I’ve been thinking about a conversation I had recently. The kind that doesn’t leave you when the ride ends. The kind that quietly stays with you and reshapes how you see things.

This one started in an Uber.

I had just landed at San Francisco airport after a long trip from India. Tired, slightly jet-lagged, and ready to get home, I booked a ride. A striking RAM truck pulled up clean, powerful, and clearly cared for. As someone who appreciates trucks, I instinctively sat in the front seat and started chatting with the driver.

What followed wasn’t small talk.

The Observer of the Human Condition

We moved quickly across topics geopolitics, California’s infrastructure, taxes, traffic, and the quality of roads. My driver, Richard, spoke with depth and clarity, drawing from years of observing people and life. As an Uber driver, he interacts with everyone doctors, pilots, engineers, architects and you could tell he had been listening, learning, and synthesizing those conversations over time.

Then we got into AI.

Highlights from Our Conversation

At first, I was curious. Then I was surprised. And soon, I was fully engaged. He didn’t talk about AI in abstract terms; he spoke about how it shows up in his everyday life. He walked me through his intentional use of technology:

• Optimizing Logistics: Using AI to manage Uber assignments and improve his ratings.
• Precision Navigation: Navigating with Google Maps and voice tech.
• Life Management: Managing stock investments, tracking weather, and even organizing medications.
• Lifelong Learning: Richard is using an Uber scholarship to attend AI courses at Arizona State University simply to make his own life better.

At one point, he said something that stayed with me: his life is already deeply engaged with and in many ways dependent on AI and technology.

The 74-Year-Old Student

When I asked his age, he simply said, “Seventy-four”.

What I was hearing didn’t match the assumptions we often carry about age and technology. Richard told me he had already researched programs and shortlisted courses at ASU. He wasn’t doing it for career growth or financial gain. He was doing it for himself.

He shared how, after years as a general contractor, his doctor advised him to stop lifting heavy weights. So he adapted. He chose to drive Uber — not out of necessity, but to stay active and meet people. He invested in a high-end truck because he cared about comfort and safety for himself and others.

Redefining the “Why” of Learning

That conversation stayed with me long after I reached home because it challenged something subtle but important in me.

As someone who works in AI and design, I often tie learning to outcomes — impact, career progression, and achievement. This felt different.

This was learning without pressure. Learning driven by curiosity. Learning as a way to live better, not just achieve more.

In many ways, Richard is doing what we talk about in design, but rarely apply to ourselves:

1. He is intentionally shaping his life.
2. He is adjusting to constraints.
3. He is leveraging technology thoughtfully.
4. He is staying curious.

The Ultimate Design Project

He is designing his life. Not loudly. Not performatively. But consistently.

We often think of design as something we apply to products, systems, or experiences. But maybe the most important thing we ever design… is how we choose to live.

This 74-year-old Uber driver didn’t just share a ride; he shifted my perspective. He reminded me that curiosity doesn’t age, learning doesn’t expire, and purpose doesn’t have a deadline.

What is the “front seat” conversation in your life right now? Are you designing your life, or just riding along?

In the traditional world of SaaS, design followed a linear path: Step A to Step B. We built workflows. But as AI transforms the enterprise, the rules have fundamentally shifted. We are at an inflection point where design isn’t just about aesthetics , it’s a strategic business engine.

Here are the core key points which i shared with Dr. Manish Arora who interviewing me for The Inference Show regarding the future of AI-driven product design.

1. Reverse the Design Process: Outcome First

In traditional design, you build a sequence of steps (1, 2, 3, 4, 5). In AI design, you start with the end goal.

• The Shift: Instead of asking “What is the next step?”, ask “What is the desired outcome?”
• The Strategy: Use AI to work backward from that outcome, removing unwanted steps and automating the “middle” to reach the goal faster with applied intelligence and with less effort.

2. The TRACE Framework for Human-Centered AI

To build trust in AI systems, i have built TRACE framework, which ensures AI remains human-centered and this always helps to architect the AI expeirences.

• T — Transparency: Users must know exactly what the AI is doing and why.
• R — Responsible: Designing with ethics at the forefront.
• A — Agency: Maintaining human control. Even with autonomous agents, “human-in-the-loop” is essential for approvals and oversight.
• C — Compassion/Context: Ensuring the AI understands the user’s specific situation and needs.
• E — Equity: Focusing on accountability and accessibility.

3. From Chatbots to Agentic Experiences

While natural language interfaces (like ChatGPT) made AI accessible, the future lies in Agentic Experiences.

• Autonomy: Agents don’t just talk; they act on behalf of the user.
• Orchestration: Future enterprises will use “agent orchestrators” that manage multiple specialized agents to complete complex tasks , like cleaning data or running quality checks , in minutes rather than weeks.
• The “Second Brain”: AI shouldn’t just be a tool; it should act as an “intelligent second brain” that assists users with insights while they maintain ultimate control.

4. Why Startups Should Prioritize Design

I strongly advocate startup fouders that, design is a competitive advantage.

“Anyone can build code today… but the approach you solve with design will be different, its an magical experience that matters! ” ~Ranjeet Tayi

Design creates the user emotional attachment, trust, and confidence that differentiates one product from a sea of similar technical solutions. It is a strategic tool for value creation, not just a “decoration” for the interface.

5. The Future: Zero UI and Dynamic Canvases

Looking ahead, i see a move toward Zero UI and Dynamic UX Canvases.

• Zero UI: Situations where AI runs in the background, performing tasks without needing a traditional interface. Zero UI is a design paradigm that eliminates traditional screens and visual interfaces, relying instead on natural interactions like voice, gestures, touch, and environmental context for seamless technology engagement.
• Dynamic Canvases: Interfaces that generate themselves on the fly based on the user’s specific context and needs, rather than having a static, pre-designed layout. Dynamic UX Canvases refer to adaptive, AI-powered frameworks that evolve in real-time, blending traditional UX planning tools like Lean UX Canvas with generative interfaces for personalized enterprise experiences.

6. My 4 Rules for AI Product Design

1. Outcomes > Features — Solve the right problem
2. Trust = Design Requirement — Not a nice-to-have
3. Agents > Chat — Build intelligence, not just interfaces
4. Measure adoption — Business impact beats benchmark scores

Final Thought: Data is the Foundation

None of this is possible without a solid data foundation. My advice to leaders is simple: Get your data right first. AI is only as good as the data it feeds on. If your data is “messy,” your AI experiences will fail, regardless of how well they are designed. I always say to many silicon valley AI startup founders that “Design is the ultimate advantage for Statups, they should learn design and get obsessed about design for real users”

Watch the full episode on The Inference Show Video: The New Rules of AI Product Design

The New Rules of AI Product Design: Outcomes over Workflows was originally published in Bootcamp on Medium, where people are continuing the conversation by highlighting and responding to this story.

A Reflection on Leading Design in the Age of AI

As 2026 begins, I’ve been reflecting on how design leadership itself must evolve in an AI-first world. The rise of generative, predictive, generative and conversational AI has transformed not just products, but the creative process behind them, redefining what leadership, collaboration, and systems thinking mean in the age of intelligence.

My Journey with AI Design

My exposure to AI began early. Back in 2015, I started experimenting with conceptual AI design frameworks, exploring how intelligent systems could improve enterprise user experiences through good data, prediction, automation, and guidance with rich user interface's & interactions. I have filed and awarded few patents in the areas of Intelligent Data Privacy and Data Security innovations.

By 2018, at the Informatica World conference, our CEO unveiled CLAIRE — Informatica’s AI engine, built on my team’s design vision and concepts. Seeing some of those prototypes come to life on the keynote stage was both validating and humbling; it marked the beginning of a sustained, multi-year evolution in AI-driven experience design.

Since then, we’ve worked across many AI modalities, from predictive AI and generative AI, to conversational AI, AI Agents and cross-product experiences powered by CLAIRE GPT and CLAIRE Copilot experiences. Our mission has been clear: to make AI an intelligent second brain, invisible partner across every product within the Informatica Intelligent Data Management Cloud (IDMC). I had a boss who is super passionate on AI “Wai On Lee” who was our deisgn head and we spent so much of time working together bouncing many ideas and i learnt a lot from him.

Scaling AI design across such a vast ecosystem has been a profound learning journey. Each product team has different priorities, business goals, and maturity curves. Creating a consistent AI design system, shared experience patterns, and cross-product coherence demands patience, iteration, a deep culture of collaboration and depth of knowledge.

Building AI experiences at scale isn’t just about design systems or machine learning , it’s about orchestrating people, processes, and principles across evolving constraints. We learned to stay humble, adaptive, and relentlessly curious because trends change fast, capabilities shift, and user expectations rise with every product cycle. Every thing still feel like a fresh beginning because AI is evolving as keep building.

That’s why UX needs to lead from the front: continuously experimenting, learning, and upskilling ahead of the curve and keep iterating.

Building AI UX Team: The True Engine of AI Design

I’m incredibly fortunate to lead an exceptional AI UX team- a group of handpicked designers, researchers, and thinkers who bring deep passion for AI and diverse perspectives to our work. One of my biggest investments as a leader has always been in hiring, finding talent with complementary, not identical, skills. I look for people from varied backgrounds, disciplines, and locations who share curiosity, independence, and a systems-thinking mindset. Great AI design requires individuals who think deeply, challenge assumptions, and innovate fearlessly, people who transform ideas into experiences that influence both users and business outcomes. Our recruiters says i am one of the toughest leader when comes to hiring. They are the people who make things happen at ground level, i am proud and thankful to them.

Redefining Design Leadership for an AI-first Era

Traditional UX leadership focused on clarity, systems, and scalability. In contrast, AI design leadership extends into governance, responsibility, and human oversight.

It’s not enough to make AI useful , we must make it trustworthy.
Design now shapes how AI learns, decides, and explains itself, not just what it looks like on screen.

The design leader’s role has evolved from shaping interfaces to shaping intelligence.

Key Learnings on AI Design Leadership from 2025

1. Speed with clarity beats speed alone. Clear intent prevents us from over-automating or building untrustworthy interactions.
2. Strong process enables strong ethics. A mature UX process ensures that innovation stays responsible.
3. PMs, designers and engineering must co-own intent. Collaboration ensures AI objectives stay aligned with real human needs.
4. Context is the key and context drives coherence. Teams need shared understanding across AI, data, and experience layers.
5. Vision keeps AI purposeful. A clear AI experience vision connects multiple products under a single philosophy.
6. Design-led innovation is essential. UX should continually initiate new AI use cases, not just react to them.
7. Customer collaboration sharpens trust. Human feedback grounds AI ambition in practical empathy.
8. Design ahead of development. Foresight enables refinement; parallel design leads to reactive compromises. But get into waterfall trap, AI is realtime.
9. AI design systems need ownership. Rapid iteration in AI requires flexible, designer-led governance. Keep AI related stuff separate but collaborative.
10. AI design leadership is about empathy, innovation and influence. The best leaders teach, advocate, and design for both human and business value. Strong partnership between PM Leader + Eng Leader + Design Leader will bring strong focus and support.

AI Is All About UX

AI is fundamentally a user experience problem. People don’t just want powerful AI, they want explainable, predictable, and empathetic AI. Design for AI centers on three principles: data, trust, and control.

• Data: What the system learns and how transparently it uses it.
• Trust: How decisions are communicated and justified.
• Control: The degree of user agency and override built into every experience.

When these principles are designed intentionally, AI feels like collaboration, not automation.

The experience is only as intelligent as its data. Designers & engineers must engage in data quality, governance, and management , it’s our responsibility to make intelligence reliable and ethical.

If data is messy, biased, or incomplete, even the best interface can’t save the experience. Data transparency creates trust. We visualize how AI reaches decisions, its confidence levels, and what powers it, bridging understanding between humans and systems.

The TRACE Framework for Human-Centered AI

To ground AI ethics and usability into daily practice, I developed the TRACE framework, a design compass for building trustworthy intelligence:

Transparency — Show clear decision-making to build trust.
Respect — Uphold ethics, fairness, and responsibility.
Agency — Ensure users stay in control and can override AI when needed.
Compassion — Adapt empathetically to human context and emotions.
Equity — Commit to inclusivity and accessibility for all.

These aren’t philosophical principles , they’re practical tools for designing AI experiences that people actually believe in and adopt. TRACE helps embed human values directly into interaction design and system architecture.

When AI systems embody T.R.A.C.E principles, they build trust and serve human flourishing

Looking Ahead to 2026

If 2025 was about learning what it means to lead AI design at scale, 2026 is about perfecting the craft.

My focus this year includes:

• Embedding TRACE into AI design and review processes.
• Measuring AI experience success in human trust metrics.
• Scaling consistent AI experience & Agentic AI response frameworks across platforms and modalities.
• Strengthening and expanding AI UX design talent and get top AI UX talent.
• Continuing to explore the balance between system intelligence and human judgment. Talk to more customers and look into diverse use cases.

AI design leadership is not about making machines smarter, it’s about ensuring intelligence stays human-centered.

The best AI experiences amplify human capability, not replace it. And the best AI leaders ensure innovation is guided by clarity, empathy, and purpose.

As we step into 2026, the challenge for all of us is clear:
to build AI that earns trust, operates transparently, and reflects the best of human design. We just got acquired by SalesForce in Nov 2025, and now stepping into new chapter with more support from AgentForce and Data 360 muscle. Looking forward for AI advances and make a dent with Design & AI.

A Recap of the UXINDIA25 Design+AI Panel Discussion

In a world buzzing with AI jargon and “hype cycles,” a group of industry titans recently sat down at UXINDIA25 to strip away the noise. IModerated by (me) Ranjeet Tayi, Senior Director of AI Experiences at Informatica, the panel featured voices from Amazon (AWS), Freshworks, Cognizant, and Happening Design.

The story they told wasn’t one of machines replacing humans, but of a profound shift in how we relate to the products we use.

FULL PANEL VIDEO: https://www.youtube.com/watch?v=yrrDwJwwFH8&t=1321s

The Story: From “Filling Fields” to “Natural Conversations”

The panel opened with a striking observation from Sridhar Gare (Freshworks): For a century, humans had to learn the computer’s language. Now, for the first time, the computer is learning ours.

Imagine the traditional “Ticket Creation” screen — cluttered with 20 fields and complex menus. The panel envisions a future where that entire UI is replaced by a single, empty text box that simply asks: “What do you want to do today?” This isn’t just a technical upgrade; it’s a philosophical one. We are moving from static workflows to adaptive orchestration.

Key Outcomes & Insights

The discussion delved deep into why 95% of AI pilots fail and how designers can steer the ship toward success. Here are the key takeaways:

• Linear is Dead: Mohan Krishnaraj (Cognizant) emphasized that user journeys are no longer a straight line. Systems must now learn the user in real-time, delivering “contextual moments” that are meaningful and purposeful.
• The “Unicorn” Designer: Aditi Sharma (AWS) challenged the community to become “unicorn designers” — professionals who don’t just push pixels but lead product strategy, write documentation, and understand the business P&L as deeply as a CFO.
• Trust as a Designed Outcome: Trust isn’t an accident; it’s a designed pattern. The panel identified three ways to embed trust:

1. Invisible: AI working proactively in the background.
2. Embedded: AI offering recommendations you can choose to ignore.
3. Engaging: Direct interaction where the AI shows its “thinking” (transparency).

• Responsible AI (RAI): Sridhar Gare highlighted that RAI is about building guardrails. From “Privacy by Design” to “Explainability,” designers must be the ones to ensure AI doesn’t become a “black box” but remains a traceable, accountable partner.
• Efficiency vs. Craft: While AI can generate a website in 10 minutes, the panel agreed that “taste” and “intent” remain human-exclusive. AI can fill pixels, but only a designer can provide the purpose.

The Evolution of the Role

Rohan (Happening Design) reminded the audience that while the tools change, the core philosophy of UX remains: solving human problems. The difference now is the scale and speed. Designers are no longer just creators; they are enablers and auditors of a new relationship between humans and technology.

Conclusion: The Best Time to Be a Designer

As the panel drew to a close, the energy in the room was electric. The consensus was clear: despite the fears of job displacement, the reality is the opposite for those willing to evolve.

I have concluded with a powerful message for the next generation: “This is the best time to be a designer.” AI is going to change everything — from the way we design to the way we interact with every product on the planet.

Final Advice from the Panel:

• Aditi Sharma: Find your purpose and lead with “Data-Driven Storytelling.”
• Mohan Krishnaraj: Design for “Emotional Integrity.”
• Sridhar Gare: Use AI to handle the repetitive, but keep the “human at the center.”
• Rohan Krishna: Understand the technology, but “don’t chase the clout” — stay honest to the user’s needs.

The era of AI doesn’t mark the end of UX; it marks the beginning of its most influential chapter.

This panel & speakers are amazing and fortunate to moderate this amazing AI leaders: Featuring Aditi Sharma (AWS), Sridhar Gare (Freshworks), Mohan Krishnaraj (Cognizant), and Rohan Krishna (Happening Design).

Design+AI | How Artificial Intelligence is Redefining the Future of UX was originally published in Bootcamp on Medium, where people are continuing the conversation by highlighting and responding to this story.

I was honored to moderate a recent panel called “Design as a Strategic Advantage in the Age of AI” at the AI Innovators Forum at Google. I also had the chance to share some of my own insights about AI and to learn from two outstanding design leaders on the stage.

• Arin Bhowmick — Chief Design Officer at SAP, who has a background at IBM and Oracle.
• Kim Lenox — Head of Design at Amplitude, previously with Adobe, LinkedIn, and Zendesk.

We dived into a topic that’s becoming more and more pressing in the era of AI: how design isn’t just a tool but a key factor in determining how AI adds value, fosters trust, ultimately affects society and overall value for users and business.

A Room Full of Innovators

The event gathered AI executives, founders, venture capitalists, and product innovators — a vibrant crowd that really showcased the energy and importance of this moment. The discussions brought to light a key insight: the success of AI isn’t just about powerful algorithms; it’s about the quality of the human experience that goes with it.

Key Takeaways from the Discussion

1. The biggest hurdle for AI isn’t technical, it’s human. It’s not just about having better models or quicker algorithms; it’s about whether people can trust, understand, and engage with AI in a meaningful way.
2. The human experience is what matters most. As Arin pointed out, human needs and control need to be at the core of AI experiences. Without that focus, even the most advanced systems could fall flat.
3. Mental models need to sync up. Arin also stressed that AI tends to fail when its mental model doesn’t match with how humans think. Designers have a vital role in connecting how AI functions with how people expect it to behave. Many AI failures we see today come from this disconnect.
4. Design teams as sparks of innovation. Kim shared how her design team at Amplitude conducts internal design thinking workshops and vibe-coding sessions to use AI for generating and prototyping ideas quickly. These fast experiments help pinpoint valuable opportunities sooner. AI serves as a creative partner for rapid conceptualization and prototyping, speeding up decision-making. The result? Improved user experiences and quicker innovation cycles.
5. UX is the new AI. Our panel reached a consensus on this point: in the Age of AI, user experience is what sets you apart. AI is ultimately judged not by how complex it is technically but by whether it meets user needs and helps them get things done efficiently. UX is inherently human-centered, and in an AI-driven world, experiences need to be intentionally designed to gain trust and engagement. This requires designers to closely collaborate with cross-functional teams in product, engineering, and data. Experiences don’t just happen, they need to be crafted.
6. Design enhances validation and business impact. I mentioned during our session that design-focused approaches are crucial not just for big companies but also for small AI startups. When pitching to customers or investors, having AI-first design integrated into a product’s DNA boosts adoption and builds credibility. The market increasingly understands that AI’s success hinges on how intuitively and effectively it’s experienced. These days design founders are much encouraged by VC’s.
7. Agentic AI opens up new possibilities. With agentic AI and multi-agent systems, there are now chances for autonomous collaboration, deep reasoning, and adaptive execution of complex tasks. But once again, design plays a critical role: Users need to stay in the loop with appropriate control and transparency. Systems must be clear and accountable. Designers are key in ensuring that autonomy doesn’t leave users out, but rather gives them power.

The Road Ahead

In my work at Informatica, we are very focused on advancing Agentic AI Expeirences and design approaches / frameworks that reflect these principles, creating solutions where AI systems not only compute but also gain trust, empower users, and facilitate seamless collaboration between people and technology. The phase of AI advances is changing very fast and we are trying to do our best and always learning with collaborating with AI/ML teams with a fail fast approach. The user research is going tobe a key factor because its all about users and experiences.

The conversations at brought to the forefront an important truth:

• Arin reminded us that success depends on aligning human and AI mental models.
• Kim shared how design teams can leverage AI to speed up creativity, prototyping, and decision-making. Vibe coding to prototype ideas quickly.
• And I pointed out that design-led approaches aren’t just nice-to-haves , they’re essential for AI adoption, validation, and making a real impact in the long run.

In short: design isn’t optional anymore; it’s a strategic advantage in this Age of Intelligence.

Final Thoughts

AI is going to shape the next decade of innovation, but only if we tailor it to address human needs clearly, trustworthily, and impactfully. At its heart, UX in the Age of AI is all about aligning mental models the way humans think, anticipate, and act with how AI comprehends and responds.

When there’s a mismatch between human and AI mental models, you end up with frustration and failure. But when they’re in sync, that’s when trust, engagement, and meaningful collaboration can really thrive.

Designers need to step up to the table not as mere decorators of the future, but as architects of intelligence itself, aligning human and AI mental models to create meaningful, trusted, and empowering experiences.

Here’s to pushing the limits of AI experience and making design the true competitive edge in this Age of AI.

#AI #AIExperience #DesignValue #AgenticAI

As a curious AI design leader & design activist who has been deeply tracking Figma’s AI strategy and tracking all the AI PLG companies including Canva, i keep attending and regularly tracking their keynotes and product launches to decode their product AI and GTM Strategy. Later this year, i attended COnFIg — Figma’s annual confernece where i was part of their “Leadership Collective” — an event curated by Figma for design leaders. I got an opportunity to speak to many Figma design and product leaders along with my other Chief Design Officer friends from various organizations.I have witnessed fiest hand on how Figma has transformed from a design tool into a largest community- driven ecosystem that gas captivated both deisgn and developer communities. Their IPO success with multifold returns has suprised entire industry, establishing new benchmark how A-Powered design platforms achieved unpredictable market valuations.

User Expeirence Foundation of Figma’s Success

Collaborative-First Design Philosophy

Lot of people think Figma’s success is beacase of Browser based design Tool. Figma’s core innovation wasn't just being browser basee, it was fundamentally reimagining design as a collaborative and real time activity in one tool. Traditional design tools treated as a afterthought, but Figma took this seriously as a core design principle and built their entire user experience and design is inherently social.

Key UX Differentiators:

• Real-time multiplayer editing: Like Google Docs for design, enabling simultaneous collaboration
• Instant Comment and feedback systems: Integrated directly into the design canvas
• Simplified Version control : Automatic saving and intuitive version history
• Cross-platform access: Web-based approach eliminated OS barriers

Easy Onboarding and Adoption

Figma’s freemium model isn't just the a pricing strategy, its a design philosophy that remove every possible barrier to entry. They figured out end to end user journeys and every touch points.

• Instant Access: No download, setup or installation required.
• Intuitive User Interface: Consistant, familiar paradigms and interactive behaviors reduce the learning curve and tool usage.
• Progressive Disclosure: Advanced features revealed as users grow more and using more complex tasks.
• Template and Library marketplace: Rich library and templates of starting points for immediate productivity.

The strategic game play and talent investment lessons from a UX perspective

Building products before they exist

Figma did a counterintuitive approach by hiring talent for their products taht didnt yet exists. This is a genius strategy that Figma pen down and planned from pre product stage and then start acquiring required talent right during the research phase, targeted market down time to attract talent and getting ready production at speed.

1. AI-First UX Research (Pre-Product)

• Hired industry leading AI researchers before launching Figma AI
• Conducted extensive user research on AI-assisted design workflows and top usecases.
• Built understanding of designer pain points with existing AI tools
• Developed design solutions and UX patterns for human-AI collaboration in creative workflows

2. Deep focus on Developer Experience (DevX)

• Recruited top developer tooling experts before Dev Mode existed
• Studied the designer-developer handoff problem extensively from real world
• Created seamless design-to-code workflows that reduced friction with collaboration as focus.
• Built tools that developers actually wanted to use, not just what designers thought they needed
• Made this a important product development tool that product teams needed.

3. No-Code Movement Anticipation

• Hired product teams for Make and Sites before the no-code boom peaked
• Researched how designers wanted to go from design to live website
• Built UX flows that made complex functionality accessible to non-technical users

The Community Takeover: A Design Leader’s Perspective

Witnessing the Figma Revolution Firsthand

As someone who has been deeply tracking Figma’s AI strategy evolution, the transformation has been nothing short of revolutionary. Preparing for CONFIG Conference in San Francisco, I’ve observed how Figma has systematically captured not just the design community, but created an unprecedented bridge to the developer ecosystem — something that traditional design tools never achieved. I was amazed to see more than 50K designers and they captured amost every design leader was present. They had a seperate sessions for design leaders too.

The Community Conquest Strategy:

1. Design Community Domination

• Education-First Approach: Free resources, masterclasses, and certification programs
• Influencer Ecosystem: Turning power users into community evangelists
• Open Design Systems: Making enterprise-grade design systems accessible to everyone
• Real-Time Collaboration Culture: Shifting industry mindset from individual to collaborative design

2. Developer Community Infiltration What amazed me most was Figma’s ability to win over developers — historically the most skeptical group when it comes to design tools:

• Dev Mode: Finally bridging the designer-developer handoff gap with actual code generation
• API-First Architecture: Allowing developers to build tools and integrations
• Version Control Integration: Git-like workflows that developers understand
• Component-to-Code Pipeline: Automated design token generation and component libraries

3. The Network Effect Amplification Figma created a flywheel where each new user made the platform more valuable:

• Plugin Marketplace: 1000+ plugins solving niche problems
• Template Economy: Designers monetizing their work while expanding adoption
• Educational Content: Community-generated tutorials and best practices
• Cross-Pollination: Designers and developers working in the same environment

CONFIG Conference Insights: The AI Strategy Masterclass

Anticipating CONFIG 2024, several strategic moves become clear about Figma’s AI roadmap:

1. AI as Community Catalyst

• Democratizing Advanced Design: AI tools making sophisticated design accessible to non-designers
• Accelerating Learning Curves: New users achieving professional results faster
• Expanding Addressable Market: Non-traditional design roles adopting Figma through AI assistance

2. Intelligence Layer Strategy Rather than replacing human creativity, Figma’s AI acts as an intelligence layer that:

• Amplifies Human Intent: Understanding design context and suggesting relevant improvements
• Automates Tedious Tasks: Component generation, layout optimization, accessibility checks
• Provides Design Intelligence: Pattern recognition across millions of designs for best practices

3. The Community Data Advantage With millions of designs created daily, Figma has an unprecedented training dataset that competitors cannot replicate:

• Real-World Design Patterns: Learning from actual user behavior, not synthetic data
• Cross-Industry Intelligence: Understanding design patterns across different sectors
• Collaborative Context: AI that understands team dynamics and design processes

The IPO Success: AI and UX Convergence Analysis

Why the Market Rewarded Figma’s Strategy with Multifold Returns

The $67B valuation and 250% first-day jump wasn’t just market exuberance — it represented the market’s recognition of Figma’s unique position at the intersection of AI, UX, and community-driven growth. BTW I love the tagline “Design is everyone’s business”

1. The AI-UX Multiplication Effect

From my analysis tracking their AI strategy, Figma achieved something unprecedented: they made AI feel native to the design process rather than bolted-on. This created exponential value: Every one know that they are still not there much on AI but they made a noise that they are working on it.

• Productivity Multiplier: Teams reporting 3–5x faster design iterations with AI assistance
• Quality Enhancement: AI-powered accessibility and design system compliance
• Skill Democratization: Junior designers producing senior-level work with AI guidance

2. The Community as Competitive Advantage

What surprised the industry was how Figma’s community became their strongest defensive and competitive advantage. They captured almost every company and there is no designer exists without knowing the tool name Figma or assocaition with it.

• Switching Costs: Once a team’s entire workflow, plugins, and design systems are in Figma, migration becomes virtually impossible
• Innovation : Community-driven feature requests and plugin development outpacing traditional R&D cycles
• Talent : Design schools and bootcamps standardizing on Figma, creating generational lock-in

3. The Revenue Model Revolution

Figma’s IPO success demonstrated a new revenue model that combined:

• Product-Led Growth: Self-serve adoption with minimal sales overhead
• Community-Driven Expansion: Users organically expanding usage across organizations
• AI-Powered Value Creation: Premium features that genuinely accelerate productivity
• Platform Economics: Revenue sharing with plugin developers and template creators

Deep Dive: AI Strategy Success Factors

1. Timing and Market Readiness

• Launched AI features when designers were ready for automation but still wanted creative control. No one expected this growth.
• Positioned AI as augmentation rather than replacement, reducing adoption resistance
• Built on existing collaborative workflows rather than requiring new behavior patterns

2. Data Network Effects

• Every design created improved AI recommendations for all users
• Cross-team learning accelerated design system evolution
• Community feedback loops refined AI suggestions in real-time

3. Integration Philosophy Unlike competitors who added AI as separate features, Figma embedded intelligence throughout the entire user journey: Still lot to do but their nailed the timing.

• Smart Suggestions: Context-aware component recommendations
• Automated Workflows: Background processing of design systems and accessibility
• Predictive Features: Anticipating user needs based on design patterns

AI Integration & Brand building : The UX Advantage

Figma’s AI tools are designed to “get started faster, find what you’re looking for, and stay in the flow” with features that empower rather than replace human creativity. Their UX approach to AI integration demonstrates several key principles and provide any one to design easily nut stick to the tool.

1. Context-Aware Assistance

• AI suggestions based on current design context
• Smart component generation that understands design systems + styling
• Intelligent layout suggestions that maintain visual hierarchy

2. Workflow Integration

• AI tools embedded directly in existing workflows
• No context switching required to access AI features
• Maintains designer agency while providing intelligent assistance

3. Privacy-First Design Team-level settings allow admins to control customer content sharing with Figma for AI training, showing how UX considerations extend to data governance, feedbck loop and user trust.

The Community-Driven Growth Engine

UX of Community Building

Figma’s $20B valuation was significantly driven by its community, but this wasn’t accidental, it was designed into the product experience and they have taken right pain point and excel init.

1. Plugin Ecosystem UX

• Seamless plugin discovery and installation
• Rich ecosystem of AI-powered plugins like UX Pilot that revolutionize the Figma experience
• Community-driven feature development that responds to user needs

2. Resource Sharing Infrastructure

• Component libraries and design systems sharing
• Template marketplace with intuitive browsing and filtering
• Knowledge sharing through tutorials and educational content

3. Social Proof Mechanisms

• Public profiles showcasing designer work
• Community recognition and featuring systems
• Easy sharing and collaboration invites

The Failed Acquisition as UX Strategy

The Adobe acquisition failure became a powerful UX narrative tool:

1. Validation Signal

• “Even Adobe validates us” became powerful social proof
• Positioned Figma as the future while Adobe represented the past
• Created urgency around joining the winning platform

2. Security and Stability Messaging

• $1B cash position communicated financial stability
• IPO trajectory suggested career growth opportunities
• Market position reinforced through competitive dynamics

Product Velocity as UX Compound Interest

Four Products, Unified Experience

Figma’s ability to ship four major products in 18 months wasn’t just impressive- it demonstrated UX consistency and platform thinking:

1. Figma AI: Enhanced existing workflows without disruption 2. Dev Mode: Bridged the designer-developer gap with familiar interfaces 3. Make: Extended design into interactive prototyping with consistent paradigms 4. Sites: Completed the design-to-deployment pipeline with seamless transitions

Each product leveraged the same underlying UX principles: collaboration, accessibility, and workflow integration.

The Compounding Effect of UX Investment

Why UX-Driven Hiring Worked

Figma’s growth has been primarily product-led, with their freemium model and short implementation cycle contributing to sales efficiency. The strategic hiring amplified this by:

1. Faster Feature Development

• More researchers meant deeper user insights
• Larger design teams enabled parallel product development
• Specialized roles (AI, DevX, no-code) allowed focused expertise

2. Quality Maintenance at Scale

• Design system teams ensured consistency across new products
• UX research teams validated assumptions before development
• Accessibility specialists maintained inclusive design standards

3. Market Expansion Through UX

• 57% of APAC businesses using cloud-based design tools see increased revenue, showing how UX quality drives business outcomes
• International teams built culturally appropriate experiences
• Enterprise UX teams addressed complex organizational needs

Lessons for UX Leaders

1. Bet on Talent During Uncertainty

When competitors freeze hiring, exceptional UX talent becomes available. Figma’s contrarian approach created a talent moat that competitors couldn’t quickly overcome.

2. Build Products Users Don’t Know They Need Yet

The best UX innovations solve problems users can’t articulate. Figma’s pre-emptive hiring for AI and developer tools positioned them to lead market shifts.

3. Collaboration is the Ultimate Competitive Moat

Real-time collaboration capabilities, like those found in Figma, are incredibly useful for remote teams who want to collaborate, track changes, and streamline workflows. Once teams adopt collaborative workflows, switching costs become enormous.

4. Community-Driven Product Development

The most successful UX platforms become ecosystems. Figma’s plugin architecture and community features created network effects that made the product more valuable as more people used it.

5. AI as UX Enhancement, Not Replacement

Figma’s AI features remain free during the beta period through 2024, showing commitment to user adoption over immediate monetization. The best AI integrations feel like natural extensions of existing workflows.

Conclusion: The UX-Driven PLG Playbook That Redefined an Industry

Figma’s $67B success story demonstrates that in the modern software landscape, exceptional user experience isn’t just a competitive advantage- it’s the foundation of sustainable growth. Their strategic investment in talent during market uncertainty, combined with relentless focus on collaborative UX and forward-thinking AI integration, created a compound effect that traditional marketing or sales approaches couldn’t match.

The Figma Formula: Community + AI + UX = Market Dominance

As an AI Design Leader tracking this evolution, three key insights emerge:

1. Community-Led Businesses Win at Scale Figma proved that building for the community first creates unstoppable network effects. When your users become your biggest advocates, growth compounds exponentially.

2. AI Strategy Must Enhance, Not Replace Their AI approach — augmenting human creativity rather than replacing it — created user love instead of user fear. This philosophical choice was crucial to adoption and retention.

3. UX Innovation Creates Category Transformation Figma didn’t just build a better design tool; they reimagined how creative collaboration happens. This category-defining innovation justified their premium valuation.

Looking Ahead: CONFIG and Beyond

Attending CONFIG Conference this year, I expect to see Figma unveil their next phase: deeper AI integration that pushes the boundaries of design automation while maintaining their community-first philosophy. Their IPO success provides the capital to accelerate R&D in ways that will likely surprise the industry again.

The lesson for UX leaders is clear: when everyone else is cutting costs, investing in user experience talent and innovation creates unassailable competitive advantages. Figma didn’t just build better design tools — they reimagined how creative work happens in the digital age, and their UX-first approach made that vision irresistible to users worldwide.

Their story proves that markets don’t care about headcount — they care about output. But great output requires great people building great experiences. Figma’s bet on talent during uncertainty wasn’t just bold; it was the strategic foundation that enabled their product-led growth engine to operate at unprecedented scale and speed.

The multifold returns on their IPO weren’t just a market anomaly — they were the inevitable result of building a community-driven, AI-enhanced platform that fundamentally changed how millions of designers and developers work together. That’s the power of UX-driven strategy executed at the highest level.

Love triangles are deep waters, especially when they involve your software stack! Enterprise software, consumer software, and the AI-augmented future can all be compared together, all while bringing some of that “vibe coding” energy. As always, I look forward to sharing some fresh humorous perspectives mixed with wisdom putting UX as a dimension.

The Cast: Enterprise Software, Consumer Software, and AI Vibe Coding

Consumer Software (“The Girlfriend”)

Audience: Literally everyone — from social app swipers to e-commerce addicts.

Vibe: Trendy, energetic, and always eager to impress.

UX: Effortless and fun. Download, try, move on if it’s not love at first sight.

Focus: Personal enjoyment, instant results, and user-friendly design.

Flexibility: Super easy to switch — swipe left or try something new at any time.

Enterprise Software (“The Wife”)

Audience: Businesses and organizations with mission-critical needs.

Vibe: Reliable, steady, essential to your routine (and a little harder to surprise).

UX: Can be complex, sometimes needs a “how-to” manual and lots of patience.

Focus: Long-term productivity, security, data management, and reliability.

Flexibility: Not a casual commitment! Changing can require effort, negotiation, and usually a long migration.

AI Vibe Coding (“The Superpartner”)

Audience: Both businesses and individuals — anyone ready for a new workflow.

Vibe: Smart, curious, reads your mind (almost), and always ready for a new “adventure.”

UX: Conversation-driven; just tell it what you want. No more “lost in documentation.”

Focus: Empathy, adaptability, and creative collaboration. AI crafts what you imagine, on your schedule.

Flexibility: Mixes loyalty with spontaneity — AI adapts, learns, and evolves as you go

Here’s a table visualizing the differences between Consumer Software (“Girlfriend”), Enterprise Software (“Wife”), and AI Vibe Coding (“Superpartner”)

What, AI Vibe Coding Exactly?

AI vibe coding is an innovative, AI-powered software development method where your goals are articulated in natural language, and AI transforms them into code. Picture a deeply empathetic collaborator who absorbs your suggestions and brings them to fruition, needing no help with syntax after a few clarifying questions.

How does it work?

Prompt: State your instructions in plain English and AI will generate your desired output.

AI Response: In accordance to your goals, AI creates code, evaluates it, and performs modifications if necessary.

Iteration: AI has the ability to adapt, debug, and evolve your project based upon your instructions.

Why is it vital?

Empowerment: Every single person has the ability to software without difficulty.

Efficiency: Software development has never been this modular in structure and this fast.

Co-Creation: Every one crosses the traditional boundaries, when described separately, the creator of the software and the one who tells the programmer what to do.

The Newer Workable Romance (With Comedy)

Consumer App: “I’m easy to use and entertaining. Just don’t expect payroll support from me.”

Enterprise App: “I won’t leave you, and you won’t leave me; we’ll have marriage-like binds, yet don’t even think we won’t have scheduled breaks.”

AI Vibe Coding: “What do you aspire to? I’ll work on transforming them, and I won’t stop there. I’ll also prompt you to drink water on a timely basis and remind you to stay hydrated!”

When Should You Use Each?

Consumer Software: When you are looking for straightforward, a balance of elegance, and simple joys.

Enterprise Software: When you need structured reliable systems to handle multifaceted business problems.

AI Vibe Coding: When you are set for a blast of ingenuity, advanced automations, and a sprinkle of wonder on your technology stack.

The Takeaway: Fun, Fidelity, and Ready for the Future

Now it is possible to have seamless customer interactions, effective support from the enterprise systems, and even the “superpowers” of AI vibe coding, all on the same desk. The most notable factor is knowing the capabilities of each and the optimal blend to achieve for your needs.

Updating our relationship status will be a breeze once AI figures out the secret to making office coffee taste great. Now design is the basic foundational skill set and i was telling one of the startup founder who is working on kids design empowerment that design in next generation is a life skill and that teaches empathy and innovation. This is much needed in the new era to deal with any one weather its a Girl friend, wife or super power AI to accomplish your tasks and goals ;)

Just for fun… i like this UX triangle love story and hope you do!

Attending the SkyDeck 2025 Demo Day last week at UC Berkeley was an electrifying experience that blended the thrill of innovation with the lot of positive vibrations at the venue. As a partner at Progressive Ventures, I was excited not only to scout the next wave of AI-driven startups but also to reconnect with friends, colleagues, and fellow investors in the heart of Silicon Valley’s entrepreneurial ecosystem12. This is my first demo day at this scale and this left me a great deal of impression.

A Vibrant Gathering of Innovators and Investors

The event took place in a high-end auditorium, buzzing with over 600 attendees venture capitalists, mentors, founders, advisors and students. It was my first time at a SkyDeck Demo Day in Berkeley, and the energy was palpable. Meeting up with my friends and partners from Progressive Ventures, we dove into deep discussions about the most promising startups, their business models, and the nuances of their go-to-market (GTM) strategies. We know some of the startup founders outside of SkyDeck. The Demo Day wasn’t just about pitches and startup showcase it was about building relationships, sharing insights, and exploring potential seed investment opportunities12. Every idea is unique and has a strong business potential and a purposeful use of AI to solve problems effectively.

Unplanned Encounters and Lasting Connections

One of the evening’s highlights was an unexpected reunion with Sajeeve Kumar, who had hired me at Informatica back in 2011. Now a respected AI professional, venture capitalist, and startup advisor, Sajeeve’s journey is a testament to the power of lifelong learning and networking. I had a very strong relationship with Sanjeev and he is always a data person. We both worked on some interesting Hackathons and Innovation days back then for Informatica. I also caught up with Sahil Agarwal, a Yale PhD graduate and founder of Enkrypt AI. Watching Sahil deliver a polished pitch on enterprise AI security and compliance was a proud moment , his company is setting new standards for safe and responsible AI deployment12. Sahil is at Series A and i am sure he has a strong team and advisors, his passion on enterpise software will make him reach new heights. I have also invited him to join for a event “AI Innovators Forum” at Google next day and we did got time again discuss about his startup, AI and User experience's.

A World-Class Showcase of Agentic AI Startups

Batch 19 featured 22 startups, most harnessing the power of Agentic AI — autonomous, adaptable, and goal-driven systems. The diversity was striking: healthcare, fintech, enterprise software, robotics, sustainability, and more. Each pitch felt like a TEDx talk, with founders articulating not just their technology but also their vision, business model, and GTM strategies12 and they all were very prepared. The SkyDeck Executive Director Caroline Winnett was amazing and her passion and her commitment showed up in every time she shows up on Stage she did the MC for this event introducing each startup. BTW she was also the Forbes 50 over 50.

Standout Startups from the Evening

Key Learnings from the Pitches

The evening was a masterclass in startup storytelling and business strategy. Here’s what stood out:

Clarity of Vision: Each founder clearly defined the problem, their unique solution, and the market opportunity.

Diverse GTM Strategies: Startups showcased tailored approaches — from SaaS and hardware pilots to strategic partnerships.

Real Traction: Many were already generating significant revenue ($500K–$3M) and had secured key partnerships or pilots.

Mentorship and Community: The SkyDeck program’s structure — advisors, weekly events, and a global network — was evident in the polish and confidence of the pitches12.

Beyond the Auditorium: Networking and New Ideas

After the formal pitches, the evening continued with networking events at local bars, where UC Berkeley PhD students presented cutting-edge Agentic AI research posters. These informal conversations with founders, researchers, and fellow investors sparked new ideas and potential collaborations. The unplanned moments — like reconnecting with old colleagues and discovering new perspectives — were as valuable as the scheduled programming12.

Agentic AI: Shaping the Future Across Industries

The Demo Day reinforced how Agentic AI is reshaping key sectors:

Healthcare: Automating compliance, clinical support, and administrative tasks.

Fintech: Accelerating lending and payments with explainable, real-time AI.

Enterprise Productivity: Delivering scalable, cost-effective AI-driven analytics and operations.

Sustainability: Enabling smarter recycling and supply chain optimization12.

Final Reflections: The Power of Curiosity and Community that accelerates

SkyDeck Demo Day Batch 19 was more than a showcase, it was a window into the future of entrepreneurship, motivator, learning pad and a reminder of the value of staying curious and engaged. Every demo day is a unique opportunity to learn from diverse domains, challenge your thinking, and build lasting connections. Most of these startups are latest in technology and AI advances. If i have more money i would definitely do multiple seed investments, thats how you get lot of returns when a startup gets successful and exists. This is my secret to continuous learning (Attending meaningful event / pitches) and it’s a habit I recommend to anyone passionate about innovation. Keep attending demo days, startup pitches, stay curious, and be eager to learn; you never know what new ideas or connections await you at the next event12. You might even get a new startup idea or can find your cofounder and can pave a path for your startup entrupurnership.

As artificial intelligence (AI) reshapes industries, product designers face the challenge of crafting user experiences (UX) that align with AI’s capabilities, evolution, and ethical implications. Designing for AI is always beyond visuals, it’s about creating trust, enabling collaboration, and ensuring adaptability while prioritizing ethics and making users in control. Here is a guide by a designer to design for AI-driven products, from regular systems to autonomous AI agents and assistants.

Core Principles for Designing AI Experiences

I always call AI is nothing but UX because AI is all about User Experiences. Here are some core principles of AIX.

1. Design to create Human Trust

Trust is always psychological and Trust is the foundation of AI interactions. Users must feel confident in the AI’s reliability without unknown or scary expectations.

• Be Transparent: Clearly communicate the AI’s capabilities and limitations on what it can do.
• Set Expectations: Use visual cues and confidence score so that users know how much they can realy upon the AI recommendations and to explain AI decisions.
• Balance Anthropomorphism: Don’t make AI too human, its not real human and its just assistant software, not a replacement for human connection.

2. Design for Quick Iteration, AI Evolution, and Hyper Personalization

AI is evolving fast and user needs evolve rapidly, so designs must be adaptable with trends and useless.

• Build Modular Systems: Use flexible frameworks which are modular to accommodate updates and upgrading to newer stuff.
• Prioritize Scalability: Design is never done and its a living system, not a static User Interface, it needs updates and scale.
• Enable Personalization: Provide control to the user and lets user edit the outputs to match their expecations and support rapid iteration.

3. Design for Ethical Use

The big thing in AI is biases and it can be amplify easily, invade privacy, or hide ownership. Designers must mitigate these risks and consider while designing the system.

• Reduce Bias: Check the system AI responses and create feedback loops for users to flag issues and raise concerns.
• Data Privacy: Must be very transparent about data usage on where data is coming, where data is going and provide required details and provide control to the users.
• Showcase Ownership: The AI generated outputs should be defined well about the ownerships weather its uers, platform or proivude so there is now confusion and its very clear.

4. Design for Collaboration

AI should be always a partner or a assistant. It should not be a dictator or show authority, it should be a good collaborator to help the users accomplish their tasks.

• Enable to Refine: Allow users to adjust AI response outputs tweaking the tone, sliders or adjusting the values, regenerate with variations, etc.
• Foster Dialogue: Design interfaces for conversation and interaction and just consumption and implementation. This will fetch better results.
• Encourage Co-Creation: AI is a tool which learns based on its training and keep training the system so it learn better and serves your better.

Designing for AI Agents

AI agents are autonomous systems that perceive, decide, and act to achieve goals, from simple rule-based bots to complicated machine learning models. Poor design can lead to wrong responses, misinterpretations, biased outputs, or unpredictable behavior, risking trust and accuracy. Thoughtful design ensures agents align with human values, handle both positive and alternative use cases, remain transparent and unbiased.

Behavioral Attributes of Agentic AI

Agentic AI systems are defined by these 6 key attributes and they are are the foundational elements of Agnetic user experiences. There is a general agreement on the key attribute of the simplest form of an AI agent.

1. Goal-orientation — following given objectives.
2. Perception — understanding the environment and context.
3. Reasoning — ability to deduce.
4. Acting — do things. Either by itself or use “tools” to do it.
5. Learn and Adapt — ability to update its memory and improve reasoning.
6. Autonomy — a degree of independence and self-governance.

Agentic UX and Designing for Agents

Agentic UX focuses on intuitive, AI experiences for users interacting with autonomous AI agents building trust. Unlike traditional UX, it accounts for the dynamic, proactive nature of agents. Designing for Agents is all about building systems that ensuring seamless user interaction with these 3 major principles craft expeirence.

1. Agentic UX Principles

• Transparency in Autonomy: Clearly communicate actions on what its going to do and aligning with Goal-orientation and Autonomy.
• Contextual Feedback: Use Perception and Reasoning for relevant responses tying to Proactivity and provide details.
• User Empowerment: Let users override with contextual actions balancing Autonomy with user control to what user want to do.
• Proactive Assistance: Offer non-intrusive suggestions which are relavant ensuring Proactivity feels helpful.
• Adaptive Tone: Adjust style of tone of language so it could be personal ,professional or soft or harder, etc.

2. Designing for Agents

• Support Acting: Enable tool integration and provide required API’s and inform users of actions with transparency.
• Enable Learning: Build memory systems to track preferences and prioritize reflecting Learn and Adapt.
• Facilitate Perception and Reasoning: Provide required data inputs for context-aware responses.
• Design for Proactivity: Use predictive models to anticipate the necessity and predict accordingly.
• Balance Autonomy: Create permission settings for user oversight and balance between system and human.

3. Integration with AI Assistant Design

• Optimize Interaction Flows: Add Transparency and Proactivity where ever required and balance accordingly.
• Visualize Context: Support Learning by showing past interactions and data insights.
• Keep It Simple: Use Proactivity subtly like notifications, inline instructions and streamline with right Actions.

Conclusion

There is so many agents growing and AI agents is the the future of intelligent software products. Designing for AI is very tricky and it requires balancing trust, adaptability, and ethics. By aligning designs with AI’s capabilities, supporting rapid interaction and evolution, prioritizing ethical use, and fostering collaboration, designers can create great user experiences that empower users. For AI agents, Agentic UX and thoughtful system design ensure better autonomy, disrupts the user experience. This holistic approach of designing thoughtful AI Agents builds AI systems that are capable, ethical, and delightful to interact with, fostering trust and collaboration. This all will result in a greater AI expeirences that delight users.

Some people in the industry are calling now as “DESIGN IS DEAD”. But i disagree with it totally. I have been in design from my childhood and experienced the transformation from telephone, cable TV, internet, ATM, mobile, smart phone, IOT, smart things, cloud, autonomous cars, conversational bots and now AI and its advances. As we see every single transformation has boosted design opportunities and added to its GDP.

Now people think design is dead, i don’t agree with this because i think DESIGN IS REDEFINING with AI and DESIGNER plays a major role to make an impact.

The only thing is design needs other skills beyond design which is business and strategic value generation using the power of technology + design influence. The entire spectrum of design activities have to change weather its the process of doing design, research, implementation and measurements. There are lot of AI tools shaping up where any one can do it but the questions how can designers make use of them better with an empathic and user centric mindset balancing AI + Business Impact. For this change design leadership and management plays a bigger role in this new evolution of design and all great wake-up calls that must give us the next push and energy to change.

I believe this shift is critical Manager to Founder Mode. In the age of AI, design leadership is changing a profound transformation, going out from traditional “Manager Mode” to a dynamic “Founder Mode.” This will completely change how things work right from thinking, ownership, responsibilities, growth and risk taking ability just like an intrapreneur within an organization.

AI is UX because it creates magical, seamless experiences for users. UX is business because it drives results that sell. Businesses care about the bottom line, and as UX leaders, we wield a powerful set of metrics to build a compelling case for our work:

“Measuring the Design is key to showcase the Business VALUE”

Focus on Results: For businesses, it’s all about the final outcome and the the bottom line. The UX metrics matter because they quantify the value of user experience. Without these, UX leaders struggle to demonstrate their impact and value it has generated. Showcaing the strategic value of UX is the key to unlocking business support and proving what you got back.

User Needs and Business Needs if you want understand better, Imagine UX as a bridge between these two pillers. We connect the world of user needs with the realm of business objectives. We’re not just designing the user interfaces, we’re crafting experiences that fuel business success and returns. We should able to translate user insights into actionable strategies.

Shifting to Strategic Leadership

Strategy is nothing but a thoughtful plan and tobe strategic, we must evolve from order-takers to problem-solvers. This is a mindset change and requires understanding the business landscape and figuring out where UX can deliver the biggest impact. Communication is the key and we must learn to speak the language of business, mastering key performance indicators (KPIs), numbers, competition, brand and UX metrics in terms that resonate with executives.

Aligning UX with business objectives is critical and this is our biggest job duties. To secure executive buy-in, we need to tie UX initiatives to broader company goals, showing how our efforts drive targeted outcomes in point of time. Collaboration with other departments ensures UX becomes a core part of the strategic conversation and we become the advocate for bring everyone together as we are very good at moderating workshops and connecting the dots in the big pitcure.

Communicating ROI Effectively

Executives are driven by data. Demonstrating the return on investment (ROI) of UX isn’t about showcasing pretty mockups and animations: It’s about quantifying the design impact. Did a redesign increase conversion rates by 20%? Did fixed usability issues cut support tickets by 30%? Any expectations miss match from user feedback?

Use data visualization to tell a compelling visual story. Highlight the before-and-after, showcase UX’s effect on the key metrics, shwocase the future benefits. Numbers and visuals will make it more compelling.

A UX Strategic Framework for Business Value

I’ve developed a UX Strategic Framework built on three foundational steps and four key pillars to create business value:

1. Upskill: Educate leadership and teams, encourage experimentation, and integrate feedback.
2. Envision: Identify design opportunities, launch pilot projects, and refine with feedback.
3. Showcase: Present innovations to executives and customers, validated by real results.

Key Pillars for Value Creation: Data, AI and Design are the three key tenents for creating the value. To create more value we have to uPlift Design, Deepen data and Expand AI. Together, these elements create a synergy that drives innovation and amplifies value.

The Strategic UX Playbook: 9 Elements for Success

To help UX leaders navigate this transformation, I’ve crafted a Strategic UX Playbook with nine essential moves that helped me and this shouldmhelp you aswell.

1. Align: Align your UX design efforts with business goals.
2. Envision: Keeping north start in mind, Paint a compelling vision of the future.
3. Pilot: Create small projects and Prove value through small, measurable wins.
4. Collaborate: Build cross-functional teams for broader impact and support.
5. Cultivate: Nurture a design & AI first culture with curiosity.
6. Educate: Teach leadership and people about the value & ROI of UX.
7. UXecute: I coined this name which is executing UX and embedding into the process to deliver high quality results.
8. Network: Build strong relationships with stakeholders and executives, make this own this project alogn with you.
9. Impact: Deliver measurable design, bottom-line results is what matters.

Your win with Executives

Securing executive support isn’t easy like a magic wand. It’s about executing a very thoughtful strategy. These nine tenants form a my playbook to drive transformative UX initiatives and pitching rightly will earn the buy-in for design success.

By aligning UX with all the business priorities, leveraging right data, and right collaboration, we position ourselves as indispensable strategic design partners. This is how we lead in an design era of unprecedented change, bridging user needs and business success, one impactful experience at a time and not multi tasking which losses focus.

This is very exciting time for design leaders as UX is transforming with AI. AI’s rapid rise is reshaping businesses - upskilling teams, shifting jobs, and posing big challenges. UX stands out as a key differentiator, driving success and winning users’ hearts. By leveraging AI, we can create innovative, more empathetic experiences that shape the future that are going to live in.

These days, tiny startups, some under a year old, are hitting $10M ARR with 5–10 people or solo founders, powered by simple UX and AI. These new-age companies grow fast, operate lean, and race toward unicorn status, proving small teams can disrupt giants with creativity and agility. This is super fascinating because previously companies take 7–10 years to become a unicorn. Some Examples: Lovable ($10M, 15, $667K), Cursor ($100M, 20, $5M), Mid-journey ($300M, 131, $2.3M).

Designers have to think beyond design, focusing on business side of things and users to create real value for work. It’s not just about pretty interfaces. it’s about driving measurable impact. I would say Embrace AI, simplify, and lead with empathy to unlock opportunities and make UX a business partner.

Design Leadership in the Age of AI: From Manager to Founder Mode

This is a mind set but this shift is critical. In the age of AI, design leadership is undergoing a profound transformation, moving from traditional “Manager Mode” to a dynamic “Founder Mode.” This change is about taking ownership, embracing responsibility, and driving growth like an intrapreneur within an organization.

• Shift from Manager to Founder Mode: Transition from managing processes to leading innovation with hands-on team involvement.
• Key Principles: Take charge of the design vision, foster a growth mindset, and act with intrapreneurial zeal.
• Intrapreneurial Leadership Skills: Align vision with strategic goals, develop talent for AI, and build networks to advocate for design.
• Benefits: Improve innovation, teamwork, and business impact.
• Challenges and Opportunities: Harmonize human creativity with AI, ensuring ethical, user-focused outcomes.

This shift matters, it’s how we stay relevant and impactful in a fast-changing world and you will quote me later.

Design is Business and Bottom Line is Key!

AI has expanding the Total Addressable Market (TAM) for all products, and the customers ready to pay more for enhanced services. The real value of AI-driven design lies in its ability to increase the bottom line, get revenue growth, and deliver tangible business value. Crafting the right AI experience amplifies this impact even further.

AI + UX can enhance services, expanding both business and customer impact. This leads to GROWTH++, BRAND++, and VALUE++. Ultimately, this broadened horizon drives increased revenue and an expanded TAM, fueled by AI and technology through market expansion. At the end of the day, it’s all about increasing the bottom line constantly.

Conclusion: Your UX Strategic Journey

Transforming your organization’s approach to UX isn’t about a single magical solution or a instant coffee. It’s about implementing a holistic, data-driven strategy based on your organization vision. By aligning user needs with business objectives, leveraging right AI technologies, and consistently demonstrating value, UX leaders can drive unprecedented innovation and business value.

DESIGN IS NOT DEAD, its getting REDEFINED in the age if AI. AI will enhance producitivty of designers and also allow non designers get basic design done. The AI generated outputs are not matured and dont yet create right design, look at this as first draft. Imagine AI is your intern and its helps you to accelerate faster. Out traditonal linear process and Tools can be DEAD but not design. This is a big mindset change and the future is very unpredictable. This is same for every field including engineering, product management, marketing, etc. But the point here is are we ready for this change and i would say we have to and there is no way you cant be in the frog in the well. Design community and design leaders should proactivity start sharing and their success / failure stories and be very flexible to adopt and experiment new methords of doing things and remember dont for business value and impact. If you cannot show value, there is no need for designer for sure and thats the hard truth.

The future of UX is not just about designing interfaces , it’s about creating magical experiences that bridge human potential with technological innovation and increasing the bottom line. Which indeed will incrase Growth ++; Brand ++ and Value ++.

Design Leadership in the Age of AI: From Manager to Founder Mode was originally published in Bootcamp on Medium, where people are continuing the conversation by highlighting and responding to this story.