McKinsey Corporate Finance Platforms
Empowering leaders with real-time value insights for strategic decision‑making
Context & Role
At McKinsey & Company, I led product and UX design for a suite of internal corporate finance platforms supporting strategy, private equity, and transformation teams.
The platform was used to analyze portfolio performance, identify value levers, and simulate strategic scenarios across active client engagements.
My scope: end-to-end product design across research, UX architecture, dashboard systems, AI-assisted insight tools, and design systems — working closely with partners, consultants, data scientists, and engineers.
Scale: Adopted across 40,000+ employee teams globally at McKinsey & Company, supporting strategic analytics for 100s of consulting engagements per year.
The Business Problem
MVI (McKinsey Value Intelligence) was designed to centralize financial models and value frameworks for corporate finance and transformation work. However, before the redesign, it functioned mainly as an analytical backend, not as a true decision-support platform.
Despite strong underlying analytics, teams still depended on fragmented spreadsheets, disconnected tools, and static decks. This led to:
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Slow time-to-insight
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High manual analysis and reporting effort
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Inconsistent outputs across teams
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Difficult onboarding and scalability
Consultants spent disproportionate time preparing data and rebuilding views, while partners lacked fast access to clear, decision-ready insights.
The opportunity was to reshape MVI into a continuous strategic decision platform — compressing time-to-insight, standardizing value analysis, and enabling teams to move from report production to real-time decision support.
Product Strategy & Success Metrics
The goal was not to redesign MVI’s interface, but to change how teams used the platform — from a data repository into an active decision-support system.
Together with partners, engagement teams, and engineering, we defined three product pillars:
Speed to insight — surface value drivers and risks faster
Decision clarity — elevate executive-level signals, not just raw data
Platform scalability — enable consistent use through reusable UX systems
Success was measured through:
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Time to first meaningful insight
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Usability task success and comprehension
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Reduction in manual analysis and reporting effort
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Adoption across teams and practices
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Design and development velocity through systemization
These metrics guided prioritization and iteration, ensuring the redesign delivered measurable product and business impact — not just visual improvements.
Final Product & Impact
I reshaped MVI as a connected product ecosystem rather than a collection of screens, structuring the solution across four integrated layers.
Insight-first experience layer
Rebuilt the information architecture around value drivers and executive questions. High-impact KPIs and signals were front-loaded, visual hierarchy simplified, and progressive disclosure introduced to reduce cognitive load and accelerate insight discovery.
Scenario & simulation layer
Designed interactive what-if and comparison tools to replace static spreadsheets and decks, enabling teams to test strategic options, model outcomes, and explore trade-offs directly within the platform.
AI-assisted intelligence layer
Introduced natural-language interactions and automated insight summaries to accelerate analysis, surface patterns, and support faster movement from data to narrative.
Design system & platform foundations
Built a modular design system for analytics products, standardizing dashboards, tables, and simulation components to ensure consistency, scalability, and faster product delivery across teams.
Discovery & Validation
I led discovery and validation with partners, consultants, analysts, and data scientists to align MVI with real consulting workflows.
Through workshops, workflow mapping, and iterative usability testing, we examined how teams explored value drivers, tested hypotheses, and communicated insights.
Three patterns consistently emerged:
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Teams thought in hypotheses and value levers, not dashboards
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Leaders needed narrative clarity, not dense data
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Complexity had to be progressive, not upfront
Early tests showed users were missing key insights and taking too long to complete core tasks, directly informing a re-architecture of the information hierarchy, navigation model, and interaction patterns.
