establishments; with a mere 10% confident about their regulatory compliance readiness. This information proceeds from a novel study that analyses the current sentiments towards generative artificial intelligence (GenAI).

The research, which surveyed 300 US data analytics decision makers and GenAI strategists, looked into investment scopes and the prominent hurdles companies struggle with. 

The research found that while organisations demonstrate considerable enthusiasm for GenAI, recognising its potential to augment both business and human productivity, this enthusiasm is clouded by several challenges, including understanding gaps, an absence of strategic planning, and a talent deficit.

Marinela Profi, Strategic AI Advisor at SAS, explains, “GenAI should be treated as an ideal contributor to hyper automation and acceleration of existing processes and systems rather than a shiny new toy that will realise all business aspirations.”

Profi emphasises the importance of developing a progressive strategy and the need to invest in technology that efficiently integrates, governs, and explains large language models (LLMs).

Organisations are hitting stumbling blocks in four key areas of implementation:
• Increasing trust in data usage and achieving compliance. Only one in 10 organisations has a reliable system in place to measure bias and privacy risk in LLMs. Moreover, 93% of U.S. 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. Organisations 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, organisational 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 organisations now realise 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," said Profi.

"In an era where AI technology evolves almost daily, competitive advantage is highly dependent on the ability to embrace the resiliency rules.”