Pay Optimization
BILL
Overview
At BILL, I worked on reimagining how businesses prioritize and schedule their bill payments, moving beyond manual processing toward a more intelligent, recommendation-driven experience.
Paying bills involves more than executing transactions. Businesses must continuously balance due dates, available funds, payment methods, processing times, and vendor relationships. Yet much of this decision-making remained manual, requiring customers to evaluate multiple variables before making each payment.
I helped shape an AI-assisted bill payment experience that brought prioritization, contextual recommendations, and payment decisions directly into the existing workflow.
The goal was to help businesses spend less time figuring out what to pay and more time confidently managing their cash flow.
My role
As Senior Product Designer, I partnered with product management and engineering to explore how intelligent recommendations could improve the bill payment experience.
My focus spanned two connected areas: helping customers prioritize outstanding bills and optimizing payment decisions when they were ready to pay.
I worked on defining the information hierarchy, designing recommendation patterns, exploring AI-assisted interactions, and connecting these concepts into an end-to-end experience.
My design approach focused on balancing automation with user control—making recommendations useful, understandable, and easy to act on without removing the customer's authority over financial decisions.
Key Context
The initiative addressed recurring inefficiencies in how businesses managed and executed payments.
37% — Customers with at least one late bill
60%+ — Payment runs requiring manual optimization
2 workflows — Bill prioritization and payment optimization
These figures represented the problem baseline and opportunity, rather than measured outcomes from the proposed experience.
paid >1 bill late
required optimization
The Problem
Businesses often know which bills they owe, but deciding which ones to pay first isn't always straightforward.
A bill might be due tomorrow but qualify for an early-payment discount. Another might already be overdue, while a third could be scheduled later to preserve available cash. Payment methods, delivery speeds, and banking holidays introduce additional complexity.
BILL provided the tools to process payments, but customers still needed to interpret these competing factors and make many decisions manually.
With 37% of customers experiencing at least one late bill and more than 60% of payment runs requiring manual optimization, we saw an opportunity to reduce the decision-making burden within the payment workflow.
The problem wasn't simply how to pay bills faster. It was how to help businesses make better payment decisions before executing them.
Product Opportunity
The opportunity was to evolve bill payment from a transaction-processing experience into a more proactive decision-support system.
Rather than expecting customers to evaluate every bill independently, we explored how BILL could identify important payment considerations and surface recommendations at the moment they were most useful.
This meant answering three questions throughout the workflow:
What should I pay? Help customers distinguish urgent and high-priority bills from payments that can wait.
When should I pay? Recommend appropriate processing dates based on due dates, payment delivery times, and relevant constraints.
How should I pay? Surface payment methods and speeds that align with a customer's timing and payment needs.
The intent wasn't to automate financial decisions without oversight. It was to help customers make informed decisions with less manual effort.
Product Strategy: Intelligence Across the Payment Journey
1. Prioritize before payment
The Unpaid Bills experience needed to help customers understand which bills required immediate attention and why.
I explored a prioritization model that grouped bills into actionable time horizons: Pay Today, Pay This Week, and Pay Later.
Rather than relying on due dates alone, the experience could communicate relevant factors behind a recommendation, such as approaching deadlines or early-payment opportunities.
2. Optimize during payment
Once customers selected bills, the payment experience presented another opportunity for intelligent assistance.
I explored recommendations across payment source, payment method, processing date, and delivery speed—helping customers evaluate tradeoffs without leaving the workflow.
Together, these opportunities established a product direction centered on guiding decisions across the payment journey, from identifying obligations to configuring payment execution.
Outcomes & Business Impact
The project established a design direction for bringing intelligent recommendations into two important parts of BILL's accounts payable experience: bill prioritization and payment configuration.
The work produced a prioritization approach for outstanding bills, contextual recommendation patterns, and three AI-assisted interaction concepts for optimizing payment runs.
More broadly, the design exploration demonstrated how AI could support financial decision-making without turning the experience into an opaque or fully automated system.
The intended business value was to reduce manual payment configuration, improve awareness of time-sensitive bills, and help customers make more informed payment decisions.
As these were design explorations, I would not attribute specific reductions in late payments, processing time, or manual work to the initiative without post-launch measurement.
Reflection & Learnings
Bill Pay Optimization reinforced that introducing AI into complex enterprise workflows requires more than generating useful recommendations.
The challenge is deciding where intelligence belongs, how much autonomy it should have, and how to help users understand the reasoning behind its suggestions.
I learned to approach AI as a decision-support capability rather than a separate feature—integrating recommendations into familiar workflows while preserving transparency, predictability, and customer control.
The most important product shift was from helping businesses execute payments to helping them make more informed decisions about those payments.
Good AI assistance doesn't simply make decisions for users. It helps them make better decisions with less effort.