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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.

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How Predictive Analytics can add Value to Applications

Mind the Product

Every software application today is fighting for space in an increasingly crowded market, so software vendors need to differentiate their offerings with valuable features to avoid losing out to competitors. Why predictive analytics? Getting Started With Predictive Analytics.

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462: Using qualitative data to drive product management prioritizations – with Daniel Erickson

Product Innovation Educators

How product managers can use AI to get more actionable insights from qualitative data Today we are talking about using qualitative data to drive our work in product and consequently improve sales. Before founding Viable, he held senior leadership roles in engineering, technology, and product.

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Effective customer engagement is business critical – insights from Harvard Business Review Analytic Services

Intercom, Inc.

New research from Harvard Business Review Analytic Services reveals that businesses of all sizes – from small businesses to enterprises – are realizing the business value of personal, efficient customer engagement. Below, we take a deeper dive into the report’s key data and trends. But they’re facing big barriers.

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How to Leverage AI for Actionable Insights in BI, Data, and Analytics

In the rapidly-evolving world of embedded analytics and business intelligence, one important question has emerged at the forefront: How can you leverage artificial intelligence (AI) to enhance your application’s analytics capabilities?

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Why Machine Learning Solutions are Difficult to Implement without Machine Learning Operations?

The Product Coalition

According to Gartner , 85% of machine learning solutions fail because they use raw data. Data scientists work in isolation from operations specialists, and enterprises spend up to three months deploying an ML model. MLOps is an innovative format for working between data scientists and operations specialists.

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The new dawn of Machine Learning

Intercom, Inc.

In the past five years, we’ve seen neural network technology really take off into its own. We wanted to know what’s up with this surge, so we’ve asked our Director of Machine Learning, Fergal Reid , if we can pick his brain for today’s episode. It’s all about artificial intelligence and machine learning.