Who should have ultimate ownership of machine learning model quality, approvals and ongoing usage?

Team & Organizational DesignMachine Learning (ML)

Technicians who created the model4%

Business leaders who use the model67%

Data and Analytics leadership22%

Business leadership4%

Not sure

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Most of the application team I lead is in Brazil and the global IT leadership (US-based) now wants to move some of that work to a new global delivery center in India to be more cost effective, but I have doubts. The Brazil team is delivering good quality — is cost efficiency enough reason to relocate the work? What are the biggest pros/cons we need to consider?

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Has your organization deployed AI agents?

Yes71%

No29%

Other (comment below)

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What is the one business pain point / challenge that you regularly face, that you wish AI had solved it? The problem could be a time consuming, routine task that is frustrating to handle. It may happen daily / weekly / monthly, and you may lose many hours on it which results in delays / added cost.

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Are you excited for any of the new Bard integrations announced at Google I/O (Replit exporting for code, image creation with Adobe Firefly, syncing Bard with other GSuite tools, etc.)?

Yes70%

No18%

Not sure — I haven’t looked into these yet12%

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We are in the process of implementing a chatbot for our local government site with intentions to deploy for internal use only for testing.  We are having problems quantifying is the cost around token consumption.  I know that cost varies depending on the model used, but does anyone have any useful estimators.  Consumption calculators I have found don't seem particularly useful.  What methods for estimating cost did you use?  Did you use number of searches against the site?

Who should have ultimate ownership of machine learning model quality, approvals and ongoing usage? | Gartner Peer Community