In our previous article, we examined the fundamental differences between rule-based automation and artificial intelligence. Now, using concrete procurement examples, we will explore when each solution is more effective in practice.
Our experience shows that a significant number of companies struggle to identify the areas where artificial intelligence truly adds value.
The following criteria help clarify the differences between the two technologies.
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Most companies struggle to identify where AI creates real value in procurement processes. These four criteria help guide the right technology choice:
1. Data structure
Rule-based automation excels with structured data (numbers, dates) - fast, reliable, auditable. Examples: catalog search by exact SKU, invoice data capture with OCR, template contract population.
AI adds value with unstructured data (PDFs, emails, free text). Examples: product recommendations based on technical specifications, invoice anomaly detection, analyzing thousands of invoices, summarizing 50-page contracts.
2. Repeatability
For stable, repeatable processes, rule-based solutions are efficient. Examples: automatic three-way match approval, standard quarterly survey distribution.
In dynamic, case-specific situations, AI provides added value. Examples: exception handling with email drafts, helpdesk chatbot with natural language responses, QBR performance analysis preparation.
3. Predictive capabilities
For checking clear conditions, rule-based logic suffices. Examples: alerts for document expiration or threshold breaches.
Trend recognition, anomaly detection, and forecasting require AI. Examples: supplier risk assessment monitoring news, suspicious pattern identification from bank account changes, unstructured comment analysis for quality themes.
4. Explainability
When complete auditability is critical, rule-based automation is recommended. Examples: document validation with clear rules, automatic payment scheduling.
For handling complex relationships, AI is necessary. Examples: validating document content against unique requirements, intelligent approver selection.
The best solution: Often combines both - rule-based automation handles routine tasks, while AI takes over where interpretation and adaptation are needed.
The structure of available data
The primary distinction between the two technologies lies in the types of data they can handle effectively.
Structured data is stored in predefined formats with clear fields, such as numbers, dates, and categories. This type of data can be processed efficiently using fixed rules.
Unstructured data, on the other hand, consists of freely formatted texts, PDF documents, and emails written in natural language, all of which require interpretation.
Rule-based automation is effective for structured data, providing speed, reliability, and auditability. However, artificial intelligence becomes necessary when extracting value from unstructured information or interpreting natural language.
Examples
Examples
Process
Rule-based approach
AI-based approach
Catalog management
Catalog search: Keyword search for a specific SKU or item number (e.g., "SKU 123-ABC").
Product recommendation: Suggesting a suitable replacement for out-of-stock "SKU 123-ABC" based on technical specifications and business needs.
Invoice processing
Invoice data entry: Using OCR (optical character recognition) to extract text (e.g., "invoice number", "amount") from PDF invoices and insert it into appropriate fields.
Invoice anomaly detection: Analyzing thousands of invoices to identify suspicious patterns (e.g., a supplier's suddenly changed bank account number).
Contract management
Auto-filling template contracts: Insert the company name, address, tax ID, and bank account number from the database.
Catalog search: Keyword search for a specific SKU or item number (e.g., "SKU 123-ABC").
AI-based approach
Product recommendation: Suggesting a suitable replacement for out-of-stock "SKU 123-ABC" based on technical specifications and business needs.
Invoice processing
Rule-based approach
Invoice data entry: Using OCR (optical character recognition) to extract text (e.g., "invoice number", "amount") from PDF invoices and insert it into appropriate fields.
AI-based approach
Invoice anomaly detection: Analyzing thousands of invoices to identify suspicious patterns (e.g., a supplier's suddenly changed bank account number).
Contract management
Rule-based approach
Auto-filling template contracts: Insert the company name, address, tax ID, and bank account number from the database.
The stability and predictability of processes are important factors to consider.
Repetitive processes always occur in the same way, following a set sequence of steps. In contrast, dynamic processes are unique and require decisions that depend on the specific context of each case.
Rule-based automation is efficient for stable, predictable processes, while artificial intelligence is beneficial when flexibility and tailored handling of individual cases are necessary.
Examples
Examples
Process
Rule-based approach
AI-based approach
Three-way matching
Automatic matching: Automatic matching of purchase order, receipt, and invoice. Automatic approval in case of exact match or allowable variance.
Exception handling: An invoice is flagged due to a discrepancy (e.g., price variance). AI summarizes the issue and drafts an email to the supplier requesting clarification.
Supplier communication
Status check: Supplier logs into a portal to see their invoice's static status (e.g., "Approved", "Scheduled for payment").
Helpdesk chatbot: Supplier writes an email: "Where is the payment for my PO 456?" AI understands the email, checks the status in the system, and generates a human-like response: "We have processed the payment for PO 456, and it is due on Friday."
Quarterly business review (QBR) preparation
Survey distribution: Automatic distribution of a standard supplier satisfaction survey once per quarter.
Performance analysis: Summarizing a quarter's performance data, open issues, and stakeholder feedback.
Three-way matching
Rule-based approach
Automatic matching: Automatic matching of purchase order, receipt, and invoice. Automatic approval in case of exact match or allowable variance.
AI-based approach
Exception handling: An invoice is flagged due to a discrepancy (e.g., price variance). AI summarizes the issue and drafts an email to the supplier requesting clarification.
Supplier communication
Rule-based approach
Status check: Supplier logs into a portal to see their invoice's static status (e.g., "Approved", "Scheduled for payment").
AI-based approach
Helpdesk chatbot: Supplier writes an email: "Where is the payment for my PO 456?" AI understands the email, checks the status in the system, and generates a human-like response: "We have processed the payment for PO 456, and it is due on Friday."
Quarterly business review (QBR) preparation
Rule-based approach
Survey distribution: Automatic distribution of a standard supplier satisfaction survey once per quarter.
AI-based approach
Performance analysis: Summarizing a quarter's performance data, open issues, and stakeholder feedback.
Predictive capabilities
An important question to consider is whether the system can identify recurring patterns and anticipate expected events.
Deterministic logic means the system follows predefined rules - for example, if event X occurs, then response Y will follow.
On the other hand, predictive capabilities allow the system to recognize patterns based on historical data and make informed predictions.
Rule-based automation is effective for checking specific conditions, while artificial intelligence is essential for tasks such as trend monitoring, anomaly detection, and forecasting.
Examples
Examples
Process
Rule-based approach
AI-based approach
Supplier risk assessment
Supplier due diligence: Automatic flagging if a mandatory document expires (insurance or quality certificate) or a required form is missing.
Risk assessment: Continuous monitoring of recent news, financial reports, identifying geopolitical risks, financial problems, and reputational incidents.
Invoicing anomalies
Automatic alert: Alert if the invoice amount exceeds a predefined threshold.
Identifying suspicious patterns: From hundreds of invoices: Bank account changes, unusual amounts, deviations from the supplier's invoicing habits.
Performance evaluation
Performance data display: Calculating quantifiable KPIs based on a given formula for a supplier performance evaluation interface - e.g., 98% on-time delivery, 2% late delivery, 5-day average delivery time.
Performance analysis: Analyzing unstructured comments from stakeholder surveys and emails to identify quality themes such as "poor communication" or "proactive innovation".
Supplier risk assessment
Rule-based approach
Supplier due diligence: Automatic flagging if a mandatory document expires (insurance or quality certificate) or a required form is missing.
AI-based approach
Risk assessment: Continuous monitoring of recent news, financial reports, identifying geopolitical risks, financial problems, and reputational incidents.
Invoicing anomalies
Rule-based approach
Automatic alert: Alert if the invoice amount exceeds a predefined threshold.
AI-based approach
Identifying suspicious patterns: From hundreds of invoices: Bank account changes, unusual amounts, deviations from the supplier's invoicing habits.
Performance evaluation
Rule-based approach
Performance data display: Calculating quantifiable KPIs based on a given formula for a supplier performance evaluation interface - e.g., 98% on-time delivery, 2% late delivery, 5-day average delivery time.
AI-based approach
Performance analysis: Analyzing unstructured comments from stakeholder surveys and emails to identify quality themes such as "poor communication" or "proactive innovation".
Explainability
When choosing the right technology, it’s important to evaluate the level of transparency in the decision-making process.
Complete transparency: Every decision can be traced back to specific rules.
Limited explainability: The system utilizes complex, non-linear relationships.
Rule-based automation is fully auditable, meaning that each decision can be linked to a specific rule. In contrast, artificial intelligence makes decisions based on more intricate patterns, making the reasoning behind those decisions less straightforward.
Examples
Examples
Process
Rule-based approach
AI-based approach
Document validation
Supplier onboarding verification: The supplier cannot proceed on the platform without uploading an insurance certificate.
Document validation: Reading the uploaded insurance certificate to verify that the coverage amount and policy type actually meet the specific requirements stated in the contract.
Approval workflow
Automatic approval management: After invoice approval, automatic scheduling of payment to the payment deadline ensures timely payment.
Intelligent approver selection: Selecting the person with appropriate approval authority based on procurement complexity, urgency, and historical data.
Document validation
Rule-based approach
Supplier onboarding verification: The supplier cannot proceed on the platform without uploading an insurance certificate.
AI-based approach
Document validation: Reading the uploaded insurance certificate to verify that the coverage amount and policy type actually meet the specific requirements stated in the contract.
Approval workflow
Rule-based approach
Automatic approval management: After invoice approval, automatic scheduling of payment to the payment deadline ensures timely payment.
AI-based approach
Intelligent approver selection: Selecting the person with appropriate approval authority based on procurement complexity, urgency, and historical data.
How to decide in practice?
Based on the criteria provided, a clear distinction can be made between when to use rule-based automation and when to leverage artificial intelligence:
Rule-based automation is appropriate when:
Data is structured and in predefined formats
The process is repetitive and rarely changes
No need for forecasting or pattern recognition
Complete transparency and auditability are critical
Artificial intelligence delivers real value when:
Working with unstructured data (PDFs, emails, free text)
Each case is unique and requires context-dependent handling
Pattern recognition, trend tracking, or forecasting is needed
Managing complex relationships is the goal
The most effective solutions often combine both approaches: rule-based automation efficiently manages routine, well-defined tasks, while artificial intelligence takes over in situations that require interpretation, analysis, or adaptation.
Conclusion
Successful automation doesn't mean incorporating AI into every process. Instead, the objective is to use the most suitable technology for each task.
The Fluenta One AI-native workflow automation platform embodies this philosophy by implementing rule-based automation where it is most effective and utilizing artificial intelligence where it adds real value. This strategy ensures that procurement processes are automated in an intelligent and flexible manner.
If you're interested in understanding how rule-based automation and artificial intelligence can fit into your company's procurement processes, please contact us.
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