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3 Ways to Enable Smarter Product Development with AI

Planview

Even more so, when discussing AI and its purpose and value in product development, it can be easy to fall deep into a rabbit hole of all the possibilities and potential. In this post, we outline three more general applications of AI/ML in product development that deliver results in this quickly expanding field.

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Turn the Tables on Product Development Disruptions

Planview

Disruptions to effective product development are constant but don’t need to be detrimental to an organization’s ability to deliver innovation with speed and maintain a healthy ROI. Suppose organizations make adaptability a cornerstone of their product development strategy and overall culture.

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3 Key Risks for IoMT Product Development

Planview

But as with any innovation, IoMT product development and innovation brings risks that need to be considered. 3 Challenges of IoMT Product Development. As with any growing technology, there are risks IoMT stakeholders and product developers need to consider to ensure their products offer strong ROI.

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Innovation on Steroids: Next Generation AI-Powered Phases and Gates

Leapfrogging

The typical stages might include concept development, design, testing, and launch. At each gate, key criteria are assessed by a cross-functional team, which may include factors like market viability, technical feasibility, and financial projections. Enhance the quality of products developed.

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New Study: 2018 State of Embedded Analytics Report

Why do some embedded analytics projects succeed while others fail? We surveyed 500+ application teams embedding analytics to find out which analytics features actually move the needle. Read the 6th annual State of Embedded Analytics Report to discover new best practices. Brought to you by Logi Analytics.

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Moving from Project to Product: A Five-Stage Journey

Planview

And so is the mindset around productivity and team structure. In the traditional project-centered model, companies are organized around tasks, with each group focused on one element of a project. But greater emphasis is being placed on moving from project to product, which focuses on enabling teams to become end-to-end experts.

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Unleashing the Power of AI in Innovation Management

Leapfrogging

It consists of a series of phases (stages) where specific tasks are performed and milestones (gates) where decisions are made about whether to proceed to the next stage, halt, or redirect the project. Risk Assessment : By predicting potential hurdles, AI helps in identifying and mitigating risks early in the product development lifecycle.