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Process Map for AI Automation

AI doesn’t work “all or nothing.” Success lies in choosing the right process, the right level of detail, and the right starting point. This map is your compass.

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What is a process map for AI automation?

This is a structured analysis of current operations, focusing on identifying those can be automated using AI — without rewriting the entire business logic. The map doesn’t just list tasks; it evaluates them based on three criteria: repeatability, Structure of input data and availability of examples for training. Result: a prioritized list of processes for the pilot.

Decision-making flowchart: Is the process suitable?

flowchart TD  
    A[Process] --> B{Repeats more than once per week?}  
    B -->|No| Z[Not suitable for AI]  
    B -->|Yes| C{Input data is structured? (text, table, JSON)}  
    C -->|No| Z  
    C -->|Yes| D{Are there examples of the result (at least 20)?}  
    D -->|No| Z  
    D -->|Yes| E[Suitable for AI automation]  
    E --> F[Evaluate ROI: time/errors/satisfaction]

Example: AI in processing incoming support emails

Current process: Client writes in chat/email → Agent reads, classifies (e.g., “bug”, “question”, “complaint”), assigns tag and priority → Routes to CRM.

How AI improves: The model analyzes the email text, predicts the category (85–92% accuracy on typical requests), extracts key entities (order number, date, device), and creates a preliminary CRM ticket with tags and a recommendation (e.g., “redirect to Department X”).

Result: Processing time is reduced from 5–7 minutes to 30–60 seconds; 30% of requests are resolved without agent involvement (FAQ, order status).

Common mistakes

  • We choose an “interesting” process, not a “suitable” one. — AI won’t replace creativity, but it excels at handling routine tasks.
  • Ignoring data quality — If CRM has 40% empty fields or inconsistent status names, AI will be “guessing.”
  • Set the goal: “100% automation” — Better to start with 70% manual work + AI recommendations, rather than 0%.
  • We don’t measure the baseline metric. — Without timing/error measurement before launch, ROI cannot be assessed.

Checklist: Is the Process Ready for an AI Pilot?

CriterionCheck
Frequency≥5 times per week, steady flow
Input dataIs there a template (form, JSON, CSV), or is it easily structured?
Output DataResult — a structured object (ticket, report, response)
Examples≥20 historical cases with correct labels
IntegrationIs there an API or CSV export in the current system?
OwnerAssigned a tester and feedback contact

What does a “minimalist” pilot (pseudocode) look like?

flowchart LR  
    A[Incoming Text] --> B[Preprocessing: Tokenization, Cleaning]  
    B --> C[Classification Model (BERT-light)]  
    C --> D[Entity Extraction (spaCy)]  
    D --> E[JSON Ticket Generation]  
    E --> F[Send to CRM via API]

Example JSON ticket structure:

{
  "source": "email",
  "category": "question",
  "priority": "medium",
  "entities": {
    "order_id": "ORD-2025-04-117",
    "device": "iPhone 15",
    "issue": "confirmation code not received"
  },
  "ai_recommendation": "Reply using template #FAQ-12",
  "confidence": 0.89
}

Next step: from map to pilot

After filling out the card and checklist — select one High-priority, low-complexity process. Launch a 2-week pilot: AI operates in “recommendation” mode; human makes the final decision. Collect metrics: task completion time, number of correct recommendations, agent satisfaction. Use the data to decide: scale up, refine, or defer.

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