This week's roundup of interesting links covers:
AI and the Workplace
Applied AI
Communities
Online Learning/Training
Product Management Interviews
AI AND THE WORKPLACE
How Professional Service Firms will capture value in the AI agent era. [CB Insights]
Four priorities
1) orchestrating the AI agent tech stack. Guide clients through implementation, integration, scaling, and governance of AI agents across fragmented infrastructure, from model selection to trust and performance management.
2) activating proprietary data for smarter agents. Turn firm and client data into fuel for more capable, context-aware agents — differentiating through domain-specific IP, data infrastructure, and strategic partnerships.
3) turning services into scalable AI products. Evolve from custom consulting engagements to platform-based delivery, industry-specific solutions, and new pricing models.
4) leading the buildout of a human-AI workforce. Redesign internal talent models to work alongside AI agents while shaping offerings to help enterprises adopt and manage AI, from agents to robots.
APPLIED AI
Addressing Gen AI’s Quality-Control Problem [HBR]
What Amazon learned when it automated the creation of product pages
AI-generated recap:
° Amazon introduced Catalog AI in 2023 to improve the quality and reliability of its massive online product catalog using generative AI.
° Traditional quality control relied on thousands of employees and hundreds of machine-learning models, which were costly, specialized, and limited in scalability across product categories.
° The new system initially produced unreliable outputs, but Amazon implemented audits, guardrails, and dual-AI review mechanisms to detect and block errors.
° With these improvements, Catalog AI now generates and tests tens of millions of hypotheses annually, with 8% of its suggestions already boosting sales revenue.
° The approach shows that structured guardrails, experimentation, and AI self-checks can make generative AI practical at scale, offering lessons for other organizations seeking to control AI output quality.
COMMUNITIES
Women Defining AI (Site, Podcast)
A paid membership ($100/year as of this writing) where members gain access to community learning events. What you get out of it is entirely dependent on how much effort you put in.
ONLINE LEARNING/TRAINING
Building with Cursor (Site)
Public-facing version of an internal onboarding guide at Cursor provided to GTM + non engineering hires. This guide walks through getting started from scratch to a built out, deployed project.
Q3 2025 Google Certifications/Training (Blogpost with full list of offerings)
For non-technical users:
Develop AI-Powered Prototypes in Google AI Studio - shows you how to use Google AI Studio, our developer playground for the Gemini API, to quickly sketch and test your ideas.
PRODUCT MANAGEMENT INTERVIEWS
The definitive guide to mastering product sense interviews.
The product sense interview has five core dimensions: clear communication, product motivation, segmentation, problem identification, and solution development. This guide offers a step-by-step plan for handling each dimension under time constraints.
The definitive guide to mastering analytical thinking interviews (truncated by paywall).
Analytical thinking (AT) interviews test a candidate’s ability to contextualize a product, establish metrics, set goals, and weigh tradeoffs, with the focus on structured thinking rather than “right answers.” This guide highlights that mastering AT interviews requires preparation, structured communication, and awareness of business and market context.