post

Footer About

Footer Contribute

Footer Get Involved

Footer Links

Mind the Product

Product Management

How to measure impact in Product Operations

Artificial intelligence (AI)

How to navigate the explosion of AI tools aimed at product managers

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! 🎉

Why self-care is your secret weapon as a product manager

What if pricing was your best feature?

Navigating the workplace as a young product manager

Klarna goes on recruitment drive after ditching AI-first policy

How Stack Overflow is competing with AI – Jody Bailey (CPTO, Stack Overflow)

Eira is an editor for Mind the Product. She's been a business journalist, editor, and copywriter for longer than she cares to think about.

Eira Hayward

This week’s Sunday Rewind is a case study from <a href="https://www.linkedin.com/in/aefidler" target="_blank" rel="noopener">Ashley Fidler</a> in which she lays bare a catalogue of common errors made by her and her team when launching a cybersecurity software product.
The case study relates to Ashley’s time at a <a href="https://www.mindtheproduct.com/machine-learning-for-product-managers-a-quick-primer/" target="_blank" rel="noopener">Machine Learning (ML)</a> platform company that pivoted to cybersecurity in 2016.
The company had a ML platform that was focused on financial services and differentiated by ease of use for non-technical users, but it had hit a common set of issues facing ML companies. It was service-heavy, targeted at enterprises who expected a lot of customisation, and was burning cash without the repeatability and scale of a SaaS product that would justify this spend.
The company made a number of errors when it pivoted:
Lack of data: It pivoted to a new market quickly, based on many untested assumptions, without adequate data.
Relied on expertise: It relied on expertise, rather than fully validating the customer need and budget before building the product
Wishful thinking: They deluded themselves about technological readiness, build time, and the importance of cybersecurity knowledge
Rushing to market: Once these errors became clear, they rushed to market then recovered slowly.
Ashley says that in the end, the company was acquired and nearly everyone got a position with the parent company.  She says: “We made tons of mistakes, endemic to young teams without adequate experience and support to take on the hard challenges we were facing. It wasn’t a  successful outcome, but we escaped without much lasting damage” Her final take away from this experience is, “honestly, humility. Product is hard. It requires patience, curiosity, good judgement, and a level of openness and emotional maturity that takes time to develop”.
Read the original post <a href="https://www.mindtheproduct.com/common-product-development-mistakes-and-how-to-avoid-them-all-a-case-study/" target="_blank" rel="noopener">Common Product Development Mistakes and How To Avoid Them All: A Case Study</a>

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

What we get wrong about technology by Tim Harford

Product management in the age of ChatGPT by Yana Welinder

How Canva uses AI-powered features to drive PLG

SUNDAY REWIND: Common Product Development Mistakes and How To Avoid Them All: A Case Study

ProductTank Stuttgart

ProductTank Bratislava

ProductTank Stockholm

ProductTank Bonn

ProductTank Milano

ProductTank Madrid

ProductTank Columbus

ProductTank Sofia

ProductTank Belfast

ProductTank Toronto

ProductTank Bangkok

ProductTank Kraków

ProductTank Barcelona

ProductTank Montréal

ProductTank Chicago

ProductTank Seattle

ProductTank Linköping

ProductTank Rotterdam

ProductTank Cardiff

ProductTank San Francisco

ProductTank Auckland

ProductTank Newcastle Upon Tyne

ProductTank Abu Dhabi

ProductTank Lausanne

ProductTank Ljubljana

ProductTank Valencia

ProductTank Luxembourg

Product Management Foundations BST

Product Management Foundations ET

ProductTank Edinburgh

ProductTank Des Moines

ProductTank Philadelphia

ProductTank Reston

ProductTank Dubai

ProductTank Prague

ProductTank Reading

ProductTank Jacksonville

ProductTank Rio de Janeiro

ProductTank Atlanta

ProductTank Los Angeles

ProductTank Bielefeld

ProductTank Köln

Communication & Alignment BST

Communication & Alignment ET

ProductTank Paris

ProductTank Frankfurt

ProductTank Milton Keynes

ProductTank Norwich

ProductTank Pittsburgh

ProductTank Manchester

ProductTank Exeter

ProductTank Malmö

SUNDAY REWIND: Common Product Development Mistakes and How To Avoid Them All: A Case Study

Eira Hayward

Read more