Lead to Cash

An Agentic AI solution to reimagine the Lead-to-Cash journey, simplifying complex workflows and creating a more cohesive, user-centered experience. This solution was delivered for a global utilities client.

The transformation covered three connected business processes: Lead-to-Order, Order-to-Invoice, and Invoice-to-Cash.

My Role and Collaborators:

I led the UX strategy and AI interaction design across the Lead-to-Cash journey, defining how autonomous agents, human approvals, exceptions, and cross-functional handoffs work across sales, orders, credit, billing, and collections.

UI/UX Design for AI — Alina Baiko
Lead Product Design — Alina Baiko
UI/UX Design — Rachana Nilesh Desai & Sayanta Ganguly
Creative Direction — Lauren Schwartz
Copilot/Agentic AI Managing Direction — Anoop Gopinatha

Understanding Our Users

Focused on designing how AI agents support users across the Lead-to-Cash journey from lead qualification to order processing, credit management, and invoicing. The solution was tailored to the needs of three core user groups:

Enterprise Sales Leader:
"I want an intelligent assistant that streamlines lead qualification and accelerates deal progression. It should help me quickly identify opportunities, make informed decisions using real-time insights, and drive consistent deal closure despite time pressure, complex client needs, and evolving market dynamics."

Revenue Assurance Agent:
"I want a smart platform that organizes tasks, corrects data errors, and streamlines order-to-invoice workflows. It should help me manage orders efficiently, resolve discrepancies proactively, and maintain data integrity despite complex systems, time-sensitive payments, and high transaction volumes."

Communications & Operations Lead:
"I want an intelligent system that automates billing workflows and enhances financial oversight. It should help me manage approvals, resolve disputes, and ensure timely cash flow despite operational complexities, shifting schedules, and high volumes of data."

Understanding the Problem

Enterprises still rely heavily on manual order-to-cash workflows from lead origination to cash application:

Manual Processes & Data Silos:

Manual processes cause errors, delays, and higher costs, while fragmented data creates communication gaps and inconsistencies.

Customer Service Issues:

High customer expectations for fast and accurate order processing are difficult to meet with current systems.

Credit Management:

Assessing credit risk and managing payment plans is time-consuming and impacts cash flow and financial stability.

Strategic Insight:

“Reimagining Lead-to-Cash is about unifying the journey. The breakthrough lies in designing a cohesive ecosystem where AI agents proactively surface insights, turning fragmented processes into a smooth, accelerated revenue engine while giving users full operational oversight.”

Value Proposition

Automating Manual Tasks: AI agents reduce manual workload, allowing employees to focus on strategic work.

Enhance Data Integration: Improved data accuracy and flow across departments eliminate silos and enhance decision-making.

Optimize Customer Service: AI-driven processes ensure timely and accurate order processing, improving customer satisfaction.

Streamline Credit Management: Automated credit assessments and payment plans enhance cash flow and reduce financial risks.

Defining Human–AI Operating Model

Mapped each Lead-to-Cash decision by risk, reversibility, data quality, and required human judgment to define where agents could act independently, where they should recommend an action, and where explicit human approval was required.

Our Results

The program defined a phased Lead-to-Cash transformation with internal estimates of $9.8M in Year 1 benefit, $13M in annualized value after full deployment, and up to 245 FTE redeployment opportunities.
‍
My contribution was translating the complex multi-agent operating model into a coherent human-agent experience and a reusable interaction framework. This helped stakeholders understand the future-state vision, align around the value of Agentic AI, and move toward implementation.

$9.8M

Estimated Year 1 benefit

$13M

Estimated annualized run-rate value

~245 FTE

Redeployment opportunities