post

Footer About

Footer Contribute

Footer Get Involved

Footer Links

Mind the Product

Guest Post

Creating metrics that matter

A case study: Launching a product process in a project-based company

B2B product management

Artificial Intelligence (AI) & Machine Learning (ML)

Building Product Teams

Business Strategy

Data Driven Product Management

Digital Transformation

Diversity and Inclusion

Minimum Viable Product

OKRs

Prioritisation

Product Design

Product Research

Product Development Process

Product Ethics

Product Discovery

Product Leadership

Product Management Career

Product Strategy

Stakeholder Management

🌎 <a href="https://www.mindtheproduct.com/world-product-day/" style="color:#FFFFFF;text-decoration:underline;">Happy World Product Day!</a> Be sure to celebrate product, yourself, and your peers today! 🎉

How much are product managers getting paid in 2024?

Leading product ops at Delivery Hero with Koen Dubelaar

Connecting fans to creators at Spotify (Dariusz Dziuk, Product Lead, Spotify)

“You’re not as competent as you think” 5 lessons from Ken Chin on navigating a product career (CPO, Spark Networks)

Everything wrong with the product management job market. And how to fix it

Annie Witcombe produces editorial for Mind The Product on a freelance basis. She has experience in conducting client research and telephone interviews, and she keeps relevant information accessible and up to date. 

Annie wrote for five business publications during the final year of her undergraduate degree, and she has worked on various stages of the editorial process since. The experience has sparked her enthusiasm for journalism, which she hopes to continue in her future career.

Annie Witcombe

<p>Zara Tam loves building things that make an impact. Now at Verve, she was formerly a product manager at Founders Factory, where she led a revolutionary AI recruitment product called Chosen. Built with the latest machine learning and search technologies, Chosen can help recruiters to find brilliant talent in a matter of minutes.</p>
<p>In this ProductTank London talk, Zara shows how AI can transform the time-consuming processes that companies face when they scout for talent. Zara’s product provides an AI-powered service with a enormous database of candidates at the touch of a button. Zara explains that if a recruiter finds a promising profile on LinkedIn, they can “copy the profile URL and put it into Chosen&#8221;. The inbuilt AI will return a comprehensive list of similar candidates based on information from that profile, and the legwork of the search is complete.</p>
<h2>How Does it Work?</h2>
<p>Chosen requires an enormous amount of data to work, so Zara and her team gathered 400 million candidates from nine million companies. The language-processing AI software extracts relevant information from this database, then sorts the candidates based on the following four categories:</p>
<h3>1) The Role Title</h3>
<p>The AI can sort the list of candidates into senior or generic-level piles.</p>
<h3>2) Function</h3>
<p>Chosen analyses the profiles by industry and by speciality.</p>
<h3>3) Size</h3>
<p>The software can sort profiles based on the size of company they work for. It separates people from big and small businesses, and can identify a start-up or scale-up background.</p>
<h3>4) Time</h3>
<p>Chosen analyses how much time the candidates have spent in each role, be it a general role, a product management position, or a senior management role. If recruiters would like to hire an experienced member, they can see the time in industry straight away.</p>
<h2>What’s Next?</h2>
<p>As Zara and her team refine their AI, she continues to share their learnings with product people. She says:</p>
<ol>
<li>Sprinting doesn’t work! Trial and error and research takes time.</li>
<li>You can find ways to put your data scientists in the shoes of your users.</li>
<li>As a product manager, try to understand how AI works, not just why.</li>
</ol>

core/freeform

AI ethics advice from former White House technologist – Kasia Chmielinski (Co-founder, The Data Nutrition Project)

How Google makes AI work at Enterprise scale – Miku Jha (Director, AI/ML and Generative AI, Google Cloud)

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

The future of product management: Insights from ProductTank San Francisco

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

A year with ChatGPT and product innovation: Navigating the AI landscape

How to keep your head about generative AI (when everyone is losing theirs) by Claire Woodcock

AI at an Early Stage Startup by Zara Tam

Annie Witcombe