AI Product Workflows

The workflow needed to reduce product translation, not generate more content.

Enterprise product initiatives required coordination across product, UX, research, data, engineering, implementation, and business stakeholders. A single feature could involve customer needs, existing product behavior, data availability, business commitments, desktop and mobile requirements, and dependencies across multiple teams.

The problem was not a lack of information. It was that the information existed in different conversations, documents, design files, and delivery systems.

The AI product workflow was designed to preserve that context as work moved from an initial problem statement to a production-ready requirement.

Shared context became the foundation of the workflow.

Product knowledge was organized around the problem being solved rather than the meeting or tool where the information originated.

Discovery notes, requirements, user needs, product decisions, unresolved questions, and delivery dependencies were structured so they could be reused throughout the lifecycle of a feature. This prevented each phase from restarting the product narrative.

Claude helped make product reasoning visible.

Claude helped turn fragmented inputs into structured product artifacts while preserving source context and identifying gaps that required human follow-up.

It was used to organize meeting notes, compare stakeholder requests, develop requirement structures, identify contradictions, draft user stories, and surface missing decisions.

The goal was not to accept generated output as complete. The goal was to give the team a stronger starting point for review.

Requirements became the bridge between user needs and implementation.

The workflow moved beyond writing isolated user stories. Each initiative was framed through the user problem, business objective, workflow, data requirements, system behavior, edge cases, dependencies, and definition of success.

Seemingly simple feature requests were expanded into the product decisions required to make them actionable.

AI supported the initial organization of these requirements, but product, design, engineering, data, and implementation partners reviewed the resulting structure before it became delivery work.

Product decisions were translated into a consistent ticket structure.

To reduce ambiguity between product definition and engineering delivery, work was organized through a repeatable ticket model. A master ticket captured the overall business objective and feature scope. Supporting tickets separated discovery, desktop design, mobile design, and implementation support.

The model also created a controlled entry point for AI-generated content. AI could prepare the first draft of a requirement or ticket, but each artifact still required an accountable owner and explicit review before entering the backlog.

Figma became the visual reasoning layer of the workflow.

Once requirements were structured, Figma was used to test whether the proposed workflow made sense in an actual product experience.

AI-assisted exploration helped generate alternative information hierarchies, interface content, workflow questions, and early interaction concepts. These outputs accelerated exploration but did not replace direct design work or user validation.

Trust required visible boundaries around AI-generated work.

The framework established a clear distinction between generated content, reviewed content, and approved product requirements.

Generated outputs were treated as drafts. Important assumptions were identified explicitly. Unsupported claims were removed. Open questions remained visible instead of being resolved through invention.

The process was tested through active product delivery.

The framework was applied across interconnected dashboard, reporting, analytics, data-visualization, and decision-support initiatives.

It helped organize metric prioritization, comparison logic, data requirements, visualizations, incomplete-data states, responsive workflows, and implementation dependencies.

Across these efforts, AI helped the team move more efficiently between conversations, product definitions, interface concepts, and implementation requirements without separating generated artifacts from the evidence behind them.

The final framework connected AI assistance with the operating rhythm of the product team.

The framework established a continuous path from discovery to delivery, then returned implementation discoveries and customer feedback to the knowledge system so future work began with stronger context.