Building a culture of innovation around Generative AI & LLMs
Generative AI (GenAI), and Large Language Models (LLMs) in particular, took the world by storm in 2023.
ChatGPT went from 0 to 100M users in just two months. What is even more impressive is the blistering speed at which GenAI technologies themselves have developed—advancing from toy models to levels that compete with human intelligence in mere months.
While these developments have been exciting for those of us with a strong appetite for innovation, they have been extremely challenging for large organizations that have had to develop policies around how these technologies should be used. Despite what the immediate policies might be (with many companies taking the safe route and forbidding the use of tools such as ChatGPT), productivity and quality gains are clear among those who have adopted them. In one study from Harvard, BCG consultants who used ChatGPT were found to have a 40% performance boost and a 25% speed boost in their work.1
Statistics like these, which are bound to become more impressive as the tools improve and the user experience becomes more streamlined, mean that companies looking to remain competitive are unlikely to remain on the "safe" side for very long. The lure of cutting-edge technology, coupled with tangible benefits, makes it inevitable that more organizations will start to explore if not fully embrace, the use of Generative AI and Large Language Models. This shift towards adoption, however, requires a delicate balance. On one hand, it offers the promise of unprecedented efficiency and innovation; on the other, it poses ethical, security, and societal challenges that cannot be ignored.
The journey towards a culture of innovation around Generative AI and LLMs in large organizations involves more than just deploying new technologies. It requires a holistic approach that encompasses policy development, ethical considerations, and culture change.
While companies might approach the first two aspects using their rulebooks, culture change relies on a deep understanding of organizational psychology. This is where insights from the behavioral science of innovation might prove helpful.
References
- Marshall, M. (2023). Enterprise workers gain 40 percent performance boost from GPT-4, Harvard study finds. VentureBeat. https://venturebeat.com/ai/enterprise-workers-gain-40-percent-performance-boost-from-gpt-4-harvard-study-finds/
About the Author
Dr. Sekoul Krastev
Sekoul is a Co-Founder and Managing Director at The Decision Lab. He is a bestselling author of Intention - a book he wrote with Wiley on the mindful application of behavioral science in organizations. A decision scientist with a PhD in Decision Neuroscience from McGill University, Sekoul's work has been featured in peer-reviewed journals and has been presented at conferences around the world. Sekoul previously advised management on innovation and engagement strategy at The Boston Consulting Group as well as on online media strategy at Google. He has a deep interest in the applications of behavioral science to new technology and has published on these topics in places such as the Huffington Post and Strategy & Business.
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