Remove Experimentation Remove Marketing Remove Risk Remove ROI
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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.

ROI 110
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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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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.

IT 92
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What you need to know about product management for AI

O'Reilly on Data

You’re responsible for the design, the product-market fit, and ultimately for getting the product out the door. The need for an experimental culture implies that machine learning is currently better suited to the consumer space than it is to enterprise companies. AI doesn’t fit that model.

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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. While GCash has been growing exponentially as a disruptor in the financial market, the importance of being able to bring everyone along on the journey—even non-technical stakeholders—is crucial.

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Getting ready for artificial general intelligence with examples

IBM Big Data Hub

While leaders have some reservations about the benefits of current AI, organizations are actively investing in gen AI deployment, significantly increasing budgets, expanding use cases, and transitioning projects from experimentation to production. A key trend is the adoption of multiple models in production.

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Belcorp reimagines R&D with AI

CIO Business Intelligence

As Belcorp considered the difficulties it faced, the R&D division noted it could significantly expedite time-to-market and increase productivity in its product development process if it could shorten the timeframes of the experimental and testing phases in the R&D labs. This allowed us to derive insights more easily.”