Remove Experimentation Remove Measurement Remove ROI Remove Uncertainty
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What you need to know about product management for AI

O'Reilly on Data

Machine learning adds uncertainty. Underneath this uncertainty lies further uncertainty in the development process itself. There are strategies for dealing with all of this uncertainty–starting with the proverb from the early days of Agile: “ do the simplest thing that could possibly work.”

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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. The AGI would need to handle uncertainty and make decisions with incomplete information.

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How to Set AI Goals

O'Reilly on Data

Customer stakeholders are the people and companies that advertise on the platform, and are most concerned with ROI on their ad spend. Technical sophistication: Sophistication measures a team’s ability to use advanced tools and techniques (e.g., Technical competence results in reduced risk and uncertainty.

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Product Management for AI

Domino Data Lab

Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. These measurement-obsessed companies have an advantage when it comes to AI.