A Centralized, Trust-First System for Proposing, Reviewing, and Approving Compensation Changes
Helping managers and admins make confident compensation decisions at scale
Overview
Compensation changes are one of the most sensitive workflows inside HR software. A single mistake can affect employee trust, payroll accuracy, and legal compliance. Despite this, many teams still rely on spreadsheets, email threads, and manual approvals to manage raises, promotions, and adjustments.
This project focused on designing GoCo’s Compensation Management feature — a centralized, role-aware system that allows managers and admins to propose, review, approve, and audit compensation changes with confidence.
The goal was not just to replace spreadsheets, but to preserve their flexibility while reducing risk, improving visibility, and enabling better decision-making.
The Problem Space
Before this feature, compensation changes were:
Managed across disconnected tools (Excel, email, internal notes)
Error-prone and difficult to audit
Stressful for managers due to fear of making irreversible mistakes
Time-consuming for admins to validate and reconcile
Lacking context for approvers reviewing changes
Compensation workflows also introduce unique complexity:
Multiple roles (manager, admin, approver)
Budget constraints and approval rules
Conflicting edits and timing issues
The need for a clear audit trail
High expectations around fairness and transparency
Designing for compensation meant designing for trust, safety, and clarity, not just efficiency.
Goal & Success Criteria
Centralize compensation changes into a single system
Maintain the flexibility managers expect from spreadsheets
Reduce fear by making changes visible, reversible, and reviewable
Support complex approval workflows and permissions
Provide clear context for every decision
Lay groundwork for AI-assisted insights without eroding trust
Recognition Journey
User Types:
Admins
Managers
Employees
Recognition journeys were broken down for different user type.
Competitive Analysis
Direct Competitors:
BambooHR
Rippling
Lattice
Paylocity
Paycom
ADP
Compensation Grid (Spreadsheet Familiarity, System Safety)
Design highlight: Editable grid with inline validation and visual safeguards
The core of the experience is an Excel-like compensation grid, designed to feel immediately familiar to managers while enforcing system-level guardrails.
Key decisions:
Inline editing to reduce context switching
Visual differentiation between editable, read-only, and calculated fields
Real-time validation to prevent invalid entries
Clear affordances for pending vs committed changes
Why this matters:
Managers keep the speed and mental model they’re used to, while the system quietly prevents costly mistakes.
An Excel-inspired grid allows managers to propose compensation changes inline, while validations and permissions reduce error risk.
2. Manager Request Flow (Confidence Without Commitment)
Design highlight: Draft-first changes with clear submission states
Managers can propose compensation updates without immediately finalizing them. Changes remain in a draft state until reviewed and submitted.
Key decisions:
Explicit “pending” vs “submitted” states
Visual indicators for modified cells
Ability to review all changes before submission
Inline comments to explain intent
Why this matters:
This reduces anxiety and encourages thoughtful proposals instead of rushed edits.
Managers can review and comment on proposed changes before submitting them for approval.
3. Admin & Approver Review (Context-Rich Decision Making)
Design highlight: Side-by-side review of changes, history, and comments
Approvers don’t just see the final numbers — they see:
What changed
Who changed it
Why it was changed
How it compares to previous compensation
Key decisions:
Change summaries grouped by employee
Full audit trail accessible inline
Comment threads attached directly to changes
Clear accept / reject actions with rationale
Why this matters:
Approvals become informed decisions, not blind sign-offs.
Approvers review proposed changes with full context, including comments and historical compensation data.
4. AI Summaries & Recommendations (Assist, Don’t Decide)
Design highlight: AI used for summarization and insight — not automation
AI was introduced carefully to support, not replace, human judgment.
AI provides:
Summaries of proposed compensation changes
Context across role, history, and trends
Recommendations framed as guidance, not decisions
Key decisions:
Clear labeling and explainability
No auto-approval or silent changes
Positioned as a review aid, not a replacement
Why this matters:
Trust is preserved by keeping humans in control while reducing cognitive load.
AI-generated summaries help approvers quickly understand compensation changes without removing human oversight.
5. Conflict Handling & Visibility
Design highlight: Clear surfacing of conflicting edits
In multi-user workflows, conflicts are inevitable. Instead of hiding them, the system makes conflicts explicit.
Key decisions:
Visual indicators when changes overlap
Centralized conflict review
Clear resolution paths without data loss
Why this matters:
Transparency builds trust and prevents silent overwrites.
Conflicting compensation changes are surfaced clearly, allowing admins to resolve issues before approval.
Why This Feature Is Complex (and Why That Matters)
This wasn’t just a UI problem. It was a systems design challenge involving:
Permissions and roles
Editable data with high risk
Multi-step approvals
Trust in AI-assisted insights
Familiar mental models vs safe constraints
Every design decision balanced flexibility vs control.
Research Goal
The goal of usability testing was to validate whether the Compensation feature:
Felt intuitive despite its complexity
Reduced fear around making compensation changes
Provided enough context for confident approvals
Clearly communicated AI assistance without eroding trust
This phase focused less on “can users complete the task” and more on how confident they felt while doing it.
Methodology
Moderated usability testing
8 participants (customers, prospects, and internal stakeholders)
Participants included managers and admins responsible for compensation changes
Two primary flows tested end-to-end:
Creating and submitting compensation changes
Reviewing, approving, and auditing changes
1. Familiar Mental Model Reduced Friction
Participants immediately understood the grid-based interaction.
Insight:
Users felt comfortable because the experience mirrored tools they already trusted (spreadsheets), without feeling “too rigid.”
Design validation:
Inline editing
Familiar column layouts
Immediate visual feedback on edits
Users quickly understood how to interact with the compensation grid due to its spreadsheet-like structure.
2. Visibility Built Confidence
Participants consistently mentioned feeling more confident reviewing compensation changes compared to their current workflows.
Why:
Clear change indicators
Side-by-side comparisons
Centralized comments and history
Participant feedback (paraphrased):
“This feels much safer than what we do today.”
Design validation:
Surfacing context reduced anxiety around approving high-impact changes.
3. AI Was Seen as a Differentiator
AI summaries were one of the most positively received elements.
Insight:
Users valued AI when it summarized and explained, not when it tried to decide.
Design validation:
AI framed as guidance
Clear boundaries between human decisions and system insights
Transparent language and tone
4. Centralization Was a Major Win
Participants appreciated having everything in one place:
Proposed changes
Historical compensation
Comments
Approval actions
This removed the need to cross-reference spreadsheets, emails, and notes.
Impact:
Reduced cognitive load and faster review cycles.
1. Action Clarity in Dense Screens
Some users hesitated when multiple actions were available at once.
What we learned:
Primary actions needed stronger visual hierarchy
Secondary actions needed clearer grouping
Design response:
Refined button hierarchy
Improved spacing and grouping of actions
Clearer labeling in review states
2. Notifications & Status Feedback
A few users wanted clearer confirmation of:
When changes were officially submitted
When approvals were completed
Design response:
Improved status indicators
Added clearer submission and approval feedback
Reinforced notifications for key moments
3. Discoverability of Comments
While comments were valued, some users didn’t notice them immediately.
Design response:
Stronger visual cues for existing comments
Clearer entry points for adding context
Overall Validation
Across sessions, participants consistently described the experience as:
Clear
Safer than current tools
Less stressful
More transparent
One participant summarized it best:
“This is worlds better than what we’re doing now.”
This feedback reinforced that the feature successfully balanced flexibility with control — the core challenge of compensation workflows.
Key Learnings
Familiar patterns lower fear in high-risk workflows
Visibility and auditability matter as much as speed
AI adds the most value when it explains, not decides
Small clarity issues become amplified in complex systems
What This Feature Unlocked
Foundation for benchmarking and budget planning
Future enhancements to approval workflows
Expanded use of AI for insights across performance and compensation
Strong positioning against competitors relying on manual processes
Final Takeaway
Designing compensation tools isn’t about moving faster — it’s about helping people make confident decisions.
By combining familiar interaction patterns, strong system safeguards, and carefully positioned AI assistance, this feature transforms a stressful, error-prone process into one that feels controlled, transparent, and trustworthy.
