TLDR Product Management 2026-10-23

We asked 1,000 product leaders how AI is actually changing their work. 80% say they are shipping faster, but customer value is not landing any sooner. Join Atlassian CPO Tamar Yehoshua and Claire Vo as they discuss what we learned from our 2027 State of Product report.

Register now

This article outlines workflow principles for building software with AI agents rather than traditional sprints. Key strategies include running multiple agents continuously, auditing apps with customer personas, and converting successful experiments into reusable skills.

In enterprise product development, choosing between buying tools and building them internally has become a user experience decision driven by AI. While coding agents reduce initial build costs, ongoing maintenance, data silos, model retirement, and user context switches introduce hidden complexities that product teams must manage.

Product quality involves setting and evaluating against a quality bar across a product's lifecycle. Often overlooked in favor of new features, poor quality risks sudden or gradual customer churn. Maintaining quality is even harder in AI products due to model drift and non-deterministic interfaces.

AI can make vague requirements look complete by quietly inventing business rules. Instead, use it to identify assumptions and unanswered questions and resolve key decisions with stakeholders, and only then draft acceptance criteria.

Your customers need websites. But offering them yourself means owning hosting, security, provisioning and maintenance. Is that where your engineering team should spend its time?

Wix Enterprise breaks down how software companies are offering customer websites without building the underlying platform.

Read the build-vs-partner breakdown →

This six-step growth playbook outlines tools used from analytics to product communication, including Mixpanel, FullStory, UserTesting, VWO, Pendo, and HubSpot.

Product teams can boost feature adoption by applying behavioral science strategies: requiring features through paternalism, adding costs to declining, and reducing perceived setup friction. Timing feature requests to match user intent within a workflow significantly increases success.

Product teams frequently trap themselves in featurism and outdated backlogs by focusing on speed rather than a clear direction. Teams should start with a clear destination, delete stale backlog items untouched for over 90 days, and prioritize what is necessary over what is easy.

Setting tight constraints is more effective than focusing on broad, outcome-based goals when the path forward is unclear. Constraints reduce overwhelming choices, prevent scope creep, block default thinking, and help teams solve complex problems.

Leaders must visibly commit to the changes they ask of their teams.

Measuring a tech organization's success solely by roadmap execution is insufficient.