Intuit Enterprise Suite · Enterprise SaaS · AI-native workflows
Defining an AI-native target state for enterprise finance
I helped define and communicate AI-native product direction for complex enterprise finance workflows — translating ambiguity into clearer information architecture, trust patterns, prototype concepts, and leadership-ready product narratives.
This public case study is intentionally sanitized. It focuses on approach, systems thinking, and design patterns rather than confidential product details, internal screenshots, or roadmap specifics.
- Role
- Product design, information architecture, prototyping, product storytelling
- Focus
- AI-native workflows, enterprise finance, trust, explainability, human judgment
- Output
- Frameworks, sanitized patterns, prototype narratives, alignment artifacts
- Status
- Public-safe case study
Context
Enterprise finance workflows are dense, high-stakes, and full of dependencies. People need to understand what changed, why it matters, what evidence supports it, who needs to act, and what happens after an action is taken.
As AI becomes more present in business software, the design challenge is not simply making systems more automated. The challenge is making intelligent systems understandable, trustworthy, and useful in moments where human judgment still matters.
The challenge was not “add AI.” It was make AI legible.
In high-trust financial workflows, a smart recommendation is only useful if people can evaluate it. The experience needed to make reasoning visible, clarify risk, show supporting evidence, and preserve user control before any meaningful decision or action.
Make the system legible before making it smart.
My role
I contributed to the product direction by translating complex enterprise finance concepts into clearer experience models, information architecture, prototype concepts, and leadership-ready narratives. My work focused on helping teams reason through ambiguity, trust, evidence, authorization, and the role of human oversight in AI-native workflows.
How I made the system legible
Structure the system
Map the entities, relationships, decisions, constraints, and failure points before designing the interface.
Make evidence visible
Show the signals and source context behind a recommendation so people can understand why it matters.
Preserve human judgment
Treat approval, escalation, and review as core parts of the experience — not friction to remove.
Close the loop
After a decision, show what changed, what was recorded, and what can be reviewed later.
Sanitized design patterns
Evidence rail
A supporting layer that gives users a clear trail of signals, assumptions, and source context.
Decision packet
A structured object that brings together the recommendation, rationale, impact, risks, and next step.
Human judgment checkpoint
A deliberate moment where the system asks for review, confirmation, or escalation before meaningful action.
Approval receipt
A closing artifact that records what was approved, what changed, and where the user can review it later.
Abstract visuals
Synthetic diagrams showing the thinking behind trust patterns — generic labels only, no confidential product UI.
A simplified model for turning ambiguous financial state into inspectable action.
A deliberate pause before meaningful action, preserving review and control.
Supporting context that helps users evaluate why a recommendation matters.
- Source context
- Policy check
- Materiality signal
- Confidence note
A structured object that brings together recommendation, rationale, risk, and next step.
- Recommendation
- Review classification variance
- Evidence
- 3 supporting signals attached
- Risk
- Medium — requires human review
- Human review
- Pending approval
- Approval state
- Awaiting decision
A closing artifact that records what changed and where it can be reviewed.
- Decision recorded
- Classification review approved
- Reviewer
- Finance lead
- Time
- Synthetic timestamp
- Follow-up
- Audit trail available
What changed
The work helped make an abstract AI-native ambition easier to understand, evaluate, and discuss. By turning complex financial workflows into clearer frameworks, patterns, and narratives, the direction became more tangible for cross-functional partners and leadership audiences.
Reflection
The most important lesson was that trustworthy AI is not only about model capability. It is about the surrounding experience: the information architecture, evidence, controls, language, and moments of human judgment that help people understand what the system is doing and why it matters.