Open to Principal, Staff & Design Manager roles

Principal Product Designer

Enterprise SaaS • AI Products • Design Systems • Product Strategy

I partner with product and engineering leaders to untangle complex enterprise workflows — using research, product strategy, and design systems to ship experiences that reduce cost, speed up work, and scale across teams.

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12+
Years in Product & UX
15+
Enterprise Applications
8
Cross-Functional Teams Led
2M+
Users on Platforms Shipped
30–40%
Typical Gains in Task Speed or Delivery
Selected Work

Problems I was brought in to solve

Real constraints from enterprise environments — fragmented teams, legacy UX, rising support costs — and the product design responses that moved measurable metrics.

IT Operations Dashboard — workflow friction was driving support costs

Dell Technologies · Principal Product Designer · Enterprise SaaS
The Problem

Business pressure

  • Support tickets for the IT ops dashboard grew ~15% quarter-over-quarter while the platform served 2M+ enterprise users.
  • Only ~34% of newly shipped features reached meaningful adoption — leadership questioned ROI on continued investment.
  • Product needed a credible AI differentiator without introducing risk to production infrastructure.

User pain

  • Incident triage took 12+ clicks across fragmented modules; admins spent ~40% of session time navigating, not deciding.
  • Critical alert context was buried under feature-heavy navigation built for breadth, not daily workflows.
  • Generic AI suggestions were ignored — no confidence score, no tie to the active alert or system state.

Constraints

  • Zero-downtime rollout on a live enterprise platform; no full rewrite budget.
The Solution

Product strategy

  • Reframed the initiative from "dashboard redesign" to "fix the top 5 workflows first" — aligned with PM and eng via support-ticket data.
  • Phased delivery: triage workflow → contextual AI at decision points → role-based personalization.

Design decisions

  • Restructured IA around jobs-to-be-done from 12 contextual inquiries and 500+ support ticket analysis.
  • Introduced progressive disclosure — summary, detail, and action in a single viewport flow.
  • Placed AI inline at the triage decision point with visible confidence scores and expandable rationale — not a detached chatbot.
  • Contributed 12 net-new patterns to the enterprise design system for cross-product reuse.

Validation

  • 16 moderated usability sessions across 3 rounds; A/B test on triage flow with 2,400 users over 6 weeks.
Impact

Phased rollout with usability testing and an A/B on triage flow (2,400 users, 6 weeks).

30%faster task completion
40%fewer dashboard support tickets
25%higher feature adoption
Full case study →

Design System — 15 apps looked like 15 different products

Dell Technologies · Design System Lead · Platform initiative
The Problem

Business pressure

  • 15 enterprise applications had diverged into visually and behaviorally different products — eroding brand trust and increasing training cost.
  • Estimated ~30% of frontend capacity went to rebuilding identical buttons, forms, and tables across 8 engineering teams.
  • Accessibility remediation ran as a separate phase on every release instead of being designed in upfront.

User pain

  • 120+ documented UI inconsistencies; users re-learned navigation every time they switched between apps.
  • Product managers reported longer onboarding for new features because patterns never felt familiar.

Constraints

  • Legacy apps could not pause feature work for a 6-month big-bang migration; framework diversity across teams.
The Solution

Product strategy

  • Built a business case framed as cost avoidance — duplicated eng work, repeated a11y fixes, and slower GTM.
  • Pilot with 3 teams, prove velocity and consistency gains, then scale adoption to 8 teams over two quarters.

Design decisions

  • Audited UI patterns across 15 products; prioritized the 50 highest-frequency components first.
  • Defined token architecture bridging Figma to production code — one source of truth for design and engineering.
  • Established governance: contribution model, design review cadence, and WCAG 2.1 AA baked into component specs.
  • Shipped migration guides so teams could adopt incrementally without rewriting entire applications.

Validation

  • Tracked adoption per team; compared time-to-ship on pilot vs. non-pilot squads; reported ROI quarterly to platform leadership.
Impact

Adoption tracked per team; ROI reported quarterly to platform leadership.

40%less design-to-dev effort
60%faster delivery (adopting teams)
90%WCAG compliance in system
Full case study →

AI Copilot — users ignored v1 because they didn't trust it

Dell Technologies · Senior / Principal Product Designer · AI UX
The Problem

Business pressure

  • IT support costs climbing ~20% YoY despite significant investment in an AI troubleshooting layer.
  • AI feature adoption stayed under 10% after launch — leadership risked writing off the initiative.
  • Competitive pressure to ship "AI-powered" capabilities without a clear trust or safety model for production systems.

User pain

  • Admins would not auto-apply AI-suggested fixes: "One wrong action could take down production."
  • Recommendations lacked context — same generic suggestions regardless of alert type, system, or prior actions taken.
  • No audit trail for AI-assisted actions; compliance and escalation teams could not trace decisions.

Constraints

  • AI backend already built; redesign had to work within existing model outputs and engineering timelines.
The Solution

Product strategy

  • Defined AI UX principles with PM and eng: augment, don't replace; explain every recommendation; start with low-risk diagnostics before remediation.
  • Shifted success metric from "AI feature clicks" to "recommendation acceptance rate" — a proxy for trust.

Design decisions

  • Human-in-the-loop flow: suggest → explain → preview → confirm → execute, with full audit logging.
  • Inline contextual panels tied to the active alert — outperformed chat-style UI in testing on trust and completion.
  • Confidence scores with expandable rationale; users could accept, modify, or reject before any action ran.
  • Added 8 AI-specific patterns to the design system (confidence indicators, preview states, audit views).

Validation

  • 15 contextual inquiry sessions; 5 interaction patterns tested with 20 admins; phased rollout behind feature flags.
Impact

Phased release with feature flags; trust measured via acceptance rate, not just clicks.

45%reduction in task effort
35%workflow efficiency gain
30%faster issue resolution
Full case study →
Product Leadership

How I Drive Product Success

A strategic design process that connects user needs to business outcomes at enterprise scale.

Discover Define Prioritize Design Validate Scale
Discover

Understand the problem space

  • Stakeholder interviews & alignment workshops
  • User research & contextual inquiry
  • Analytics review & support ticket analysis

→ Research brief & opportunity map

Define

Frame the right problem

  • Journey mapping & service blueprinting
  • Problem statements & success metrics
  • Persona & jobs-to-be-done synthesis

→ Product brief with measurable KPIs

Prioritize

Focus on highest impact

  • Impact vs. effort prioritization
  • Roadmap alignment with PM & engineering
  • Executive stakeholder buy-in

→ Prioritized roadmap & business case

Design

Explore & craft solutions

  • Concept exploration & prototyping
  • Design system contribution
  • Cross-functional design reviews

→ Validated design direction & specs

Validate

Test before you scale

  • Usability testing & A/B experiments
  • Accessibility audits (WCAG 2.1 AA)
  • Iteration based on behavioral data

→ Evidence-backed design decisions

Scale

Enable teams & systems

  • Design system governance & documentation
  • Team enablement & design ops
  • Post-launch measurement & optimization

→ Sustained adoption & org-wide impact

Case Studies

Deep dives on each engagement

Full write-ups covering research, product strategy, design exploration, validation, and business results.

Enterprise SaaS AI Products

AI-Powered Enterprise Dashboard

The Problem
  • 12+ clicks to triage incidents; support tickets up 15% QoQ
  • 34% feature adoption; users navigated 40% of session time
  • AI recommendations ignored — no context or confidence signals
The Solution
  • Workflow-first IA around top 5 daily admin jobs
  • Progressive disclosure and inline AI at decision points
  • Validated via 16 usability sessions + 6-week A/B test
30%Faster Task Completion
40%Fewer Support Requests
25%Higher Adoption
Read case study →
Design Systems Platform Strategy

Enterprise Design System Transformation

The Problem
  • 120+ UI inconsistencies across 15 apps, 8 teams
  • ~30% eng capacity lost to duplicate component builds
  • WCAG fixes repeated every release; no shared patterns
The Solution
  • W3C token architecture + 50 components with governance
  • Pilot-then-scale: 3 teams first, then org-wide adoption
  • 94% WCAG 2.1 AA; $2.1M projected annual savings
40%Less Design-to-Dev Effort
60%Faster Delivery
94%WCAG Compliance
Read case study →
AI Copilot Workflow Design

AI Copilot for Enterprise Workflows

The Problem
  • AI v1 under 10% adoption; support costs still rising 20% YoY
  • Admins rejected black-box fixes on production systems
  • No audit trail for AI-assisted troubleshooting actions
The Solution
  • Human-in-the-loop: suggest, explain, preview, confirm, execute
  • Contextual inline panels with confidence + rationale
  • Trust measured by acceptance rate, not feature clicks
45%Reduced Task Effort
35%Workflow Efficiency
30%Faster Resolution
Read case study →
Data Platform Enterprise SaaS

Enterprise Data Platform Unification

The Problem
  • 7 disconnected data tools; $1.2M/year in overlapping licenses
  • 68% of data requests took 24+ hours to fulfill
  • 12% of executive dashboards showed conflicting KPIs
The Solution
  • Unified search and federated query on existing warehouses
  • Role-based templates + data lineage visualization
  • 22 contextual inquiry sessions; 6-week pilot with 180 analysts
50%Faster Data Retrieval
38%Fewer Integration Errors
42%Higher Daily Active Use
Read case study →
Service Portal Employee Experience

IT Service Portal Redesign

The Problem
  • 65% of IT tickets were password resets or FAQ-level queries
  • Portal search returned irrelevant results 73% of the time
  • $2.8M/year in agent time handling deflectable requests
The Solution
  • NLP-powered semantic search + conversational request wizard
  • 18-field form replaced with 4-question guided flow
  • A/B on search relevance with 3,200 employees over 4 weeks
55%Ticket Deflection
48%Faster Resolution
34%Higher CSAT
Read case study →
Cloud Migration Self-Service

Cloud Migration Portal

The Problem
  • Only 22% of eligible customers adopted; 70% abandoned mid-migration
  • Average migration took 4–6 months with 15+ manual steps
  • Cost estimates required manual spreadsheets and took days
The Solution
  • Guided migration wizard with real-time cost estimation
  • Automated compatibility scoring with remediation suggestions
  • 18 contextual sessions; 3-month beta with 45 customers
60%Faster Migration Time
45%Higher Adoption
38%Fewer Support Tickets
Read case study →
View All Case Studies
Product Thinking

Product Principles

The beliefs that guide every strategic design decision I make.

01

Outcomes over outputs

02

Research before assumptions

03

Simplicity scales

04

Measure what matters

05

Design is a business function

Leadership & Influence

Leading design at organizational scale

Beyond screens — shaping product direction, enabling teams, and building design maturity across the enterprise.

Mentoring designers on strategic work

Coached designers moving from visual execution to problem framing — review sessions focused on research quality, metric selection, and how to present trade-offs to PMs, not just pixel polish.

Aligning PM, engineering, and design

Ran cross-functional workshops to resolve roadmap conflicts — e.g., when engineering wanted a component library rebuild but users needed workflow fixes first. Outcome: phased plan tied to support ticket data.

Making the case to leadership

Presented design system ROI and dashboard redesign metrics to VP-level stakeholders — framed as cost avoidance (duplicated eng work, support volume) rather than design aesthetics.

Influencing product direction early

Joined discovery before solutions were scoped — contributed JTBD synthesis and opportunity sizing that shifted one initiative from "add AI chat" to "fix triage workflow, then layer AI at decision points."

Design quality without bottlenecks

Set up lightweight design reviews and accessibility checklists for a multi-team org — caught WCAG issues pre-dev instead of in production remediation sprints.

Scaling design across 8 teams

Built onboarding docs, office hours, and Figma libraries so distributed teams could adopt the system without waiting on a central design queue.

Design Systems

Systems thinking that scales design impact

15+ design systems delivered — here's how I handle the constraints that usually kill them: legacy apps, skeptical eng teams, and leadership asking for ROI proof.

When teams can't wait for a big-bang migration

Problem: Legacy UIs coexisted with new apps; a full redesign wasn't fundable.
Approach: Systemized the highest-frequency patterns first (buttons, forms, tables, alerts) and gave teams a migration guide — not a mandate to rewrite everything at once.

When design and code drift apart

Problem: Figma specs didn't match production; accessibility regressions slipped through handoff.
Approach: Shared design tokens and component specs both teams referenced — designers and engineers reviewed the same source of truth in critiques.

When accessibility is treated as a phase

Problem: WCAG fixes were bolted on after launch, costing sprints and delaying releases.
Approach: Built accessibility into component definitions and review checklists — 90%+ AA compliance in the system, fewer emergency remediation cycles.

When leadership asks "what did the system save us?"

Problem: Design systems often lack proof of value.
Approach: Tracked component adoption, time-to-ship on pilot vs. non-pilot teams, and reported quarterly — 60% faster delivery where adoption was highest.

Differentiator

UX Engineering Advantage

I can prototype and discuss feasibility with engineers — which shortens feedback loops on complex enterprise UI, but product judgment and research still drive the work.

Fewer "can we build this?" loops

Understand component constraints enough to design within platform limits upfront — fewer late-stage cuts when engineering discovers edge cases in data-heavy tables or real-time dashboards.

Prototypes that stress-test the idea

Build interactive flows for multi-step enterprise tasks before a squad commits a sprint — especially useful for AI interactions and permission-sensitive workflows.

Design systems that ship

Contributed patterns knowing how they're implemented — tokens, states, and responsive behavior specified in ways engineering can adopt without reinterpretation.

Figma User Research Prototyping Design Tokens WCAG 2.1 AA Design Ops Product Analytics Workshop Facilitation
About

Product designer with enterprise depth

I'm a Principal Product Designer based in Mumbai, currently working on enterprise platforms at Dell Technologies — IT operations tools, design systems, and AI-assisted workflows used by administrators managing infrastructure at scale.

Most of my work sits in the messy middle: legacy constraints, multiple stakeholder groups, and metrics that leadership actually tracks — support volume, time-on-task, adoption, and delivery speed. I use research to find where users struggle, align with PM and eng on what to fix first, and measure whether the shipped solution moved the number.

Before platform and systems work, I spent years on enterprise application UX across consulting and product teams — which is why I'm comfortable jumping from journey maps and stakeholder decks to component specs and pilot rollouts.

Dell Technologies Principal Product Designer — Enterprise Platforms
Mumbai, India Remote • Hybrid • Global opportunities
Immediate Availability Principal, Staff, Lead & Design Manager roles
Core Expertise Enterprise SaaS, AI Products, Design Systems, UX Leadership
Contact

Let's discuss your next design leader

Open to Principal Product Designer, Staff Product Designer, Lead Product Designer, and Design Manager opportunities at enterprise SaaS and AI-first companies.

📧 prashantgadge87@gmail.com 💼 linkedin.com/in/prashantgadge 📱 +91 77388 41006

Ready to move forward?

Review my case studies and resume, then reach out to schedule a conversation.

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