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520: The future of AI in product management – with Mike Todasco

Product Innovation Educators

How product managers are transforming innovation with AI tools Watch on YouTube TLDR In this deep dive into AI’s impact on product innovation and management, former PayPal Senior Director of Innovation Mike Todasco shares insights on how AI tools are revolutionizing product development.

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517: How to conduct an AI Design Sprint – with Mike Hyzy

Product Innovation Educators

Using a custom ChatGPT model combined with collaborative team workshops, product teams can rapidly move from initial customer insights to validated prototypes while incorporating strategic foresight and market analysis. Instead of focusing solely on today’s customer problems, product teams need to look 2-5 years into the future.

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Choose the Right Large Language Model (LLM) for Your Product

The Product Coalition

Let’s talk confidently about how to select the perfect LLM companion for your project. The AI landscape is buzzing with Large Language Models (LLMs) like GPT-4, Llama2, and Gemini, each promising linguistic prowess. Data Richness: A wealth of data opens doors to training bespoke models.

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529: Is this the best AI-powered market research approach? – with Carmel Dibner

Product Innovation Educators

How AI captures customer needs that human product managers miss Watch on YouTube TLDR In my recent conversation with Carmel Dibner from Applied Marketing Science, we explored how artificial intelligence is transforming Voice of the Customer (VOC) research for product teams. However, these early efforts faced significant limitations.

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How Banks Are Winning with AI and Automated Machine Learning

Banks have always relied on predictions to make their decisions. Estimating the risks or rewards of making a particular loan, for example, has traditionally fallen under the purview of bankers with deep knowledge of the industry and extensive expertise. How today’s banks can handle the data science talent shortage.

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How to Succeed with AI in 2025

Piyanka Jain

And not because AI itself is broken, but because companies keep treating it like a science project instead of a tool that actually needs to solve problems. Some common AI failurestories: The Data Hoarders : Companies that think collecting more data will somehow lead to an AI breakthrough. Ready to see where data is headednext?

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Large Language Models for the Rest of Us

The Product Coalition

AI is having its Cambrian explosion moment (although perhaps not its first), led by the recent developments in large language models and their popularization. And best yet, I don’t need to come prepared with a vast data library. Using other models together with LLMs can help solve those problems.

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How Banks Are Winning with AI and Automated Machine Learning

Banks have always relied on predictions to make their decisions. Estimating the risks or rewards of making a particular loan, for example, has traditionally fallen under the purview of bankers with deep knowledge of the industry and extensive expertise. How today’s banks can handle the data science talent shortage.