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The AI Fueled Product Manager – The Best Version of You

Product Management University

The pace of product management has accelerated exponentially in recent years because of agile development and the relentless focus on metrics. Agile is a great software development methodology but it has consumed product managers to the point where they have almost no capacity to do product management. It’s not AI.

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From Stacking Features to Shaping Value: A New Product Building Model

UX Planet

Photo by Ricardo Gomez Angel on Unsplash For decades, software development has mirrored the construction industry — a manpower-heavy model where effort, hierarchy, and sequencing define how value is created. They’re Living Systems of Value The way people engage with digital products has fundamentally changed. Not for users.

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Strategies to mitigate AI security and compliance risks

Modus Create

AI models are susceptible to adversarial attacks where malicious actors manipulate input data to deceive the system. AI systems often process large volumes of personal or sensitive data, challenging compliance with these regulations. AI systems can perpetuate or amplify existing biases, leading to unfair or discriminatory outcomes.

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Software Bugs Are Expensive: How Session Replays Help You Spot and Fix Them Fast

Userpilot

Negative user experience = lost customers Users get frustrated when they encounter functional bugs like a payment system failure, a broken feature, or a poorly functioning app. Wasted time on bug reproduction and fixes The software development process is lengthy. Heres how such bugs result in lost revenue and increased churn: 1.

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The Secret of Diminishing Returns

Tyner Blain

You could judge a city’s traffic system in terms of how many people can commute from outside the city to a stadium for a sporting event. The more people you can get to the event in a given timeframe, the better the system is.

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Are you ready to transform life sciences with intelligent apps? Five questions to help kickstart your AI journey

Modus Create

These challenges manifest in various forms, from poor data quality and reliability to inaccessible data sources, stemming from disparate systems and a lack of governance. Yet, numerous companies often encounter setbacks or outright failures attributable to data maturity-related issues. Data quality is fundamental to any AI project.

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The Ultimate Guide to Embedded Analytics Requirements for 2025

Reveal

SDKs and APIs : SDKs (software development kits) and APIs (application programming interfaces) offer a more robust and flexible approach to embedding analytics. Leverage robust APIs to connect to any data source, enabling real-time insights across multiple systems.