The use of AI, machine learning, and natural language processing tools is critical to transforming business enterprises, Fleming posits. As such, Fleming proposes a cognitive enterprise model which uses AI to structure data and build predictive models. The data structures are then used to improve a variety of business aspects, such as leadership, employee skills training, business processes, and organizational infrastructure. His advice to organizations with disorganized data is to work with the data and focus on the adoption of cognitive tools, which involves teaching senior leaders how to use the tools and making them advocates of the change.
Fleming observes that the number of jobs in the U.S. has held relatively steady, but the tasks that make up those jobs have changed significantly, largely because of changes in technology and productivity. As nearly all of the tasks that make up occupations change, Fleming concedes that job loss due to AI is inevitable. But, the displacement effects of AI are small compared to its productivity benefits. In order to cope with changes to the future of work, Fleming reinforces the need for skills education training. Policy should protect those facing job loss by encouraging skill retraining and continued education.
During the Q&A, Fleming discussed the role that AI could play in facilitating financial transactions, especially for developing countries. Countries faced with supply chain problems may be able to use AI and blockchain technology to tie the movement of goods in with their paper transactions, generating economic benefits for a developing nation
Fleming concluded his talk by re-emphasizing the importance of continued learning and education, notably for workers faced with mid-career transitions. The idea that education consists of a fixed number of years is of a bygone era, says Fleming. Organizations and governments should promote policies that encourage workers to learn new skills to keep up with the pace of change that AI presents.