Microsoft Senior Product Designer Interview Preparation Guide

Situation & goal
I'd run a tightly scoped usability study to validate the friction, surface real user pain, and produce measurable options that balance UX and business needs.

Study design (lean + rigorous)

• Recruit 8-12 target users (a mix of power/novice).
• Moderated task-based testing on a clickable prototype focused only on the contentious feature (5-7 tasks).
• Collect task success, time-on-task, SUS (System Usability Scale, a 10-question survey producing a 0-100 usability score), and qualitative pain points.
• Add an A/B micro-test variant if feasible: current-stakeholder version vs. a lower-friction compromise.

Negotiation tactics with stakeholders

• Present compact evidence: impact on key metrics and verbatim quotes.
• Use risk/benefit framing: show business uplift vs. projected churn or support cost.
• Propose a time-boxed pilot or feature-flagged rollout to limit exposure.
• Offer measurable KPIs and rollback criteria to reduce perceived risk.

Compromise solutions

• Progressive disclosure: hide complex controls behind an "advanced" toggle.
• Inline help and contextual defaults to reduce cognitive load.
• Phased feature with analytics gating (only to a small percent / power users).

Phased-rollout metrics & gates

• Quantitative: task success rate + completion time, conversion/activation, support tickets, retention delta.
• Qualitative: NPS (Net Promoter Score, a 0-10 likelihood-to-recommend survey used as a loyalty proxy) snippets, top 3 usability complaints.
• Gates: advance to 25% if task success improves by 10% or more and support tickets stay neutral; advance to 100% only if conversion and retention are stable or improved.

Documenting residual risks

• Create a concise risk register: description, severity (UX/business), likelihood, mitigation, owner, and rollback plan.
• Attach raw data snapshots, user quotes, and the A/B results.
• Share as a short one-pager for stakeholders and add it to the product PRD.

Result: an actionable compromise, clear success criteria, and minimized organizational risk while honoring stakeholder priorities.

Direct answer

Treat the cluttered feeling as a spacing problem before assuming it's a content problem: first check the micro whitespace directly around and within individual elements, then check the macro whitespace between whole sections, and fix the relationship between those two scales before removing anything from the screen.

Structured elaboration

• Micro whitespace is the small-scale spacing attached to a single element or a tight group of elements, the padding inside a button, the gap between a label and the input it belongs to, the space between an icon and its text. It's what makes one element feel resolved on its own.
• Macro whitespace is the large-scale spacing that separates distinct groups or sections from each other, the margin around a card, the gutter between a sidebar and the main content, the breathing room around a page's header. It's what tells the eye where one idea ends and the next one starts.
• The actual diagnosis: a screen usually feels cluttered not because any single element is wrong, but because the gap between related things and the gap between unrelated things are too close to the same size. When a macro gap is barely bigger than a micro gap, the eye can't tell where a group ends, so the whole screen reads as one undifferentiated block, which is what "cluttered" usually means even when every individual button and label looks fine in isolation.
• The fix is a spacing scale (a fixed set of allowed spacing values, for example 4, 8, 16, 24, 40px) applied so that each larger relationship in the layout gets a clearly bigger gap than the smaller relationship it contains, rather than picking spacing ad hoc per screen.
• Why the steps on that scale are spread out: adjacent steps on 4, 8, 16, 24, 40 are 2x, 2x, 1.5x, and 1.67x apart, so every jump is at least 1.5x. That spacing between the steps is the whole point. It guarantees that any two adjacent values are far enough apart to be seen as different tiers rather than as a slightly bigger version of the same gap, which is exactly the perceptual failure the diagnosis above describes. A scale with 8, 10, 12, 14 in it would satisfy the letter of "use a scale" and still produce a cluttered screen.

Worked example

A settings screen has the label "Email notifications" sitting 8px above its toggle (that's the micro gap, and 8px is the right value for it), but the whole row is also separated from the next unrelated row, "Push notifications," by that same 8px. Because the row-to-row gap matches the micro gap exactly, there is nothing to distinguish "these two things belong together" (the label and its own toggle) from "these are two separate settings" (this row and the next one), so the eye reads the entire list as one undifferentiated block, and the screen feels cluttered even though no individual row is badly designed. The fix: keep the micro gap (label to its own control) at 8px, exactly where it started, since that relationship was never the problem, but grow the gap between separate settings rows from 8px to 24px, and grow the gap between whole sections, "Notifications" versus "Privacy," to 40px with a heading or divider marking the boundary. That 8 / 24 / 40 progression steps up by 3x and then by a further 1.67x, so each tier is unmistakably bigger than the one nested inside it, and someone can glance at the screen and immediately tell which items are grouped and which aren't, without touching a single element's own design.

A second, smaller example on the same screen: a product card had only 2px between the price and the "Add to cart" button, so the two visually fused into one shape. Increasing that internal micro gap from 2px to 8px, the same value used for label-to-control elsewhere on this screen, separates them and resolves the crowded feeling without changing type size, color, or content at all. If the price and the button are meant to read as two distinct blocks rather than one price-and-action unit, the correct value is the next step up, 16px, and that choice is itself the grouping decision, not a matter of taste.

What the choice is not is 12px. 12 is not on the scale, and reaching for an off-scale value because 8 felt slightly tight and 16 felt slightly loose is exactly how a spacing system dies: once one screen has a 12, the next has a 10, and the tiers stop being distinguishable again. If a screen genuinely needs a value the scale doesn't have, the fix is to change the scale once, deliberately, for everyone, not to bypass it on one card.

Trade-offs and pitfalls

Adding whitespace uniformly everywhere doesn't fix clutter, it just makes the whole page bigger while the underlying grouping problem, gaps that don't reflect real relationships, stays exactly as confusing. What actually matters is the ratio between the micro and macro scale, not the absolute number of pixels, which is why the scale is built from multiplicative steps rather than evenly spaced ones. Cutting content is often the first instinct when a screen feels busy, but if the real problem is inconsistent spacing, removing an element just repeats the same ungrouped feeling on a shorter page. And macro whitespace can be overdone too: separating related sections with too much space can make a screen feel disjointed or force excessive scrolling, so the goal is a deliberate ratio, not maximum space everywhere. A coarse scale also has a real cost, since sometimes 8px truly is tight and 16px truly is loose for one specific case; the discipline is to accept the nearest step and keep the system legible rather than to win that one card and lose the tiering everywhere else.

Direct answer. Start from the capacity: derive what fits, define the one job the MVP (minimum viable product: the smallest product that tests the core idea) must prove, sort features with MoSCoW, and agree the cut order before work starts. Then explain cuts in terms of the goal, not preferences.

Capacity (illustrative: 2 engineers, 6 weeks). 2 x 6 = 12 person-weeks. Keep a 20% buffer (time held back for surprises): 12 x 0.2 = 2.4, leaving 9.6 person-weeks to plan.

MoSCoW (Must, Should, Could, Won't have this time; an approach from rapid application development, a style of building software in short cycles) with estimates, for a community app where people post and follow each other:

Musts total 8.0, plus the Should 1.5 gives 9.5, within 9.6. Coulds (2.0) are not in the committed plan; they only start if we are ahead. Check the Must share against the DSDM (Dynamic Systems Development Method) guideline. MoSCoW was created by Dai Clegg in 1994 for rapid application development and is used heavily in DSDM, whose published guidance is typically no more than 60% Must Have effort, measured against the planned effort for the timeframe (Won't items excluded), with around 20% Could Have effort as contingency. Planned effort here is 8.0 + 1.5 + 2.0 = 11.5 person-weeks, so Musts are 8.0 / 11.5 = 69.6%, Shoulds 13.0% and Coulds 17.4%. That is above the 60% guideline, so this plan is at the risky end: confidence that every Must lands is lower than DSDM recommends. (Against the whole 12 person-weeks the Musts are 67%, and against the 9.6 plannable weeks 83%, but neither is the basis the guideline uses.) The 2.4-week buffer is my own addition, not part of DSDM's rule, and it does not fix the ratio; the fix is to narrow Musts before the build starts, for example a simpler profile (1.0 instead of 1.5) and a chronological feed (1.5 instead of 2.0), which brings Musts to 7.0 of 10.5 planned (66.7%), closer to the guideline though still above it, and to keep the buffer for the surprises that remain.

What to cut first if it slips. Reverse order of value. Because the Coulds are not in the committed plan, the first cut is simply not starting them (notifications, then analytics beyond one launch event) and spending that time on the buffer. If the slip continues, cut comments next, keeping likes only. Musts are never cut; if even they slip, move the date or the launch audience, not quality of safety features like report and block.

Choosing in/out. I use a PR/FAQ (a press release and customer FAQ written as if launched). Write the headline first, for example: "Neighbours can now sign up, post, follow each other, see a feed and stay safe with report and block." If a feature is not needed to write that headline, it is not a Must; direct messages do not appear in it, so they are a Won't. Another way to cut is to serve one segment first. Suppose the app has moderators (volunteers who review reports), power users (who post daily) and casual users (who mostly read). Pick the segment whose need is sharpest, for example moderators, who need report and block to keep a new community safe, and give the others a clear later path.

Explaining cuts. Show the table and the rule: "This is what the first version must prove. Each cut buys the buffer that protects the date." Give the owner of each Won't a date to revisit.

Direct answer. When stakeholders prioritize speed or visual polish over accessibility, the most effective move is reframing the conversation around a specific, scoped trade-off rather than an abstract "we should care about accessibility" appeal, proposing a minimal viable accessibility improvement that fits the existing timeline instead of demanding the full fix immediately.

Framing the trade-off. State the specific, concrete cost of not addressing it ("this custom dropdown will be completely unusable for anyone navigating by keyboard alone or with a screen reader, a mainstream assistive-technology workflow, not a rare edge case") rather than a general values statement; pair it with a specific, bounded ask, since a stakeholder under time pressure responds better to "can we ship the native <select> now and revisit the custom design after launch" than to "we need to fix all of this before we ship."

Stakeholders to consult and measurable criteria. Loop in whoever owns the launch timeline (to find genuinely available slack) and whoever owns technical risk (to confirm the minimal fix is actually low-cost); measurable criteria might be a specific WCAG success criterion the current approach fails, or a quick keyboard-only test recording that makes the problem concrete and undeniable rather than abstract.

A worked example. A checkout flow launching in one week has a custom-styled radio-button group with no visible focus indicator. The minimal viable fix (adding :focus-visible styles, roughly an hour of work) ships in the current timeline; the larger accessibility pass on the whole checkout flow's information architecture gets scoped as a fast-follow ticket with an owner and a target sprint, not left as an unowned "someday" item that never gets picked up.

Trade-offs and pitfalls. Insisting on the complete fix when the team is genuinely timeline-constrained often results in accessibility being cut entirely rather than partially addressed, since an all-or-nothing ask gives a time-pressured stakeholder only two choices, ship broken or slip the deadline, and slipping the deadline usually loses; a scoped minimal-viable-fix ask that still meaningfully reduces harm, with the larger fix genuinely scheduled rather than vaguely deferred, gets more real accessibility improvement shipped over time than a rejected all-or-nothing demand.

Direct answer

Once you know a partner function is measured on something different from you (speed versus quality or risk, for example), you scope your asks to be small and cheap under their metric, and you change what "status" means when you talk to them: short, action-oriented signals instead of the detailed risk narrative you'd give your own stakeholders. You're not changing what you need, you're changing how you package it so it doesn't read as a tax on the thing they're rewarded for.

Structured elaboration

1. Diagnose the incentive, don't assume it. Confirm what the partner function is actually measured on (deploy velocity, ticket close time, uptime, cost) rather than inferring it from how they push back. Different sub-teams within the "same" function can be measured differently.
2. Scope the ask to the smallest unit that gets you what you need. If they're speed-measured, don't ask for a broad, standing review of everything; ask for a narrow, well-bounded check on the specific surface that carries the risk you actually care about, and let everything else pass without friction.
3. Translate the ask into their currency. Instead of framing a request around your risk language, frame it around what it costs (or saves) them in their terms: incident response hours avoided, rework avoided, a compliance gate they'd otherwise hit later and more expensively.
4. Change the shape of status, not just the ask. For a speed-measured partner, give a compact signal (blocked/not blocked, a count, a single risk flag) they can act on in seconds. Save the fuller narrative for your own stakeholders who need the detail. Sharing the same long-form update with both audiences under-serves the partner who needs to move fast.
5. Keep a floor. Adapting your ask to their incentive has a limit: there's a minimum you can't compromise below without failing your own mandate. Know that floor before the conversation so "scoping down" doesn't quietly become "giving up the requirement."
6. Revisit as trust builds. Early asks are necessarily narrow and low-trust. As the partner sees your asks are well-scoped and your status updates are reliable, you can often widen the ask (a slightly broader review surface, more lead time) because they've learned you're not going to slow them down for nothing.

Worked example

A platform team is measured on release velocity; a security-minded partner function is measured on defect and incident rates. Rather than asking the platform team to route every change through manual security review (a direct tax on their velocity metric), the ask is scoped to only changes that touch a named risk surface, such as authentication or payment code. Everything else ships without added friction. Status to the platform team is a single weekly line: "2 changes in the review queue, 0 blocking, both cleared by Thursday." The fuller write-up, with rationale and residual risk, goes to the security function's own leadership, not to the platform team, because that's not the audience that needs it to act.

Trade-offs & pitfalls

• Pitfall: scoping the ask down so far it stops actually managing the risk it exists to manage. Know your floor before you negotiate.
• Pitfall: assuming the incentive instead of confirming it. Guessing wrong (e.g., treating a team as purely speed-driven when they're also on the hook for a compliance metric) leads to asks that miss what would actually land.
• Pitfall: sending the same status update to every audience. It either over-informs the speed-measured partner (who tunes it out) or under-informs your own stakeholders (who need the detail to make decisions).
• Senior differentiator: treating the ask size and the status format as things you design deliberately around the incentive gap, and revisiting that design as trust changes, rather than a fixed communication style you use with everyone.

Direct answer

I serve both with a safe default for everyday users and progressive disclosure for power users: the common path is simple and works out of the box, and fine-grained control lives one step deeper (an "Advanced" area, a rule editor), with guardrails. I decide per control, using who needs it, how often, and how safely it can be hidden.

Decision test for each requested control

1. Is a real job blocked without it? Ask power users what they are trying to achieve, not which control they want.
2. Can the default path stay unchanged? If adding it clutters the main screen, it goes behind a deeper layer.
3. Is it safe to misconfigure? If a wrong setting can cause harm, add a preview, a confirmation, and an undo.
4. How many people need it? Check usage evidence and who the power users are (revenue, influence, advocacy), but do not let the loudest segment set the default.
5. Can a preset do the job? Presets cover most of the need without exposing every knob.

Worked example (illustrative): alert rules in a monitoring product

Everyday users get three presets: "Recommended", "Quiet" and "Everything". Power users get a rule editor behind "Advanced" with a preview of how many alerts the rule would have sent last week and an undo for edits.

Five requests: one on the main screen, three behind Advanced, one declined in favour of a safer route. Measure the share of everyday users who keep the default and whether power users complete their jobs without support.

For an AI-based product

Expose a few outcome-level choices (for example a "creative" versus "precise" mode) as the default, and keep model-level settings in an advanced layer.

Pitfalls

• Settings sprawl: every extra control is a decision for every user.
• Hiding power features so deeply that experts leave.
• Treating the loudest segment as the whole user base.
• No safety net on advanced controls.

Direct answer

Present the same decision three times in three currencies: outcome and trade-off for the Product Manager, scope and risk for the Engineering Lead, cost and strategic bet for the VP. The facts stay identical across all three; only the framing changes.

Structured elaboration

Short structure (about 5 minutes total):

1. Decision summary (30s): state the choice and the problem it solves.
2. Evidence (1-2 min): the data or user signal behind it.
3. What it looks like / how it works (2-3 min): a walkthrough, demo, or diagram.
4. Implementation and cost (1-2 min): effort, timeline, risk.
5. Next steps (30s): what happens after this meeting, and the rollback path if it doesn't work.

Product Manager: emphasize the outcome bet and the trade-off. What metric should move, and what are we giving up to try it. PMs need to know if this is reversible and how you'll know it worked.

Engineering Lead: emphasize scope and risk. What gets simpler, what gets harder, what needs a dedicated sprint or a design review before it can ship.

VP of Product: emphasize cost against a metric they already track, and the size of the bet. A VP needs enough to say yes or no in 30 seconds, plus a rollback path so "no" isn't the safe default.

Scaling past three audiences. The same discipline holds when the room grows: absorbing a richer variant of this same ask, explaining the same feature to four audiences at once (engineers, executives, UX, and support), the structure above doesn't change, you add one point per added group. UX needs to know whether the change still fits the design system's existing states and patterns. Support needs to know the one new failure mode they'll see in tickets and how to triage it. The discipline that holds at three audiences, same facts, one owns-the-outcome line per group, holds at four.

Worked example

Decision: replacing a multi-step account-setup wizard with a single inline form.

To the PM: "We think the inline form raises setup completion, because the wizard's biggest drop-off is step 2 of 4. We're betting the shorter path outweighs losing the step-by-step guidance, and we've scoped an A/B test to confirm before a full rollout."

To the Engineering Lead: "This removes three of the four wizard screens' state management, so it's a net simplification. But validation now has to happen inline instead of per-step, and we need about one sprint for the accessibility pass on the new error states before this ships."

To the VP: "This is roughly one engineer-sprint against a metric we already track, setup completion rate, with a rollback path if the A/B test comes back flat."

Extending to UX and Support (the four-audience version): UX gets "does this still meet the design system's error-state and focus-order patterns, or do we need a variance." Support gets "the one new failure mode you'll see in tickets is inline validation blocking submit without an obvious reason, here's how to triage it."

Trade-offs & pitfalls

The failure mode is telling the VP "low risk" while telling engineering "we're not fully sure the accessibility pass fits in a sprint." That's not audience-tailoring, it's two different claims, and it surfaces the moment the two rooms compare notes. The other common pitfall is letting the executive's 30-second version drop the one caveat that would actually change their decision (needing a rollback plan, or a dependency on another team) purely to keep the pitch tight. Keep the caveat, cut the sentence around it instead.

Direct answer
Keep exactly one source of truth (semantic design tokens) and generate platform-specific artifacts from it through explicit, documented adapters, rather than hand-maintaining three parallel component libraries; let components diverge in native interaction details (navigation, gestures, haptics) while the tokens that drive their visual identity stay identical by construction.

Structured elaboration

Token mapping strategy

• Define tokens semantically (color.surface.primary, space.md, elevation.1), never as raw platform values, so the same token name means the same intent everywhere even when its rendered value differs.
• A build pipeline (Style Dictionary or equivalent) compiles the semantic token source into CSS custom properties for web, a Swift value type for iOS, and XML resources for Android, from a single JSON/YAML source file, so no platform's token file is hand-edited independently of the others.
• Where a value must legitimately differ by platform (for example, elevation is a shadow on web and iOS but a literal elevation attribute on Android), the mapping itself documents the intentional divergence, rather than leaving it as an unexplained discrepancy someone finds later.

Platform adapters

flowchart TB
    T[Canonical tokens<br/>color, space, type, motion]
    T --> W[Web adapter:<br/>CSS custom properties]
    T --> IOS[iOS adapter:<br/>Swift value types]
    T --> AND[Android adapter:<br/>XML resources]
    W --> WC[Web components]
    IOS --> IC[iOS components]
    AND --> AC[Android components]
    WC --> R[Coordinated release:<br/>token version tag]
    IC --> R
    AC --> R
    R --> QA[Cross-platform<br/>visual + a11y QA]

• Each platform's component library consumes only its own adapter's output, never the raw token source, so a platform team can never accidentally reference a value meant for another platform.
• Components themselves follow a shared anatomy spec (states, accessibility roles, motion intent) with platform-specific implementation notes attached: for example, a shared "primary button" spec that documents ripple feedback on Android, a subtle scale-down on tap for iOS, and a focus ring on web, all expressing the same underlying "pressed" state contract.

Accommodating native interaction paradigms without divergence
Respecting platform conventions means the interaction pattern differs while the token-driven visual identity (color, type, spacing) stays fixed: iOS gets swipe-to-dismiss and native haptics on a sheet, Android gets a back-button-dismiss and Material ripple, web gets a close button and keyboard Escape, but all three sheets use the same color.surface.elevated token and the same corner-radius token, so they read as unmistakably the same product even though they behave like citizens of their own platform.

Coordinating releases

• Token changes get a version tag; all three platform component libraries pin to a specific token version rather than "latest," so a web-only token update can't silently break iOS builds before that team has reviewed it.
• A synchronized release cadence (for example, quarterly for token-level changes, with hotfix releases as needed) with a shared changelog keeps the three platforms from drifting apart silently between coordinated releases.
• Cross-platform QA runs a shared checklist (does this component's tokens match across platforms, do the accessibility roles map correctly on each platform's assistive technology) as a release gate, not an afterthought.

Worked example
A semantic token space.md is defined once as an abstract "medium spacing" value. The adapter pipeline compiles it to --space-md: 16px for web, Spacing.md: CGFloat = 16 for iOS, and dimen/space_md = 16dp for Android, three different unit systems (px, points, dp) representing the same design intent, all traceable to the single source definition. If a designer changes the source value, all three platform outputs regenerate from that one change on the next token build, rather than requiring three separate manual edits that could each drift independently.

Trade-offs & pitfalls
The platform-adapter pattern adds real coordination overhead: every token or component-anatomy change now needs sign-off or at least visibility across three platform teams before it ships, which is slower than letting one platform move independently. The most common wrong turn is treating "respecting native conventions" as license to let each platform's component silently diverge in ways the token pipeline doesn't capture, for example a designer approving a slightly different corner radius on iOS "because it looks more native," without updating the shared spec; that kind of untracked exception is exactly the drift this whole approach exists to prevent, and it accumulates invisibly until a rebrand or an accessibility fix has to be manually reconciled across three codebases instead of one.

Direct answer

Match fidelity to what each flow is actually testing, not to a uniform standard: build real, interactive fidelity for whatever differs between the flows (the part that actually carries risk), and fake or mock everything else, since it does not inform the decision you are trying to make.

Structured elaboration

The fidelity decision rule
For each flow, ask: what question does the difference between this flow and the others answer. Build only enough fidelity to let a user's behavior actually answer that question. A flow that differs in navigation structure needs working navigation. A flow that differs only in wording needs working wording, not working backend logic.

What to simulate versus mock, generally

• Simulate (build for real): whatever the flows differ on, plus the immediate interaction around it (form validation, navigation, visible state changes), since that is what generates the signal.
• Mock (fake convincingly): whatever is constant across flows and does not affect the comparison, such as real payment processing, backend order fulfillment, or account creation; canned success and failure screens are enough.

Sequencing across two weeks
Roughly front-load build time on the parts that differ and carry risk, run tests once each flow's differentiating piece is real enough to interact with, and leave a short buffer near the end for one fast iteration on whichever flow's early sessions raised a fixable, cheap issue. The split does not need to be even across the three flows; the flow with the most novel interaction earns the most build time.

Worked example

Three checkout flow directions:

• Flow A changes the structure (fewer steps, different sequencing). This is a high-fidelity interactive prototype, since the sequencing itself is the variable being tested; a static mock would not let anyone experience the actual navigation.
• Flow B keeps the same structure as the current flow but changes labels and microcopy. This needs only a well-labeled clickthrough, since wording, not interaction, is the variable; building full interactivity here would spend time without adding signal.
• Flow C introduces a new payment method. Build the new payment step at real fidelity, since that is the unproven part, and mock everything after it (a canned confirmation screen), since order processing is identical across all three flows.

Testing approach: small moderated sessions per flow, watching for where people hesitate or make errors, not just whether they complete the task, since the goal at this stage is diagnosing which direction to pursue, not confirming readiness at scale.

Trade-offs and pitfalls

• Building every flow to the same high fidelity "just in case" burns most of the two weeks before any testing happens.
• Under-building the flow that differs in interaction, not just copy, produces an unreliable signal, since users cannot feel the actual friction in a static mock.
• Testing everything unmoderated trades away the diagnostic "why" this stage of the process actually needs; unmoderated at scale is a better fit once you are closer to a launch decision than a direction decision.

Situation & goal
At ~3 designers decisions were ad-hoc; by 30 we need repeatable, decentralized decision-making that keeps teams autonomous while ensuring product consistency and shared standards.

Approach / Framework

• Establish decision tiering:
  • Team-level (local UX flows, microcopy) — owner decides.
  • Cross-team (shared patterns, platform UX) — lightweight council.
  • Strategic (brand, design principles) — design leadership + PM/Eng partners.

Documentation patterns

• Living Design Playbook (single source of truth): guidelines, component rationale, when to deviate, decision records (who, why, date).
• Component-level RFCs for major changes with pros/cons and migration plan.
• Constrain-by-default recipes: “accept, adapt, escalate” steps for ambiguous cases.

Governance & meetings

• Design Council (monthly): reps from squads + lead designers — triage RFCs, align on cross-cutting trade-offs.
• Tactical syncs (weekly): rapid design critiques per squad; reduce council load.
• Office hours: leadership availability for escalations.

Knowledge transfer

• Onboarding dossier: key RFCs, design principles, decision log.
• Apprenticeship program: pair new hires with senior designers for first 3 months.
• Quarterly demos + postmortems: capture wins, failed assumptions, and update the playbook.

Success metrics

• Reduced escalations to leadership, faster RFC resolution time, consistency score from cross-team UX audits, time-to-onboard metric.

This balances autonomy with repeatable practices and ensures institutional knowledge grows and remains accessible.