2024

4 Months

Nokia / AI-Powered Enterprise UX

Supplier Contract Risk Analysis

Designed an AI-powered enterprise platform that helps Nokia's IT Procurement team analyze supplier contracts against approved Nokia templates. The solution automates the identification of contractual discrepancies, assesses risk severity, and provides actionable recommendations, reducing the manual effort required from procurement and legal experts.

Problem statment

Manually compared supplier contracts against approved templates, making contract reviews slow, inconsistent, and difficult to scale.

Supplier contracts frequently deviated from Nokia's approved legal templates, requiring procurement and legal teams to manually compare documents, identify discrepancies, and assess contractual risks. This process was time-intensive, difficult to scale, and prone to inconsistencies, creating delays in vendor onboarding and contract approvals.

Time Consuming

Heavy reliance on manual document comparison.

Inconsistent

Review outcomes varied across reviewers.

Difficult to scale

Contract volume growth increased review overhead.

Risk visibility was limited

No standardized severity assessment or reporting.

Discovery & Research

Conducted stakeholder workshops with Procurement and Legal teams to understand:

We also analyzed existing contract templates and supplier agreements to identify patterns, section mappings, and common discrepancy types. The analysis revealed that many sections had partial or non-linear mappings, requiring more than simple document comparison logic.

Current review workflows

How contracts were compared, escalated, and approved.

Pain points

Where reviewers lost time, duplicated effort, or lacked visibility.

Risk assessment needs

How severity should be defined, communicated, and prioritized.

Success criteria

What would make the tool trustworthy, useful, and adoptable.

Key Design Decisions

Guided analysis workflow

Designed a clear sequence: Upload Contract → Select Template → Analyze → Review Risks → Export Report.

Severity-based risk visualization

Used labeled severity levels (Low, Medium, High) with clear visual indicators so users could prioritize review efforts without relying on color alone.

Contextual explanations

For every discrepancy, users could view the reason it was flagged, the severity rationale, and suggested modifications.

Job-based processing model

Because AI analysis could take several minutes, analysis requests were treated as jobs with status tracking (Running, Completed, Failed) and historical access to results.

Design Process

To meet aggressive delivery timelines, we followed a lean design process: understand the current state, align with stakeholders, define the solution strategy, and rapidly iterate on designs. This approach allowed us to validate requirements early, reduce ambiguity, and move quickly from discovery to delivery.

01.

Discovery

We began by reviewing client documentation, understanding the existing contract review process, and gathering business requirements through stakeholder discussions. Our analysis revealed that contract comparisons were performed manually by procurement and legal experts, making the process time-consuming, difficult to scale, and prone to human error. This presented an opportunity to leverage AI to automate contract analysis while keeping users in control of reviewing and approving recommendations.

02.

Define

Following discovery, we focused on translating business requirements and user needs into a clear product vision. The primary challenge was designing an experience that could simplify complex contract comparisons while making AI-generated insights easy to understand, review, and act upon.

03.

Design

Leveraging the Nokia Design System, we designed the experience iteratively, focusing on one workflow at a time. Concepts were regularly reviewed with stakeholders, allowing us to validate solutions early, incorporate feedback, and continuously refine the experience to meet both user and business objectives.

04.

Deliver

Delivered high-fidelity designs for the complete platform, ensuring consistency across workflows and alignment with AI-powered processes. Worked closely with engineering teams to provide detailed specifications and implementation support, enabling a smooth transition from design to development.