In AI systems, automation refers to how much control is delegated to the AI vs user. This is a strategic UX pattern to decide degree of automation based upon user pain-point, context scenarios and expectation from the product.
Levels of automation
No automation (AI assists but user decides)
The AI system provides assistance and suggestions to the user but requires the user to make all the decisions. E.g., Grammarly highlights grammar issues but the user accepts or rejects corrections.
Partial automation/ co-pilot/ co-editor (AI acts with user oversight)
The AI initiates actions or generates content, but the user reviews or intervenes as needed. E.g., GitHub Copilot suggest code that developers can accept, modify, or ignore.
Full automation (AI acts independently)
The AI system performs tasks without user intervention, often based on predefined rules, tools and triggers. Full automation in GenAI are often referred to as Agentic systems. E.g., Ema can autonomously plan and execute multi-step tasks like researching competitors, generating a report and emailing it without user prompts or intervention at each step.
How to use this pattern
Evaluate user pain point to be automated and risk involved: Automating tasks is most effective when the associated risk is low without severe consequences in case of failure. Low-risk tasks such as sending automated reminders, promotional emails, filtering spam emails or processing routine customer queries can be automated with minimal downside while saving time and resources. High-risk tasks such as making medical diagnoses, sending business-critical emails, or executing financial trades requires careful oversight due to the potential for significant harm if errors occur.
Evaluate and design for particular automation level: Evaluate if user pain point should fall under — No Automation, Partial Automation or Full Automation based upon user expectations and goals.