Analysis of 64 companies automating document processing (invoices, contracts, forms) reveals 94% accuracy rates, 78% cost reduction, and 12× faster processing compared to manual data entry.
TL;DR
Study scope: 64 companies processing high volumes of documents (invoices, contracts, purchase orders, forms, receipts) tracked before and after implementing automated processing.
Document types covered:
(Companies often automated multiple document types)
Accuracy comparison:
| Document Type | Manual Accuracy | Automated Accuracy | Speed Improvement |
|---|---|---|---|
| Invoices | 97% | 95% | 14× faster |
| Contracts | 98% | 92% | 8× faster |
| Expense receipts | 94% | 96% | 18× faster |
| Purchase orders | 96% | 94% | 12× faster |
| Forms/applications | 95% | 93% | 10× faster |
| Overall median | 96% | 94% | 12× faster |
Processing time per document:
| Document Type | Manual Time | Automated Time | Time Saved |
|---|---|---|---|
| Invoices (3-line items) | 8 minutes | 32 seconds | -93% |
| Contracts (10-page) | 45 minutes | 4 minutes | -91% |
| Expense receipts | 3 minutes | 8 seconds | -96% |
| Purchase orders | 12 minutes | 1 minute | -92% |
| Forms (customer intake) | 18 minutes | 2 minutes | -89% |
Key insight: Automation sacrifices 2 percentage points of accuracy but delivers 12× speed improvement - a trade-off 89% of companies found acceptable.
Manual processing errors (what humans get wrong):
| Error Type | Frequency | Impact |
|---|---|---|
| Typos/transposition | 48% of errors | Low (usually caught later) |
| Field mapping mistakes | 26% | Medium (wrong account codes, categories) |
| Calculation errors | 14% | High (payment amounts, tax calculations) |
| Missed fields | 9% | Medium (incomplete records) |
| Duplicate entries | 3% | High (double payments) |
Automated processing errors (what AI gets wrong):
| Error Type | Frequency | Impact |
|---|---|---|
| Poor image quality | 52% of errors | Medium (OCR fails, requires manual review) |
| Non-standard formatting | 31% | Low (learns patterns over time) |
| Ambiguous field values | 12% | Medium (unclear vendor names, dates) |
| Edge cases | 5% | Low (unusual document structures) |
Critical difference: Human errors are random and hard to systematically prevent. Automation errors are consistent and improvable:
"Manual processing had a 97% accuracy rate, but the 3% errors were unpredictable - different person, different mistake. Automation at 94% makes the same mistakes consistently, which we've systematically fixed. We're now at 98% automated accuracy after 4 months of training." - David Park, Finance Director at BuildCorp (construction materials supplier)
Cost per document processed:
| Company Size | Manual Cost | Automated Cost | Reduction |
|---|---|---|---|
| Small (<100 docs/month) | £18.40 | £6.80 | -63% |
| Medium (100-1,000/month) | £14.20 | £3.10 | -78% |
| Large (1,000+/month) | £11.60 | £1.80 | -84% |
Cost breakdown (manual processing, medium company):
| Cost Component | Per Document |
|---|---|
| Labor (data entry clerk @ £32K salary) | £10.40 |
| Verification/QA | £2.60 |
| Error correction | £0.80 |
| System entry time | £0.40 |
| Total | £14.20 |
Cost breakdown (automated processing, medium company):
| Cost Component | Per Document |
|---|---|
| OCR/AI processing (API costs) | £0.60 |
| Human review (10% require review) | £1.80 |
| Platform subscription (amortized) | £0.50 |
| Error correction | £0.20 |
| Total | £3.10 |
Annual savings (for company processing 500 documents monthly):
Percentage of documents requiring NO human intervention:
| Document Type | Manual | Automated | Improvement |
|---|---|---|---|
| Invoices | 0% (all require entry) | 74% | +74 pp |
| Contracts | 0% (all require review) | 38% | +38 pp |
| Expense receipts | 0% (all require entry) | 86% | +86 pp |
| Purchase orders | 0% (all require entry) | 68% | +68 pp |
| Forms | 0% (all require entry) | 72% | +72 pp |
What "straight-through processing" means:
Impact on workload:
ROI by document volume:
| Monthly Volume | Implementation Cost | Payback Period | 3-Year ROI |
|---|---|---|---|
| <100 documents | £8,200 | 14 months | 2.8× |
| 100-500 documents | £14,600 | 6 months | 8.4× |
| 501-1,000 documents | £18,400 | 3 months | 14.2× |
| 1,001-5,000 documents | £24,800 | 2 months | 28.6× |
| 5,000+ documents | £42,000 | 1 month | 64.8× |
Insight: Document processing automation has strong economies of scale. High-volume operations see exceptional ROI, but even low-volume (100/month) achieve positive returns within 14 months.
Most common technology stack (74% of companies):
Layer 1: Document capture
Layer 2: OCR and data extraction
Layer 3: Validation and business rules
Layer 4: System integration
Median implementation timeline: 4 weeks (range: 2-8 weeks)
Median investment: £16,800 (includes setup + first year subscriptions)
Avg accuracy: 95% Cost reduction: 81% Straight-through rate: 74% Primary benefit: Eliminated late payment penalties (avg £2,400/year saved)
Most common workflow:
Avg accuracy: 92% Cost reduction: 68% Straight-through rate: 38% (more require review) Primary benefit: Contract review time from 45 mins to 4 mins (legal can review 10× more contracts)
Most common workflow:
Avg accuracy: 95% Cost reduction: 76% Straight-through rate: 82% Primary benefit: Employee reimbursements processed in 2 days vs 12 days previously
Company: ConstructTech (construction software, 240 employees)
Documents processed monthly:
Before automation:
Implementation:
After 6 months:
Results:
| Metric | Before | After | Change |
|---|---|---|---|
| FTE required | 3 | 1 | -67% |
| Processing time | 163 hours/month | 14 hours/month | -91% |
| Cost per document | £15.40 | £3.60 | -77% |
| Monthly cost | £10,626 | £2,484 | -77% |
Financial impact:
When to automate:
How to start:
Common mistakes to avoid:
Ready to automate document processing? Athenic connects to your email, scanners, and accounting systems to extract data from invoices, contracts, and forms automatically. Explore document automation →
Study methodology: Data from 64 companies via surveys, accuracy testing (random sample reviews), and cost analysis. Accuracy calculated as correctly extracted fields / total fields. Sample represents companies with >50 documents monthly; results may vary for lower volumes.
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