Product design Management

AI enhanced workflow for how I manage product design development

Scoping

Determining scope of design discovery with intelligence

The Discovery Playbook is a tool to guide discussions about right-sizing the approach to design discovery work, but now it's supercharged with AI analysis that identifies patterns we might miss. Before we even sit down for the scoping conversation I feed data into an AI partner (Claude, Perplexity, etc.) to generate an initial landscape of where we should focus our discovery efforts. It can include:

• Customer data in the form of goals, needs, and pain points

• Customer feedback

• Competitive research

• Sales team inputs

• Corporate goals

• Industry trends

• Data analytics

• Previously captured ideas in backlog

The chart below represents different levels of design discovery work that could be done for any given product design effort (in pink), but now each level is informed by AI analysis of user behavior data, and risk assessment algorithms. It was born out of need to communicate with other disciplines who were new to incorporating design work into their planning.

AI-Enhanced Discovery Planning Process:

1. Pre-Discovery Data Analysis: AI analyzes existing user data to identify patterns and suggest focus areas

2. Risk Assessment Generation: AI evaluates project complexity and suggests appropriate discovery depth

3. Timeline Intelligence: AI suggests realistic timelines based on similar project patterns

It helps answer questions like "How do we right size our approach to design? How do we align cross functionally on risks and desired outcomes for product MVPs?"

To better understand all the things that should be considered for scoping design work, see my case study on Dual Track Agile.

Once a "right sized" approach is agreed upon, product design managers should work with other disciplines to create a discovery plan to operationalize the effort—a plan that's now informed by AI analysis ensuring we ask the right questions from the start.

Managing in Asana

To make this work viewable and shareable across the program review efforts, the timeline, once agreed upon, should be tracked in a program that lets others see it. Here Asana is used.

Design team tracking

In addition to being able to share the aligned upon work effort in program reviews, I was able to easily set up templates in Asana for other managers to utilize as part of planning for the Dual Track Agile Process, and create an all-up view of design resource availability:

Kick Off!

A cross-functional kickoff meeting is typically conducted, during which I would highlight the key objectives of the work, outline the Discovery Track Plan, and clarify roles and responsibilities of designers on the team.

UX Research

Product design managers and AI enhanced research

Depending on the needs of the project and the skillset of the team, I typically like to be involved in UX research for a project. This ensures that I'm on the same page as the designers when it comes to what insights are driving the design solution. AI doesn't replace this involvement—it amplifies it.

AI research enhancement process:

Planning phase: AI generates comprehensive research protocols based on project objectives and user segment analysis. Instead of starting from scratch, we begin with AI-generated interview scripts, survey questions, and testing scenarios tailored to our specific context.

Execution phase: During user interviews, AI provides real-time transcription with automatic theme identification. As patterns emerge across interviews, AI suggests follow-up questions and flags important insights for deeper exploration.

Synthesis phase: What used to take weeks now happens in real-time. AI analyzes interview transcripts to identify themes, extract key quotes, and generate preliminary user journey maps. The human researcher focuses on strategic interpretation rather than manual data organization.

There are many kinds of research, but generally, product design managers should be involved in writing up the research plan, the research sessions when applicable, and the synthesis and outputs of the work. AI makes each of these phases more thorough and efficient.

Sample AI-Enhanced Research Outputs:

• Dynamic user personas generated from behavioral data analysis

• Journey maps with emotional state tracking and pain point clustering

• Insight prioritization based on frequency, impact, and business alignment

• Automated research reports with key findings and recommendations

Research Software Integration

UX Managers and designers ideally have access to AI-enhanced UX research software for this collaboration. Modern tools like Dovetail integrated with AI, or Miro with AI plugins, create a research ecosystem that maintains methodological rigor while dramatically accelerating insight generation.

AI Research Capabilities:

• Cross-interview pattern recognition as research progresses

• Automated coding of qualitative data with human oversight

• Predictive user behavior modeling based on research findings

• Insight correlation across multiple research projects

Example of user interviews in Dovetail.

Principles

UX Principles & Archetypes: Outputs from UX research

As a result of gathering insights from the research phase, I often find that helping designers translate those insights into UX principles is a useful step. Depending on the maturity of the team, this may be a product design manager’s task, or may fall directly to a designer.

Requirements

PRDs with AI analysis

Depending on how your organization works, someone is generally creating an initial Product Requirements Document. This may be a Product Manager or other stakeholder. Now, before we dive into story mapping, AI analyzes these requirements to identify gaps, suggest enhancements, and predict potential edge cases.

AI requirements enhancement:

• Completeness analysis against industry best practices

• User story gap identification based on journey map analysis

• Risk factor assessment with mitigation strategy suggestions

• Cross-functional dependency mapping

Story mapping for cross functional alignment on scope - AI accelerated

In terms of how we translate this initial set of documents into a cross-functional scoping conversation, I expect product design managers to help facilitate story mapping sessions with well written tickets. This is the single most important task I have found for setting cross functional expectations and getting work into a backlog in a timely manner. AI now makes this process dramatically more comprehensive and efficient.

AI-enhanced story mapping process:

Pre-session intelligence: Before the story mapping session, AI analyzes user research findings and requirements to generate initial story map structures. Teams arrive with a data-driven foundation rather than starting from blank sticky notes.

Real-time story generation: During the session, AI suggests user stories with acceptance criteria based on the conversation flow and research insights. Instead of struggling to remember edge cases, the team has AI-generated comprehensive story options to refine.

Dependency intelligence: AI identifies technical and design dependencies that human teams commonly overlook, ensuring we account for integration complexity and sequencing requirements upfront.

Comprehensive coverage: AI cross-references similar products and user journeys to suggest stories and scenarios the team might miss, ensuring comprehensive coverage of user needs.

The ultimate goal of a Story Mapping Session is to gather and clarify the requirements and outcomes necessary for the new User Experience, not to clarify the specific design of those requirements. AI ensures we capture these requirements more completely and with greater confidence in our coverage.

Tracking Work

UX Kanban & backlog

Depending on how your organization work, a product design manager needs to be keeping and eye on where the design work is tracked. We used a UX project in Jira with a Kanban board that different engineering teams could pull work in from, which was handy as UX work often doesn’t fit cleanly into Agile workflows. This was an effective solution.

Meetings Cadence

Sample UX ceremonies cadence

A product design managers needs to set up a series of reviews to manage and evangelize the work. Below is an example that accounts for Design Discovery ceremonies and where it intersects with engineering’s Agile ceremonies.

Review Work

Review in progress work with intelligent analysis

Product design managers should regularly review work with designers and ensure cross-disciplinary teams stay informed on progress. See notes below for discussion points in design work reviews, now enhanced with AI analysis that ensures comprehensive and consistent evaluation.

AI-Enhanced Review Framework:

Before each review, AI analyzes the design work against multiple frameworks:

UX Solution Intelligence:

• Heuristic evaluation results with specific improvement recommendations

• User research correlation analysis showing design-to-insight alignment

• Accessibility compliance assessment with remediation guidance

• Conversion optimization suggestions based on behavioral data patterns

Design System & Pattern Analysis:

• Component usage consistency across platform and brand guidelines

• Pattern alignment with established design system standards

• Cross-platform experience continuity verification

• Visual hierarchy assessment against accessibility and usability principles

User Experience Validation:

• Cognitive load analysis with simplification recommendations

• Task flow efficiency evaluation with bottleneck identification

• Error prevention assessment with safety net suggestions

• Mobile and responsive design validation across device contexts

Technical Feasibility & Performance:

• Implementation complexity scoring for engineering estimation

• Performance impact prediction based on design choices

• Cross-browser compatibility analysis

• Asset optimization recommendations

Rigor questions:

Instead of relying solely on experience, AI generates contextual review questions:

• "Can the designer explain how this feature product is meeting both user and business needs?" (with supporting data from user research)

• "Is this pattern in agreement with our design system or other accepted industry standards?" (with specific compliance analysis)

• "Why is this the right pattern to meet the users need?" (with alternative pattern suggestions and trade-off analysis)

• "Have you thought through the accessibility concerns?" (with specific WCAG compliance recommendations)

The human elements of creative judgment, strategic thinking, and empathetic user advocacy remain central to the review process. AI ensures we don't miss technical details and provides data-driven insights to support design decisions.

UX Documentation

UX Managers need to ensure that proper documentation is included in design work. Below are some examples, but the game has completely changed with AI. What used to be the most time-consuming part of design management is now largely automated, freeing designers to focus on creative problem-solving.

AI Documentation Pipeline:

Automated Generation: AI analyzes Figma files, user stories, and research insights to automatically generate comprehensive design documentation including component specifications, user flow descriptions, and design rationale explanations.

Living Documentation: Instead of static documents that become outdated, AI maintains documentation that evolves with the design. When designs change, documentation updates automatically with change impact analysis and stakeholder notifications.

Comprehensive Coverage: AI ensures documentation completeness by cross-referencing designs against established patterns and identifying missing specifications before engineering handoff.

Sample AI-Generated Documentation:

Component Documentation:

• Automated component behavior descriptions with interaction states

• Usage guidelines generated from design system analysis

• Implementation notes with engineering-friendly specifications

• Accessibility documentation with compliance verification

User Flow Documentation:

• Step-by-step user journey descriptions with decision point analysis

• Edge case and error state documentation

• Cross-platform experience mapping

• Integration point specifications for engineering teams

Design Rationale Documentation:

• Research-backed design decision explanations

• Alternative solution evaluation with trade-off analysis

• User impact predictions based on behavioral data

• Business goal alignment verification

Handoff Optimization: AI generates engineering handoff packages that include:

• Asset organization with naming conventions

• Responsive behavior specifications

• Animation and interaction details

• QA testing scenarios with expected outcomes

The result is documentation that's more comprehensive, more consistent, and more useful than manual documentation, created in a fraction of the time. Designers can focus on solving user problems while AI handles the systematic documentation of those solutions.

Design System & Brand

Ensure that the visual design is aligned with the brand and UX patterns

Product design managers, in addition to ensuring that the user experience is meeting both business and end user needs, must ensure that the design is on brand and being worked back into the design system, if applicable.

Group Critique

Critiques with AI-Generated Insights

Depending on how your organization is set up, you may want to have your designer(s) review their work with the broader team in addition to a product design manager review. Group critiques are a good tool for this, now enhanced with AI analysis that makes feedback more objective, actionable, and grounded in data.

AI Critique Preparation:

Before critique sessions, AI provides comprehensive design analysis:

• Objective assessment against established UX principles and accessibility standards

• User research correlation showing how design decisions align with insights

• Performance prediction based on similar design pattern analysis

• Comparative analysis with successful projects in similar domains

Enhanced Rules of Play:

Sign up with AI prep: Critique participants receive AI-generated prep materials tailored to their expertise—technical reviewers get implementation analysis, UX reviewers get user impact assessment, business stakeholders get goal alignment evaluation.

Mind the time with AI focus: AI agenda optimization ensures the most impactful discussion topics get appropriate time based on project phase, stakeholder needs, and identified design risks.

Listen before asking with data support: AI provides background context for design decisions, helping reviewers understand the research and constraints that influenced the design before offering alternatives.

Write it down with AI capture: AI generates structured feedback summaries with action item prioritization, ensuring nothing gets lost and follow-ups are clearly defined.

Be specific with AI insights: Instead of vague feedback, AI helps generate specific, actionable recommendations based on user research findings and best practice analysis.

Yes and... with AI synthesis: AI identifies opportunities to combine different feedback perspectives into stronger solutions, suggesting ways to build on ideas constructively.

AI-Enhanced Critique Questions:

Instead of generic critique prompts, AI generates contextual questions based on:

• Project phase and specific design challenges

• User research findings and business objectives

• Technical constraints and implementation feasibility

• Team dynamics and reviewer expertise

The human elements of empathy, creative collaboration, and strategic thinking remain at the heart of critiques. AI ensures these conversations are grounded in data, focused on the most important issues, and result in actionable improvements that serve users and business goals.

Present Out

Communicating out to the org

Once work has hit a certain point in development, it typically needs to be evangelized to stakeholders for the use cases below. A product design manager should be keeping an eye on the work and where it will benefit from the broader communication and make sure it winds up in the right channel.

Integration Summary

These AI enhancements transform your methodology from:

Sequential → Continuous: Analysis and insights flow throughout the process rather than in discrete phases

Manual → Augmented: Designers focus on creative strategy while AI handles systematic analysis and documentation

Assumption-Based → Data-Driven: Every decision is informed by user research, historical patterns, and predictive analysis

Individual → Collaborative: AI provides consistent analysis that all team members can build upon

Reactive → Predictive: AI identifies potential issues and opportunities before they impact the project

The core human elements of your methodology—empathy, creativity, strategic thinking, and collaborative problem-solving—remain central. AI amplifies these capabilities, making teams more effective at creating user-centered design solutions that achieve business objectives.