72% of executives plan to increase their AI investments this year. But large enterprises, small companies, and governments are still uncertain about how to adopt this technology, artificial intelligence consultancy Scale Enterprises says in a survey of more than 1,600 executives and Machine Learning practitioners. “Generative AI is rapidly transforming the world, and businesses need to understand how to adopt this technology quickly or get left behind.”
“Executives are quickly realizing that you can’t take these models off the shelf and expect to get a unique business advantage – you need to train them to perform for your specific business needs, with your proprietary data.”
The report says that with the right foundation in place for implementing AI, employee productivity increases, customer experience improves, and revenue and profitability grow.
“Generative AI is rapidly transforming the world, and businesses need to understand how to adopt this technology quickly or get left behind.”
“The most significant AI and ML readiness trend has been the enormous impact of Generative AI on companies, large and small, across all industries.”
The survey found that many companies plan to work with or experiment with foundation models, but many lack the expertise and tools to get these models into production.
“Most companies are adopting AI to enhance the customer experience, optimize operational efficiency, or improve profitability. Generative models will become increasingly more useful and widely accessible, making them essential to every organization’s business strategy with a similar impact to the internet.”
“Early adopters of AI are seeing the improved ability to develop new products or services, enhanced customer experience, and better collaboration across business functions, in addition to improved revenue and profitability.”
“Enterprises widely use older non-generative models like BERT, but have realized they must adopt more generative models to stay ahead of the competition.”
The report says that companies that fine-tune foundation models find their most significant challenges are acquiring training data, ML infrastructure, and comparing experiments across different models.”
“Human evaluation has replaced benchmarks as the de-facto method to analyze large generative models and determine how they will work in a specific enterprise.”
“Enterprises and governments need to leverage their unique data to unlock the full potential of generative models.”