RPS // Blogs // The Component Priority List: What to Build First in a Design System
Component priority list: what to build first in a design system

Build a design system in this order: tokens, then the components that appear on every screen, then the patterns that combine them. Teams that build in the wrong order spend months producing components nobody uses while shipping no faster than before.

Start with tokens. Color, type, spacing, and radius defined once, before anyone draws a button.

Then build the atoms that appear everywhere: buttons, inputs, and icons, with every state defined.

Payback depends on how much of your team’s time goes to repeat interface work, not on team size alone.

Aim for the fifth of components that cover most use cases. Full coverage is where budgets go to die.

Two sources of truth is not a design system. If Figma and the code disagree, you have a suggestion.

Introduction

Build your design system in this order: design tokens first, then high traffic atoms, then patterns, and stop well short of full coverage. That order matters because the common failure is not building a bad system, it is building the right components in the wrong sequence and shipping nothing faster for a quarter.

A design system is an internal product, and like any internal product it has a scope, an owner, and a return that can be calculated before you commit. This article covers the build order, how to decide what comes next when your team asks for a new component, what the return actually looks like, and a calculator for running the numbers on your own team.

What a Design System Is, in Business Terms

A design system is a shared library of values, components, and patterns that design and engineering both build from, so that a decision made once is applied everywhere instead of being remade on every screen.

The Lego comparison holds if you push it further than most people do. Anyone can buy a set of bricks. What makes Lego work is that every brick locks to every other brick, because the stud spacing was decided once, in 1958, and never changed. That decision is worth more than the bricks. Your token layer is the stud spacing.

That is also why the build order matters. Producing 40 components before you have agreed on spacing and color is producing 40 bricks that do not lock together.

When to Build One, and When to Wait

Build a design system when the same interface decision is being made more than once by more than one person. Before that point, you are building infrastructure for traffic you do not have.

Four signals mean the time has come. Any two together are usually enough.

More than one team ships interface work

A single team holds consistency in shared memory. Two teams cannot, and the divergence starts in the first quarter.

You have a second theme or brand coming

Dark mode, a white label deal, or a second product. Each doubles the values you maintain, and doing that without a system multiplies the work rather than adding to it.

Onboarding a designer or front end engineer takes weeks

If new joiners spend their first month asking which component to use, the answer exists in people rather than in a system, and it leaves when they do.

Your team is generating interface work with AI tools

Generated screens without a defined system produce a different blue on every screen, because nothing constrains the output.

Wait if you are one designer on one product with no theming requirement and a roadmap that is still changing shape monthly. Tokens and a tidy component file give you most of the benefit at a fraction of the upkeep, and the components you would build today are the ones your product will stop using next quarter.

There is a middle case worth naming, because it catches most companies. You are past the point where a component file is enough and short of the point where you can staff a system. The answer there is to build the foundations only, tokens and the five components on every screen, and stop deliberately. That is a two to three week commitment rather than a two quarter one, and it buys you time without accumulating maintenance you cannot service.

The failure this section is trying to prevent is not building too late. Late is recoverable. It is building a full system at seed stage because a competitor announced one, then abandoning it half finished, which leaves the product worse off than never starting.

Phase One: The Foundations, Before Anyone Draws a Button

Tokens, type scale, and spacing come first because everything built afterward inherits them, and anything built before them has to be rebuilt.

Design tokens

Named references for your values, so color-action-primary is defined once instead of a hex code being typed into 40 files. This is what makes a rebrand or a dark mode a configuration change rather than an audit. Without it, every later phase compounds the problem.

Type scale

A fixed set of sizes and weights with defined use. The saving is not the typography, it is the end of the conversation about whether this heading should be 22 or 24 pixels, which recurs weekly and is never worth the time it consumes.

Spacing and grid

A rigid scale, commonly based on multiples of four or eight. This one is unglamorous and produces the most visible consistency improvement per hour spent, because uneven spacing is what makes a product look unfinished even when every component is correct.

Foundations are cheap and fast, usually a week or two for a single product. They are also the only phase where skipping ahead is genuinely expensive, because everything downstream inherits the mistake.

Phase Two: The Atoms That Earn Their Place

Button component states: default, hover, focus, active, disabled, loading

Build the components that appear on nearly every screen, and build them completely rather than building more of them.

Buttons, with primary, secondary, and tertiary variants, and every state: default, hover, focus, active, disabled, loading

Buttons are where users take the actions your business depends on, and a missing loading state is how double submissions reach production.

Inputs and form fields, including text fields, selects, checkboxes, and radios, with error, focus, filled, and disabled states

Forms are where conversion is won and lost, and inconsistent validation behavior across forms is one of the most reliable sources of support tickets.

Icons, as one unified set at one grid size

Mixed icon sets are the visual tell that a product was built by several teams who never spoke.

Completeness beats quantity here. A button with seven properly defined states is worth more than seven half defined components, because the states are what engineers otherwise invent screen by screen.

Phase Three: The Molecules

Combine atoms into the modules that repeat across your product, once the atoms are stable.

Navigation and app bars come first, because they are the most viewed components in any product and the most damaging when inconsistent. Cards follow, as the container for most list and dashboard content. Modals and dialogs come next, and they are worth defining strictly, since a confirmation dialog that behaves differently in three places is how users learn not to trust confirmations.

Resist building this phase in parallel with phase two. A molecule built on unstable atoms gets rebuilt when the atoms change, which is the most common source of wasted effort in a system build.

How to Decide What Comes Next

Three questions to ask before building a new design system component

When someone requests a new component, score it on three questions before agreeing.

Does this block a launch?

A component holding up a committed release outranks everything else, regardless of how elegant the alternative would be.

How many places will use it?

A component used in five features is infrastructure. A component used in one dashboard is a feature, and it belongs in that feature’s codebase, not in the system.

Is there an adequate alternative today?

A standard select instead of a custom date picker often costs three days instead of three weeks, and the difference is rarely visible to users.

The third question is the one that saves the most money and gets asked the least. Most component requests are solvable with something that already exists, and the request is really a preference rather than a requirement.

What the Return Actually Looks Like

The published evidence on design system return is real but comes with a conflict of interest worth naming.

Forrester, in a study commissioned by Figma, found that teams using prebuilt components saved more than half their design time in the ideate and create phase, and put the efficiency gain for that phase at 60%. A separate Forrester study, also commissioned by Figma, estimated a 20% to 30% increase in developer output and more than 90 minutes saved per developer per week using Dev Mode, with a modelled ROI of 351% over three years.

Both studies were paid for by the vendor whose product they assess. That does not make them wrong, and Forrester’s methodology is disclosed and reasonable, but it does mean they represent an optimistic ceiling rather than a neutral expectation. Treat those figures as the best case and model your own numbers lower.

The variable that decides your outcome is not team size, it is how much of your team’s time goes to repeat interface work. A team of 20 building one screen a quarter gets almost nothing from a system. A team of six shipping continuously across three surfaces gets a lot. That is why a calculator is more useful here than a benchmark.

The calculator defaults to gain figures below the published research, includes the cost of retrofitting existing screens, and applies an adoption ramp, since a system pays back as teams adopt it rather than on the day it ships. On a team of three designers and eight engineers with typical time splits, it returns a payback of roughly ten months. On a single designer working mostly on new feature exploration, it correctly says not to build one.

Three Mistakes That Waste the Budget

Design system mistake: Figma library and code disagree

Copying a system built for a company with different problems

Material Design solves Google’s problem, which is thousands of engineers building for billions of users across a dozen platforms. Adopting its component list wholesale means maintaining components your product will never render.

Chasing full coverage

The last 20% of components take longer than the first 80% and serve edge cases that appear once a year. Systems die of exhaustion more often than they die of rejection.

Running two sources of truth

If the Figma library and the code library disagree, you do not have a design system. You have a design suggestion, and engineering will correctly ignore it. This is the failure that undoes the return calculated above, because a system nobody trusts still costs upkeep while saving nothing.

Our guide to building a UI design system covers the mechanics underneath this, and the next article in this series covers the pattern layer, which is where most of the remaining value sits.

Who to Approach

Whoever builds your system should be able to work in the codebase, not only in the design file, because a system that exists only in Figma cannot enforce anything.

Three questions separate a partner who will build you a system from one who will build you a component file.

Ask what they would refuse to build

A partner who agrees to everything in the scoping call is selling hours, not judgment. Scope control is most of the value in this work.

Ask how the system reaches your code

If the answer stops at handover of Figma files, you are buying an artifact that starts decaying at delivery. The answer should involve tokens exported to your repository and components mapped to their code counterparts.

Ask who owns it after they leave

The correct answer names a person on your side and a handover that transfers ownership rather than files.

At Rock Paper Scissors we build systems for enterprise fintech and insurance products, where the constraint is usually regulatory and operational rather than aesthetic. The Nuvama wealth management platform is a representative example. The platform served Relationship Managers handling large volumes of leads, approvals, and client context, and the core problem was not that it looked dated. It was that inconsistent patterns across the product increased the learning curve, so users had to interpret different controls, layouts, and status behaviors on every screen, which pushed up training time. The work replaced that with standardized interaction patterns and a reusable component structure built to extend across lead management, admin, and task workflows.

That project is also why article two in this series exists. Our own writing on why components are cheap and patterns are hard covers the layer where that kind of work actually gets decided.

If you are choosing between building in house and bringing in a partner, the honest split is this. In house is better when you already have a design engineer and the roadmap is stable, because the knowledge stays with you. A partner is better when you need the build compressed and want the sequencing decisions made by someone who has made them before. Either way, name your internal owner before the work starts.

FAQ

What should you build first in a design system?

Design tokens, before any component. Color, type scale, spacing, and radius defined once as named values. Everything built afterward inherits them, so components built before tokens exist have to be rebuilt. Foundations usually take one to two weeks for a single product.

How many components does a design system need?

Fewer than most teams build. Target the components that appear across multiple features, typically twenty to forty for a single product. Full coverage is where budgets are lost, because the final components serve rare cases while consuming disproportionate build and maintenance time.

How do you calculate design system ROI?

Compare the cost of repeat interface work today against the same work with reusable components, then subtract the build and upkeep cost. The deciding variable is what share of your team’s time goes to repeat UI production, not headcount alone.

When is a design system not worth building?

When little of your team’s time goes to repeat interface work. One designer on one product with no theming requirement gets more value from tokens and a tidy component file than from a full system, at a fraction of the maintenance cost.

Should we use Material Design or build our own?

Material Design solves problems specific to Google’s scale and platform range. Most products need a fraction of its components and a different visual language. Borrow its structural thinking, then build your own component list from what your product actually renders on screen.

What is the difference between a component library and a design system?

A component library is a set of reusable elements. A design system adds tokens underneath, patterns above it, plus documentation, a named owner, and a governance process. The library is the artifact. The system is what keeps that artifact accurate over time.

Conclusion

Building in the right order is the difference between a system that pays for itself inside a year and one that quietly becomes a maintenance obligation. Tokens, then the components on every screen, then patterns, then stop. The teams that struggle are almost never the ones that built too little.

There is a check you can run tomorrow without any of this. Ask your lead engineer and lead designer to count the button variations in your codebase. If there are more than three, you already have the problem this article describes, and the calculator above will tell you what it is costing.

Let’s build with intent.

RPS // Blogs // AI as design thinking partner: the shift that changes everything

The most consequential change in how design teams use AI isn’t about generating mockups faster. It’s about thinking better. Across every major design publication and industry survey from 2024 to 2026, a clear consensus has emerged: AI’s greatest value to designers lies in ideation, critique, research synthesis, and strategic reasoning, not in producing visual assets. The data backs this up decisively. Foundation Capital’s 2025 survey of 400+ designers found that 84% use AI during exploration phases (research, ideation, strategy) versus just 39% during delivery. Figma’s own research shows only 33% of designers use AI to generate design assets, while 40% use it for data analysis and 38% for desk research. The tools haven’t caught up to the hype. Nielsen Norman Group’s May 2025 review found AI design tools still produce generic results with poor information hierarchy. But the thinking-partner use case is already delivering real returns. For design leads managing cross-functional teams in fintech and enterprise SaaS, this reframing isn’t academic. It changes how you staff projects, run critiques, synthesize research, and communicate with stakeholders.

From “enthusiastic intern” to outcome orchestrator

The design industry has converged on a spectrum of mental models for AI’s role, and understanding where your team sits on this spectrum matters more than which tools you’re using. Nielsen Norman Group, still the most cited authority in UX, has moved from their 2024 “AI as intern” metaphor to a far more ambitious 2026 framework called “outcome-oriented design.” In this model, designers stop crafting individual interfaces and instead define adaptive frameworks that respond to individual user goals. As Kate Moran and Sarah Gibbons wrote in March 2026, designers shift “from designing for the average to designing for the individual,” setting guardrails and constraints rather than pixel-level specifications.

John Maeda, now Microsoft’s CVP of Engineering for CoreAI Design & Research, frames this through his “UX → AX” (Agent Experience) paradigm: interfaces designed not just for humans but for AI agents that act on their behalf. His 2025 SXSW keynote outlined a taxonomy of collaboration spaces (chat, document, table, canvas) where human-AI partnership takes different forms. IDEO, meanwhile, has built an entire certificate program around “AI × Design Thinking,” with Managing Director Jenna Fizel positioning AI explicitly as a brainstorming partner.

But the most useful framing for practitioners comes from Dave Goyal of Think AI Corp, who distinguishes between three levels. AI as “tool” executes commands and generates outputs. AI as “copilot” assists within workflows and suggests completions, what Goyal calls “a glorified autocomplete engine.” AI as “co-thinker” engages in bidirectional dialogue, challenges assumptions, and surfaces blind spots. The difference, Goyal argues, “is not in capability. It is in design intent.” Paul Boag of Smashing Magazine operationalized this most concretely: he creates Claude and ChatGPT projects loaded with client research, personas, survey results, and documentation, then uses the AI as “a co-worker who never gets tired and has a perfect memory.” Not to generate designs, but to challenge his thinking, review his work, and ask hard questions.

The data tells a more nuanced story than the hype

The adoption numbers are impressive on the surface but reveal important fault lines when you dig deeper. Figma’s State of the Designer 2026 (906 respondents across five regions) found that 72% of designers now use generative AI tools and 98% increased their usage over the past year. Adobe’s October 2025 survey of creative professionals reported that 99% use generative AI in some capacity, with 97% using it across multiple workflow stages. Yet these headline numbers mask a critical insight: designers are far less satisfied with AI than developers. Figma’s 2025 AI Report (2,500 users across seven countries) found developer satisfaction with AI tools at 82% versus just 69% for designers. Only 32% of all respondents said they could rely on AI output, and only 54% of designers said AI improves the quality of their work.

The Foundation Capital/Designer Fund survey adds crucial context. While 89% of designers report improved workflows, the adoption curve follows a clear pattern: heavy use in early-phase exploration, moderate use during creation, and minimal use in delivery. 96% of designers learned AI entirely through self-teaching, through side projects, peer tips, and social media. Formal training barely exists. Startups lead adoption, with early-stage designers more than twice as likely to fully integrate AI compared to enterprise teams. The “last 40% problem” persists: AI gets designs roughly 60% of the way there, but the nuance, polish, and judgment that distinguish good work from great work still require human hands.

The sentiment data is perhaps most telling. Figma’s 2026 survey found designers split almost perfectly into thirds on whether design has gotten better (36%), worse (35%), or stayed the same (29%) since AI’s rise. NNGroup declared 2026 “the year of AI fatigue.” Yet 82% of hiring managers said their company’s need for designers has increased or held steady, and 56% specifically report increasing demand for senior designers, those who bring the judgment and strategic thinking that AI cannot replicate.

Six ways design teams are using AI to think, not produce

The most compelling evidence for AI as thinking partner comes from practitioners documenting specific workflows. These fall into six categories that map directly to how agency design leads structure their work.

For brainstorming and ideation, Eleken, a SaaS design agency, has replaced traditional brainstorming’s awkward silence with a structured approach: AI generates 20+ possible solutions to a design problem, each team member selects 2-3 concepts that intrigue them, and those become starting points for deeper human exploration. IDEO U teaches a similar pattern: let AI generate divergent ideas, then use human creativity to “build on, combine, or completely flip these ideas on their head.” The key nuance, supported by a CHI 2024 study, is that AI image generators during ideation can actually increase design fixation and reduce originality. Text-based ideation with LLMs avoids this trap by keeping ideas abstract and malleable.

For design critique, Nicole Riemer of ROSE Digital published the most detailed framework. Her five-step process uses role prompting to stress-test features from multiple perspectives simultaneously: a UX researcher conducting a usability audit, an accessibility specialist evaluating WCAG compliance, a behavioral economist analyzing cognitive biases, and stakeholder perspectives including customer support leads asking “What could go wrong that would flood your queue?” The critical insight: never ask AI “Would you use this feature?” because it will always say yes. Instead, ask open-ended exploratory questions grounded in real persona data. Markiian Bobyliak of Supermega Design takes this further by connecting Claude Code directly to his project folder via MCP, giving AI access to Figma files, research documents, and stakeholder notes so it can review onboarding flows against actual user interviews and documented requirements.

For user research synthesis, the most rigorous approach comes from Great Question’s six-step pipeline: transcribe, summarize each segment, generate codes, apply codes to summaries, group into themes, and transform into actionable insights. The crucial principle is breaking complex analysis into discrete steps with clear inputs and outputs. UX researcher Dominika Mazur’s side-by-side comparison of human versus AI analysis found ChatGPT “reliable in coding and synthesizing” with a particular strength in processing speed, while human researchers excelled at “generating non-obvious insights.”

For stakeholder communication, Bobyliak’s workflow stands out: after finishing designs, he instructs Claude to create documentation tailored for different audiences from the same underlying information. Technical specs for developers, design rationale for future designers, business-goal alignment for product managers, and presentation content for clients that pulls real quotes from user interviews. Sandra Herz, an impact communication consultant, uses a similar approach, creating detailed stakeholder personas for critical decision-makers and then using AI to adapt pitches for each audience.

For strategic thinking, the emerging pattern is multi-model triangulation. Elizabeta Kuzevska of Revenue Experts AI runs the same competitive analysis prompt across ChatGPT, Claude, Gemini, and Perplexity simultaneously, keeps responses separate, then creates a synthesis prompt to identify agreements, contradictions, and unique insights. If you’re using one AI model for competitive intelligence, you’re building strategy on one model’s biased perspective.

For problem framing, the most structured approach is the C.S.I.R. framework (Context, Specific Info, Intent, Response Format) from AiforPro.net, which includes specific prompt patterns for root-cause mapping via the 5 Whys, reframing How Might We questions, and clustering problems by journey stage. Designer Erin Wilson demonstrated the power of reframing by comparing solutions generated from a conventional problem statement about climate change versus a reframed “How might we help people experience the impact of their carbon footprint?” The conventional framing produced conventional solutions while the reframed version generated innovative concepts like interactive VR simulators and personalized carbon impact stories.

Exercises that work on Monday morning

The gap between understanding AI-as-thinking-partner conceptually and using it effectively in practice is where most design teams stall. These five exercises, drawn from documented practitioner workflows, require no special setup beyond access to Claude or ChatGPT.

  • Rubber duck design review: Prompt AI with “Act as my rubber duck. I’ll talk through my design decisions and I need you to help clarify my thinking by asking questions and reflecting back what I say, without giving direct answers.” Walk through your latest design decision. The forced articulation reveals gaps in reasoning and prepares you for stakeholder presentations.
  • Multi-perspective stress test: Take your current feature design and run it through Nicole Riemer’s role-prompting framework. Feed the AI real persona data (not assumptions) and ask it to respond as a UX researcher, accessibility specialist, behavioral economist, and customer support lead, each evaluating the same feature from their perspective.
  • Edge case scenario generation: Describe your feature’s happy path and prompt: “Generate 10 contextual edge cases where real human behavior deviates from this path. Consider users under stress, users with malicious intent, users with disabilities, and users in crisis situations.” This exercise directly addresses the blind spots that cause major UX incidents.
  • Design rationale document creation: After making a key design decision, prompt AI with the decision context and ask it to draft a rationale document covering “why it matters,” a decision summary, supporting evidence, alternatives considered, and trade-offs accepted. Then create versions tailored for your engineering lead, your product manager, and your client.
  • Competitive “what if” analysis: Feed AI your product’s current positioning alongside competitor data and ask: “What if our primary competitor launched [specific feature] tomorrow? How should our design strategy adapt? What assumptions in our current roadmap become invalid?”

Building a persistent “design copilot” amplifies all of these exercises. Avani of ADPList published a detailed guide recommending designers treat this like onboarding a new team member: write a “hiring brief” defining the AI’s personality and role, upload brand guidelines, design system documentation, accessibility standards, past research, and product strategy, then use the prompt “Please review what I’ve shared and ask me sequential questions to help complete your understanding of our brand, design system, product, and design culture.” Once onboarded, this persistent context transforms every subsequent interaction from a cold start into a conversation with a knowledgeable colleague.

What this means for design leadership in 2026

The most important finding across this research isn’t any single statistic or framework. It’s the emerging consensus that AI’s impact on design is primarily cognitive, not productive. The teams gaining the most advantage aren’t the ones generating assets faster; they’re the ones thinking more rigorously, considering more perspectives, and communicating design rationale more effectively. NNGroup’s insight crystallizes it: the skills that matter now are “curated taste, research-informed contextual understanding, critical thinking, and careful judgment,” precisely the skills that define senior design leadership.

For design leads at agencies working across fintech and enterprise SaaS, this has immediate structural implications. The “last 40% problem” means AI won’t replace your production designers anytime soon, but the exploration-heavy adoption pattern (84% in exploration versus 39% in delivery) suggests your biggest ROI comes from integrating AI into discovery, research synthesis, and strategic framing. These are the phases where agency teams often face the tightest time constraints and where client value is highest. The multi-audience communication capability alone, translating the same design rationale into developer specs, PM strategy docs, and client presentations, addresses one of the most persistent bottlenecks in cross-functional agency work.

The counter-narrative matters too. The Psychology Today research finding that “while generative AI enhances individual creativity, it simultaneously narrows collective diversity” is a genuine risk for teams that over-rely on AI for ideation without maintaining diverse human perspectives. And the CHI 2024 finding about AI-assisted ideation increasing design fixation suggests that how you integrate AI into brainstorming matters as much as whether you do. The practitioners getting the best results treat AI as a sparring partner that expands their thinking, not a shortcut that replaces it. That distinction, between amplification and automation, will define which design teams thrive and which find themselves, as NN Group warned, already replaceable.

RPS // Blogs // UX Audit Checklist – The 10-Point Framework That Exposes Flaws in Minutes
UX Audit Checklist - The 10-Point Framework That Exposes Flaws in Minutes

Let’s talk about the “A-word.”

Audit.

Just saying it out loud makes people want to take a nap. It sounds expensive. It sounds corporate. It sounds like a guy in a suit charging you $500 an hour to tell you that your font size is too small or that your logo is slightly off-center.

In the design world, we treat audits like root canals: painful, expensive, and something to be avoided until the patient is screaming.

But you don’t need a consultant to tell you your website sucks. You likely already know it. You feel it in your gut when you watch a user struggle with a form you built. You see it in the analytics where the drop-off rate looks like a cliff edge. You just don’t know where it sucks specifically.

Most UX audits are massive overkill.

Agencies love to deliver 100-page PDFs filled with jargon like “cognitive friction” and “information scent.” Those documents usually end up in a folder called “Old Stuff,” never to be read again. You don’t need a thesis. You don’t need a philosophy lecture. You need a 10-point checklist that exposes the ugly truth in under 20 minutes.

The “Common Sense” Framework

Most usability issues stem from a violation of basic heuristics—fancy words for “stuff Jakob Nielsen figured out 30 years ago.” These aren’t trends; they are the laws of physics for the web.

If you want to fix your product, stop looking at analytics for a second and look at the interface. Analytics tell you what is happening (they are leaving), but an audit tells you why.

Here is the framework. It’s not about perfection; it’s about triage. You are looking for the “bleeding neck” problems—the ones causing users to rage-quit—before you worry about the “paper cuts.”

1. Visibility of System Status (Don’t Leave Me Hanging)

Does the user know what’s going on? Is the button loading? Did the save work?
The Context: Silence is the enemy of UX. If a user clicks “Buy” and nothing happens for 3 seconds, they assume it’s broken. They will click again. They will double-charge their card. They will hate you.
The Check: Does every action have a reaction? Spinners, progress bars, and “Success” checkmarks aren’t decoration; they are reassurance.

2. Match Between System and Real World (Speak Human)

Are you speaking “Developer” or “Human“?
The Context: Users don’t know your internal terminology. They shouldn’t have to.
The Check: Look for jargon. Instead of “System Error 404,” try “We couldn’t find that page.” Instead of “Execute Protocol,” try “Run Backup.” If your grandmother wouldn’t understand the label, rewrite it.

3. User Control and Freedom (The Emergency Exit)

Is there an “Undo“? Can they get out of a flow easily?
The Context: Users make mistakes. They click the wrong link. They change their mind. If they feel trapped in a flow, they panic.
The Check: Ensure there is always a “Back,” “Cancel,” or “Home” button. Never trap a user in a modal or a multi-step form without a way out.

4. Consistency & Standards (Don’t Gaslight Me)

Does your “Submit” button say “Submit” on one page and “Save” on the next? Does the logo go to the homepage on the desktop app but not the mobile site?
The Context: Inconsistency makes users feel stupid. They learn a rule on page 1, and you break it on page 2. That creates cognitive friction.
The Check: Audit your terminology and placement. Pick a style and stick to it religiously.

5. Error Prevention (The Best Error is the One That Never Happens)

Don’t just fix errors; design them out of existence.
The Context: Error messages are a failure of design. Why did you let the user click that button if the form was empty?
The Check: Gray out invalid options. Disable the “Submit” button until the password is strong enough. Guide the user before they stumble.

6. Recognition Rather Than Recall (Don’t Make Me Think)

Don’t make the user memorize stuff from page 1 to page 2.
The Context: The human brain is lazy. It doesn’t want to hold information.
The Check: Are menu options clearly visible? Do form fields show examples (e.g., “[email protected]”) inside the box so the user knows the format? Make the options visible, not hidden in memory.

7. Flexibility and Efficiency of Use (Speed for Pros)

Can a power user speed through the task?
The Context: New users need guidance; experts need shortcuts.
The Check: Do you have “Skip” buttons for onboarding? Do you support keyboard shortcuts (Tab, Enter) for forms? Don’t slow down the experts just to hand-hold the newbies.

8. Aesthetic and Minimalist Design (Less is More)

Every extra element is competing for attention.
The Context: If everything is bold, nothing is bold. If you have three “Call to Action” buttons, you have zero.
The Check: Remove, remove, remove. If a paragraph doesn’t help the user achieve their goal, delete it. If a button isn’t critical, hide it.

9. Help Users Recognize, Diagnose, and Recover from Errors (Be Nice)

When things break, explain exactly how to fix them.
The Context: “Invalid Input” is useless. “Password must contain a symbol” is helpful.
The Check: Read your error messages. Are they blaming the user (“Illegal operation”) or helping them? Use plain English and highlight the specific field that needs fixing.

10. Help and Documentation (The Last Resort)

Ideally, the design is so good you don’t need a manual. But if you do…
The Context: Sometimes things are complex.
The Check: Is your help searchable? Is it context-aware (a help button right next to the complex feature)? Don’t bury the “Contact Support” link five levels deep.

How to Run the Audit (The “Fresh Eyes” Protocol)

As the folks at Eleken point out, a structured approach to reviewing your product against standard usability heuristics is the fastest way to spot those “tiny imperfections” that ruin the user experience.

But you can’t do it alone. You know where the bodies are buried. You know why that button is weirdly placed (because of a legacy API from 2019). Your users don’t care.

  1. Print the Checklist: Physical paper helps. It feels like a detective’s notebook.
  2. The “Jerk” Test: Go through your product and try to break it. Click randomly. Leave fields empty. Type gibberish.
  3. The “Mom” Test: Watch someone who isn’t in tech try to use your site. Don’t help them. Just watch where they pause.

Downloadable Asset

We’ve turned the heavy theory into a lightweight tool. Grab our “Is This Trash?” UX Audit Checklist. It’s a single-page PDF that walks you through the 10 critical heuristics listed above. Print it out, tape it to your monitor, and go to town.
[📥 Download the “Is This Trash?” Checklist]

FAQs

Q: Can I audit my own design?
A: You can try, but you’re blind to your own children’s flaws. You know why you built that confusing button (it seemed like a good idea at 2 AM). Your users don’t. Get a friend to do the checklist.

Q: What if I fail the audit?
A: You will fail the audit. Everyone fails the first audit. That’s the point. If you passed, you weren’t looking hard enough. Now fix it.

Q: Is a checklist better than user testing?
A: No. User testing is king. But a checklist is free and takes 10 minutes. Do the checklist first to fix the obvious, stupid stuff before you pay real humans to test it. Save your money for the complex problems.

Also Read: UX Design Patterns – Why Your “Unique” Design Is Hurting Your Users

RPS // Blogs // UX Design Patterns – Why Your “Unique” Design Is Hurting Your Users
UX Design Patterns - Why Your "Unique" Design Is Hurting Your Users

We all want to be the artist. The visionary. The one who reinvents the wheel.

We look at sites like Awwwards or Dribbble and see interfaces that float, glide, and defy gravity. We see elements that don’t look like buttons but feel like portals. We think, “If I build that, I will be famous.”

But in UX design, reinventing the wheel is usually just a fancy way of saying “confusing the hell out of your users.”

There is a dangerous myth in our industry that “unique” equals “good.” Agencies and junior designers alike often chase the Dribbble aesthetic—interfaces that look like sci-fi movie props but function about as well as a chocolate teapot. They build portfolios to impress other designers, forgetting that real users aren’t looking for art; they are looking to get a job done.

If your user has to learn how to use your interface, you have failed.

The Tyranny of Learning Curves

Users bring with them a “mental model“—a set of expectations based on every other app, site, and tool they’ve ever used. They know that a “hamburger menu” hides navigation. They know that a magnifying glass means search. They know that a trash can deletes things.

This isn’t laziness; it’s efficiency. The human brain is an energy-conserving machine. It loves patterns because patterns require less processing power.

This is known as Jakob’s Law: Users spend most of their time on other sites.

When you decide that your “Close” button should be a rotating hexagon in the bottom left corner instead of an “X” in the top right, you aren’t being creative. You’re being selfish. You are forcing the user to burn cognitive calories just to figure out how to leave the page. You are disrupting the flow they have established over thousands of hours of internet usage.

Every time a user pauses to ask, “Wait, where is the menu?” you are extracting a “mental tax.” If the tax gets too high, they close the tab and go to a competitor who respects their time.

The “Selfish Designer” Syndrome

Why do we break patterns? Usually, it’s ego. We want our work to stand out. We fear that if we use a standard left-sidebar navigation, our app will look “generic.”

Usability is invisible.

When a design works perfectly, the user doesn’t notice the design; they notice the task getting easier. If your design is loud, flashy, and confusing, the user notices you. And in B2B SaaS or e-commerce, the user doesn’t want to notice the designer. They want to pay their invoice, book their flight, or send their email.

The best design is the design that gets out of the way.

When to Use Patterns (and When to Break Them)

Does this mean you should copy-paste Bootstrap and call it a day? No. That’s laziness, not design. But you should use established UX design patterns as your foundation. You should only break the rules if you have a solution that is objectively 10x better than the standard.

Until then, stick to the script:

  • Navigation: Stick to standard layouts (top bar, left sidebar). Users shouldn’t need a map to find the “Home” button. If you hide your navigation inside a gesture-based mystery menu, you are playing a game of hide-and-seek that your user didn’t sign up for.
  • Input Forms: Don’t reinvent the radio button or the checkbox. These patterns exist because they work. We’ve all filled out thousands of forms. Don’t make us re-learn how to select “Male/Female/Other” or how to check a box.
  • Feedback: When something loads, show a spinner. When something saves, show a checkmark. Don’t invent a new language of “success.” If your error message is a cryptic riddle, you have failed.

“The usage of UX design patterns in your design process promotes creating usable and high-convertive websites… we know it from our experience.” — Eleken

Skeleton vs. Skin: How to Be Unique Without Being Confusing

This is the part where designers panic. “If I use common patterns, my app will look exactly like my competitor’s!”

False. This is where the distinction between Skeleton and Skin comes in.

  • The Skeleton: This is the structure. The placement of the navigation, the layout of the form fields, the position of the primary CTA. This should be standard. This should be boring.
  • The Skin: This is the visual design. The typography, the color palette, the iconography, the micro-interactions, the copywriting. This is where you can be as unique as you want.

You can have the most boring, standard left-sidebar layout in the world, but if you pair it with bold illustration, witty micro-copy, and a vibrant color palette, your brand will shine through. You can be distinct without being difficult.

Use a pattern library. There are tons of them (like GoodUI or UI Patterns) that offer battle-tested solutions based on A/B testing. These aren’t “crutches”; they are cheat codes for usability. They free up your brain power to solve the actual hard problems of the product, rather than wasting time deciding if your “Login” button should be oval or square.

Be Boring to Be Brave

Real creativity in UX isn’t making a button look like a banana. It’s solving a complex problem so seamlessly that the user never notices the design at all.

It takes guts to say, “We’re going to use a standard tab bar because it’s what our users expect.” It takes confidence to know that your product’s value lies in its utility, not in its novelty.

So, go ahead. Be boring. Your users will thank you for it. And by “thank you,” I mean they will actually use your product instead of rage-quitting.

Downloadable Asset

We’ve compiled a “Don’t Be Weird” Pattern Library. It’s a collection of the most effective, standard UI patterns for navigation, forms, and data tables that you can drop into your project to ensure users feel instantly at home.
[📥 Download the Pattern Library]

FAQs

Q: But what if my brand is ‘quirky’ and ‘different’?
A: Your brand voice can be quirky. Your navigation should be predictable. Don’t make me solve a riddle to find the “Login” button. You can be a comedian without hiding the exit sign.

Q: Are carousels (sliders) a safe pattern?
A: Lord, no. Carousels are often terrible for UX. They hide content, they auto-scroll when you’re trying to read, and mobile users hate swiping them. Only use them if you hate conversion rates.

Q: If I use standard patterns, won’t my site look generic?
A: Customizing the skin (typography, color, spacing) allows for branding without breaking the skeleton (usability). Don’t break the skeleton. A skeleton with a broken arm doesn’t look “edgy”; it looks like it needs a doctor.

Q: What is the one time I should break a pattern?
A: Only when the existing pattern is fundamentally broken for your specific use case. But be prepared for the learning curve. And test it. If your users fail, you were wrong. Go back to the standard.

Also Read: Profile Page Design – The Underrated Conversion Goldmine You’re Ignoring

RPS // Blogs // Profile Page Design – The Underrated Conversion Goldmine You’re Ignoring
Profile Page Design: The Underrated Conversion Goldmine You’re Ignoring

Let’s be honest. When was the last time you got excited about a “Settings” page?

Exactly. Never.

In the design world, the profile or settings page is the digital equivalent of the utility closet. It’s where we shove the brooms, the mismatched Tupperware, and the holiday decorations. As long as the door closes and the clutter is hidden, we’re happy. We prioritize the landing page, the dashboard, and the analytics graphs. We treat the profile page like a tax form, a necessary evil.

Your profile page is a retention killer.

We spend months agonizing over landing page hero images and CTA button colors. But the moment a user signs up, where do they go to set up their account? The profile page. If that experience is cluttered, confusing, or ugly, you’ve just poured gasoline on your churn rate.

You can have the sexiest onboarding flow in the world, but if the user feels lost the second they try to upload an avatar or change their password, they’re gone. The “First Time User Experience” doesn’t end at the signup screen; it ends when the user successfully customizes their space.

Why “Boring” Pages Matter (The Psychology of Ownership)

A profile page isn’t just a data dump; it’s a user’s personal space within your product. It’s the only part of the app that truly belongs to them. In a sea of charts, graphs, and other people’s data, the profile page is the only mirror.

If you treat it like an afterthought—hiding it behind a vague gear icon or making it look like a 1990s database form—you are subtly telling the user that they are an afterthought. You are telling them that their personal comfort is secondary to the system’s efficiency.

This creates a disconnect. In SaaS, specifically, trust is currency. If a user feels they don’t have control over their own identity (can they change their name? can they delete their credit card?), they subconsciously stop trusting the platform with their work.

According to insights from top agencies like Eleken, a well-structured profile page isn’t about looking cool; it’s about information hierarchy. You need to separate the “critical” (email, password, billing) from the “nice-to-have” (newsletter preferences, dark mode toggle, social links). This distinction reduces cognitive load. A user shouldn’t have to scan 50 options just to figure out how to log out.

How to Stop the Clutter

The biggest mistake in profile page UI is trying to show everything at once. We’ve seen massive enterprise tools where the settings page is a literal wall of text—300 form fields with no visual break. That is not a page; that is a resignation letter.

Here is how to fix it:

1. Group the Logic (Don’t Mix “Danger” with “Delight”)

Don’t put “Delete Account” next to “Change Profile Picture.” That’s anxiety-inducing. Group things by intent: Identity (photo, name, bio), Security (password, 2FA), and Notifications. Keep the destructive actions (Delete Account, Downgrade Plan) in a separate, clearly marked “Danger Zone” or bottom of the page. This gives the user psychological safety while navigating.

2. Visual Hierarchy is King

The user’s name and photo should be the stars of the show. Give them real estate. The billing info should be visible but not screaming for attention. Use whitespace aggressively. If a section looks dense, users assume it will be difficult to use.

3. Empty States are Marketing

If a user hasn’t filled out their bio yet, don’t just show empty lines or a gray placeholder. That’s a wasted opportunity. Use that space to teach them the value of the feature. instead of a blank box, try: “Add a bio so teammates know who you are” or “Upload a logo to appear on your invoices.”

“The page that belongs to users… Most profile pages don’t make headlines… But open any app you’ve used, and chances are you’ll find your fingerprints all over that little corner.” — Iryna Parashchenko, Eleken

4. Don’t Forget Mobile

This is where most profile pages die. A layout that looks clean on a 27-inch monitor becomes a thumb-twisting nightmare on an iPhone. On mobile, complex forms should be broken into bite-sized steps or collapsible accordions. If you are forcing a user to pinch-and-zoom just to update their phone number, you have failed mobile UX 101.

The “Invisible” Design Goal

The best profile pages are the ones you don’t notice. They feel obvious. They feel like the app was built specifically for that one user. When the design is “invisible,” the user feels a sense of agency and competence. They feel smart because they didn’t have to hunt for anything. And when a user feels smart, they stay.

FAQs

Q: Can I put a link to my design portfolio in the user’s profile for them?
A: You can try, but they will likely change it to a link to their cat’s Instagram. Prioritize their content, not yours.

Q: Is dark mode mandatory for settings?
A: Only if you want users to like you. Yes, it’s mandatory. Blinding white settings at 2 AM is a crime against eyes.

Q: How many tabs is too many tabs?
A: If you need a compass to navigate your settings page, you have failed. Stick to three or four max. Group the rest under “Advanced.”

Q: Should I hide the “Delete Account” button to reduce churn?
A: Never. Hiding it makes you look shady and desperate. Make it accessible, but ask for confirmation. If they really want to leave, a hidden button won’t stop them, just make them angrier.

Also Read: Apple’s Cross-Device UX: How Continuity & Handoff Drive 800M+ Active Users

RPS // Blogs // Amazon One-Click Checkout: The UX Case Study That Revolutionized E-Commerce
Amazon One-Click Checkout: The UX Case Study That Revolutionized E-Commerce

Learn how Amazon’s 1-click checkout increased conversions by 300% and reduced cart abandonment. The UX design principles behind $738B in e-commerce.

In 1999, Amazon filed a patent for something that sounds impossibly simple: letting customers buy something with a single click. Today, “One-Click Checkout” sits alongside the iPhone and Post-it Notes as one of those inventions so intuitive we forget what shopping was like before it existed. Yet when Amazon introduced this feature, it was genuinely revolutionary. The average e-commerce checkout required customers to navigate through 4–5 separate steps: cart review, account login, address entry, payment details, and order confirmation. Each step was a decision point. Each decision point was an opportunity for buyers to reconsider, procrastinate, or abandon altogether.

The problem wasn’t complexity—it was friction. Even highly motivated customers faced friction from decision fatigue. Enter a shipping address. Enter a billing address (sometimes different). Enter credit card details. Verify everything. Confirm the order. By the time checkout completed, the psychological momentum that drove the initial purchase had dissipated. Mobile shopping made this even worse. Typing a 16-digit credit card number on a three-inch screen felt so cumbersome that many shoppers simply abandoned their carts rather than complete the transaction.

Amazon Checkout UX Design Case Study — EMILY PAK
Amazon Checkout UX Design Case Study — EMILY PAK

Amazon’s data revealed something striking: checkout cart abandonment rates hovered around 70% across the e-commerce industry. Worse, roughly 18% of shoppers abandoned carts specifically because the checkout process felt too complicated or time-consuming. This represented catastrophic revenue leakage. Industry analysis suggested that e-commerce stores could recover an estimated $260 billion in lost orders simply by optimizing the checkout experience. The mathematical incentive was undeniable, but the design challenge was equally clear: how do you remove friction without compromising security or data collection?

One-Click Checkout solved this through a progressive disclosure strategy. Instead of asking for all information upfront, Amazon securely stored customer payment and shipping details. On subsequent visits, users could complete a purchase with literally one click. The cognitive load dropped from “fill five forms” to “confirm one decision.” This wasn’t about removing information requirements—it was about removing the moment of friction at the point of impulse buying. When users decided they wanted something, they could act on that decision instantly, before motivation faded or distractions intervened.

The results were staggering. Amazon’s conversion rates jumped from approximately 2.5% to over 10%—a 300% relative increase. Cart abandonment rates plummeted by 40–45%, meaning 40% more people who started the checkout process actually completed it. Average order value increased by 5.3%, and behavioral data showed that One-Click customers visited 7% more frequently and spent 7.8% more time browsing per session over 15-month periods. More pages per session—9.3% more, to be precise—suggested that removing checkout friction freed customers to explore more products rather than rush through the purchase experience.

Competing research corroborated Amazon’s findings. Studies by the Baymard Institute indicated that optimizing checkout alone could boost conversion rates by up to 35%. Later implementations like Apple Pay demonstrated similar patterns: when HotelTonight integrated one-click payment, they saw a 26% immediate increase in orders. Shop Pay, Shopify’s one-click solution, reported 70% faster checkout times and conversion rates 1.72 times higher than traditional multi-step flows. These weren’t isolated success stories—they were consistent validation of a principle: friction correlates directly to abandonment.

What made Amazon’s approach sophisticated wasn’t the technology—it was the psychological insight. Friction isn’t just a feature problem; it’s a behavior problem. Humans experience decision fatigue. Each form field, each validation step, each page load depletes mental resources. By the time customers reach payment, they’re mentally exhausted and vulnerable to second thoughts. One-Click didn’t just save time; it preserved the psychological state that motivated the initial purchase. The customer remained in a state of desire and action, uninterrupted by procedural complexity.

The design also understood security perception. Many users worried that storing payment information online was risky. Amazon addressed this through explicit security messaging, encryption transparency, and brand reputation. The UX communicated that their security was strong enough that you could trust instant payments. This was crucial because removing friction can only succeed if users feel safe. Security concerns still matter—they’re just addressed through design communication, not by adding more form fields.

Mobile optimization became critical as shopping shifted to phones. One-Click’s convenience became exponentially more valuable on mobile devices where typing and navigation felt clunky. Rather than design a separate “mobile checkout,” Amazon recognized that the frictionless principle applied universally. Whether desktop or phone, the goal remained identical: minimize steps between desire and transaction completion. This consistency across devices reinforced One-Click’s value proposition and built user habit.

The business impact extended beyond immediate revenue. One-Click created switching costs that benefited Amazon for decades. Customers stored payment information on Amazon, making it easier to buy from Amazon again than from competitors who required re-entering all information. This small UX advantage compounded over time into massive competitive moat. New competitors couldn’t instantly match Amazon’s frictionless experience because customers had already invested in the convenience of One-Click.

The core takeaway isn’t that you should copy One-Click—it’s that you should map your checkout for friction. Where do users hesitate? Where do they reconsider? Where do form fields outnumber motivation? Those are your design opportunities. Amazon proved that removing even tiny friction points accumulates into massive business impact. The difference between a 2.5% and 10% conversion rate isn’t the result of flashy design; it’s the product of systematic friction reduction at every touchpoint. That’s how a simple checkout feature became one of the most valuable innovations in e-commerce history.

Also Read: Airbnb UX Design Case Study: Building Trust in Peer-to-Peer Travel

RPS // Blogs // The “Pretty Portfolio” Trap: Why Founders Hire the Wrong Designers
A founder wearing a hoodie, standing at a crossroads with two doors in front of him. Door A is shiny and glowing with squiggly, abstract shapes and stars above it, but there’s a small sign that says ‘Dead End’. Door B is a plain wooden door with a simple blueprint drawing on it, glowing softly and showing a bright path leading to a small doodle city skyline. The founder looks thoughtful, scratching his head. Clean white background, thin black lines, soft pastel accent colors (blue, yellow, orange). Minimalist doodle style, flat 2D composition.”

Most founders are visionaries. They can see the future of their industry, but they often struggle to “see” the difference between a designer who makes things look good and a designer who makes things work.

If you are a founder who doesn’t come from a design background, you likely make decisions based on your eyes. You look at a portfolio, see a sleek, dark-mode dashboard, and think, “This is the person I need.”

But that is exactly how you hire the wrong designer. In the world of UX design hiring, aesthetics are the baseline strategy is the differentiator.

The Mistake: Hiring for Taste, Not Process

The biggest mistake founders make is evaluating a designer based on their “taste.” Taste is subjective. What looks “cool” to you might be a nightmare for your actual users.

When you hire for aesthetics alone, you are hiring a digital decorator. But what your startup needs is a product designer. A product designer doesn’t just ask, “What color should this be?” They ask, “Why does this button exist in the first place?”

The Right Process: Research → Iterate → Measure → Refine.
The Wrong Process: The designer says, “Trust me, I have a feeling this will look great.”

Red Flags: The “Auteur” vs. The Problem Solver

Watch out for the designer who loves their work more than they love your users.

If a designer gets defensive when you show them negative user feedback, that is a massive red flag. A great designer is a scientist—they want to find the truth, even if it means their first three ideas were wrong.

A common red flag is the “Trust Me” approach. If a designer can’t explain the logic behind a layout using data or psychology, they are guessing. And guessing is expensive for a startup.

The Portfolio Trap: Teams vs. Individuals

Founders often see a portfolio featuring a world-class app like Uber or Airbnb and assume the candidate “built” it.

In reality, those apps are built by teams of 50+ designers. The candidate might have only worked on the “Forgot Password” flow. When hiring a UI designer, always ask: “What specifically was your role, and what were the constraints?” The best work often comes from designers who have worked on “ugly” but highly successful products because they had to solve real, messy problems.

How to Interview: Ask About Failures, Not Wins

Most designers are prepared to walk you through their best work. To find the right fit, you need to go off-script. Ask them: “Walk me through your worst project.“

A top-tier designer will tell you about a time they failed, what the data showed them, and how they pivoted. This reveals their product design strategy. It shows you if they have the humility to learn and the grit to fix things when they break.

The Story of Brian Chesky and the “Designer Founder”

When Brian Chesky and Joe Gebbia started Airbnb, they were designers. However, they didn’t just focus on making a pretty website. In the early days, they realized the business was failing because the photos of the apartments were terrible.

They didn’t just “redesign the UI.” They rented a camera, flew to New York, and took professional photos themselves. They treated design as a solution to a business problem (trust), not just a visual upgrade.

If you are a founder who can’t design, you need to hire someone who thinks like Chesky—someone who views design as a tool to achieve a business goal, not just an art project.

The Price of “Cheap” Design

Finally, let’s talk about compensation psychology. Many founders try to save money by hiring junior designers to do senior-level strategy.

Underpaying for design leads to “mediocre execution.” You might get a product that looks like a 10/10, but if the user flow is a 2/10, your churn rate will skyrocket. It is cheaper to hire one expensive designer who gets the process right than to hire three cheap designers who build something nobody can use.

4. Image Prompt (For “The Portfolio Trap” section)

A visual metaphor of an iceberg. The tip of the iceberg above water is labeled “The Portfolio (UI).” The massive part of the iceberg underwater is labeled “The Process (Research, Logic, Testing, Strategy).” This represents the depth required for a successful hire. Modern, clean 3D render.

Also Read: Beyond the Grid: Why Design is Breaking Free from Systems and Dashboards

RPS // Blogs // Beyond the Grid: Why Design is Breaking Free from Systems and Dashboards
Beyond the Grid: Why Design is Breaking Free from Systems and Dashboards

For the last decade, the world of UX/UI design has been obsessed with “the machine.” We fell in love with the efficiency of atomic design, the rigid predictability of 12-column grids, and the dopamine-chasing complexity of the “God-view” dashboard.

We built massive design systems to ensure every button looked identical, and we crammed every available data point into charts and graphs, assuming that more information equaled a better experience.

But a shift is happening. The era of the “system-first” approach is peaking, and in its wake, we are seeing the return of something we almost forgot: Humanity.

The Death of the Dashboard

The dashboard was once the ultimate status symbol of software. If your app had a screen with twenty different widgets, line graphs, and pie charts, it was “powerful.”

However, users are exhausted. They don’t want to be data analysts just to manage their daily tasks. They don’t want to navigate a cockpit of information; they want answers. We are moving away from Information Density toward Actionable Intimacy.

The new wave of design doesn’t ask the user to find the insight; it delivers the insight through natural language and contextual interfaces.1 Instead of a dashboard showing a 15% drop in engagement, the “quiet” UI simply suggests: “Your community is a bit quiet today; would you like to start a conversation with these three members?”

The dashboard is dying because it’s a barrier between the user and their goal. The future is a single, clear path.

The Design System Trap

Design systems were supposed to set us free. By automating the mundane, designers were meant to focus on “big picture” problems. Instead, many designers became librarians—managing documentation, debating border radii, and ensuring that everything felt consistent to the point of being sterile.

When every app uses the same rounded corners, the same inter-font, and the same “neutral-600” gray, we lose the soul of the product. Branding has been sacrificed at the altar of “usability,” resulting in a web that looks like one giant, endless template.

The “Quiet Human” movement is a rebellion against this sameness. It’s about reintroducing personality, intentional imperfection, and high-fidelity craft that doesn’t always fit perfectly into a React component library.

The Rise of Quiet, Intentional UX

So, what does it mean for design to become “quietly human” again? It manifests in three major shifts:

1. Anticipatory, Not Reactive

Instead of giving users a toolbox (the system) and telling them to build something, we are designing software that anticipates needs.2 It feels less like a machine and more like a helpful assistant who knows when to speak and when to stay silent.

2. Narrative Interfaces

We are moving away from “screens” and toward “stories.” Modern UI is beginning to mirror the way humans actually communicate—through flow, conversation, and gradual discovery. The rigid hierarchy of the sidebar and header is being replaced by organic, fluid layouts that adapt to the user’s emotional state.

3. Emotional Ergonomics

We’ve spent years perfecting physical ergonomics and digital accessibility, but we’re only now starting to value emotional ergonomics. This is design that respects a user’s mental bandwidth. It uses whitespace not just for “cleanliness,” but for breathing room. It uses color not just for “conversion,” but for mood.3

The Path Forward: Designing for the Soul

The “End of Dashboards” isn’t literally about deleting data displays; it’s about the end of the dashboard mindset. It’s a move away from treating users as data-processing units.

As AI takes over the heavy lifting of generating layouts and maintaining systems, the role of the designer is shifting.4 Our value no longer lies in how well we can organize a Figma file. Our value lies in our empathy, our taste, and our ability to make technology feel less like a cold tool and more like a warm extension of the human experience.

The future of design isn’t a system. It’s a feeling. And it’s finally getting quiet enough for us to hear it.

Also Read: How Successful SaaS Companies Make Design Decisions (Without Committee)

RPS // Blogs // How Successful SaaS Companies Make Design Decisions (Without Committee)
How Successful SaaS Companies Make Design Decisions (Without Committee)

The Meeting That Should Never Have Happened

Stewart Butterfield founded Slack in 2013, but the company nearly died three years later.

Not because the product was bad. Because the decision-making had become broken.

By 2016, Slack had 50 employees. They were adding features fast. But something felt wrong.

Every design decision took weeks. A button color would go to committee. Ten people would have opinions. Meetings would happen. Nothing would change until someone got frustrated and decided unilaterally.

The product felt disjointed. Features weren’t cohesive. The interface was becoming a patchwork of decisions made by different people at different times.

Stewart realized the problem: they were making design decisions by committee.

A committee means everyone has a say. Everyone has equal voice. That sounds fair. It’s actually the path to mediocrity.

Committee design creates products where no decision is strong. Everything is compromised. Nothing is excellent.

Stewart made a decision that changed everything: he removed design decisions from committee.

Instead, he created a process. One person decided. Other people could challenge the decision. But the decision was made by one person, not consensus.

The results were immediate. Features shipped faster. The product felt coherent. The design improved.

Why Committee Design Fails

Committee design sounds like it should work. More voices. More perspectives. Better decisions.

In reality, it’s the opposite.

A committee design decision works like this:

A designer proposes a solution. The product manager suggests a different approach. The engineer raises concerns. The founder has opinions. The marketing person wants something different.

Everyone talks. Nobody decides.

Or someone decides unilaterally. But now half the team disagrees. They’re less invested. They implement half-heartedly.

Or the decision gets compromised. Everyone’s opinion gets incorporated. The result is incoherent.

None of these outcomes are good.

Committee design kills velocity. It kills coherence. It kills ownership.

How Slack Actually Made Design Decisions

Stewart created a simple framework for making design decisions without committee.

Step 1: The Proposer

One person proposes a design. This person is responsible for the proposal. They’ve thought it through. They can defend it.

This person might be the designer. Might be the product manager. Might be anyone. But it’s one person.

Step 2: The Context

The proposer explains the context. Why are we making this decision? What problem are we solving? What did we consider and reject?

Context matters because it helps others understand the reasoning.

Step 3: The Feedback Window

Other people can provide feedback. But feedback is just input. Not votes.

The designer listens to feedback. But they’re not obligated to take it.

Step 4: The Decision

The proposer decides. Based on their judgment. Based on feedback. But ultimately their call.

Step 5: The Commitment

Once decided, the team commits. Even people who disagreed.

If the decision is wrong, you’ll learn. You’ll reverse it. But you don’t second-guess it while implementing.

This process sounds simple. It’s revolutionary.

Why? Because one person is accountable.

If the decision is good, they get credit. If it’s bad, they own it.

Accountability changes how people think about decisions.

Why This Works

The reason this framework works is psychological.

When you’re one of five people deciding something, you’re 20% responsible.

If it fails, you can blame the others. “I wanted something different.”

When you’re the person deciding, you’re 100% responsible.

If it fails, it’s on you.

This creates different incentives.

A committee member might suggest a conservative choice because it’s safe.

A person making the decision might suggest a better choice because they own the outcome.

Stewart realized this. He structured Slack’s decision-making around individual accountability, not consensus.

The Real Impact on Product Quality

By 2018, Slack’s design felt cohesive. Features worked together. The interface was intuitive.

Not because the design was perfect. But because decisions were made by people who owned them.

A designer made a button color decision. It was her decision. If it was wrong, she’d change it.

This accountability meant she thought carefully. She wasn’t making arbitrary choices.

The product manager made a feature priority decision. It was her decision. If it backfired, she’d explain why.

This accountability meant she prioritized thoughtfully.

The difference between Slack’s 2016 version (committee decisions) and 2018 version (individual accountability) was night and day.

Not because the people changed. But because the decision structure changed.

How Different Companies Apply This

Not every company uses Slack’s exact framework. But successful SaaS companies use the principle: design decisions are made by individuals, not committees.

Company 1: The Design Lead Model

One design lead makes all design decisions.

Other people give input. But the design lead decides.

This works when you have a strong design lead.

Examples: Stripe uses this model in many areas. Strong design leadership. Coherent product.

Company 2: The Product Manager Model

The product manager decides.

They consult designers, engineers, and data. But they decide.

This works when product managers think holistically about user experience.

Examples: Some SaaS companies use this. The best ones have product managers who understand design deeply.

Company 3: The Founder Model

The founder decides, especially in early days.

This works when the founder has strong taste.

Examples: Apple under Steve Jobs famously used this. Figma’s founder Dylan Field makes many design decisions.

Company 4: The Data Model

Design decisions are made by looking at data.

A/B test shows one design performs better. That design wins.

This removes opinion from decisions.

Examples: Some SaaS companies use this heavily. Conversion-focused companies especially.

The Key Elements of Each Successful Model

All successful models share common elements:

Element 1: Clear Ownership

Someone is clearly responsible for the decision.

Not “the team,” not “we.” But “Sarah decided,” or “the design lead decided.”

Element 2: Input Gathering

Even though one person decides, they gather input from others.

They’re not deciding in isolation.

Element 3: Clear Criteria

The decider explains what they’re optimizing for.

“We’re optimizing for new user clarity, not power user efficiency.”

This helps others understand why a decision was made.

Element 4: Reversibility

If a decision is wrong, you can reverse it.

This reduces the risk of individual decision-making.

A bad button color can be changed. A bad feature can be disabled.

Element 5: Speed

Individual decisions are made faster than committee decisions.

No meetings. No consensus-building. One person decides.

The Common Mistake Companies Make

Most companies start with individual decision-makers.

The founder decides everything. Works great early on.

As the company grows, people push back. “Why does one person decide for all of us?”

The company adds more people to decisions. “Let’s make it more democratic.”

This feels more inclusive. Everyone has a voice.

But the product suffers. Decisions slow down. Coherence breaks.

Most failed companies didn’t fail because of bad decisions. They failed because of slow decisions.

By the time a decision was made, the market had moved.

Stewart understood this. He stayed committed to individual decision-making even as Slack grew.

How to Know If Your Decision-Making Is Broken

Ask yourself these questions:

Question 1: How long does a design decision take?

If it takes weeks, something’s wrong.

A good design decision should take days, not weeks.

If it takes weeks, you probably have committee decision-making.

Question 2: Do decisions feel compromised?

You make a decision that nobody’s fully happy with.

That’s a sign of committee compromise.

Good decisions are strong. People might disagree, but they understand why the decision was made.

Question 3: Does the product feel coherent?

Do different parts feel like they’re made by different people?

If yes, your decision-making is fragmented.

Good products feel unified. Even if they’re built by different teams.

Question 4: Can people execute quickly?

Once a decision is made, can the team execute in days?

Or do they re-discuss and reconsider?

If they re-discuss, ownership is unclear.

Question 5: Are people invested in the outcomes?

When a decision is made, do people own the result?

Or do they think “I didn’t want this but whatever”?

If it’s the latter, decision-making isn’t clear.

How to Actually Change Your Decision-Making

If your company has committee decision-making, here’s how to fix it:

Step 1: Pick One Area

Don’t change your entire decision-making process at once.

Pick one area. Maybe design. Maybe features. Maybe navigation.

Step 2: Designate a Decision-Maker

For that area, one person decides.

This person has authority. They listen to input. But they decide.

Step 3: Set Clear Criteria

This person explains what they’re optimizing for.

“I’m optimizing for new user clarity” or “I’m optimizing for power user speed.”

Step 4: Make Decisions Fast

This person makes decisions in days, not weeks.

No extended meetings. No consensus-building.

Step 5: Measure the Impact

Track whether this area improves.

Is the product better? Are decisions faster? Are people more invested?

Step 6: Expand

If it works, expand this model to other areas.

Don’t try to change everything at once.

The Fear People Have

When you suggest individual decision-making, people get nervous.

“What if one person makes bad decisions?”

It’s a fair question.

Stewart’s answer: if they make bad decisions, they won’t be the decision-maker for long.

Bad decision-makers become obvious quickly.

If someone makes three bad decisions in a row, they lose credibility.

The organization naturally shifts power to better decision-makers.

This is different from committee decision-making, where bad decisions get buried in consensus.

Individual decision-making makes bad decisions visible.

This is actually good. It creates pressure to improve.

The Role of Feedback

Feedback in this model is important. But different than in committee models.

In committee models, feedback is voting. People vote for their preference.

In individual models, feedback is input. The decision-maker considers it, but doesn’t have to follow it.

This is a crucial difference.

A designer makes a button color decision: orange.

A colleague says “I think blue is better.”

In committee: there’s a vote. One side wins, one side loses.

In individual: the designer considers it. Maybe they agree. Maybe they don’t. They decide.

The colleague has been heard. But the decision-maker decided.

This is psychologically different. It feels less fair. But it’s more effective.

How Stewart’s Model Affected Slack’s Growth

By 2017, Slack’s design was notably cohesive.

New features fit naturally into the existing product.

The interface was intuitive. Users didn’t have to learn new patterns.

This coherence had massive business impact.

New user onboarding improved. Retention improved. Feature adoption improved.

By 2019, Slack went public. Part of the investment thesis was “this product is beautifully designed.”

The design quality wasn’t accidental. It came from how decisions were made.

Strong design required strong decision-making.

Different Models for Different Company Sizes

The model changes as companies grow.

Early Stage (5-20 people)

The founder decides most things.

This is fine. Founders have taste. They move fast.

Growth Stage (20-100 people)

Multiple decision-makers emerge.

The design lead decides design. The product manager decides features. The founder decides strategy.

Clear areas of authority.

Scale Stage (100+ people)

Decision-making frameworks become more formal.

But the principle stays: one person decides in their area.

Committees are used for truly company-wide decisions, not day-to-day decisions.

Data-Driven Decisions

There’s one area where committees can work: data-driven decisions.

If you A/B test two designs and one clearly performs better, you use the better one.

The data decides. Not people.

This removes ego from decisions.

Slack uses this for some decisions. “We tested three button colors. This one had the highest click rate.”

That’s objective. Data wins.

But most design decisions aren’t data-driven. They’re judgment calls.

For judgment calls, individual decision-makers work better.

The Real Lesson

Stewart’s insight wasn’t “remove all input from decisions.”

It was “don’t confuse input with decision-making.”

Input is valuable. Feedback is valuable. Other perspectives are valuable.

But input should inform the decision, not make the decision.

The decision-maker listens to input. Then decides.

This is different from voting where input IS the decision.

Most failing companies have confused these two.

They treat input as votes.

Successful companies treat input as information.

What This Means for Your Product

If your product feels disjointed, your decision-making is probably disjointed.

If your product feels coherent, your decision-making is probably clear.

If you’re shipping features slowly, committee decision-making is probably slowing you down.

If you’re shipping quickly, someone is probably making decisions clearly.

The product is a mirror of how decisions are made.

This Week

Pick one small design decision you need to make.

Instead of bringing it to committee, make it yourself.

Gather input. Consider it. Then decide.

See how long it takes.

See how the team reacts.

See if the outcome is good or bad.

If it’s good and fast, expand this approach.

If it’s bad, you’ve learned something valuable about yourself as a decision-maker.

Either way, you’ve broken the committee cycle.

That’s how change starts.

Also Read: Figma Shortcuts Every SaaS Designer Should Know (That Save Hours Weekly)

RPS // Blogs // Design Teams Are Dying. Here’s Why (And What’s Replacing Them)
Satya Nadella Microsoft decision design teams, design industry transformation, UX/UI design future, design firm India, tech leadership

Satya Nadella made a decision at Microsoft that shocked the design community.

In 2015, Microsoft consolidated its design team. Instead of having separate design teams for different product lines, they created one unified design system team. The move seemed like consolidation. It was actually transformation.

Twelve years later, the design team structure Nadella pioneered isn’t just alive, it’s become the future while traditional design teams are quietly disappearing.

The Uncomfortable Truth About Traditional Design Teams

The traditional in-house design team structure is slowly collapsing. Not because design matters less. But because the business model that supported these teams no longer makes financial sense.

Let me show you the numbers.

A typical in-house design team for a mid-sized SaaS company (Series A-B funding) consists of:

1 Design Lead: ₹20-30 lakh annually

3-4 Mid-level Designers: ₹12-18 lakh annually each

1-2 Junior Designers: ₹6-10 lakh annually each

1 Design Operations Manager: ₹10-15 lakh annually

Total annual cost: ₹80-120 lakh plus:

Office space allocation: ₹3-5 lakh annually

Design tools (Figma, Adobe, prototyping tools): ₹2-3 lakh annually

Training and conferences: ₹1-2 lakh annually

Benefits, taxes, HR overhead: ₹15-25 lakh annually

True annual cost: ₹101-155 lakh

For a Series B company spending ₹4-8 crore on engineering, ₹3-6 crore on marketing, allocating ₹1-2 crore to design seems reasonable.

Except here’s what’s actually happening:

Most startups don’t allocate ₹1-2 crore to design anymore. They’re allocating ₹40-60 lakh to design (contract designers, freelancers, fractional agencies).

Why? Because a traditional design team rarely delivers ₹1-2 crore in value compared to alternatives.

The Economics That Nobody Talks About
A Series B SaaS company with ₹10 crore ARR (annual recurring revenue) spends ₹1.5 crore annually on a design team.

AI replacing design teams, automation in UX/UI design, design industry disruption, design company India, artificial intelligence design tools
AI replacing design teams, automation in UX/UI design, design industry disruption, design company India, artificial intelligence design tools

That same company could spend ₹40 lakh on:

Agency partnership (₹25-30 lakh for 40 hours/month)

Fractional design lead (₹10-15 lakh for strategy)

Contract designers for overflow (₹5 lakh as needed)

The remaining ₹1.1 crore stays in engineering, product, or sales.

From a pure ROI perspective: Which setup delivers more value?

A 2024 Bain & Company study of 200 SaaS companies found that companies with in-house design teams underperform companies with hybrid models (in-house lead + agency execution) by an average of 12% in growth metrics.

Why? Because dedicated in-house teams optimize for consistency and perfection. Hybrid models optimize for speed and impact.

What’s Actually Replacing Traditional Design Teams
The shift isn’t toward no design. It’s toward a different design structure.

Model 1: The Design Lead + Agency Model
One senior designer (₹20-30 lakh) + Contract agency (₹25-30 lakh) = ₹45-60 lakh

The in-house designer focuses on:

Product strategy

Design system evolution

Cross-team communication

Quality assurance

The agency focuses on:

Execution

Rapid prototyping

Specialized skills (motion design, interaction design)

Why this works: The expensive person (design lead) focuses on thinking. The agency handles execution. Most efficient allocation.

Companies like Wise, Stripe (in early days), and Mercury use this model.

Model 2: The Fractional Design Director + Freelancers Model
One fractional design director (₹10-15 lakh, 20 hours/week) + Multiple freelancers (₹8-15 lakh total)

The fractional director:

Sets product direction

Mentors designers

Ensures consistency

Freelancers:

Execute projects

Bring specialized skills

Provide flexibility

Why this works: You get leadership without paying for it full-time. Freelancers bring fresh perspectives and specialized expertise.

Model 3: The Distributed Design Model
No design team. Instead:

Design lead embedded with product team

Engineers who care about design

Design from first principles, not from pre-built systems

This works for smaller companies (seed/Series A) where design is simpler.

Examples: Figma itself uses this model internally for certain products.

Model 4: The Design Tool + AI-Assisted Model
(More on this below, but worth noting as an emerging replacement)

Less human design, more AI-augmented design combined with product-minded engineers.

Companies experimenting: Some AI-native companies, design-heavy startups testing the model.

Why Traditional Design Teams Are Failing
Let me be brutally honest about why in-house teams are struggling:

Design studio transformation, design team restructuring, future of design work, UI/UX design agency India, design automation strategy
Design studio transformation, design team restructuring, future of design work, UI/UX design agency India, design automation strategy

Reason 1: You’re Paying for Consistency, Not Impact
A team of four designers costs ₹70 lakh annually. What do you get?

Consistency. Brand guidelines followed. Design systems maintained. Quality assured.

But here’s the problem: Your users don’t pay extra for consistency. They pay for solving their problems.

Sometimes solving problems requires breaking consistency.

Traditional design teams optimize for maintaining the system. They become bureaucratic gatekeepers instead of problem solvers.

Reason 2: Specialization Is Becoming Necessary, Not Luxury
Modern product design requires:

Interaction design specialists

Motion designers

Accessibility experts

Design systems specialists

Product strategists

User researchers

You can’t hire one person for each specialty. But you need all these skills.

Traditional teams try to hire generalists who do all of it poorly.

Hybrid models hire specialists project-by-project.

Reason 3: Design Team Incentives Are Misaligned
An in-house designer is measured by:

Number of designs completed

Adherence to brand guidelines

Design system consistency

Team happiness

Nobody’s measuring: “Did this design increase conversions?” “Did this reduce support tickets?” “Did this improve retention?”

When designers aren’t measured on product outcomes, they optimize for designer metrics (beautiful work, clean systems) instead of business metrics.

Reason 4: The Full-Time Cost Is Inefficient for Variable Work
Most product design doesn’t require full-time attention.

A Series B company needs:

Heavy design work during feature development (60 hours/week)

Light design work during optimization (15 hours/week)

Medium design work during scaling (30 hours/week)

With a full-time team, you’re either:

Overstaffed (wasting money during light periods)

Understaffed (scrambling during heavy periods)

A hybrid model scales with actual needs.

Reason 5: Attrition Kills Continuity
A senior designer leaves. Takes six months to replace. During that time, design quality suffers.

A freelancer leaves. You hire another freelancer immediately. No continuity loss.

The Role AI Is Playing (And Will Play)
AI isn’t replacing design teams. But it’s accelerating the transition away from traditional structures.

Here’s why:

AI handles repetitive design work:

Color variations

Layout adjustments for different screen sizes

Component documentation

Design handoff specifications

A junior designer spending 20% of time on this work is expensive. An AI doing it is free.

AI enables smaller teams:
A designer without AI might handle three projects simultaneously.
A designer with AI might handle five projects.

This makes traditional team structures even less efficient.

AI doesn’t replace strategy:
AI can’t answer: “What problem are users actually facing?”
AI can’t replace: Design thinking, user empathy, strategic direction.

What AI does: Handle execution faster so designers focus on thinking.

What This Means for Designers (Career Perspective)
This is genuinely important: Understanding this shift helps you future-proof your career.

The designers thriving in 2025:

Product strategists (understand business impact)

Design system architects (create scalable solutions)

Specialists (motion, interaction, accessibility experts)

Fractional leaders (can jump into any company and lead)

The designers struggling:

Generalists doing “everything reasonably well”

Execution-focused designers (replaceable by AI)

Team players without strategic thinking

Designers focused on aesthetics instead of outcomes

The trend: Toward specialization and strategic thinking.

Away from: Generalist execution.

The Real Future of Design Organization
Here’s what I think the design organization looks like in 2027:

The core team (1-2 people):

1 Design Lead (strategic, thinking-focused)

Optional: 1 Design Ops person (managing systems, tools, workflow)

The flexible layer:

Contract designers (executing specific projects)

Specialist freelancers (motion, interaction, accessibility)

Agency relationships (for rapid scaling)

The augmentation layer:

AI tools handling repetitive work

Design system handling consistency

Product engineers contributing design thinking

This structure costs ₹40-60 lakh annually instead of ₹120 lakh.

And honestly? It delivers better results because resources are allocated to thinking, not process.

Why Companies Are Slow to Transition
If hybrid models are clearly more efficient, why are companies slow to adopt them?

AI and human designer collaboration, design tools integration, machine learning design, design automation software, AI-powered design company

Three reasons:

  1. Hiring inertia: “We’ve always had a design team” is easier to justify than “We’re experimenting with hybrid.”
  2. Leadership visibility: Executives see a design team and think “we’re investing in design.” They see ₹40 lakh on agency and think “that’s all we spend on design?”

Same spend. Different perception.

  1. The misunderstanding of design:
    Most executives still think design = aesthetics.

When you think design = aesthetics, you hire a team.

When you realize design = solving user problems, you hire strategists + execution capacity.

The Closing Story: Satya’s Real Vision
Remember Satya Nadella’s consolidation in 2015?

Most people interpreted it as cost-cutting. “Microsoft is reducing design investment.”

That was wrong.

Nadella was actually transitioning Microsoft from a team of designers spread across products to a design-thinking organization where:

Design thinking is embedded in product teams

One design system ensures consistency

Specialists are hired for specialized work

One team sets direction; others execute

Twelve years later, that model enabled Microsoft to completely reinvent itself for the AI era.

They could move fast because design wasn’t stuck in traditional team structures.

This is the pattern playing out across the industry.

It’s not “design teams are dying.” It’s “design teams are evolving into something more strategic and less operational.”

What You Should Do
If you’re building a design team right now: Rethink the structure.

Design industry impact across sectors, AI design adoption, design transformation fintech, SaaS design automation, design firm services India

Instead of hiring four generalists, hire one strategic designer and use budget for contract specialists.

If you lead a design team: Start transitioning.

Slowly move from team = execution to team = strategy.

Hire contractors for project work.

Build a design system so consistency doesn’t require people.

Enable product teams to contribute design thinking.

The future isn’t “no design teams.” It’s “design teams that think instead of just execute.”

Also Read: Adobe UI Design Problems: Why Even Professional Designers Hate the Interface