How Traditional Companies Can Transform to Remain Competitive in the Age of AI with Xena Ugrinsky


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Xena Ugrinsky has spent over the last 20 years of her career in technology enabled transformation. The last eight years of those have been focused on the application of data science for corporate performance management. She has a new book titled “Enterprise AI-Your Field Guide to the New Business Normal”.   

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Here’s the timestamps for the episode: 

(00:00) – Introduction

(02:39) –At first technology was process optimization. Then there were better individual tools for doing analysis. So there you have spreadsheets. Analytics began to enable in the second phase the ability to do things like driver based forecasting trending analysis. And the third phase and the one that we find ourselves in today is the beginning of applying truly modern mathematical methods in the form of data science to technology.

(06:41) – Technology isn’t the hurdle anymore, it’s people, it’s process, it’s culture, it’s organizational structure. 

(08:23) – Spreadsheets are too deeply ingrained in how business works 

(10:58) – Siloing of functions was causing a bottleneck of data that prevented executive teams from having the right information at the right time at their fingertips to make a decision.

(13:15) – Analytics application proliferation have become more accessible to the business community, then the IT organization completely loses control of their tech portfolio and managing costs.

(16:56) – The things that have to change in an organization to become an intelligent enterprise don’t involve technology at all. You have to consider the people and a culture of inclusiveness that moves away from information as power to empowering everyone with the information.

(20:09) – Our traditional organizational structures need to be rethought. And it starts at the top with re configuring the responsibilities of the C-suite.

(23:15) – AI as a whole is not going to be the new spreadsheet, AI as a whole describes the transformation of applied data science.

(24:23) – Organizations fall into three categories: organizations built up and architected as a data company, traditional organizations that have been early adopters to solve something or to embed it in their product, and the ones that don’t know what is the right way to get started in data science.

(32:43) – Organic transformation: when you change people, you change the entire organization.

(34:00) – The winners in this race will be transforming so that their organizations are truly data-driven. If you miss this boat, you won’t exist as a company.