EXANTE product owner Nastya has cut feature release timelines from six to eight weeks down to three to four by integrating AI tools into her daily workflow. The change, she said, comes from a deceptively simple insight: "AI excels at turning a blank page into a draft. Not a finished answer, but a starting point." That distinction shapes how she applies the technology across discovery, documentation, and even delivery.
Faster discovery and user research
In the discovery phase, competitor analysis that once consumed a full week of browser tabs, PDFs and manual note-taking now takes a single day. Nastya uses structured prompts to generate comparative matrices across multiple competitors in one pass, then manually verifies every source. For user interviews, recordings are transcribed and fed into a prompt template that extracts pain points, verbatim quotes, workarounds and user segments into a structured table. Once ten to fifteen interviews accumulate, those tables are uploaded together for cross-interview frequency and clustering analysis, producing a weighted list of themes that feeds directly into backlog prioritisation. This approach to AI for Product Development turns days of manual research into hours of structured analysis.
Consistent specs across platforms
EXANTE's cross-platform product requirements used to require three separate specification iterations for desktop, web and mobile. Now, Nastya describes the core feature logic once and uses AI to expand it into platform-specific versions that account for keyboard shortcuts, touch gestures and screen-width constraints. The result is built-in consistency across interfaces from the documentation stage, something that previously required multiple sign-off cycles and still produced behavioural inconsistencies.
AI in the delivery pipeline
Perhaps the most striking development is Nastya's involvement in delivery itself. Using Claude Code, she now handles small, isolated tasks by writing code, building locally, testing the result, iterating on bugs, and opening merge requests that pass through the standard code review and QA pipeline. This does not make her a developer, she is clear about that, but it clears low-priority backlog items that would otherwise wait weeks and has deepened her understanding of the product's underlying architecture.
Where human judgement remains non-negotiable
Where AI is explicitly excluded is equally telling. Final priority decisions, regulatory fact-checking, live user conversations and architectural integrity all remain firmly in human hands. EXANTE's approach is not about replacing judgement but about reclaiming the time previously lost to blank pages, first drafts, transcriptions and reformatting, so that more capacity flows toward the decisions that actually require a person to make them.
Why this matters for product development professionals
For product teams weighing where to apply AI, the takeaway is concrete. The biggest time savings come from the earliest stages of work-turning a blank page into a structured draft. Automating that step frees senior talent to spend more time on the decisions that cannot be delegated to a machine. The discipline of verifying every AI output, rather than trusting it, is what separates a productivity gain from a liability.
Your membership also unlocks:
