Digital twin technology continues to be adopted by manufacturing industries to support business strategy and gain efficiencies in operations and customer service. Definition: Digital twin refers to a digital replica of potential and actual physical assets...
9 Key Issues To Consider When Operationalize AI For Enterprises
Jun Wu, Forbes – November 16, 2020
This year, despite the challenges from the Covid-19 pandemic, large corporations in the financial industry are operationalizing their AI initiatives. Many mature organizations already have established processes. In the last few years, they’ve been implementing process workflows, software tools, and frameworks to quickly operationalize their models to capitalize on the changing business landscape.
However, as the business environment changed during the Covid-19 pandemic, organizations observed changes in their models’ underlying assumptions. The urgency to rapidly deploy new models in a controlled environment to account for the market risks and take advantage of new opportunities proved to be challenging.
From conversations gathered from two notable roundtables from ModelOp and QuantUniversity with industry veterans from Wells Fargo, BP, Regions Bank, Vector Institute, ModelOp, and others, I’ve put together this list of key issues to consider when operationalizing AI at the enterprise level.