What Documents Can AI Actually Process?

You've heard the promises: "AI can read any document!" "Automate all your paperwork!" "100% accuracy guaranteed!"

Then you try it on your company's faded 1987 fax-of-a-photocopy, and suddenly the AI thinks your invoice total is a phone number.

Here's the truth: AI document processing has made incredible strides, but it's not magic. It works brilliantly on some documents and struggles with others. Understanding the difference will save you weeks of frustration and thousands of dollars in failed implementations.

This guide cuts through the hype. You'll learn exactly which documents AI can reliably process today, which ones require human oversight, and which ones you shouldn't bother automating yet.

The Document Processing Reality Check

Modern AI-powered OCR systems like Scanny AI use vision-based models (Google Gemini Vision, GPT-4 Vision) that can understand document context, not just extract text. This is fundamentally different from traditional OCR.

The difference matters:

Traditional OCR reads text character-by-character. It sees "123.45" but doesn't know if that's a price, a measurement, or a date.

Vision-based AI understands semantic meaning. It knows that "123.45" next to "Total:" at the bottom of an invoice is the amount due, not a product code.

But understanding context doesn't mean it works on everything. Let's break down what actually works.

Documents AI Processes Exceptionally Well

1. Invoices & Bills

Success Rate: 95-98% (on clear, digital documents)

Invoices are the gold standard for AI document processing. Why? They follow predictable patterns even when layouts vary.

What AI extracts reliably:

• Vendor name and address
• Invoice number and date
• Line items (description, quantity, unit price)
• Subtotals, tax, and total amounts
• Payment terms

Sample JSON Schema for Invoice Processing:

{
  "fields": [
    {
      "name": "vendor_name",
      "type": "string",
      "description": "Company or individual issuing the invoice"
    },
    {
      "name": "invoice_number",
      "type": "string",
      "description": "Unique invoice identifier"
    },
    {
      "name": "invoice_date",
      "type": "date",
      "description": "Date invoice was issued"
    },
    {
      "name": "due_date",
      "type": "date",
      "description": "Payment due date"
    },
    {
      "name": "line_items",
      "type": "array",
      "description": "Individual products or services",
      "items": {
        "description": "string",
        "quantity": "number",
        "unit_price": "number",
        "amount": "number"
      }
    },
    {
      "name": "subtotal",
      "type": "number",
      "description": "Amount before tax"
    },
    {
      "name": "tax_amount",
      "type": "number",
      "description": "Total tax charged"
    },
    {
      "name": "total_amount",
      "type": "number",
      "description": "Final amount due"
    }
  ]
}

With Scanny AI, you define this schema once, and the system automatically extracts structured data from every invoice format you encounter—no template training required.

2. Receipts

Success Rate: 90-95% (varies by receipt quality)

Receipts are slightly trickier than invoices because they're often thermal-printed (fading ink), crumpled, or photographed in poor lighting.

What works:

• Store name and location
• Transaction date and time
• Individual items purchased
• Payment method
• Total amount

What can be challenging:

• Faded thermal paper (common after 6-12 months)
• Handwritten notes or signatures
• Crumpled or torn receipts

Pro Tip: If you're building an expense management system, capture receipt photos immediately. Thermal receipts fade to blank within a year.

3. Purchase Orders

Success Rate: 92-96%

Purchase orders are highly structured business documents, making them ideal candidates for automation.

Reliably extracted data:

• PO number
• Buyer and supplier information
• Order date and delivery date
• Line items with part numbers
• Quantities and pricing
• Shipping instructions

4. Identity Documents (IDs, Passports, Driver's Licenses)

Success Rate: 97-99% (government-issued documents)

These documents have standardized formats and security features that actually make them easier to process.

Sample JSON Schema for ID Verification:

{
  "fields": [
    {
      "name": "document_type",
      "type": "string",
      "description": "Type of ID (passport, driver_license, national_id)"
    },
    {
      "name": "document_number",
      "type": "string",
      "description": "Official document number"
    },
    {
      "name": "full_name",
      "type": "string",
      "description": "Person's legal name as shown on ID"
    },
    {
      "name": "date_of_birth",
      "type": "date",
      "description": "Birth date"
    },
    {
      "name": "expiration_date",
      "type": "date",
      "description": "When document expires"
    },
    {
      "name": "issuing_country",
      "type": "string",
      "description": "Country that issued the document"
    },
    {
      "name": "address",
      "type": "string",
      "description": "Current address (if shown)"
    }
  ]
}

Compliance Note: When processing identity documents, ensure you're following KYC (Know Your Customer) regulations and data protection laws like GDPR.

5. Bank Statements

Success Rate: 88-93%

Bank statements are well-structured but vary significantly by institution.

What AI handles well:

• Account holder name
• Account number (partial, for security)
• Statement period dates
• Transaction lists (date, description, amount, balance)
• Beginning and ending balance

Challenge: Different banks use wildly different formatting. Your AI system needs to handle Chase's layout alongside a local credit union's format.

Documents AI Processes with Human Review

These document types can be processed by AI, but you should implement a human-in-the-loop workflow for verification.

6. Resumes / CVs

Success Rate: 85-90% (requires validation)

Resumes are semi-structured—there are common sections (Education, Experience), but candidates format them creatively.

What AI can extract:

• Contact information
• Work history (companies, titles, dates)
• Education (schools, degrees, graduation years)
• Skills lists
• Certifications

What requires human review:

• Ambiguous job titles
• Overlapping employment dates (was this a side gig or main job?)
• Unconventional formatting
• Relevance assessment (AI can't judge "culture fit")

Sample Resume Processing Schema:

{
  "fields": [
    {
      "name": "full_name",
      "type": "string"
    },
    {
      "name": "email",
      "type": "string"
    },
    {
      "name": "phone",
      "type": "string"
    },
    {
      "name": "work_experience",
      "type": "array",
      "items": {
        "company": "string",
        "title": "string",
        "start_date": "date",
        "end_date": "date",
        "responsibilities": "string"
      }
    },
    {
      "name": "education",
      "type": "array",
      "items": {
        "institution": "string",
        "degree": "string",
        "field_of_study": "string",
        "graduation_date": "date"
      }
    },
    {
      "name": "skills",
      "type": "array",
      "items": "string"
    }
  ]
}

7. Contracts & Legal Agreements

Success Rate: 80-88% (high-stakes documents need review)

AI can extract key contract data, but legal documents require precision that demands human oversight.

What AI handles:

• Party names and addresses
• Contract dates (effective, termination, renewal)
• Payment terms
• Key obligations and deliverables
• Standard clauses

What needs human review:

• Legal interpretation of ambiguous clauses
• Non-standard terms
• Amendments and addendums
• Risk assessment

Use Case: AI can flag that a contract's auto-renewal clause kicks in 90 days before expiration, but a lawyer should review the termination provisions.

8. Medical Forms & Prescriptions

Success Rate: 75-85% (highly regulated)

Healthcare documents are challenging due to handwriting, medical terminology, and strict compliance requirements (HIPAA in the US).

What works:

• Printed patient information forms
• Digital prescriptions
• Lab results with standard formats

What's difficult:

• Handwritten doctor's notes
• Prescription abbreviations (is "qd" daily or four times daily? Mistakes are dangerous)
• Diagnosis codes (ICD-10 codes are highly specific)

Recommendation: Always use human verification for medical documents. The cost of an error far exceeds automation savings.

Documents AI Still Struggles With

Let's be honest about current limitations. These document types are possible to process but require significant custom development or produce unreliable results.

9. Handwritten Notes

Success Rate: 60-75% (varies wildly by handwriting quality)

AI can read printed text brilliantly. Handwriting? It's hit-or-miss.

What works:

• Block letters (all caps, clearly separated)
• Forms with printed labels and handwritten values
• Digital handwriting (Apple Pencil, stylus input)

What doesn't:

• Cursive writing (especially older styles)
• Doctor's handwriting (the stereotype exists for a reason)
• Notes with cross-outs and corrections

Reality Check: If your workflow depends on handwritten forms, consider switching to digital forms or plan for manual data entry.

10. Degraded or Low-Quality Documents

Success Rate: 40-70% (depends on degradation severity)

Even the best AI can't read what isn't there.

Problematic scenarios:

• Faxed documents (especially multi-generation faxes)
• Photocopies of photocopies
• Water-damaged or stained documents
• Extremely low-resolution scans (below 150 DPI)
• Heavily compressed images

Minimum requirements for reliable processing:

• Resolution: 300 DPI minimum
• Format: PDF or clear JPEG/PNG images
• Contrast: Black text on white background (or vice versa)
• Orientation: Properly rotated (though AI can often auto-correct this)

11. Multi-Column Layouts & Complex Tables

Success Rate: 65-80% (improving rapidly)

Documents with newspaper-style columns, embedded tables within tables, or mixed text-and-graphic layouts can confuse AI about reading order.

Challenging formats:

• Academic journals with multiple columns
• Catalogs with product grids
• Financial reports with complex nested tables
• Forms with checkboxes and fill-in fields intermixed

The good news: Modern vision-based AI (like Gemini Vision used by Scanny AI) is significantly better at this than traditional OCR, as it understands spatial relationships.

The Manual Way vs. The Scanny AI Way

Aspect	Manual Data Entry	Traditional OCR	Scanny AI (Vision-Based)
Setup Time	None (just start typing)	Hours to weeks (template training)	Minutes (define schema once)
Accuracy Rate	92-96% (human errors)	80-85% (rigid templates)	95-98% (context-aware)
Processing Speed	5-10 min per document	30-60 seconds	10-20 seconds
Handles Format Variations	Yes (humans adapt)	No (breaks with new formats)	Yes (learns patterns)
Cost per 1,000 Docs	$200-400 (labor)	$50-100 (software + fixes)	$10-30 (API costs)
Scalability	Hire more staff	Add more servers	Instant (cloud-based)
Multi-Language	Need bilingual staff	Requires retraining	Built-in (100+ languages)

Key Takeaway: Vision-based AI like Scanny doesn't need template training. You define what data you want (the JSON schema), and it figures out where to find it—even when invoice layouts change.

How to Maximize AI Document Processing Success

Based on processing millions of documents, here are proven best practices:

1. Start with High-Volume, Structured Documents

Don't begin your automation journey with complex legal contracts. Start with invoices, receipts, or purchase orders where you'll see immediate ROI.

2. Define Clear Schemas

The more specific your schema, the better your results. Instead of a generic "date" field, specify "invoice_date" vs. "due_date" vs. "delivery_date."

3. Implement Confidence Scoring

Modern AI systems return confidence scores for each extracted field. Set thresholds:

• 95-100% confidence: Straight-through processing (no review)
• 80-94% confidence: Flag for quick human review
• Below 80%: Manual processing

4. Build Feedback Loops

When humans correct AI mistakes, feed that data back into your system. With Scanny AI, corrections improve accuracy over time through workflow learning.

5. Optimize Document Capture

Garbage in, garbage out. Train your team to:

• Use document scanners (not phone cameras) when possible
• Ensure adequate lighting
• Flatten crumpled documents
• Use color scanning for multi-color forms

Real-World Workflow Examples

Accounts Payable Automation

Input: Vendors email invoices to invoices@yourcompany.com

Processing:

1. Email integration auto-forwards to Scanny AI
2. AI extracts vendor, amount, line items, due date
3. Data pushed to your ERP (SAP, NetSuite, QuickBooks)
4. Approval workflow triggered based on amount thresholds
5. Payment scheduled automatically

Result: 80% of invoices processed with zero human touch. 20% flagged for review (unusual amounts, new vendors).

HR Resume Screening

Input: Applicants upload resumes to your ATS

Processing:

1. Scanny extracts skills, experience, education
2. JSON data sent to your ATS
3. Auto-scoring based on required qualifications
4. Top candidates flagged for recruiter review
5. Rejected candidates auto-notified

Result: Recruiters spend time interviewing, not data entry.

Expense Report Processing

Input: Employees photograph receipts via mobile app

Processing:

1. Receipt photo uploaded to Scanny
2. Merchant, date, amount, category extracted
3. Data synced to expense management system
4. Policy violations flagged (over meal limits, missing approval)
5. Compliant expenses auto-approved

Result: Expense reports submitted in seconds, not hours.

The Future of Document AI (What's Coming)

AI document processing is evolving rapidly. Here's what's on the horizon:

2025-2026 Improvements:

• Better handwriting recognition (90%+ accuracy expected)
• Real-time processing (results in under 5 seconds)
• Cross-document reasoning (AI connects invoice to PO to delivery receipt)
• Anomaly detection (flags suspicious invoices automatically)
• Multi-modal understanding (processes text + images + charts together)

What won't change: The need for realistic expectations. AI will get better, but it won't be perfect. High-stakes documents (legal contracts, medical records) will always benefit from human oversight.

Setting Realistic Expectations: A Framework

Before automating any document workflow, ask yourself:

1. What's the cost of an error?

• Low cost: Product catalog data entry (fix it later)
• Medium cost: Invoice processing (costs time to correct)
• High cost: Medical prescriptions (could harm patients)

High-cost errors require human verification, no matter how good your AI is.

2. How standardized are your documents?

• Highly standardized: Government forms, utility bills (95%+ accuracy)
• Somewhat standardized: Invoices, receipts (90-95% accuracy)
• Not standardized: Legal contracts, emails (80-90% accuracy)

3. What's your volume?

• Under 100/month: Manual processing might be faster
• 100-1,000/month: AI automation pays off quickly
• Over 1,000/month: AI is essential for scalability

4. Do you have clean source documents?

If your documents are faded faxes from 1995, spend time digitizing properly before automating.

Making the Decision: Should You Automate?

Automate immediately if:

• You process 100+ similar documents monthly
• Documents are digital or high-quality scans
• Data entry takes 5+ minutes per document
• You have clear downstream systems (CRM, ERP) to integrate with

Consider hybrid (AI + human) if:

• Documents have high variability
• Errors have moderate consequences
• You're in a regulated industry
• You have complex validation rules

Stick with manual processing if:

• Volume is under 50 documents monthly
• Documents are highly degraded
• Errors could cause legal/safety issues
• You lack systems to integrate with

Bottom Line: AI document processing works brilliantly on the right documents. The key is knowing which ones—and setting your workflows up for success.

Getting Started with Scanny AI

Ready to see what AI can do for your documents? Here's how to start:

Step 1: Identify Your Use Case

Pick one high-volume document type (invoices, receipts, resumes, etc.).

Step 2: Define Your Schema

What data do you need? Use the JSON schema examples above as templates.

Step 3: Test with Real Documents

Upload 20-30 sample documents to Scanny AI and see the results. You'll immediately see what works and what needs refinement.

Step 4: Integrate with Your Systems

Connect Scanny to your CRM, ERP, or database via API. Documents flow in, structured data flows out—automatically.

Step 5: Monitor and Optimize

Track accuracy rates, review flagged documents, and adjust your schemas based on real-world performance.

Conclusion: Work Smarter, Not Harder

AI can't process every document perfectly—but it doesn't need to. It just needs to handle the repetitive, high-volume documents that drain your team's time.

The reality:

• Invoices, receipts, IDs, bank statements? AI handles these brilliantly (95%+ accuracy).
• Resumes, contracts, forms? AI extracts data reliably, but plan for human review.
• Handwritten notes, degraded faxes? AI struggles here. Fix your document capture process first.

The companies winning with document automation aren't trying to automate everything. They're strategically automating the 80% of documents that AI handles well, freeing their teams to focus on the complex 20% that requires human judgment.

Ready to automate your document workflows? Start your free Scanny trial today and see which of your documents AI can process in minutes, not hours.

Already using document automation? Log in to explore new document types and integrations.

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Have questions about whether AI can handle your specific document types? Reach out to the Scanny team—we've processed millions of documents and can tell you exactly what to expect.