The rapid rise of generative artificial intelligence (Gen AI) models like ChatGPT is opening up exciting new possibilities for businesses across a variety of industries. These powerful language models assist with a variety of tasks, from content creation to coding to customer service. However, the rapid adoption of Gen AI also exposes enterprises to significant data privacy risks that require urgent attention.
The main concern is the training data used to develop these AI systems. Large language models ingest large amounts of online information such as websites, books, articles, and social media posts, much of which can contain personal data, intellectual property, and other sensitive information. there is. Companies leveraging these models must navigate the complex legal and ethical implications of using data not intended for this purpose.
Every day, the world generates 5 exabytes of data. By 2025, this is expected to increase to a rate of 463 exabytes per day with increasing adoption of Gen AI. But as businesses continue to adopt AI at an accelerated rate, today's organizations are increasingly concerned about how the technology will impact their valuable data.
According to a SAS study released at this year's SAS Innovate event, 80% of leaders are concerned about data privacy and security, and business leaders admit to a lack of governance frameworks.
According to the report, US organizations are enthusiastic about Gen AI's potential to improve business and employee productivity. But amidst growing enthusiasm, leaders see gaps in understanding, lack of strategic planning, and talent shortages as obstacles to realizing and measuring the full value of technology.
Challenges faced by organizations implementing Gen AI
Organizations working to implement Gen AI face several key challenges. First, they struggle to increase trust in data usage and achieve compliance. According to a report from SAS, only 1 in 10 organizations have reliable systems in place to measure bias and privacy risks in large-scale language models (LLMs), an astonishing 93% of U.S. companies. % lack a comprehensive governance framework for Gen AI, and the majority risk not complying with new regulations.
Second, organizations run into compatibility issues when trying to integrate Gen AI into existing systems and processes. Seamlessly integrating these new technologies with traditional infrastructure remains a major hurdle.
Another third challenge involves talent and skill. As HR departments face a shortage of suitable hires, organizations are finding that there is a significant lack of in-house GenAI expertise. Organizational leaders are concerned about not having access to the skills they need to take full advantage of their Gen AI investments.
Finally, predicting the costs associated with the use of LLM has proven to be a major challenge. Although modelers provide initial cost estimates, leaders cite prohibitive direct and indirect costs associated with private knowledge preparation, training, and model operational management. The actual economic impact of GenAI implementation is complex and often underestimated.
“Organizations are realizing that large language models alone cannot solve business challenges,” said Marinella Profi, strategic AI advisor at SAS. “Gen AI needs to be treated as an ideal contributor to hyperautomation and acceleration of existing processes and systems, rather than a new shiny toy that helps organizations realize all their business aspirations. Investing time in developing a strategic strategy and investing in technology that provides LLM integration, governance, and explainability are important steps all organizations should take before jumping in with both feet and getting “locked in.” .
“The end result will be identifying real-world use cases that provide the best value and solve human needs in a sustainable and scalable way. Through this research, we hope that organizations will In an era where AI technology is evolving almost daily, competitive advantage lies largely in the ability to embrace the rules of resilience. It depends.”