AI Articles and Analysis about Large Language Models

One of the key strategies for future-proofing an organization is recognizing the limits of AI models and filtering out the misconceptions that AI is a one-solution-fixes-all narrative for businesses. Enterprises build an AI strategy supported by use cases and how LLMs can change the workforce going forward.

Lilly and Company is a global pharmaceutical corporation founded in 1876 and headquartered in Indianapolis, Indiana. The company has offices in 18 countries and sells its products in approximately 125 countries worldwide. As of 2024, Lilly employs over 38,000 people globally and has an annual revenue of $34.12 billion. 

KeyBank is a financial services institution with a rich history dating back to 1825 and headquartered in Cleveland, Ohio. With 17,000 employees, operations across 15 states, and assets totaling $187 billion, KeyBank's commitment to innovation is evident in its strategic application of AI technologies to enhance both workforce management and customer service. 

Nestlé is a global leader in the food and beverage industry, operating across 189 countries with a workforce of over 339,000 employees. Formed in 1905 and headquartered in Vevey, Switzerland, it is the largest publicly held global food company, according to Forbes's Global 500 list, with over $98 billion in sales just last year. 

Like much of the financial services space broadly, the insurance industry is growing interested in adopting AI. The technology holds immense potential to revolutionize insurance, enhancing efficiency and accuracy across various processes such as claims and underwriting. However, concerns surrounding data privacy and integration complexities have prompted a measured approach among industry leaders.

Nationwide Mutual Insurance Company, commonly known as Nationwide, is a primary US insurance and financial services company headquartered in Columbus, Ohio.

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The asset management industry is going through several challenges, like the prolonged low-interest rate environment, dwindling margins, increased cost pressures, and squeezed profitability. Meanwhile, exponential growth in data volumes has overwhelmed legacy data management systems and analytical tools, making it tiring to process and extract valuable insights from the deluge of information.