AI Opportunities
Understand the AI automation opportunities identified in your workflows and how to take action on them.
What are AI Opportunities?
AI opportunities are specific points in your workflow where artificial intelligence can automate, enhance, or optimize tasks. When you analyze a workflow, CogniFlow identifies these opportunities and maps them to one of six AI primitives.
How Opportunities are Found
Our AI analyzes your workflow descriptions, looking for patterns that match common automation use cases. It considers the type of task, data involved, and potential for improvement.
The 6 AI Primitives
Every AI opportunity maps to one of these fundamental AI capabilities:
Automatically categorizing items, routing requests, or making decisions based on defined criteria.
Creating content, documents, responses, or other outputs from input data.
Converting data from one format to another or restructuring information.
Pulling specific information from documents, forms, or unstructured data.
Making logical conclusions, analyzing information, or providing recommendations.
Forecasting future outcomes, trends, or behaviors based on historical data.
Viewing Opportunities
After running analysis, you can view AI opportunities for each node:
Taking Action on Opportunities
Once you've identified AI opportunities, here's how to move forward:
Prioritize
Start with high-impact, low-complexity opportunities. Look for tasks that are repetitive and time-consuming.
Evaluate Tools
Research AI tools that match the identified primitive. Many solutions exist for common use cases.
Pilot First
Test automation on a small scale before full implementation. Measure results and refine.
Scale Up
Once proven, expand the automation to similar processes across your organization.
Common Automation Patterns
Quick Wins
- • Email triage - Classification to auto-route or prioritize
- • Document processing - Extraction from forms and invoices
- • Report generation - Generation of summaries and updates
- • Data entry - Transformation of data between systems
- • Customer inquiries - Classification and suggested responses
Implementation Considerations
- • Ensure data quality before automating - AI needs good input to produce good output
- • Consider compliance and security requirements for your industry
- • Plan for human oversight, especially for critical decisions
- • Train your team on working alongside AI tools
Need help?
Check our troubleshooting guide for common issues.