Mariusz Cieśla
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Mariusz has been working with the internet since when "Webmaster" was a job title. He's currently Staff Design Engineer at Contentsquare, focusing on core experience and the design system, as well as building internal AI tools for product teams. Before that, he worked across a wide range of startups and consultancies in roles spanning product, design and engineering, including LILT, airfy, MING Labs, anynines and tracdelight, and co-founded the mentorship platform Lifetramp.
A talk with
Jane Austin
SVP Product Design
Contentsquare
< About >
This talk explores what it really takes to use AI to accelerate product development, beyond simply generating more output, more quickly. Through real examples from Contentsquare, it follows the journey from a raw idea in Slack to a buildable, validated product experience.
The talk shows how teams are creating the right context for AI to work effectively: capturing ideas in structured backlogs, connecting design systems directly to code, documenting language and interaction rules, and making product knowledge available across tools. It also examines the cultural shifts required to use that context responsibly, including a write-first approach that clarifies whether an idea should exist before a screen is designed, and lightweight evaluation practices that help teams understand the quality of the inputs and outputs they are working with.
The central message is that AI can accelerate every stage of product development, but speed alone is not the goal. To truly harness its value, teams need the right context, the right culture, and clear human accountability for what gets built and shipped.
< Key learnings >
AI is only as good as the context it receives. Shared knowledge, clear documentation, design-system rules, and well-defined requirements make AI outputs more accurate and trustworthy.
Speed makes accountability more important, not less. As AI makes it easier to create prototypes and features, teams need clear ownership and visibility into who created something, why it exists, and what stage it is in.
Human judgment must remain part of the process. AI can draft, rank, generate, and evaluate, but people still need to decide what should move forward and verify that the result is correct.
Write before you build. Taking the time to understand the problem you are trying to solve, the opportunity, and the end user is the route to better, faster results.
Judgement is becoming the scarce skill. As AI produces more of the first draft, what is scarce is knowing what good looks like and being able to express it precisely enough for the model to improve on its next attempt.
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