Morgan Stanley

A wealth management platform where advisors were working around the tools, not through them

Morgan Stanley's wealth management platform serves thousands of financial advisors managing complex investment portfolios. The existing workflows were functional but inefficient — advisors spent too long on portfolio analysis, data visualizations were hard to parse, and the platform's adoption was stalling because the experience didn't match how advisors actually worked.

I joined as Product Designer to lead end-to-end redesign of the core advisor experience — from investment portfolio dashboards and financial data visualizations to advisor workflows and the enterprise design system that ties it all together.

Advisors weren't rejecting the platform. They were spending too long fighting it.

When I joined the team, portfolio analysis was taking advisors far longer than it should. Investment dashboards were dense and hard to parse, workflows required too many steps, and the overall experience created friction at every turn. In a high-stakes wealth management environment, that friction directly cost advisor productivity and client satisfaction.

Three sources of evidence pointed to the same root cause

I treated the activation drop as a research problem before treating it as a design one. The thesis wasn't "the UI is confusing" — it was "advisors don't trust outputs they can't trace." Three independent evidence streams converged on the same answer.

What the data didn't say — and how I filled the gap

Internal analytics could tell us where advisors dropped off, but not why a specific recommendation got rejected. To get to mechanism, I partnered with the AI/ML team to build a lightweight feedback loop: a one-tap "this wasn't useful" signal, with optional reason codes. Within three weeks we had categorised reasons for 1,200+ rejections. The top three: "no source visible" (38%), "can't edit before sending" (24%), "confidence not stated" (19%). 81% of rejections were trust-layer problems, not AI-quality problems.

Three approaches. Two rejected. Here's the reasoning.

We chose explainability over speed. Here's what we gave up to do that.

Every AI recommendation now takes 2.4 seconds longer to render than it did pre-redesign. That's not a bug — that's the cost of rendering confidence scores, retrieving citations from the source data layer, and laying out the edit affordances inline. The product team initially pushed back on the latency hit. Here's how we defended it.

The three trust patterns we shipped

Confidence visibility. Every recommendation displays a 0–100 confidence score with a visual band (high/medium/low). Advisors can filter the queue to "high confidence only" if they want to triage faster. The number is generated by the model's actual logit distribution — not a vibe.

Citation-backed recommendations. Every claim links inline to the underlying portfolio data, market signal, or client preference that triggered it. Click any citation; the source panel opens with the raw data. This is the single most-used feature in the release — 84% of activated advisors use it at least daily.

Editable AI responses. Advisors can amend any AI output before sending to a client. Edits are tracked, attributed, and don't get lost on regeneration. This was the compliance team's hard requirement — and it turned out to be the design feature advisors valued most.

Every UI decision pointed back to one question: can the advisor defend this to compliance?

The recommendation card — annotated decisions

Onboarding flow — sequencing the trust narrative

The activation funnel had a specific failure point: the first AI recommendation. So we rebuilt onboarding to teach trust before testing it. First screen: a guided tour through one pre-built recommendation, narrating the confidence score and citation system before the advisor sees their own data. Second screen: a sandbox recommendation against fake client data, where the advisor can test editing without risk. Only on screen three does live client data appear. The sequence: watch → practice → do.

Design system contribution

I built and maintained the centralised Figma design system that the recommendation card patterns now live in — with reusable components, governance standards, and explicit documentation on which components are "AI-output" vs "advisor-output" (a critical compliance distinction). Engineering handoff time on AI-pattern work dropped substantially after the system shipped, and the cross-team consistency made downstream features faster to design and faster to review.

Advisor productivity +35%. Platform adoption +28%. Portfolio analysis 40% faster.

Measured outcomes

Advisor productivity improved by 35%. Measured via internal product analytics post-redesign. The improvement was driven by streamlined workflows, clearer data visualizations, and reduced steps in core advisor tasks.

Platform adoption increased by 28%. Redesigned onboarding and intuitive financial workflows aligned the product experience with business objectives, improving user satisfaction and driving adoption across the advisor base.

Portfolio analysis became 40% faster. Reimagined investment dashboards and financial data visualizations enabled advisors to analyze complex portfolio performance and make informed investment decisions significantly faster.

Design system & delivery impact

Built and scaled a reusable enterprise design system that reduced design inconsistencies by 45% while accelerating product delivery by 35%. High-fidelity Figma prototypes, user flows, and interaction designs streamlined engineering implementation and reduced design rework by 28%.

Cohort-specific confidence visualisations — A/B test in next release

The 67% AI acceptance rate is strong, but the residual 33% is informative. Reason-code data shows senior advisors (15+ yrs) reject recommendations because the confidence display feels "too prescriptive" — they prefer a range to a single number. Junior advisors (under 5 yrs) reject because they want more guidance, not less. One UI doesn't serve both cohorts.

Hypothesis: rendering confidence as a range (e.g., 78–84%) for senior advisors and as a single number + plain-language explanation for junior advisors will lift acceptance by another 8–12 percentage points. Instrumentation is already in place: tenure-segmented analytics, cohort-segmented A/B framework, and a rollback plan if the senior cohort sees acceptance drop instead.

A regulated industry. A black-box AI. A two-release window. Pick your battles.

Morgan Stanley's wealth management platform doesn't have the luxury of consumer-app constraints. Every design decision had to clear three filters before it shipped: compliance, model interpretability, and advisor trust. The constraint wasn't time — it was the surface area I could realistically influence inside a two-release product cycle.

The triangle: I picked Scope + Quality. Time was the dial I managed.

Compliance reviews don't compress. The AI model couldn't change. So the conscious sacrifice was timeline: the trust-layer redesign shipped across two releases instead of one, with the confidence pattern in release 1 and citations + editability in release 2. Cutting scope was unsafe (an incomplete trust layer is worse than no trust layer). Cutting quality was unthinkable (a regulated industry). Time stretched. That was the right call.

Three lessons that will shape how I design AI products from here