ReloPortal Redesign
Phase 1: Scope and Research
ReloPortal Redesign
Phase 1: Scope and Research
ReloPortal Redesign
Phase 1: Scope and Research
Home Depot - Pricing & Merchandising Decision Systems
A strategy-led concept for helping enterprise merchants move from browsing pricing data to managing outcomes
faster, safer, and with intelligent guardrails.
A strategy-first AI pricing workspace vision that helps merchants move from browsing performance data to confidently managing outcomes
faster, safer, and with intelligent guardrails.
Company: The Home Depot | Role: UX Designer & Strategist | Industry: Enterprise Retail, Pricing & Merchandising Systems | Tools: Figma, Miro, Pendo | Platforms: mPulse (Internal Pricing Platform)
Overview
This work explored how AI could support enterprise pricing decisions inside mPulse without forcing premature automation.
Instead of “AI sets prices,” the strategy centered on “AI helps merchants detect opportunity, understand tradeoffs, and execute safely.”
My contribution focused on experience strategy, shared language, and a vision that balanced proactive AI guidance
with merchant control, transparency, and governance.
The Problem
Merchants were expected to make pricing decisions using fragmented dashboards, delayed signals, and manual workflows
that often surfaced issues after performance degraded. Recommendation concepts also risked low adoption if they felt like a black box
or removed merchant control. The core challenge was designing an AI-enabled experience that made recommendations trustworthy
and actionable while preventing risky outcomes through guardrails.
UX Strategy Canvas Overview
This artifact shows how I set shared language and scope early, aligning stakeholders around outcomes,
constraints, and the role AI should play before any screen-level design began.
This canvas framed the effort in a way cross-functional partners could rally around.
It clarified what “price recommendation” and “optimization” meant in human terms, identified who the work served,
and established what must remain explicit due to enterprise constraints (pricing risk, volatility, and accountability).
Value Hierarchy - Pricing
This artifact demonstrates strategic translation: turning enterprise pricing goals into experience priorities that guide
what the product should optimize for beyond “model accuracy.”
The value hierarchy connected business intent to experience intent. It mapped enterprise goals
like margin growth, market competitiveness, and operational efficiency to the experience qualities merchants need
in order to act confidently: visibility, confidence, control, and safe execution pathways.
Insight Optimization Summary
This artifact highlights how I defined an AI experience as a set of user-facing promises
making trust, explainability, and outcome clarity part of the product definition.
This board articulated the “promised capabilities” of a future-state AI pricing system.
It framed AI not as automation, but as a decision partner: proactive insight, guided action, transparent execution,
and learning over time. The intent was to define what the system should promise merchants so trust can form
before deeper automation is introduced.
Five Pillars
This artifact shows the UX principles that would later become screen-level requirements,
especially the guardrails and transparency needed in enterprise AI decision-making.
This artifact defined the UX governance model for AI-driven pricing. It distilled the experience into pillars that merchants
must feel in the workflow: control over signal sensitivity, transparency into rationale, confidence through projected outcomes, visibility into who approved what and why, and guardrails that prevent catastrophic pricing moves.
Visual Strategy Mapping
This artifact demonstrates systems thinking: how I designed the experience as a loop that supports learning, accountability, and iteration, not just a dashboard view of metrics.
This visual strategy map translates AI capabilities into an end-to-end merchant decision journey.
Rather than focusing on isolated screens, the map shows how signals, insights, actions, and feedback connect across time.
It clarifies where AI intervenes, where human judgment remains essential, and how the system
supports continuous optimization without overwhelming the user.
By grounding AI behavior in a clear workflow, this artifact helped align Product, UX, and Engineering around
when AI should act, how much it should recommend, and where merchants retain control. It also became
a shared reference for evaluating future capabilities against the same decision model.
How Ai Was Intended to Behave
The AI was designed to act as a continuous decision-support system rather than an autonomous pricing engine.
Instead of making opaque price changes, it surfaced signals, projections, and confidence indicators that helped merchants understand why action was recommended, when it mattered, and what tradeoffs existed. The goal was to shift AI
from “black box automation” to a trusted partner in high-risk pricing decisions.
Designing for Trust, Control, and Confidence
Because pricing decisions directly impact margin, inventory, and brand perception, trust was a core UX requirement.
The experience emphasized transparency through visible inputs, projected outcomes, and constraint awareness.
Merchants could tune sensitivity, review rationale, and choose how far to act, reinforcing human accountability
while still benefiting from machine intelligence.
Visual Strategy Mapping
This visual strategy map connected business goals, AI capabilities, and user actions into a single model.
It ensured that each surfaced recommendation aligned to a measurable outcome, supported by explainable signals,
and routed through a governed execution path. This artifact was used to align product, data science,
and leadership on what “AI-driven” actually meant in practice.
Research & Validation Direction
Research focused on understanding how merchants currently detect pricing risk, validate opportunities, and coordinate execution across tools. Behavioral insights emphasized cognitive load, trust thresholds, and the need for early warnings over reactive analysis. These findings informed how AI signals were framed, timed, and escalated within the experience.
Strategic Outcome
This work established a foundation for AI-assisted pricing that balanced automation with human judgment.
Rather than replacing decision-makers, the system elevated them, accelerating insight discovery, reducing risk,
and enabling continuous optimization through feedback loops. The result was a scalable AI strategy grounded in usability, governance, and real-world decision behavior.