Comparing Predictive AI, Generative AI, and Agentic AI
Let's distinguish between the three common types of AI.
Predictive AI estimates what is likely to happen.
Examples include:
- Predicting whether a customer will pay late
- Forecasting product demand
- Estimating the likelihood of equipment failure
- Identifying transactions that may contain an error
Generative AI creates new content.
Examples include:
- Drafting a collections email
- Creating a demand-planning summary
- Writing a maintenance instruction
- Generating an explanation of a financial variance
Agentic AI works toward a goal by planning and executing multiple actions.
Examples include:
- Investigating a blocked invoice and routing it for resolution
- Monitoring overdue receivables and initiating approved collection activities
- Reviewing failed integrations and creating remediation tasks
- Coordinating test execution and following up on unresolved defects
- Gathering information from several systems to prepare a period-end close review
A single business scenario may use all three.
For example:
- Predictive AI determines that a customer is likely to pay 15 days late.
- Generative AI creates a summary and drafts a follow-up email.
- Agentic AI reviews the customer account, checks open disputes, selects the appropriate follow-up process, creates a collection task, and routes the email for approval before it is sent.
Reviewing AI Terminology
AI is not one single technology. It is a broad category of technologies that allow machines to perform tasks associated with human intelligence.
| Technology | Description |
|---|---|
| Machine Learning | Enables systems to learn patterns from data rather than relying only on explicitly programmed rules. |
| Pattern Recognition | Allows systems to identify similarities, relationships, recurring structures, and anomalies in that data. |
| Neural Networks | Are a machine-learning technique used to recognize complex patterns. |
| Deep Learning | Uses neural networks with many layers to process large amounts of complex data, including language, images, audio, and documents. |
| Language Models | Use deep learning to identify patterns in human language. |
| Large Language Models (LLMs) | Are language models trained at a very large scale and can apply those patterns to more complex language-based tasks. |
| Predictive AI | Uses learned patterns to estimate what is likely to happen. |
| Generative AI | Uses learned patterns and the context provided to create new content. |
| Agentic AI | Uses AI capabilities to plan and perform a series of actions toward a defined goal. |
In an ERP environment, AI can help identify issues, predict outcomes, explain information, generate useful content, and coordinate business activities.
It does not replace the need for sound process design, clean data, system knowledge, governance, security, or human judgment.
It gives consultants and business users another set of tools for working with information and executing business processes more efficiently.