Work / Product and organizational systems

Selected systems, not a screen archive.

The cases below show three different jobs: define trust boundaries, turn scattered evidence into product direction, and reduce decision load without removing necessary judgment.

Flagship work

Three cases do most of the heavy lifting.

Each case uses a different proof strategy rather than repeating one portfolio template.

01
Indeed / system evidence
What the employer believes
What the system is allowed to do
01 / BurdenRecruiting effort is too highTargeting, qualification, outreach, prioritization.
02 / GuideProduct can narrow choicesMake the next decision easier without hiding why.
03 / DelegateSystem may act only inside clear boundsExplain action, rationale, and recovery path.
04 / ApproveEmployer remains accountableHuman control stays visible at high-trust moments.
Trust contract mapThemed reconstruction

Flagship product strategy

Virtual Recruiter / D.I.F.M.

Defining explainable delegation for recruiting automation: what the product can do, what it must explain, and where employer control remains visible.

Employer effort → trust contract → delegated product system

02
Indeed / system evidence
SignalsMessy evidence from many surfaces
  • Vision + whiteboard → app-home ambition
  • Feedback synthesis → repeated employer friction
  • App reviews + churn → trust and usability gaps
  • A11y + localization → inclusive rollout constraints
  • Interview / agenda → time-sensitive mobile moments
  • Career Scout / AI → assistance boundary questions
SynthesisTurn signals into comparable choices
  • Cluster recurring themes
  • Separate symptom from cause
  • Map confidence and evidence gaps
  • Expose tradeoffs across teams
  • Connect evidence to employer decisions
DecisionsConvert evidence into product calls
  • Prioritize reliability and comprehension fixes
  • Sequence app-home status and next actions
  • Support interview / agenda moments
  • Defer unclear AI bets until boundaries exist
  • Route dependencies to design-system work
Supported outputsWhat the synthesis helped clarify
  • Prioritized: fix confusing mobile moments
  • Sequenced: app home → interviews → assistance
  • Deferred: broad automation without trust model
  • Framed: roadmap discussion around employer decisions
What made it complexDifferent signal typesMobile moment constraintsRegional rollout needsDesign-system dependenciesAI boundary uncertainty
Direction

Mobile employer experience can be discussed as a product-intelligence system: app home explains status, interviews get timely support, inclusive foundations stay explicit, and AI assistance waits for clear employer control.

Signal-to-roadmap synthesis mapThemed reconstruction

Fresh professional evidence / mobile product system

Employer Mobile App / EMA

The work was not finding more signals. It was making the signals useful enough to discuss what the mobile app should help employers do.

Signals → synthesis → priorities → decision support

03
Old burden11 pages
59 clicks
ResponsibilityKeep · remove
defer · prefill
New modelOne page
+ checklist

Product simplification / decision-load reduction

The Flash Funnel

The work was not making a long funnel look shorter. It was deciding which burden the employer should carry, and which burden the product should absorb.

Decision load → responsibility sorting → mobile momentum → behavior signals

Leadership and foundation systems

The pattern is older than AI.

Four contexts show how product direction, governance, service capability, and distributed craft developed over time.

Leadership and capability / 2019

Indeed University UX Integration

UX moved from late support into product-learning infrastructure: advisor roles, facilitation guides, critique rituals, mentoring, and a support network that made better framing repeatable.

  • First UX Indeed University Lead
  • Four non-UX direct reports
  • 10+ person UX support network

Design leadership scales when the method becomes teachable.

Regional systems / 2013–2015

SEEK Regional Marketplace and Design Governance

Employer products, candidate journeys, local-market context, shared standards, and release quality had to move together without flattening regional reality.

  • Seven regional contexts
  • Five-engineer / two-UX taskforce
  • Shared jobsDB / JobStreet design-system library

Reusable decisions create coherence more reliably than repeated persuasion.

People capability / 2011–2013

Apple Service Capability System

Training, localization, coaching, observation, and service standards worked together around the first Hong Kong Apple Store experience.

  • 2,000+ employee training context
  • Cupertino and market-leader collaboration
  • Localization, retail, and instructional-design partnership

Good design leaves people more capable.

Product craft and distributed execution / 2007–2011

EF Learning Products and Modular Systems

Learning journeys, mobile surfaces, modular communications, design guidance, and content operations connected product craft with distributed execution.

  • Remote 50+ development team
  • 13+ regional/country collaborators
  • Reusable email, sales-page, and product-system guidance

Craft becomes durable when screens connect to behavior and operating structure.

Craft texture

The earlier work remains visible as foundation.

A compact archive keeps hands-on product and visual craft in the story without asking old screenshots to carry the current positioning.

Englishtown web experience

Early product craft

EF / Englishtown learning products

Hands-on UX/product craft before the leadership arc.

iPad quiz platform

Early product craft

Mobile + iPad learning experiences

Interaction design, educational UX, and platform adaptation.

Smartone intranet with CMS

Systems and operations

CMS, intranet, and internal workflow tools

Systems thinking, information architecture, and utility-product design.

Email marketing system

Systems and operations

Email marketing and lifecycle systems

Audience journeys, conversion mechanics, and communication systems.

AI as the newest chapter

Habitat and KIREI test the same beliefs in working AI systems.

The professional evidence remains the foundation. Labs show where human authority, evidence, uncertainty, approval, and recovery are being tested now.