Michelle Andrews

20+ years in design · 16 in SaaS

People bring me in when things are unclear, risks are high, and the team needs clarity before moving forward.

I work on complex, high-stakes products where teams struggle with scale, ambiguity, and competing constraints. Much of that work is consolidation: bringing fragmented products, patterns, and content onto shared foundations teams can actually adopt, alongside decision-support workflows and AI-assisted tools that reduce rework and make delivery more predictable.

I partner with product and engineering to turn unclear problem spaces into systems teams can rely on.

Multi-product consolidation  ·  Design systems built 0 to 1  ·  AI-assisted workflows for expert users  ·  Designing in code  ·  Research end to end

Selected work

Multi-platform design system foundations

Consolidating 25 Products into Shared Foundations

Brought 25 acquired products across six brand lines onto one foundation, with two adoption paths so teams could move without pausing delivery.

Impact

Reduced drift and rework by aligning design and engineering on shared foundations, governance, and adoption practices.

Key contributions

  • •Architected a multi-brand token system across 6 verticals from a single source of truth
  • •Consolidated 25 acquired products into shared foundations, shipping first unified product in 6 months
  • •Built component libraries across web, Word add-in, and Outlook using Token Studio and Style Dictionary
  • •Led migration from MUI to Fluent UI without blocking delivery across multiple product teams
Read case study →
AI-driven document analysis platform

Designing Trustworthy AI for Legal Decision-Making

Embedded AI into high-stakes legal workflows with scope control, citations, and human oversight to support defensible decisions.

Impact

Increased defensibility by making evidence, citations, and uncertainty visible at the moment of decision-making.

Key contributions

  • •Clarified ambiguous questions before analysis to reduce false confidence in AI outputs
  • •Designed human-in-control review and override workflows for expert decision-making
  • •Unified answers, supporting evidence, and document context within a grid-based review model
  • •Made confidence, citations, and explanations visible to support defensibility and accountability
Read case study →
Grid and panel interface for expert document review

Supporting Expert Decision-Making Under Uncertainty

Designed workflows that help experts reason through ambiguity without over-relying on automation.

Impact

Prevented premature trust by separating AI output from judgment and gating automation until expert-reviewed context existed.

Key contributions

  • •Structured review experiences to separate AI output from human judgment and final decisions
  • •Made uncertainty, exceptions, and incomplete coverage visible rather than hidden behind confidence
  • •Designed clear review states and thresholds to ensure expert intervention was intentional and well-timed
  • •Prevented premature trust by gating suggestions and automation until sufficient human-reviewed context existed
Read case study →