'60 Leaders' is an initiative that brings together diverse views from global thought leaders on a series of topics – from innovation and artificial intelligence to social media and democracy. It is created and distributed on principles of open collaboration and knowledge sharing. Created by many, offered to all.
ABOUT 60 LEADERS
'60 Leaders on Artificial Intelligence' brings together unique insights on the topic of Artificial Intelligence - from the latest technical advances to ethical concerns and risks for humanity. The book is organized into 17 chapters - each addressing one question through multiple answers reflecting a variety of backgrounds and standpoints. Learn how AI is changing how businesses operate, how products are built, and how companies should adapt to the new reality. Understand the risks of AI and what should be done to protect individuals and humanity. View the leaders.
'60 Leaders on Innovation' is the book that brings together unique insights and ‘practical wisdom’ on innovation. The book is organized into 22 chapters, each presenting one question and multiple answers from 60 global leaders. Learn how innovative companies operate and how to adopt effective innovation strategies. Understand how innovation and experimentation methods blend with agile product development. Get insights from the experts on the role of the C-Suite for innovation. Discover ways that Innovation can help humanity solve big problems like climate change.
How is AI impacting the way businesses operate?
Before the COVID-19 pandemic, many businesses had begun their digital transformation and were beginning to use analytics and Artificial Intelligence (AI) technologies to understand and improve their business processes. This move towards a more general position of digitisation seeded a focus on the understanding and exploitation of existing data assets. The pandemic has fundamentally altered the timescale in which this digitisation must occur to ensure the long-term productivity of most businesses.
It has also forced a paradigm shift in the complexity of the processes, which now must also consider a wealth of external information including global health and climate data to guide their strategy decisions. I believe that this transformation from a passive data-driven enterprise to an active discovery-driven enterprise, fuelled by AI and hybrid cloud, is the next major paradigm in the way businesses operate. We call this new paradigm Accelerated Discovery.
The information-driven industry will be the early proponents of the discovery-driven enterprise. Leveraging developments in NLP from Deep Learning such as transformers will enable these companies to ingest and structure the constant flow of information faster and more accurately than ever before. From this information, we will see an eruption in the development of ever more accurate digital twins of processes and structures, driven by our ability to use high-performance infrastructure to train and deploy complex AI models at scale.
This combination of the ‘real’ world and its digital twin will also enable the rapid identification of new ‘white space’ in business processes and practices. I believe that generative models will begin to help us to collaborate with our digital worlds and drive advances in computational creativity to fill in this white space with high-performing novel solutions, which can then be linked back to the real world through automated deployment by combining AI and RPA. One example we are already seeing is materials discovery. Typically, it takes 10 years and $100M to bring a material to market, but through the combination of data ingestion, AI-accelerated digital twinning, generative models, and automated experimentation we have already seen those figures fall significantly.
As AI drives our push for accelerated discovery technologies further onwards, we must remain aware of the potential to accelerate all outcomes, both good and bad. This will mean establishing guardrails and protocols to ensure that responsible advances which provide strong societal benefits are enhanced, whilst alternate applications which do not follow ethical codes of conduct are side-lined. This will require investment into the application of technologies such as explainable AI and bias detection as well as the formalisation of ethical principles for AI development .
To conclude, AI is a key component in the evolution of business from a data-driven paradigm to a discovery-driven enterprise, but we must ensure that at all stages of this transformation, we are cognizant of the potential implications of our advances, and pledge to do so responsibly.