# The Quiet Shift: How AI Is Reshaping Product Work

DevFeed: [The Quiet Shift: How AI Is Reshaping Product Work](<https://devfeed.tech/articles/the-quiet-shift-how-ai-is-reshaping-product-work-30802.md>)

Original publisher: [Read original article](<https://devblog.kogan.com/blog/the-quiet-shift-how-ai-is-reshaping-product-work>)

Author: Nela De Silva

Published: 2026-05-03T09:11:35Z

Content type: opinion

Language: en

Sources: [Kogan.com](<https://devfeed.tech/sources/kogan-com.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Automation](<https://devfeed.tech/topics/automation.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [automation](<https://devfeed.tech/tags/automation.md>), [productivity](<https://devfeed.tech/tags/productivity.md>), [research](<https://devfeed.tech/tags/research.md>), [scheduled](<https://devfeed.tech/tags/scheduled.md>)

## AI overview

The article examines how AI is becoming part of day-to-day product work. It describes applications including scheduled automation, customer session analysis, meeting capture, team skill building, and codebase exploration, arguing that AI can reduce recurring coordination work and support higher-judgment activities without replacing prioritisation, scoping, or stakeholder alignment.

## Source excerpt

Over the last several months, AI has moved from a side experiment to an integral part of day-to-day product practice. The shift is practical rather than theoretical, but it is still very much in motion. Recurring coordination work is beginning to take meaningfully less time, while the substantive work that defines the role, such as problem framing, solution shaping, navigating trade-offs, and stakeholder alignment, is becoming sharper and better supported. This piece sets out where the shift is taking hold and the practical improvements that are pointing towards meaningful productivity gains within the product team, and, as a result, beyond it. Where product teams can apply AI From my own experience in the role, the most significant value has emerged across five principal areas: scheduled background automations, customer session analysis, meeting capture and follow-up, skill building for the wider team, and codebase exploration. These five areas serve two complementary purposes. In some, such as scheduled automations, ticket triage, and meeting capture, recurring and semi-structured work can be shifted toward AI-assisted execution, freeing capacity for higher-judgement activities. In others, such as codebase exploration and customer session analysis, AI actively extends what someone in a product role can contribute, opening up investigation and insight that would previously have depended on engineering or research support. In both cases, AI complements rather than replaces the substantive work of prioritisation, scoping, and stakeholder alignment. Let's take a closer look at how this translates into practice. Figure 1. The five areas of AI application, organised against two complementary purposes. A lightweight test for what to automate The strongest candidates for automation are rarely the most complex tasks; the opposite is usually true. The greatest returns come from work that is repetitive, predictable, and quietly consuming time in the background. A simple thre