Skip to main content

AI Automation: Customer Success Guide

How to maximize AI-powered invoice coding accuracy in your organization.

How the AI Works​

AI learns from your historical invoice data. It identifies patterns between invoice content (vendor, line descriptions, amounts, reference fields) and your approved coding decisions (accounts, cost centers, projects, dimensions).

Key insight: The AI learns from YOUR data. The quality and consistency of your processes directly impacts prediction accuracy.

Important to understand: Achieving high AI accuracy is a journey, not an instant result. As you implement the practices in this guide and build a consistent history of well-coded invoices, prediction accuracy will improve over time.

1. Establish Strong Vendor Processes​

Your ordering process is the foundation for accurate AI predictions.

Best practices:

  • Include relevant details in orders — Project IDs, cost centers, and department codes should be captured at order time
  • Standardize vendor onboarding — New vendors should follow the same data requirements as existing ones
  • Encourage format consistency — When possible, have vendors send invoices in a consistent format (either PDF or e-invoice, not alternating between them)

Why it matters: The AI learns vendor-specific patterns. A well-organized vendor process creates consistent, learnable data.

Watch out for: Vendors who mix between PDF and e-invoice formats, use different invoice layouts, change field labels, or send invoices in multiple languages. These variations make it harder for the AI to establish reliable patterns.

2. Use Systematic Codes, Not People​

Reference persons change. Project IDs and cost centers persist.

Avoid:

  • Coding based on "who requested it"
  • Using personal names as the primary coding signal
  • Relying on tribal knowledge ("Maria always handles IT invoices")

Instead, use:

  • A systematic code based on projects
  • A systematic code based on cost centers
  • A systematic code based on departments
  • A systematic code based on activities
  • A systematic code based on work orders

Why it matters: Systematic identifiers create stable patterns. When a person leaves or changes role, AI predictions based on their name become unreliable. Codes tied to organizational structure remain accurate.

3. Be Consistent When Ordering​

Everyone in your organization should follow the same ordering routine.

Best practices:

  • Document your ordering procedures — Create clear guidelines for how orders should be placed
  • Train all purchasers — Everyone should know what information to include
  • Use templates — Standardized order formats ensure consistent data capture
  • Require key fields — Make project codes, cost centers, or other critical identifiers mandatory

Why it matters: Inconsistent ordering creates noise in your data. When one person includes a project code and another doesn't, the AI receives mixed signals.

4. Be Consistent When Coding​

Apply your coding rules uniformly across all invoices.

Best practices:

  • Document coding guidelines — Clear rules for which accounts to use in different scenarios
  • Avoid ad-hoc decisions — One-off coding choices create exceptions that confuse the AI
  • Review coding consistency — Periodically audit that similar invoices are coded similarly
  • Correct mistakes promptly — If an invoice was miscoded, fix it rather than leaving inconsistent data

Why it matters: The AI learns from your approved invoices. If the same type of expense is coded differently each time, the AI cannot establish a reliable pattern.

5. AI is Smart, But Cannot Read Your Mind​

The AI predicts based on what it can see in the invoice data.

What the AI sees:

  • Invoice header fields (vendor, date, reference numbers, buyer reference)
  • Line item descriptions and amounts
  • Product codes and quantities
  • Additional fields from e-invoices or extracted from PDFs

What the AI cannot see:

  • Internal context you haven't documented
  • Verbal agreements with vendors
  • Information stored only in emails or separate systems

Best practice: If a piece of information drives your coding decision, make sure it appears on the invoice or in linked order data.

6. Strengthen the Connection Between Input and Output​

The AI is strongest when there's a one-to-one relationship between invoice lines and approved accounting lines.

For line-level coding:

Input (Invoice)Output (Coding)Connection Strength
"Consulting services - Project Alpha"Project: ALPHA-001Strong
"Professional services"Project: ALPHA-001Weak
"Services"Project: ALPHA-001Very weak

Best practices:

  • Request detailed line descriptions from vendors — Generic descriptions make prediction difficult
  • Use line references — PO line numbers, project codes, or othe codes on each invoice line
  • Let AI handle consolidation — If multiple invoice lines have the same coding, let the system consolidate them rather than manually merging lines before approval. This maintains the strong link between input and output while still producing clean accounting entries
  • Avoid manual line merging — When you manually combine invoice lines before coding, you break the one-to-one relationship and weaken the AI's ability to learn

Why one-to-one matters:

ApproachAI Learning
3 invoice lines → 3 accounting linesStrong - AI learns what each line means
3 invoice lines → 2 accounting linesWeaker - to distinguish which input drove the output

7. When Mixing Account Types, Provide Clear Signals​

Some invoices require different coding approaches: main accounts for some lines, projects for others.

The challenge: How does the AI know which lines go to projects vs. main accounts?

Solution: Ensure distinguishing signals exist in the invoice data.

Examples of clear signals:

Line DescriptionCoding ApproachSignal
"Project consulting - PROJ-123"Project dimensionProject ID in description
"Monthly software license"Main account onlyNo project reference
"Travel expenses - Cost Center 4500"Cost center dimensionCost center in description

If your vendors don't include these signals: Request that they add project codes, cost center references, or other identifiers to invoice lines.

8. Line-Level Decisions Require Line-Level Features​

If you need to make different coding decisions per line, you need different information per line.

Common issue: Vendor invoices with generic line descriptions like:

  • "Services"
  • "Products"
  • "As per agreement"

The problem: When all lines look the same, the AI cannot distinguish between them.

Solution: Work with your vendors to improve invoice quality:

  • Require itemized invoices — Each line should describe what was delivered
  • Include reference numbers — PO line numbers, project codes, or other codes
  • Specify units and descriptions — "10 hours consulting - Project X" is better than "Consulting"

How to communicate this to vendors:

"To ensure timely payment processing, please include the following on each invoice line: [your specific requirements]"

9. AI Can Decode Complex Reference Codes​

Don't worry if vendors have character limits on reference fields. The AI is smart enough to parse concatenated codes into meaningful signals.

Example: A vendor reference field limited to 15 characters might contain:

Reference CodeAI Interpretation
P123-IT-FIProject: 123, Cost Center: IT, Department: Finance
WO4521-CC200Work Order: 4521, Cost Center: 200
PRJ-MKTG-Q1Project: Marketing, Period: Q1

Why this works:

  • The AI learns patterns from your historical data
  • If you consistently code invoices with P123-IT-FI to Project 123 + Cost Center IT + Department Finance, the AI recognizes the pattern
  • Delimiters (-, _, /) help but aren't required — the AI can learn patterns like P123ITFI too

Best practice: When vendors have field length constraints, encourage them to use compact but meaningful codes rather than truncating or omitting information entirely.

Summary: The Recipe for Success​

PrincipleAction
Strong vendor processMaintain data, use POs, include codes at order time
Systematic codesUse project IDs and cost centers, not people's names
Consistent orderingSame routine for everyone, documented procedures
Consistent codingApply rules uniformly, avoid ad-hoc decisions
Visible informationIf it drives coding, put it on the invoice
One-to-one line mappingKeep invoice lines intact, let AI consolidate if needed
Clear mixed-type signalsDistinguish project lines from expense lines
Line-level featuresRequire itemized invoices with references
Compact reference codesUse meaningful codes even with character limits

Getting Help​

If you have questions about optimizing your AP automation setup, contact your representative. We can analyze your data patterns and provide specific recommendations for your organization.