Designing for Faster Collections in Autonomous Collections with AI
Designing for Faster Collections in Autonomous Collections with AI
I redesigned the core collections workflow so AR analysts could act faster, combining AI insights, in-app calling, automated summaries, and a structured follow-up system.
I redesigned the core collections workflow so AR analysts could act faster, combining AI insights, in-app calling, automated summaries, and a structured follow-up system.
Role: Product Designer (End-to-end)
Role: Product Designer (End-to-end)
Role: Product Designer (End-to-end)
Platform: Fintech & AI, B2B SAAS
Platform: Fintech & AI, B2B SAAS
Platform: Fintech & AI, B2B SAAS
Team: 1 designer, 2 PMs, 4 engineers (2 FE, 2 BE)
Team: 1 designer, 2 PMs, 4 engineers (2 FE, 2 BE)
Team: 1 designer, 2 PMs, 4 engineers (2 FE, 2 BE)
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Fewer missed follow-ups
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Fewer missed follow-ups
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Fewer missed follow-ups
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Fewer missed follow-ups
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Fewer missed follow-ups
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Less manual effort
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Less manual effort
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Less manual effort
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Less manual effort
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Less manual effort
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Faster DSO
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Faster DSO
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Faster DSO
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Faster DSO
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Faster DSO
TL;DR
TL;DR
TL;DR
Problem
Problem
AR analysts manage hundreds of accounts per cycle, but the workflow was fragmented across tools, leading to manual work, errors, and missed follow-ups that impacted DSO.
AR analysts manage hundreds of accounts per cycle, but the workflow was fragmented across tools, leading to manual work, errors, and missed follow-ups that impacted DSO.
What I did
What I did
Redesigned the end-to-end collections workflow by integrating AI-assisted insights, in-app calling, automated summaries, and a post-call follow-up system.
Redesigned the end-to-end collections workflow by integrating AI-assisted insights, in-app calling, automated summaries, and a post-call follow-up system.
Impact
Impact
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Fewer missed follow-ups
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Less manual effort
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Faster DSO
Context (Set the stakes)
Context (Set the stakes)
AR analysts don’t “just call customers.” They track invoices, prioritise accounts, prepare for calls, negotiate next steps, and log outcomes—often under time pressure and across multiple systems.
AR analysts don’t “just call customers.” They track invoices, prioritise accounts, prepare for calls, negotiate next steps, and log outcomes—often under time pressure and across multiple systems.
Why it mattered
Why it mattered
When follow-ups are missed or notes are incomplete, collections slow down, directly affecting DSO and team performance.
When follow-ups are missed or notes are incomplete, collections slow down, directly affecting DSO and team performance.
A simple workflow map: Prioritise → Prepare → Call → Log outcomes → Follow-up → Next action
A simple workflow map: Prioritise → Prepare → Call → Log outcomes → Follow-up → Next action
The Problem
The Problem
The Problem
From discovery, a pattern was clear: the product wasn’t failing because analysts didn’t know what to do—it was failing because the UI made the work hard to manage at scale.
From discovery, a pattern was clear: the product wasn’t failing because analysts didn’t know what to do—it was failing because the UI made the work hard to manage at scale.
Pain points
Pain points
Fragmented workflow → analysts jumped across tools to call, take notes, and track outcomes.
Manual logging → repetitive admin work after every call (notes, status, tasks).
Missed follow-ups → the system didn’t reliably turn call outcomes into next actions.
Fragmented workflow → analysts jumped across tools to call, take notes, and track outcomes.
Manual logging → repetitive admin work after every call (notes, status, tasks).
Missed follow-ups → the system didn’t reliably turn call outcomes into next actions.
The old collections dashboard
Goals & Success Metrics
Goals & Success Metrics
Goals & Success Metrics
Users + Business Goals
Users + Business Goals
Reduced DSO
Reduce missed follow-ups
Improve analyst productivity
Improve end to end workflow clarity
Reduced DSO
Reduce missed follow-ups
Improve analyst productivity
Improve end to end workflow clarity
How we measured success
How we measured success
Missed follow-up rate
Manual effort/time spent per account
DSO movement trend(time to collect)
Qualitative friction feedback from analysts
Missed follow-up rate
Manual effort/time spent per account
DSO movement trend(time to collect)
Qualitative friction feedback from analysts
Discovery & Insights
Discovery & Insights
Discovery & Insights
What I did
What I did
6 contextual inquiry interviews
Workflow shadowing
180+ minutes of recorded call analysis
Heuristic review of the collections dashboard
6 contextual inquiry interviews
Workflow shadowing
180+ minutes of recorded call analysis
Heuristic review of the collections dashboard
3 insights
3 insights
Work is outcome-driven, not call-driven. Analysts care about “next action” more than “call completed.”
Context switching kills throughput. Every jump to another tool increases time and errors.
Follow-ups need structure. If outcomes aren’t captured in a consistent format, they get lost.
Work is outcome-driven, not call-driven. Analysts care about “next action” more than “call completed.”
Context switching kills throughput. Every jump to another tool increases time and errors.
Follow-ups need structure. If outcomes aren’t captured in a consistent format, they get lost.
Empathy Map from the discoveries
Key Decisions
Key Decisions
Key Decisions
Decision 1: Unify the workflow in one place
Decision 1: Unify the workflow in one place
Why
Why
Fragmentation created context switching and missed follow-ups.
Fragmentation created context switching and missed follow-ups.
What changed
What changed
Core dashboard experience now supports prep → call → log → next steps in one continuous flow.
Core dashboard experience now supports prep → call → log → next steps in one continuous flow.
Result
Result
Reduced manual effort and friction (qual + time).
Reduced manual effort and friction (qual + time).
Before vs After of the main workflow area.
Decision 2: Bring calling inside the product
Decision 2: Bring calling inside the product
Why
Why
Calls are central, but the work after calls was the real bottleneck.
Calls are central, but the work after calls was the real bottleneck.
What changed
What changed
In-app calling + call context visible during interaction.
In-app calling + call context visible during interaction.
In-app calling screen + contextual panel.
Decision 3: Automate summaries to remove admin work
Decision 3: Automate summaries to remove admin work
Why
Why
Notes and summaries were repetitive and inconsistent across analysts.
Notes and summaries were repetitive and inconsistent across analysts.
What changed
What changed
Automated call summaries that analysts can review/edit quickly.
Automated call summaries that analysts can review/edit quickly.
Summary UI showing editable AI-generated text + confirmation
Decision 4: Make follow-ups structured and unavoidable
Decision 4: Make follow-ups structured and unavoidable
Why
Why
Missed follow-ups were a direct business problem.
Missed follow-ups were a direct business problem.
What changed
What changed
A post-call system that turns outcomes into tasks/next steps with due dates + status.
A post-call system that turns outcomes into tasks/next steps with due dates + status.
Result
Result
40% fewer missed follow-ups.
40% fewer missed follow-ups.
Post-call “Next actions” UI (task creation / scheduling)
Solution Walkthrough
Solution Walkthrough
Solution Walkthrough
Impact
Impact
Impact
40%
Fewer missed follow-ups
Structured post-call system
15%
Less manual effort
In-app calling + automated summaries
13%
Faster DSO
Faster, more consistent execution of next actions
Learnings + Next Steps
Learnings + Next Steps
Learnings + Next Steps
What I learned
What I learned
In enterprise tools, “speed” is mostly about reducing context switching and making next actions explicit.
In enterprise tools, “speed” is mostly about reducing context switching and making next actions explicit.
Next steps
Next steps
Expand the AI insights to better explain “why” behind recommendations
Improve QA loops (where summaries are edited most)
Add better analytics for follow-up effectiveness by segment
Expand the AI insights to better explain “why” behind recommendations
Improve QA loops (where summaries are edited most)
Add better analytics for follow-up effectiveness by segment