The key to successful product discovery is transforming a product idea into a clear business vision by aligning stakeholders around a shared goal and demonstrating early success.
This goes beyond creating prototypes — it means implementing solutions, validating their impact, and showcasing measurable business outcomes.
AI can accelerate and streamline the design process, but the critical decisions still depend on contextual understanding, ethical judgment, and the ability to build trust with stakeholders.
As we move from traditional discovery to AI-augmented discovery, I combine strategic design, AI-powered research, and rapid prototyping to compress product discovery from 6–8 weeks to 2–3 weeks.
By accelerating insight synthesis, exploring solution concepts, and validating ideas through functional prototypes, I help businesses reduce uncertainty, make faster investment decisions, and move toward development with greater confidence.
*My own crafted experiences, represented using AI
Business outcomes
Design that performs
38%
User registrations
34%
Service subscriptions
Enterprise transformation requires balancing immediate business needs with long-term strategic vision. The priority is to deliver short-term improvements that ensure business continuity and success while establishing a scalable foundation for the future — enabling consistent experiences, stronger governance, and sustainable growth across the product portfolio.
Building a design system is the relatively easy part; the real challenge is designing it for long-term business evolution. Decisions must account for the product portfolio, future acquisitions, and organisational growth.
Equally critical is establishing seamless documentation, developer handoff, cross-team adoption, and ongoing maintenance. Strong governance ensures the system remains consistent, scalable, and adaptable as the business evolves.
I bridge design and engineering to turn ideas into deployed products. Combining UX strategy, AI-assisted coding, rapid prototyping, and full-stack tools, I take products from discovery and design through implementation, testing, and deployment.
This end-to-end approach accelerates iteration, reduces handoff friction, and transforms design concepts into working solutions faster.
I'm a design practitioner partnering with global product teams.
I turn complex business challenges into meaningful product experiences.
With an M.Des. from IIT Delhi, I lead design for AI-powered enterprise platforms, staying hands-on by prototyping and building AI agents to explore possibilities, challenge assumptions, and help teams move from ideas to working solutions.
Alongside my design leadership journey, I'm pursuing an Executive MBA at S. P. Jain Institute of Management and Research, strengthening my business strategy and leadership capabilities.
My focus is to bridge design, technology, and business — building scalable products, empowering teams, and connecting design decisions to measurable outcomes.
“Amit 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.”
Philip EdgeCTO, Registrar Corp, US
User adoption
“Amit 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.”
Karim KhanManager, GContracts, Google, US
Workflow efficiency
“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.”
“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.”
Tod DixonProduct 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.”
Arvind KumarVP – Digital Banking, ICICI Bank, India
How I think about AI
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.
I came to AI design through 18 years of enterprise UX — designing contract management platforms, compliance ecosystems, banking systems, and developer tools.
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.”
Current focus
Currently, I'm focusing on two capabilities
01
AI as a productivity multiplier
How AI accelerates and improves traditional product design activities.
Insight synthesis
Rapid prototyping
AI-assisted coding
02
AI-native product thinking
How I design AI agents, AI experiences, and human–AI collaboration models.
Trust & explainability
Human override
Graceful failure
Recent work
Three case studies
Case 1 · AI in practice: enterprise application
Designing an AI-powered compliance platform
FDA compliance platform modernisation with AI — Registrar Corp × Coforge. I 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.
01
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.
02
What's the cost of being wrong?
Low stakes = more AI autonomy. High stakes (compliance, finance, health) = more human confirmation required. Stakes determine autonomy.
03
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.
04
Can the user override it?
Every AI suggestion, action, or automation needs a visible, frictionless override path. Invisible AI is untrustworthy AI.
05
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.
Case 2 · Building a design system for a large enterprise
One design system for a global legal-software portfolio
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 the 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 Registrar Corp 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.
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.
01
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.
02
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.
03
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 portable across products and themes.
04
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.
05
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.
06
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.
Case 3 · Progressive mobile application for B2C
Taking an idea to a shipped mobile app
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.
30M
User base
3000+
Users in the first 6 months
#2
Sports app in Germany
1
Designer, full ownership: research to visual design
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.
Phase 1
Map the ecosystem
Workshop with founders + engineering lead. Roles, workflows, pain points — before any screens. 5 days to map the entire ecosystem.
Phase 2
Validate with users
12 interviews across 3 countries, real personas, 4 competitor products benchmarked.
Phase 3
Design the core loop
One central action across all 3 personas, designed as one connected system. Reusable components from day one.
Phase 4
Test, measure, ship
On-field testing + analytics before and after beta release.
