Design Case Study — Enterprise AI Assistant

Goodyear – AOP Chatbot

Annual Operating Plan AI Assistant: Transforming static financial spreadsheets into interactive, conversational intelligence.

CLIENT Goodyear Tire & Rubber Co.

ROLE Lead Product Designer

PRIMARY SCREENS 3 Screens + Top Nav Copilot

ARCHITECTURE React, Goodyear UI, LLM Engine

https://aop.goodyear.com/dashboard/grid

AOP Intelligence

Ask AOP Copilot: "What is the variance for Q3 marketing spend?"

Emily (Finance Analyst)

AOP Dashboard Grid View (Excel-like High-Volume Financial Lines) Connected to Enterprise ERP

Line Item ID	Category Name	FY24 Forecast	FY24 Actuals	Variance ($)	Status
`AOP-MKT-301`	Global Marketing & Brand Campaigns	$12,400,000	$14,150,000	+$1,750,000 (+14.1%)	Over Budget
`AOP-RD-104`	Tire Tech R&D (EV Compounds)	$28,500,000	$27,800,000	-$700,000 (-2.4%)	Favorable
`AOP-LOG-502`	Supply Chain & Logistics West	$45,000,000	$48,200,000	+$3,200,000 (+7.1%)	Review Req.

80%+ Manual Excel Search Time Reduction Target

< 5s Query Response Time Target

85%+ Finance Team Adoption Target

95%+ Accuracy Target of Summaries

01 / OVERVIEW & BACKGROUND

Project Background & Solution Overview

Project Background

Goodyear’s Finance team manages large volumes of financial data across contracts, transactions, and Annual Operating Plans (AOP).

The AOP is typically maintained as a complex Excel-based financial sheet containing budget forecasts, actuals, variance data, and projections. The existing workflow required finance professionals to manually search through spreadsheets, contracts, and transaction logs to extract insights.

Legacy Workflow Pain Factors:

Time-consuming: Hours wasted manually searching through spreadsheets.
Prone to manual errors: Broken formula links and lookup omissions.
Dependent on Excel expertise: Required complex VLOOKUPs and pivot table mastery.
Difficult to scale across regions: Fragmented processes between global finance teams.

Solution Overview

Goodyear introduced an AI-powered chatbot capable of answering contextual financial queries instantly.

The AOP Chatbot is embedded directly into Goodyear's Annual Operating Plan dashboard. The interface consists of three primary screens, with a persistent chatbot embedded in the top navigation bar—similar to how Copilot is integrated within Microsoft products.

Core Interface Structure:

1. AOP Dashboard Screen: Displays the Annual Operating Plan in an Excel-like grid with high-volume financial data.
2. Contracts & Transactions Screen: Shows structured contract records and transaction history.
3. Analytics & Insights Screen: Visualizes financial trends, variances, and performance indicators.

Key Features (Full Specification)

Conversational Financial Queries: Natural language interaction for AOP, contracts, and transactions.
Excel Data Interpretation: Reads and interprets structured AOP spreadsheets.
Contextual AI Integration: Embedded into top navigation for cross-screen assistance.
Large Dataset Handling: Efficiently processes high-volume financial tables.
Smart Summarization: Converts complex financial rows into concise explanations.
Drill-Down Support: Enables follow-up questions without resetting context.
Variance & Forecast Analysis: Automated comparison between budget vs actuals.

Goals Achieved & Overall Outcomes

North Star Goal:

Transform static financial spreadsheets into an interactive, AI-powered financial intelligence platform.

Reduced manual Excel analysis time significantly.
Improved financial insight accessibility across teams.
Minimized dependency on advanced Excel formulas and pivot tables.
Enabled faster contract and transaction lookups.
Increased productivity of finance teams during planning cycles.

02 / PROBLEM, OBJECTIVES & SCOPE

Problem Statement, Strategic Value & Scope

Automate Financial Insight Retrieval

Enable conversational access to AOP data.
Reduce manual spreadsheet manipulation.

Enhance Financial Transparency

Allow instant variance and forecast analysis.
Provide structured summaries for decision-making.

Standardize Finance Workflows

Create a unified interface for contracts, transactions, and AOP.
Ensure consistent financial interpretation across teams.

Root Causes Uncovered

Finance workflows were dependent on manual Excel navigation.
Complex financial datasets increased cognitive overload.
Limited AI support for structured financial decision-making.

Guided Design Decisions

Embedding AI into the top navigation bar.
Designing an Excel-like interface to preserve familiarity.
Building structured AI prompts optimized for financial datasets.

Project Scope

Build a 3-screen financial dashboard.
Integrate AI chatbot within top navigation.
Enable conversational queries across AOP, contracts, and transactions.
Design scalable UI architecture for large datasets.

Key Components

Excel-like AOP grid view.
Contracts & Transactions module.
Analytics dashboard.
Persistent AI Chatbot assistant.
Smart response engine with contextual memory.

03 / USER RESEARCH & AUDIENCE

Target Audience, Personas & Pain Points Matrix

Primary Users

1. Finance Analysts: Use chatbot for variance analysis and reporting.
2. Financial Controllers: Review contracts, transactions, and AOP summaries.
3. Regional Finance Managers: Monitor budget alignment and forecasts.

Key Stakeholders

Finance Leadership
Product & Data Teams
Engineering & UX Teams
IT Governance & Compliance

Emily

Finance Analyst

KEY TRAITS

Detail-oriented, analytical

GOALS

Quickly analyze variance and forecasts

FRUSTRATIONS

Complex Excel formulas slow analysis

MUST-HAVE OUTCOME

Instant AI-generated summaries and insights

Michael

Financial Controller

KEY TRAITS

Risk-focused, compliance-driven

GOALS

Monitor contracts and spending accuracy

FRUSTRATIONS

Time-consuming transaction cross-checks

MUST-HAVE OUTCOME

Unified data access with contextual explanations

David

Regional Finance Lead

KEY TRAITS

Strategic, decision-oriented

GOALS

Ensure budget alignment and growth targets

FRUSTRATIONS

Delayed reporting cycles

MUST-HAVE OUTCOME

Fast executive-level insights from chatbot

Pain Point	Frequency	Severity	Supporting Insight
Manual spreadsheet navigation	High	High	Required filters, pivot tables, and formulas
Time-consuming contract lookups	High	Medium	Multiple systems required cross-referencing
Difficulty identifying variance drivers	High	High	Large datasets lacked quick summarization
Cognitive overload from dense data tables	High	Medium	High data volume reduced clarity
Inconsistent interpretation of financial data	Medium	High	Different analysts derived different conclusions

Research Key Insights & Decisions Matrix

Issue	Insight	Decision
Complex Excel workflows	Finance teams rely heavily on formulas and pivot tables	Integrate conversational AI to automate insight extraction
Fragmented financial data	Contracts and AOP stored separately	Unify screens with shared chatbot layer
Long analysis cycles	Manual filtering slowed down reporting	Enable real-time AI summaries
Low scalability	Excel-based processes don't scale well	Build structured backend integration
High cognitive load	Large datasets difficult to interpret	Add contextual AI explanations and drill-down responses

Core User Needs Matrix

User Need	Evidence	Priority
Automate Excel-based financial queries	Heavy manual filtering and pivot usage	High
Centralized financial data access	Multiple systems for contracts & AOP	High
Faster insight generation	Reporting cycles were slow	High
Clear financial summarization	Large datasets lacked clarity	High
Scalable AI architecture	Multi-year expansion expected	Medium

04 / BRAND GUIDELINES & DESIGN SYSTEM

Goodyear Brand Guidelines & UI Standards

Enterprise Brand Guidelines

Built using Goodyear’s enterprise UI standards.
Designed for desktop-first experience.
Integrated within secure enterprise environment.
Followed accessibility and compliance requirements.
Frontend built using React-based architecture.

Constraints & Opportunities

CONSTRAINTS & RISKS:

Handling large-scale financial datasets.
Ensuring data security and compliance.
Maintaining high AI response accuracy.

OPPORTUNITIES:

Transform spreadsheets into interactive intelligence tools.
Standardize financial insights across regions.
Build foundation for AI-driven financial automation.

05 / CORE INTERFACES

The 3-Screen Financial Workspace

AOP Workspace

Ask Copilot: "What is the variance for Q3 marketing spend?"

Emily (Analyst)

1. AOP Dashboard Screen

Displays the Annual Operating Plan in an Excel-like grid with high-volume financial data.

Line Item ID	Category Name	FY24 Forecast	FY24 Actuals	Variance ($)	Variance (%)	Status
`AOP-MKT-301`	Global Marketing & Brand Campaigns	$12,400,000	$14,150,000	+$1,750,000	+14.1%	Over Budget
`AOP-RD-104`	Tire Tech R&D (EV Compounds)	$28,500,000	$27,800,000	-$700,000	-2.4%	Favorable

Contracts Module

Ask Copilot: "Show contracts exceeding $500K in 2024"

Michael (Controller)

2. Contracts & Transactions Screen

Shows structured contract records and transaction history.

Contract ID	Vendor / Partner	Effective Period	Total Value	YTD Spent	Compliance
`CTR-2024-891`	Synthetic Rubber Global Supplies	Jan 2024 – Dec 2026	$4,500,000	$2,100,000	Verified

Analytics & Insights

Ask Copilot: "Why is actual revenue lower than forecast in Region West?"

David (Regional Lead)

3. Analytics & Insights Screen

Visualizes financial trends, variances, and performance indicators.

AI Smart Summarization & Drill-Down Support

Region West actual revenue ($30.0M) fell -$5.0M (-14.2%) below forecast ($35.0M) due to delayed fleet tire delivery shipments in August.

06 / LIVE DEMO PLAYGROUND

Interactive AI Copilot Demo

Preset Sample Queries

Goodyear AOP Financial Copilot Connected to AOP Ledger & Enterprise Contracts API

Enterprise Secure

07 / DESIGN PROCESS & APPENDICES

Design Process & Documentation

Low-Fidelity Data Mapping

Defined user query types.
Designed chatbot placement and behavior.

Mid-Fidelity Wireframes

Created Excel-like grid structure.
Designed three-screen information hierarchy.

High-Fidelity Prototype

Integrated chatbot within top navigation.
Optimized data density and readability.
Iterative feedback with Finance stakeholders.

Project Appendices & Technical Artifacts

Chatbot Prompt Architecture

Data Schema Mapping for AOP

API Flow Diagrams

Finance Use Case Mapping

08 / SUCCESS METRICS & BUSINESS IMPACT

Success Metrics, KPIs & Business Impact Summary

Metric	Target	Notes
Manual Excel Search Reduction	80%+ reduction	Replace manual filtering and pivot operations
Query Response Time	< 5 seconds	AI-processed structured dataset
Finance Team Adoption	85%+	Embedded workflow integration
Accuracy of Financial Summaries	95%+	Verified against structured data
Time to Insight	50% faster	Compared to traditional spreadsheet analysis
Usability Compliance	100%	Adherence to Goodyear design standards

Executive Business Impact Summary

Increased operational efficiency during annual financial planning.
Reduced reliance on complex Excel formulas and manual reconciliation.
Improved clarity in financial decision-making.
Accelerated contract and transaction analysis.
Created a scalable AI foundation for future financial tools.

Final Project Outcome

The Goodyear AOP Chatbot successfully transformed static financial spreadsheets into an intelligent, conversational financial assistant — empowering Goodyear’s finance teams to move from manual data navigation to AI-driven insight generation.

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