Who’s Accountable for AI and its Risks? Why Enterprise CEOs Need to Assign AI Ownership Now
Webinar | Tuesday, April 30th | 1pm ET
Search
Close this search box.

9 Key Issues To Consider When Operationalize AI For Enterprises

Enterprise AI Operationalization: 9 Key Issues

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.

Read the Full Article

You might also enjoy

AI Regulations: What to Know & What to Do Now

Global, federal, and state-level governments are moving quickly to implement AI regulations. While reading this, you may be asking, “If I want to use AI, what do I need to do now to prepare my organization now?”

Get the Latest News in Your Inbox

Further Reading