I facilitated a cross-functional workshop last week with WEF and several tech startups (Noodle A.I., Rage Framework, Enterra). We explored the impact of Artificial Intelligence (AI) on global production and value chain. This is part of our on-going collaboration on the World Economic Forum Future of Production initiative.
The cross-functional discussion explored many topics. Firstly, we discussed the underlying technology progress and limitations. Secondly, we brainstormed trigger points for mass adoption across individuals, firms and governments. Lastly, we ended the session by discussing the broader societal consequences of AI adoption.
Some key emerging themes from the session:
1) The very definition of A.I. itself is vague as we had many different definitions across participants in the room. We arrived at a consensus definition of AI as any algorithm system that can generate foresight and insight through learning. See below for a break-down of sub-set of A.I. algorithms and techniques:
2) The current breakthrough in AI performance, enabled by innovations in underlying processing hardware, learning software, and proliferation of big data (tagged and structured data) will continue to advance to unlock faster, smarter, and more intuitive applications
3) Progress will be limited in near term to narrow types of AI (software that can learn specific problems for targeted domains) rather than general AI (software that can generalize and learn with limited and noisy data)
4) AI use cases are already pervasive – spanning from applications that impact individuals, factory floor, business value chains and eco-systems.
5) Adoption however will be non-uniform. As use cases across functions and sectors will be largely dictated by private sector ROIs. In the short term, AI will likely eliminate more net jobs and further accelerate “winner take all” dynamics.
6) AI will further push new products to compete and generate value through their digital functionalities rather than traditional physical features. This will have significant implications on business models and supply chains.
Policies and social attitude that drives lifelong learning and entrepreneurship are key to manage society’s transition to the AI “cognitive” revolution.
Read more from 2 related papers on World Economic Forum Future of Production: “Will You Embrace AI Fast Enough” | “WEF – Technology and Innovation for the Future of Production”