case-studyARnearshore

Case study template: proving AR days reduction after implementing an AI-powered nearshore workforce

iinvoicing

2026-02-04

9 min read

Reusable case study structure and metrics to prove AR days reduction after deploying AI-powered nearshore teams. Finance-ready ROI and audit-ready methods.

Hook: Stop debating nearshore headcount — prove AR days dropped and the CFO believes you

Late payments, manual follow-ups, and opaque collections workflows are the top reasons finance leaders push back on nearshore and outsourcing proposals. Vendors promise lower costs; operators promise improved throughput — but finance asks one question first: show me the cash and the reduced AR days. This template gives you a repeatable, audit-ready case study structure and the exact metrics and calculations to prove AR days reduction after implementing an AI-powered nearshore workforce in 2026.

Why this matters now (2026 context)

In late 2025 and early 2026 the industry shifted: nearshore offers now bundle AI augmentation, supervised LLM agents, and real-time analytics, not just lower labor rates. Vendors such as the teams behind MySavant.ai launched integrated AI-powered nearshore workforces in 2025, claiming AR and collections improvements by combining human judgment with generative models and automation. At the same time, coverage in trade press (Jan 2026) emphasized the importance of governance to avoid "clean-up" work after AI adoption. Finance teams want tangible KPIs: shorter Days Sales Outstanding (DSO), faster cash conversion, and defensible ROI. This guide helps you deliver exactly that.

Executive summary — the one-page that convinces a CFO

Start your case study with a sharp one-paragraph summary that answers: what changed, how much AR days fell, and the financial impact. Use absolute dollar amounts and a 90-day payback statement when possible.

Example summary: After deploying an 80-person AI-enabled nearshore collections team and automation platform in Q3 2025, ACME Logistics reduced AR days from 56 to 38 (-18 days), accelerating $3.6M in cash and achieving a 7-month payback on implementation costs.

Reusable case study structure (section-by-section)

Title & one-line outcome — e.g., "ACME Logistics: -18 AR days, +$3.6M cash in 6 months"
Executive summary — 3–4 sentences with headline KPI and ROI
Problem / baseline — describe the AR state, tools, costs, headcount, and DSO before intervention
Hypothesis — what the AI-enabled nearshore workforce would change (e.g., increase first-contact resolution, reduce disputes, automate promise-to-pay capture)
Solution design — vendor + tech stack, team composition, SLAs, scripts, and AI features used
Implementation timeline — pilot dates, ramp milestones, governance checkpoints
Data & methodology — how KPIs were measured, baseline periods, statistical methods
Results — before/after KPIs with absolute and percent change, financial translation
ROI & sensitivity analysis — payback period, NPV, and scenarios
Operational learnings — what worked, what required iteration, governance learnings
Appendix — raw metrics, calculation worksheets, scripts, SLA wording

Key KPIs to include and how to calculate them

Finance will focus on DSO/AR days and cash flow. Collections teams care about contact rates and dispute resolution. Use the following list as the canonical KPI set for your case study.

AR days (DSO): (Average AR balance / Net credit sales) * 365. Provide baseline and post-implementation values and the rolling 30/60/90-day AR cohorts. See forecasting and cash-flow tools for translating days into working capital impact: Forecasting and Cash-Flow Tools.
Collection Effectiveness Index (CEI): ((Beginning AR + Credit Sales - Ending AR) / (Beginning AR + Credit Sales - Bad Debt)) * 100. CEI captures actual collection performance.
Cash collected earlier (dollar impact): Calculate cash acceleration as average daily sales * AR days reduction.
Promise-to-pay kept rate: Promises honored within agreed timeframe / total promises.
Dispute rate and average dispute resolution time: Percent of invoices disputed and mean days-to-resolution.
Contact attempts per account and contact rate: Measure efficiency gains from AI-powered prioritization and automated outreach.
Cost to collect (per invoice or per $100k AR): Total collections cost / number of invoices or AR balance.
FTE-equivalent productivity: Collections handled per FTE per month before and after.
Error rate / rework: Corrections required per 1,000 invoices.

Data collection & methodology — make your results auditable

Finance teams will question causation. Use a transparent methodology:

Define baseline: Use a 6–12 month pre-implementation period. Match for seasonality (e.g., shipping peaks in Q4).
Isolate cohorts: Compare matched cohorts (same customer segments, invoice sizes, geographies). Use difference-in-differences if you can keep a control group.
Standardize fields: Invoice date, due date, paid date, original invoice amount, adjustments, dispute flag, collector ID, contact attempts, promise dates.
Clean and validate: Remove cancelled invoices, internal transfers, and taxes. Reconcile AR ledger totals to general ledger.
Use rolling and cumulative views: 30/60/90 rolling AR days will smooth spikes and show trend.
Apply statistical tests: Simple t-tests or non-parametric tests to show significance of AR days reduction. For enterprise programs, run a difference-in-differences test.

Proving causation — isolate the impact of the AI-powered nearshore workforce

To show the AR days reduction is due to your deployment (not market shifts), demonstrate:

Parallel trends in the baseline period between test and control groups.
Earlier improvements in KPIs where the new team had early access (geography or customer segment staging).
Operational leading indicators that changed immediately (contact rate, dispute closures, promise-to-pay captures).
Correlation of automation adoption metrics (e.g., % of invoices auto-detected for follow-up) with AR days drop.

ROI calculation template — step-by-step with example

Finance wants dollars and months-to-payback. Use this simple model.

Step 1: Calculate cash acceleration
- Annual credit sales (A): $300,000,000
- AR days reduction (ΔD): 18 days
- Cash accelerated = (A / 365) * ΔD = ($300,000,000 / 365) * 18 ≈ $14,794,520
Step 2: Interest & working capital benefit
- Cost of capital / line of credit: 7% annual
- Annual interest saved ≈ Cash accelerated * cost of capital = $14.8M * 7% ≈ $1,035,600
Step 3: Operational savings
- FTE reduction or redeployment savings: $400,000 annual
- Reduced bad debt and dispute write-offs: $250,000 annual
Step 4: Total annual quantified benefit
- Total = Interest saved + Operational savings = $1,035,600 + $650,000 ≈ $1,685,600
Step 5: Cost of deployment
- Implementation & one-time setup: $600,000
- Annual nearshore + AI platform costs: $900,000
Step 6: Payback
- First-year net benefit = Total benefit - Annual costs = $1,685,600 - $900,000 = $785,600
- Payback on implementation = Implementation / First-year net benefit = $600,000 / $785,600 ≈ 0.76 years (~9 months)

Note: Tailor assumptions for your cost of capital, claim conservative scenario (50% of measured AR days reduction) and best-case scenario. Finance prefers conservative realism.

Sample result section — narrative + metrics

Frame the results with a short narrative and then bullets of core metrics. Example:

"Within six months of launch we saw AR days fall 18 days (56 → 38), CEI improve from 72% to 86%, disputes resolved faster (avg 21 → 8 days), and cash accelerated by ~$14.8M annually. Implementation paid back in 9 months."

AR days: 56 → 38 (-32%)
CEI: 72% → 86%
Dispute rate: 6.2% → 3.1%
Promise-to-pay kept: 44% → 68%
Cost-to-collect per $100k AR: $3,400 → $2,100 (-38%)
FTE-equivalent: 425 invoices/FTE/month → 720 invoices/FTE/month (+69%)

Operational playbook — what vendors and operators must document

Beyond metrics, finance and procurement look for governance and risk controls. Include these documented artifacts in the case study:

Team org chart and roles (AI engineers, collectors, dispute specialists)
Scripts and escalation matrix
Data lineage and field definitions (audit trail of each automated action)
AI governance: model versioning, human-in-the-loop thresholds, confidence scoring
SLA definitions (e.g., contact attempt within 24 hours of aging >60 days)
Compliance & privacy checklist for cross-border data handling

Common objections and how to preempt them

Address these head-on in your case study.

"The AR improvement is seasonal or market-driven." — Show control cohorts and apply difference-in-differences analysis.
"AI made mistakes and created more work." — Provide error rate and rework metrics; show governance steps such as human review thresholds introduced in month 1.
"Savings are just headcount arbitrage." — Present FTE-equivalent productivity, cost-to-collect improvements, and cash acceleration — not just salary delta.
"How do we trust the data?" — Include reconciliation to general ledger and an external audit of the metrics if available.

Visualization recommendations (what to include in slides)

Finance leaders prefer concise visuals. For a 4–6 slide summary include:

Slide 1: One-line outcome + $ impact
Slide 2: AR days trend (rolling 30-day) before/after with control line
Slide 3: Leading indicators (contact rate, disputes closed, PTP kept)
Slide 4: ROI table and payback timeline
Slide 5: Risk mitigations & governance
Slide 6: Next steps for scale

Lessons learned and best practices from 2025–2026 deployments

From multiple implementations observed in late 2025 and early 2026, the fastest successes share common traits:

Start with high-value cohorts: Large invoices, strategic customers, and 60–120 day buckets.
Combine human judgment and AI: Keep humans for subjective disputes, let AI prioritize routine outreach and capture promises.
Iterate scripts weekly: Early A/B tests on messaging produced meaningful lifts in promise-to-pay rates.
Instrument everything: Capture every contact, every AI suggestion, and whether it was accepted to build trust with audit trails.
Govern AI outputs: Apply conservative thresholds for autonomous actions until confidence is proven.

Appendix: Quick metric worksheet (copy/paste friendly)

Annual credit sales: ____________
Baseline AR days: ____________
Post-implementation AR days: ____________
AR days reduction (ΔD): ____________
AR days reduction (ΔD): ____________
Cash accelerated = (Annual credit sales / 365) * ΔD = ____________
Cost of capital (%): ____________
Annual interest saved = Cash accelerated * cost of capital = ____________
Operational savings estimate: ____________
Total annual benefit = interest saved + operational savings = ____________
Implementation costs: ____________
Annual nearshore + platform costs: ____________
First-year net benefit = total annual benefit - annual costs = ____________
Payback months = (Implementation costs / First-year net benefit) * 12 = ____________

Final checklist before publishing the case study

Reconcile KPIs to GL and AR ledger
Validate cohort selection and control group
Include conservative and optimistic scenarios
Attach data export and calculation workbook in the appendix (consider offline-first backup and diagram tools: Tool roundup)
Get sign-off from finance, legal, and operations

Closing: Communicating the win to executives

When you present, lead with the business outcome: AR days reduced, cash accelerated in dollars, and payback in months. Back those headlines with an auditable appendix and a short narrative about operational change — who did what differently, and why it locked in the result. Remember, in 2026 CFOs demand both advanced analytics and strong governance when AI and nearshore teams touch cash collection.

Use this template to create repeatable, finance-ready case studies that convert skeptical procurement and win executive sponsorship for scaled deployments.

Call to action

Ready to build a finance-ready case study for your deployment? Download the free worksheet and ROI calculator, or contact our editors for a tailored case study review and slide pack that your CFO will understand. Show the AR days reduction — and show the cash.

invoicing

Contributor

Senior editor and content strategist. Writing about technology, design, and the future of digital media. Follow along for deep dives into the industry's moving parts.