Mind the Product
AUGUST 19, 2019
Artificial intelligence (AI) is probably the biggest commercial opportunity in today’s economy. We all use AI or machine learning (ML)-driven products almost every day, and the number of these products will be growing exponentially over the next couple of years. What does it mean for us as product managers?
Gainsight
JUNE 1, 2022
Artificial intelligence (AI) seems to be everywhere these days. And most of that impact is being driven by machine learning, the most important and popular subfield within AI. Customer Success and Machine Learning: A Perfect Match. Identify Effective CS Strategies. Speed Up Decision-Making.
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Crafty CTO
MARCH 17, 2023
First, let’s define AI … The AI label is getting slapped on a lot of things these days, and I’m actually ok with that – it’s a short, handy, evocative term, useful as long as we get its definition straight. AI does not mean “the machine is thinking.” So if AI is not machine thought, what is it?
Bain Public
FEBRUARY 13, 2023
Session Information Title: It’s an AI product manager’s job to help an organization succeed with Predictive Machine Learning Date: March 16th, 2023 Register: [link] The workshop provides actionable strategies, tools, processes, and skills product leaders need to create AI innovations, adapt to market shifts, and anticipate what’s next.
UX Planet
FEBRUARY 2, 2024
The potential of quantum computing and artificial intelligence to enhance user research User research is crucial for the human-centered design of digital products and services. Leveraging Artificial Intelligence Alongside quantum computing, continuous advancements in AI will also revolutionize user research in the coming decades.
UX Planet
MARCH 17, 2024
What’s text-to-video AI Let’s start with a short definition. The concept utilizes artificial intelligence algorithms to automatically generate videos from text prompts. The model understands the language, so it can evoke desired emotions. It allows you to optimize production strategies.
Bain Public
JULY 12, 2021
There are a lot of complexities when it comes to building, shipping and executing AI and machine learning (ML) projects. Product managers need to ensure that data scientists are delivering results in efficient ways so business counterparts can understand, interpret, and use it to learn from.
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