AI Profit Assistant
AI Profit Assistant
AI Profit Assistant
From static reporting to conversational profit analysis
From static reporting to conversational profit analysis at transaction level
From static reporting to conversational profit analysis
An AI assistant embedded within an Enterprise Profit Management platform, enabling finance teams to move from manual analysis to instant, explainable answers grounded in a GL-reconciled profit model.
An AI assistant embedded within an Enterprise Profit Management platform, enabling finance teams to move from manual analysis to instant, explainable answers grounded in a GL-reconciled profit model.
An AI assistant embedded within an Enterprise Profit Management platform, enabling finance teams to move from manual analysis to instant, explainable answers grounded in a GL-reconciled profit model.
The Problem with Profit Visibility
The Problem with Profit Visibility
Finance Teams Don’t Lack Data. They Lack Clarity
Finance Teams Don’t Lack Data. They Lack Clarity
Answering simple questions like:
Which customers are truly profitable?
Which products are eroding margins?
This requires spending a large amount of time rebuilding models, reconciling assumptions & validating outputs across systems. This creates:
Slow decision cycles
Inconsistent answers
Limited trust in the data
Answering simple questions like:
Which customers are truly profitable?
Which products are eroding margins?
This requires spending a large amount of time rebuilding models, reconciling assumptions & validating outputs across systems. This creates:
Slow decision cycles
Inconsistent answers
Limited trust in the data
Turning Profit Analysis into a Conversation
Turning Profit Analysis into a Conversation
The Opportunity was to Remove the Friction Between Question and Answer
The Opportunity was to Remove the Friction Between Question and Answer
Instead of navigating dashboards, users could simply ask: “Who are my top customers by profit in 2024?” and receive:
A direct answer
Transparent Breakdowns
Further Exploration
Instead of navigating dashboards, users could simply ask: “Who are my top customers by profit in 2024?” and receive:
A direct answer
Transparent Breakdowns
Further Exploration
AI Assistant Built on a Profit Model
AI Assistant Built on a Profit Model
The assistant is not a generic chatbot.
It is grounded in a transaction-level profit model that:
Reconciles to the General Ledger
Updates every reporting period
Reflects real operational costs
This ensures every answer is:
The assistant is not a generic chatbot. It is grounded in a transaction-level profit model that:
Reconciles to the General Ledger
Updates every reporting period
Reflects real operational costs
This ensures every answer is:
Accurate
Accurate
Traceable
Traceable
Finance Grade
Finance Grade
Ask (Almost) Anything
Ask (Almost) Anything
Users can ask natural language questions about profitability across any dimension:
Users can ask natural language questions about profitability across any dimension:
Customer
Customer
Customer
Product
Product
Product
Facility
Facility
Facility
Supplier
Supplier
Supplier
Every Answer is Backed by Visible Logic
Every Answer is Backed by Visible Logic
Users can:
See how results were calculated
Validate outputs with confidence
Understand drivers of profit
Users can:
See how results were calculated
Validate outputs with confidence
Understand drivers of profit
Analysis Doesn’t Stop at the First Answer
Analysis Doesn’t Stop at the First Answer
Users can ask follow-ups to:
Compare Segments
Identify Key Drivers
Drill Deepers
Users can ask follow-ups to:r a collaborative environment where teams can manage all their ai tools on a unified platform.
The Assistant Adapts to User Context
The Assistant Adapts to User Context
Users can ask follow-ups to:
Embedded mode for quick insights
Full-screen mode for deep analysis
Users can ask follow-ups to:
Embedded mode for quick insights
Full-screen mode for deep analysis
From Answering Questions to
Enabling Faster, Smarter Decisions
From Answering Questions to
Enabling Faster, Smarter Decisions
From Answering Questions to Enabling Faster, Smarter Decisions
Shared Workspaces for Analysis
Shared Workspaces for Analysis
Users can save and organize analysis into shared workspaces called Islands.
These allow teams to:
Build a shared understanding of profit
Store key findings
Collaborate on insights
Users can save and organize analysis into shared workspaces called Islands. collaborative environment where teams can manage all their ai tools on a unified platform.
Designed for Enterprise Use
Designed for Enterprise Use
The assistant includes controls for:
Data Scope and Permissions
Model Confiiguration
File Uploads & Context
Ensuring flexibility without compromising governance.
The assistant includes controls for:
Data Scope and Permissions
Model Confiiguration
File Uploads & Context
Ensuring flexibility without compromising governance.
Assets That Give the Assistant Context
Assets That Give the Assistant Context
Assets That Give
the Assistant Context
The assistant was designed to work with supporting files and business context, not
just platform data.
Users can upload and manage assets such as reports, spreadsheets, documents, and
reference material, giving the assistant additional context for analysis and follow-up questions.
This helps finance teams connect structured profitability data with the supporting knowledge
needed to interpret it.
The assistant was designed to work with supporting files and business context, not just platform data.
The assistant was designed to work with supporting files and business context, not just platform data.
From Days to Seconds
From Days to Seconds
The assistant transforms how teams work:
The assistant transforms how teams work:
Reduces analysis time from days to seconds
Reduces analysis time from days to seconds
Eliminates manual model rebuilding
Eliminates manual model rebuilding
Increases trust in profitability insights
Increases trust in profitability insights
What This Enables
What This Enables
This project represents a shift from static reporting to dynamic, conversational analysis. It lays the foundation for a future where AI moves beyond answering questions to actively guiding business decisions.
This project represents a shift from static reporting to dynamic, conversational analysis. It lays the foundation for a future where AI moves beyond answering questions to actively guiding business decisions.
My Role & Contribution
My Role & Contribution
As Design & UX Lead for this project, I was responsible for:
Foster a collaborative environment where teams can manage all their ai tools on a unified platform.
Defining the product direction
Defining the product direction
Designing
the full experience
Designing the full experience
Creating interaction patterns for AI explainability
Creating interaction patterns for AI explainability
Ensuring alignment with finance workflows
Ensuring alignment with finance workflows
Defining the product direction
Creating interaction patterns for AI explainability
Designing
the full experience
Ensuring alignment with finance workflows