Design Leader · Enterprise AI Products
Amit Marathe
I lead design teams that make complex, regulated software usable — and AI inside it trustworthy.
7+ years building and scaling enterprise UX across compliance, legal, contract management, energy and banking. I have led design teams of up to [X], built 4 enterprise design systems used by 100+ product teams, and shipped 8 AI features into an FDA compliance platform serving 30K+ customers in 190 countries.
My journey so far
17+
Product Discoveries
+38%
Discovery > Engagement
14+
Enterprise Applications
4
Enterprise Design Systems
AI
As Productivity Multiplier &
AI Native Product Thinking
I build teams that ship
I have hired designers, grown 4 into leads, and run design across 3 time zones. My teams work inside product and engineering pods, not as a service desk beside them.
I scale through systems, not heroics
I have built four enterprise design systems, the largest serving 100+ product teams across 3 tech stacks. Each one shipped with governance, a contribution model and adoption metrics — because a design system is a change-management problem as much as a design one.
I tie design to business numbers
On Registrar Corp, the redesigned platform drove 38% more user registrations in two quarters and 34% more service subscriptions. I agree the metric with the product owner before the first wireframe.
I set the bar for AI
Every AI feature my team designs passes a five-question review: who benefits, cost of being wrong, explainability, override, failure state. It turns AI ambition into features that regulated customers will actually trust.
I am a design leader based in Pune, working with global product teams.
I started as an interaction designer after an M.Des. from IIT Delhi and spent 17 years in the hardest corner of UX: enterprise software where errors cost money, compliance or safety. Today I lead design for AI-powered enterprise platforms at Coforge, and I still prototype - including working AI agents - so I can judge what my team is asked to build.
Design for mass
Improve Efficiency
"Amit M. played a key role in modernising our compliance platform. ​
His expertise in design systems and UX strategy helped us create a scalable product that improved usability for our global customer base."
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Philip Edge, CTO,
Registrar Corp US​
User
Adoption
"Amit M. partnered closely with our teams to customize the Icertis platform for enterprise scale. His ability to balance design thinking with business needs made him a trusted partner in driving user adoption and innovation."
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Karim Khan, Manager, GContracts, Google US
Workflow
Effeciency
"Amit Marathe from Icertis, brought deep design expertise to Daimler’s contract management initiatives. His iterative approach and user focus helped us streamline workflows and increase efficiency across regions."
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Jens Schmezer, Manager, Procurement Systems, Daimler, Germany
Collaboration &
Cocreation
"Amit helped us reimagine complex enterprise workflows. His collaborative approach with engineering and product teams ensured that our solutions were not only intuitive but also technically feasible."
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Tod Dixon, Product Champion, Schlumberger PUTC, India
Design for
mass
"Amit was instrumental in designing intuitive retail banking experiences for our customers. His leadership in prototyping and testing ensured that new features aligned with customer expectations and business goals."
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Arvind Kumar, VP – Digital Banking, ICICI Bank India
I design the space
between human intent
and AI capability.
Most AI products get the model right and the experience wrong. I focus on the layer that makes AI feel trustworthy, controllable, and genuinely useful — not just impressive.
The best AI experience
is the one where
human feels
More capable
I came to AI design through 18 years of enterprise UX — designing contract management platforms, compliance ecosystems, banking systems, and developer tools. ​
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In every one of those domains, the biggest UX challenge was never the interface. It was cognitive load: too many decisions, too much information, too little clarity about what matters now. AI, done well, is the answer to that challenge. Done poorly, it's another layer of noise. My job is to make sure it's the former.
"The best AI experience is one where the human feels more capable — not replaced, not overwhelmed, not dependent."
Currently, I am focusing on two capabilities
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AI as a Productivity Multiplier
"How AI accelerates and improves traditional product design activities ​
AI-Native Product Thinking
"How I design AI agents,
AI experiences, and
human-AI collaboration models."
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How AI changes the way I lead design
I use AI in two places. In my team's workflow, it removes the slow parts of design so people spend time on judgement. In the products we design, it takes on work for users — but only where the cost of being wrong is understood and the human keeps control.
In my team's workflow
​
Research synthesis agent — turns interview notes into pain points and design actions. Synthesis time went from 7 days to 7 hours.
BRD intelligence agent — generates or audits 18-section business requirement documents before design starts, catching gaps early.•
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In the products we design — every AI feature passes my five-question review before wireframes: who benefits, cost of being wrong, can the user understand it, can the user override it, how does it fail.
Brief-to-wireframe skill — converts a plain-text product brief into a structured wireframe spec, with a UX risk-flagging step built in.•
AI-built prototypes — I take concepts to working, clickable products so teams test behaviour, not static screens.
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​In the products we design — every AI feature passes my five-question review before wireframes: who benefits, cost of being wrong, can the user understand it, can the user override it, how does it fail.
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Recent Projects
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PROJECT 1
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Design AI-Powered Compliance Platform
AI in practice: Enterprise Application
FDA compliance platform — platform modernisation with AI Registrar Corp × Coforge. Redesigned a fragmented compliance ecosystem unifying dashboard, marketplace, AI insights, and task management into one cohesive platform.
8
Distinct AI features
designed
38%
User registrations
in 2 quarters
+34%
Service subscriptions
30K+
Customers across
190 countries
Smart Onboarding with AI
AI search
Smart Compliance with AI
AI chatbot
Document Review with AI
Data Validation with AI
Image Editing with AI
Smart Insights with AI
How I evaluate
every AI interaction
I design?
Before any AI feature goes to wireframes, I run it through this five-question framework. It's how I decide what the AI should do, how much autonomy it should have, and what the human always controls.
Who benefits?
Is this AI serving the user — or the product? If the primary beneficiary is engagement, not the human's goal, I push back.
What's the cost of being wrong?
Low stakes = more AI autonomy. High stakes (compliance, finance, health) = more human confirmation required. Stakes determine autonomy
Can the user understand it?
If I can't explain the AI's reasoning in plain language to the user, the feature isn't ready. Explainability is a launch requirement, not a roadmap item.
Can the user override it?
Every AI suggestion, action, or automation needs a visible, frictionless override path. Invisible AI is untrustworthy AI.
How does it fail?
I design the failure state before the success state. If the failure experience is confusing or invisible, the feature isn't ready to ship.
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How I build a design system?
Building Design System for a large enterprise:
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PROJECT 2
Aderant is a global leader in business management software for law firms. With the launch of a new cloud platform, they faced the challenge of unifying a diverse portfolio of products — each with its own UI patterns, built by distributed teams across multiple tech stacks — into a single, consistent, scalable experience. I led the design system from strategy to component rollout.
10+
Distributed designers
across globe
100+
Product design teams
3
UI stacks
150+
Components, tokens and templates
Faster time-to-market via reusable templates
Reduced design-dev training costs
Centralised updates
fewer regressions
Better cross-team collaboration
A design system is not a component library. It's a product that your designers and engineers use every day. I design it that way — with governance, adoption strategy, documentation, and a contribution model — not just a Figma file.
I don't just build components.
I build the systems that let organisations design at scale.
Every design system I've built has followed this sequence — adapted for the organisation's maturity, team size, and tech stack. The phases aren't always sequential; at Icertis and RegistrarCorp I ran discovery and pilot delivery in parallel to show early ROI while strategy was still being finalised.
Phase 1
Audit & inventory
UI audit of all existing screens. Component catalogue. Inconsistency map. Identify quick wins vs foundational needs.
Phase 5
Documentation
Usage guidelines, do/don't examples, prop specs, decision rationale. Supernova or Zeroheight. Engineers and designers as co-authors.
Phase2
Foundations
Tokens first: colour, typography, spacing, radius, elevation. Brand alignment. Dark/light mode strategy. Naming conventions.
Phase 6
Pilot adoption
Select 1–2 high-visibility teams for first rollout. Learn what breaks. Fix before scaling
Phase 3
Core components
Atoms → molecules → organisms. Figma library + Storybook in sync. Accessibility built into every component, not retrofitted.
Phase 7
Scale & evangelise
Design showcases, internal workshops, before/after demos. Adoption isn't passive — it's marketed internally.
Phase 4
Governance
Contribution model, approval workflow, versioning policy, deprecation process. Define before the first PR arrives.
Phase 8
Measure & iterate
Component adoption rates, version drift, time-to-integrate, UI inconsistency regression. Treat the DS like a live product.
How I think about design systems?
Most design system projects fail not because the components are wrong, but because no one designed the system around the people who have to use it. Engineers skip it because it's not in their stack. Designers bypass it because it's easier to build from scratch. PMs deprioritise it because the ROI is invisible until it isn't.
I've built four enterprise design systems across different organisations, domains, and team sizes. Every time, the hardest challenge wasn't the token architecture or the component API — it was adoption. Getting 100 engineers across 3 tech stacks to use the same button is a change management problem as much as a design one
1
Design systems are products
Treat them like one. They need a roadmap, a versioning strategy, a contribution model, and adoption metrics. A Figma file is not a design system.
4
Figma ↔ Code parity matters
The distance between a Figma component and its code equivalent is where inconsistency lives. I work closely with frontend leads to keep Figma and Storybook in sync — not as a handoff, but as a shared source of truth.
2
Governance before scale
The moment more than one team uses a design system, you need governance. Who can contribute? Who approves changes? What happens when a component breaks? Define this before you have a crisis
5
Documentation is a design deliverable
Every component needs usage guidelines, do/don't examples, accessibility notes, and a decision rationale. Documentation that lives only in designers' heads doesn't scale.
3
Tokens are the real foundation
Components are visible. Tokens are structural. A well-defined token architecture — spacing, colour, radius, elevation — is what makes a design system actually portable across products and themes.
6
Adoption is designed in
I design the onboarding experience for the design system itself — how do engineers discover components? How do designers contribute? Adoption is not a training problem. It's a UX problem.
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PROJECT 3
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Taking idea to shipped mobile app
Progressive mobile application for B2C
The fastest path to a credible MVP isn't more features, it's a tight loop of ecosystem research, a single validated core action, and instrumented testing from the first release.
Talento Today went from founder vision to a successfully validated beta by treating research and analytics as part of the design process, not a step before or after it.
30 M
User base
3000+
Users in the
first 6 months
#2
Sports App in
Germany
What it actually takes to go from a founder's idea to a validated, beta-released consumer app condensed from designing Talento Today as sole product designer for an early-stage German startup
5 days
to map the entire ecosystem before any design work
12
User interviews across 3 countries before wireframes
3
Distinct personas designed as one connected system
1
designer, full ownership: research to visual design
Phase 1
​Map the ecosystem
Workshop with founders + eng lead. Roles, workflows, pain points \u2014 before any screens
Phase 2
Validate with users
12 interviews, real personas, 4 competitor products benchmarked
Phase 3
Design the core loop
One central action across all personas. Reusable components from day one
Phase 4
Test, measure, ship
On-field testing + analytics before and after beta release
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Productization Process
Working with the Product team
Human+AI
Better Together
Human judgement, AI capability, Shared impact
