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The Future of AI and ROI for the Enterprise

Dataiku

For many years, AI was an experimental risk for companies. Recently, Dataiku spoke with Mike Gualtieri, VP & Principal Analyst at Forrester , in “The Future of AI and ROI for the Enterprise, featuring Forrester” webinar about the current state of the market and what AI success looks like going forward.

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The portfolio approach to digital transformation: 4 keys to success

CIO Business Intelligence

Corporate projects are classically evaluated on standard matrices such as return on investment (ROI), break-even period, and capital invested. To capitalize on the gains offered by digital technologies, CIOs are building technology portfolios by allocating diverse investments based on prospective risk, reward, and value.

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How to become an AI+ enterprise

IBM Big Data Hub

While many organizations have implemented AI, the need to keep a competitive edge and foster business growth demands new approaches: simultaneously evolving AI strategies, showcasing their value, enhancing risk postures and adopting new engineering capabilities. times higher ROI. times higher ROI.

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Bring light to the black box

IBM Big Data Hub

It is well known that Artificial Intelligence (AI) has progressed, moving past the era of experimentation to become business critical for many organizations. Challenges around managing risk and reputation Customers, employees and shareholders expect organizations to use AI responsibly, and government entities are starting to demand it.

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Ferrovial puts AI at the heart of its transformation

CIO Business Intelligence

With the aim to accelerate innovation and transform its digital infrastructures and services, Ferrovial created its Digital Hub to serve as a meeting point where research and experimentation with digital strategies could, for example, provide new sources of income and improve company operations.

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AI Governance: Break open the black box

IBM Big Data Hub

It is well known that Artificial Intelligence (AI) has progressed, moving past the era of experimentation. Today, AI presents an enormous opportunity to turn data into insights and actions, to amplify human capabilities, decrease risk and increase ROI by achieving break through innovations. Challenges around managing risk.

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Rapid AI Iteration, Reducing Cycle Time: Key Learnings from the Big Data & AI World Asia Conference

DataRobot Blog

At the event, a financial services panel discussion shared why iteration and experimentation are critical in an AI-driven data science environment. This allows GCash to maintain the pace of innovation and iteration without exposing the business to significant risk. Closing the Value Gap: Reducing AI Cycle Time. Request a demo.