Generative Artificial Intelligence has captivated the world and the interest in delving into this new technology seems incessant.
In a business landscape marked by the constant search for innovation, generative artificial intelligence has emerged as a revolutionary force that has captured global attention. From various sectors, interest in exploring this modern technology in depth shows no sign of slowing down. This powerful tool, it seems, is transforming the way we think about creativity, innovation and problem solving, while facing the challenges that accompany its adoption.
According to a recent study by McKensey, it is expected that Generative AI could bill nearly $4.4 billion dollars annually in different industries and approximately 63 new use cases are being studied.
Despite the current global interest in tools and applications based on Generative AI, the incorporation and monitoring of this technology pose significant challenges for the business environment. These aspects were discussed by Raghu Raghuram, CEO of VMware in 3 important components:
- From expensive to affordable: high development cost open models such as ChatGPT will be rare.
- From specialised “AI wizardry” to democratised AI expertise: Innovation and advances in this technology will require closing technical skills gaps.
- From risk to trust: Current Generative AI models bring with them significant data security; regulatory and intellectual property protection risks that need to be addressed.