Topic

Artificial Intelligence (AI) & Machine Learning (ML)

AI and ML are no longer just buzzwords or hypotheticals; they are now practical tools shaping the way modern products are built and experienced. At their core, these technologies are about enabling systems to learn from data and make decisions with minimal human intervention. For product managers, this means knowing when to leverage AI/ML to solve real problems, deliver more personalised experiences, and unlock smarter automation. A solid grasp of the fundamentals helps you work effectively with technical teams, identify impactful use cases, and ensure these solutions support your product strategy.

How to build an AI product that doesn’t suck

How to build an AI product that doesn’t suck

In article, Archana Kumari, Senior Product Manager at Microsoft, shares her insights from her experience in spam detection, covering the evolution from traditional methods to the role of large language models (LLMs).

Archana Kumari

Poll results: What tools are product managers using the most?

Poll results: What tools are product managers using the most?

Louron Pratt

Learning from experience: Key takeaways from our GenAI project

Learning from experience: Key takeaways from our GenAI project

Lisa Murkin, Senior product Manager at Elsewhen, shares her learnings from integrating Generative AI into advertising workflows to improve campaign efficiency and targeting relevance.

Lisa Murkin

AI and product-led growth go hand in hand – Nichole Mace (SVP, Product, User Experience at Pendo)

AI and product-led growth go hand in hand – Nichole Mace (SVP, Product, User Experience at Pendo)

The Product Experience

LLM workflows for product managers: 3 key takeaways (Niloufar Salehi, Assistant Professor at UC Berkeley) – ProductTank SF

LLM workflows for product managers: 3 key takeaways (Niloufar Salehi, Assistant Professor at UC Berkeley) – ProductTank SF

Louron Pratt

20 ways to build AI/ML products

20 ways to build AI/ML products

Explore 20 AI product management best practices to streamline R&D, data analysis, customer insights, and user adoption, with expert tips.

Liz Fieno

The future of product management: Insights from ProductTank San Francisco

The future of product management: Insights from ProductTank San Francisco

Discover key insights on the future of product management from industry leaders at ProductTank San Francisco's first panel discussion.

Ketaki Vaidya

Why ML-based products require an agile approach to road mapping

Why ML-based products require an agile approach to road mapping

In this article, Ananti Gupta, Senior Product Manager - Technical at Amazon, explores the unique challenges of planning roadmap timelines and managing stakeholder and team expectations when working on Machine Learning (ML) products.

Ananti Gupta

Product lessons learned making early moves with AI in media: Lindsey Jayne (CPO, Financial Times)

Product lessons learned making early moves with AI in media: Lindsey Jayne (CPO, Financial Times)

Louron Pratt

A no-code approach to building MVPs 

A no-code approach to building MVPs 

Hardik Chawla, a product lead at Amazon, explores how no-code and low-code development platforms empower product managers to build and launch MVPs without extensive coding knowledge. This article features layered insights from his own experience with these tools.

Hardik Chawla

Can AI tools address product manager performance paradox? 

Can AI tools address product manager performance paradox? 

In this article, read how AI can be used to streamline processes and empowers managers to foster high-performance teams.

Balaji Ananthanpillai

Data-driven time management for product managers

Data-driven time management for product managers

Product Lead, Julia Ryzhkova launched an initiative to analyze work calendars. Personally, she found out that she spent 1573 hours in meetings last year, with 70% initiated by herself. In this article, she shares her insights and tips on data-driven time management.

Julia Ryzhkova