The Silent Productivity Killer
Watch your accounts payable team for an hour. Here's what you'll see:
They receive an invoice PDF via email. They open it. They manually type the vendor name into the accounting system. They type the invoice number. They type the amount. They type the line items. They save it. They move to the next invoice.
Repeat 50 times a day. Repeat every day. Forever.
This isn't unique to AP. Contract review teams manually copying terms into spreadsheets. Operations teams transferring purchase order details from PDFs to the ERP. HR teams entering resume data into the hiring system.
Manual data entry can consume up to 40% of an office worker's day. And poor data quality from manual entry? That costs organizations an average of £15 million per year according to Gartner.
40%
Of an office worker's day consumed by manual data entry
£15M
Average annual cost from poor data quality due to manual entry (Gartner)
Why This Got So Bad
Here's the frustrating part: The information is already digital. It's in PDFs, scanned documents, emails, images. It's right there on the screen.
But it's locked in formats your systems can't read. So humans become the translation layer. They read the PDF with their eyes, process it with their brain, and type it into a system with their fingers.
We've spent decades digitizing documents—scanning paper, creating PDFs, enabling paperless workflows. But we didn't automate the last mile. We just changed where humans do the copying.
What Actually Works Now
AI-powered document processing can extract data from any document format—PDFs, images, scanned documents, handwritten forms—with 90%+ accuracy. More importantly:
80-90% reduction
In processing time
Works on unstructured documents
Not just perfect forms
Handles variations and exceptions
Different formats, poor scans, handwriting
Gets better over time
Learns from corrections
This isn't OCR from 2010. This is AI that understands context, handles messy real-world documents, and actually works.
Real-World Examples
Invoice Processing
Before: AP team manually entering data from 200 invoices daily. 15 minutes per invoice. 50 hours/week of pure data entry.
After: AI extracts vendor, amount, line items, and tax details automatically. System routes for approval. Humans verify exceptions only.
Time saved: 85%. Error rate: Cut by 90%.
Contract Review
Before: Legal team manually reading contracts, copying key terms (renewal dates, payment terms, liability clauses) into a tracking spreadsheet.
After: AI reads contracts, identifies key clauses, extracts terms, populates the database. Humans review AI-flagged risks.
Time saved: 70%. Nothing falls through cracks.
Customer Onboarding
Before: Team manually entering customer information from application forms (some digital, some scanned, some handwritten).
After: AI reads all formats, extracts customer data, populates CRM, flags incomplete information. Humans handle exceptions only.
Onboarding time: Cut by 60%.
Expense Management
Before: Employees manually entering receipt data. Finance team verifying and correcting.
After: Photo of receipt gets processed instantly. AI extracts date, vendor, amount, category. Expense report auto-populated.
Time saved per expense: 5 minutes. Multiply across thousands of expenses monthly.
What This Looks Like in 30 Days
We're not trying to automate every document type in your company. We're solving one specific, high-volume problem.
Week 1: Analysis
Pick your biggest document processing pain point. Invoices? Contracts? Purchase orders? Customer forms? We analyze the document types, identify what needs extracting, and determine the downstream systems that need the data.
Week 2-3: Build
We build a system that:
- ✓Reads your specific document types (even poorly scanned or inconsistent formats)
- ✓Extracts the fields you need
- ✓Validates the data
- ✓Routes it to your systems (accounting software, ERP, CRM, whatever you use)
- ✓Flags anything that needs human review
Week 4: Production
Your team uses it in production. Real documents. Real systems. Real time saved.
The Math That Matters
Let's be specific. Company processes 5,000 invoices monthly. Each takes 10 minutes of manual data entry. That's 833 hours per month, or roughly 5 full-time employees worth of work.
Automate 85% of that = 708 hours saved monthly = 4.25 FTEs redirected to higher-value work.
At £40K/year per employee = £170K annually in redirected productivity.
Reduce data entry errors by 90% = Fewer payment errors, better vendor relationships, cleaner books.
Investment: 30-day build. Ongoing AI costs based on volume (pennies per document).
How It Actually Works
Step 1: Document Ingestion
Documents arrive via email, upload portal, or integration with your existing systems. PDFs, images, scans—format doesn't matter.
Step 2: AI Extraction
The system reads the document, identifies key fields (invoice number, vendor, amount, dates, line items), extracts the data, and validates it against business rules.
Step 3: System Integration
Extracted data flows directly into your accounting software, ERP, CRM, or database. No manual entry required.
Step 4: Exception Handling
If the system isn't confident about a field, it flags it for human review. Humans correct it. The system learns from the correction.
Step 5: Continuous Improvement
Over time, accuracy improves as the system learns from your specific document formats and business rules.
What It Doesn't Do
Let's be clear about limitations:
- •It won't be 100% accurate on day one (nothing is)
- •It needs humans to review exceptions and edge cases
- •It works best on repetitive document types (hundreds or thousands of similar documents)
- •It's not worth building for document types you only process occasionally
If you're processing the same type of document repeatedly, in high volume, and it requires manual data entry, automation probably makes sense.
The Honest Assessment
We recommend starting with one document type. The one that's crushing your team's morale. The one where errors cause the most pain. The one with the clearest ROI.
We'll spend 90 minutes analyzing your situation: What documents you're processing, how much time it's consuming, whether AI can handle the variations, what the realistic accuracy will be, and what the ROI looks like.
No sales pitch. Just an honest technical assessment.
If it makes sense, we'll build working software in 30 days. It'll process your actual documents in your actual systems. Your team will use it immediately. You'll know within weeks if the ROI is real.