AI in Automations

Intelligent automation actions that analyze, classify, and transform data using artificial intelligence

Elementum’s AI-powered automation actions enable your workflows to understand, analyze, and act on data intelligently. These actions process unstructured information, make intelligent decisions, and transform data automatically within your automation workflows.

AI Actions Overview

AI actions receive data through variables and produce structured outputs that can be used by subsequent actions in your automation sequence. Each AI action is designed to handle specific types of data processing and analysis tasks.

AI Classification

Automatically categorize and classify data using AI analysis

AI Summarization

Generate concise summaries of lengthy content and documents

Transform Data with AI

Clean, normalize, and enhance data automatically using AI

AI File Analysis

Extract structured information from documents intelligently

AI Classification

Automatically categorize and classify data using AI analysis of content and context.

How It Works

AI Classification analyzes content and assigns appropriate categories, tags, or classifications based on patterns and context. The AI understands the meaning and intent of the content to make accurate categorization decisions.

Use Cases

Support Ticket Categorization

Automatically classify incoming support tickets by type, priority, and department.

Example Flow:

Email Received → AI Classification → Route to Appropriate Team

Input: Email content from customer support

Output: Category (Bug, Feature Request, General Support), Priority (High, Medium, Low)

Lead Qualification

Analyze lead information to determine quality and sales potential.

Example Flow:

Lead Form Submitted → AI Classification → Update Lead Score → Route to Sales Team

Input: Lead form data and company information

Output: Lead Quality (Hot, Warm, Cold), Industry Category, Company Size

Document Classification

Organize documents by type, department, or business process.

Example Flow:

Document Uploaded → AI Classification → File in Appropriate Folder → Notify Stakeholders

Input: Document content and metadata

Output: Document Type (Contract, Invoice, Report), Department, Process Category

Content Analysis

Analyze customer feedback, reviews, or communications for sentiment and topics.

Example Flow:

Customer Feedback Received → AI Classification → Route Based on Sentiment → Create Follow-up Actions

Input: Customer communication or feedback text

Output: Sentiment (Positive, Negative, Neutral), Topic Categories, Urgency Level

Variables and Integration

AI Classification outputs structured data that can be used in subsequent automation actions:

ai_classification.category = "Bug Report"
ai_classification.confidence = 0.92
ai_classification.priority = "High"
ai_classification.department = "Engineering"

AI Summarization

Generate concise summaries of lengthy content, extracting key points and insights for quick understanding.

How It Works

AI Summarization analyzes long-form content and extracts the most important information into digestible summaries. The AI understands context and relationships to create meaningful condensed versions.

Use Cases

Executive Summaries

Convert detailed reports into executive-level summaries for leadership review.

Example Flow:

Monthly Report Generated → AI Summarization → Send to Executive Team → Post to Dashboard

Input: Detailed performance reports, analytics data

Output: Key metrics, trends, recommendations, action items

Customer Feedback Analysis

Summarize customer feedback to identify common themes and issues.

Example Flow:

Customer Survey Responses → AI Summarization → Identify Key Themes → Create Action Plan

Input: Survey responses, feedback forms, support tickets

Output: Common themes, sentiment summary, priority issues

Meeting Notes Processing

Extract key decisions and action items from meeting transcripts or notes.

Example Flow:

Meeting Notes Uploaded → AI Summarization → Extract Action Items → Create Follow-up Tasks

Input: Meeting transcripts, discussion notes

Output: Key decisions, action items, assigned responsibilities

Long-Form Content Digest

Create digestible summaries of lengthy documents, articles, or communications.

Example Flow:

Research Document Uploaded → AI Summarization → Create Brief → Distribute to Team

Input: Research papers, lengthy reports, technical documentation

Output: Key findings, methodology summary, conclusions

Variables and Integration

AI Summarization provides structured summary content:

ai_summarization.summary = "Key project milestones achieved with 15% budget savings"
ai_summarization.key_points = ["Budget savings achieved", "Timeline on track", "Resource optimization"]
ai_summarization.sentiment = "Positive"

Transform Data with AI

Clean, normalize, and enhance data automatically using AI-powered transformation capabilities.

How It Works

Transform Data with AI analyzes data patterns and applies intelligent transformations to standardize, clean, and enhance data quality. The AI understands data context and applies appropriate normalization rules.

Use Cases

Address Standardization

Normalize addresses into consistent formats for better data quality.

Example Flow:

Customer Data Imported → Transform Data with AI → Update Customer Records → Validate Addresses

Input: “123 main st, apt 4, NYC, ny 10001”

Output: “123 Main Street, Apartment 4, New York, NY 10001”

Phone Number Formatting

Standardize phone numbers across different input formats.

Example Flow:

Contact Information Updated → Transform Data with AI → Update Contact Records → Verify Numbers

Input: “5551234567”, “[phone]”, “555.123.4567”

Output: “[phone]”

Company Name Matching

Normalize company names to prevent duplicate entries.

Example Flow:

Lead Data Imported → Transform Data with AI → Check for Duplicates → Merge or Create Records

Input: “Microsoft Corp”, “Microsoft Corporation”, “MSFT”

Output: “Microsoft Corporation”

Data Enrichment

Enhance existing data with additional context and standardization.

Example Flow:

Customer Database Updated → Transform Data with AI → Enrich with Industry Data → Update Profiles

Input: Raw customer data with inconsistent formatting

Output: Enriched, standardized customer profiles with industry classifications

Variables and Integration

Transform Data with AI provides cleaned and enhanced data:

transform_data.standardized_address = "123 Main Street, Suite 200, New York, NY 10001"
transform_data.formatted_phone = "[phone]"
transform_data.company_name = "Microsoft Corporation"
transform_data.confidence_score = 0.95

AI File Analysis

Extract structured information from documents intelligently, understanding context and relationships within the content.

How It Works

AI File Analysis processes various document types and extracts relevant structured data based on document context. The AI understands different document formats and can identify key information automatically.

Use Cases

Contract Analysis

Extract key terms, parties, dates, and obligations from legal contracts.

Example Flow:

Contract PDF Uploaded → AI File Analysis → Create Contract Record → Set Renewal Reminders

Input: Legal contract document

Output: Parties, effective dates, terms, obligations, renewal dates

Invoice Processing

Extract vendor information, amounts, line items, and due dates from invoices.

Example Flow:

Invoice Uploaded → AI File Analysis → Create Invoice Record → Route for Approval

Input: Invoice PDF or image

Output: Vendor name, invoice amount, line items, due date, tax amount

Resume Screening

Extract skills, experience, and education from candidate resumes.

Example Flow:

Resume Uploaded → AI File Analysis → Update Candidate Profile → Score Against Requirements

Input: Resume document in various formats

Output: Skills, experience, education, contact information, certifications

Report Analysis

Extract key metrics, trends, and insights from business reports.

Example Flow:

Report Uploaded → AI File Analysis → Extract Key Metrics → Update Dashboard

Input: Business reports, financial statements, analytics reports

Output: Key metrics, trends, performance indicators, insights

Variables and Integration

AI File Analysis provides structured document data:

ai_file_analysis.contract_parties = ["Company A", "Company B"]
ai_file_analysis.effective_date = "2024-01-15"
ai_file_analysis.contract_value = "$50,000"
ai_file_analysis.renewal_date = "2025-01-15"

AI Agents in Automations

Deploy AI agents within your automation workflows to handle complex interactions and decision-making processes.

Giving an Agent a Task

AI agents can be assigned specific tasks within automation workflows, allowing them to handle complex interactions that require understanding, reasoning, and multi-step processing.

Task Assignment Process

1. Context Preparation: Gather relevant data and context for the agent
2. Task Definition: Clearly define what the agent should accomplish
3. Agent Execution: The agent processes the task using its capabilities
4. Result Integration: Use the agent’s output in subsequent automation actions

Example: Customer Support Agent Task

Support Ticket Created → Gather Customer Context → Assign Agent Task → Process Agent Response → Update Ticket

Agent Task Configuration:

• Input Data: Customer history, ticket details, product information
• Task Instructions: “Analyze the customer issue and provide a recommended solution with steps”
• Expected Output: Structured response with solution steps and escalation recommendations

Agent Task Variables

When an agent completes a task, it provides structured output:

agent_task.solution_steps = ["Step 1: Verify account", "Step 2: Reset password", "Step 3: Test access"]
agent_task.escalation_needed = false
agent_task.estimated_resolution_time = "15 minutes"
agent_task.confidence_score = 0.89

Agent Conversation Ended Trigger

The Agent Conversation Ended trigger captures the completion of agent interactions and makes the conversation context available for further automation processing.

Trigger Activation

This trigger fires when:

• An AI agent completes a conversation with a user
• The conversation reaches a natural conclusion
• The agent determines the interaction is complete

Available Context

When the trigger fires, it provides access to:

• Complete conversation history
• Agent decisions and recommendations
• User satisfaction indicators
• Conversation metadata (duration, topics, resolution status)

Example Usage

Agent Conversation Ended → AI Summarization → Create Follow-up Tasks → Update Customer Record → Send Satisfaction Survey

Use Cases:

• Generate conversation summaries for record keeping
• Create follow-up tasks based on agent recommendations
• Update customer profiles with interaction insights
• Trigger post-conversation workflows

Conversation Context Variables

conversation.summary = "Customer password reset completed successfully"
conversation.resolution_status = "Resolved"
conversation.satisfaction_score = 4.5
conversation.duration_minutes = 8
conversation.topics = ["password reset", "account security"]
conversation.next_actions = ["Follow up in 24 hours", "Send security tips"]

AI Action Patterns

Sequential AI Processing

Chain multiple AI actions for comprehensive data processing:

Document Uploaded → AI File Analysis → AI Classification → AI Summarization → Transform Data with AI → Create Records

Conditional AI Logic

Use AI results to make automation decisions:

Customer Email → AI Classification → 
  IF category = "Complaint" → AI Summarization → Route to Manager
  OTHERWISE → Standard Response → Route to Support Team

Agent-Assisted Workflows

Combine AI agents with automation actions:

Complex Issue Identified → Assign Agent Task → Agent Conversation Ended → AI Summarization → Create Action Plan → Notify Team

Best Practices

Data Quality for AI Actions

Provide Context: Include relevant background information in AI action inputs

Use Clear Variables: Name variables descriptively for better AI understanding

Validate Outputs: Check AI action results before using them in subsequent actions

Performance Optimization

Batch Processing: Group similar AI operations when possible

Selective Analysis: Use conditions to apply AI actions only when necessary

Result Caching: Store AI results in variables for reuse within the same automation

Error Handling

Confidence Thresholds: Use AI confidence scores to validate results

Fallback Logic: Provide alternative actions when AI analysis fails

Human Review: Route low-confidence results to human reviewers

Integration Examples

Customer Support Automation

Complete customer support workflow with AI integration:

Email Received → AI Classification → 
  IF category = "Technical Issue" → 
    AI File Analysis (attachments) → 
    Assign Agent Task → 
    Agent Conversation Ended → 
    AI Summarization → 
    Create Knowledge Base Entry

Document Processing Pipeline

Automated document processing with AI analysis:

Document Uploaded → AI File Analysis → Transform Data with AI → AI Classification → 
  IF type = "Contract" → Create Contract Record → Set Renewal Reminders
  IF type = "Invoice" → Create Invoice Record → Route for Approval

Lead Processing Workflow

Intelligent lead qualification and routing:

Lead Form Submitted → Transform Data with AI → AI Classification → 
  IF quality = "Hot" → Assign Agent Task → Immediate Follow-up
  IF quality = "Warm" → Add to Nurture Campaign
  IF quality = "Cold" → Add to Long-term Follow-up

Monitoring AI Actions

Performance Metrics

Track AI action effectiveness:

• Accuracy: How often AI classifications match expected results
• Confidence Scores: Average confidence levels across AI actions
• Processing Time: Time taken for AI analysis
• Success Rate: Percentage of successful AI action executions

Quality Assurance

Regular Review: Periodically review AI action outputs for accuracy

Feedback Loop: Use human corrections to improve AI performance

Version Tracking: Monitor changes in AI model performance over time

Next Steps

Automation System

Learn how to build complete automation workflows with AI actions

Actions Reference

Explore all available automation actions and their configurations

Agent Architecture

Understand how AI agents work and integrate with automations

Best Practices

Follow proven patterns for implementing AI-powered workflows