What You Should Know about Investing in AI During Economic Downturn

Our CEO, Ryohei Fujimaki, PhD discusses investing in #AI during the economic downturn for @thenextweb #DataScience #ArtificialIntelligence

As of April 28, there have been nearly 198,000 confirmed deaths and more than 2.8 million confirmed cases across the globe. California, New York, and Philadelphia had to “shelter in place” and leave their homes only for necessities. Even harsh measures will take months for families and communities to heal from the COVID-19 pandemic. The COVID-19 outbreak has affected the world’s economy by causing an economic contraction in China and a decrease in the US stock index. Planning for an economic downturn is a complex proposition, especially when the downturn is prolonged and severe.

According to Gartner, 14% of organizations have already adopted AI, while 48% plan to adopt the technology by 2020. Using AI/ML-based predictions and group contracts, a large multinational bank doubled the close rate for new clients by deploying AI/ML-based predictions. AI/ML has provided significant benefits for consumers and enterprises, but the challenges of AI/ML include critical timelines, which affect ROI. Enterprises that invest in AI/ML projects typically see high returns, but these projects also require significant capital. During an economic downturn, enterprises re-evaluate their investments in AI/ML and likely stop hiring new talent, exceptionally highly skilled, more expensive talent like data scientists and AI/ML experts.

Automation can help to accelerate the development of AI/ML solutions within existing BI organizations. Through automation, companies can accelerate AI/ML project timelines and empower a new class of users: Business Intelligence developers and Data Engineers. During economic slowdowns, enterprises might want to reduce investments in AI/ML projects that require long timelines. Business Intelligence and Data Engineers can help create a more empowered technology team by adopting new technologies like AutoML 2.0. Read the full article at thenextweb.