From: aidotengineer
Current trends in AI implementation often focus on integrating chatbots into products, which is seen as an easy but often unhelpful solution [00:00:02]. This approach is problematic because it fails to genuinely assist users [00:00:10].
Issues with Reactive AI and Chatbot Interfaces
The prevailing method for AI assistance makes AI reactive, waiting for user input and specific questions [00:01:09]. This leads to:
- Generic Interactions: AI often asks non-specific “how can I help” questions [00:01:30].
- Lack of Context: Most AI assistants do not proactively understand the user’s immediate needs or work context [00:02:03].
- Workflow Disruption: Chat interfaces introduce contact switching and extra windows, breaking the natural flow of work [00:02:51].
- Inefficiency: Users waste time trying to explain their needs to the AI [00:02:23].
A Proactive AI Approach: The Tigon Example
Inspired by the concept behind Clippy—a system with the right idea but poor execution and timing—modern technology allows for a proactive AI approach [00:00:48]. The AI issue tracker, Tigon, demonstrates this by having AI anticipate user needs rather than waiting for commands [00:01:11].
Proactive AI Modes
- Suggestion Mode: The AI tracks user input in real-time, understands the context, and offers specific, contextual questions or suggestions at the opportune moment, without needing a chat window [00:01:41].
- Example: When a user reports a bug, the AI immediately asks relevant follow-up questions instead of generic ones [00:01:22].
- Action Mode: The AI can recognize complex tasks and suggest better ways to organize work based on previous data and understanding of optimal organization [00:02:06]. This shows the AI’s understanding of the domain [00:02:29].
- Example: If a user writes an issue that could be split into sub-issues, the AI identifies and suggests this division, considering timelines and resources like a project manager [00:02:00], [00:02:38].
- Question Plus Action Mode: This combines questioning with proactive action, allowing the AI to ask questions and help manage issues within the natural workflow, seamlessly guiding the user [00:03:02].
User Control
Users retain control over proactive AI suggestions, with the ability to revert changes with a single click [00:03:11].
Principles for Proactive AI Design
To foster proactive AI in products, three simple rules can be followed:
- Supplement User Agency: AI should enhance, not replace, the user’s control [00:03:47].
- Offer Recommendations, Never Force: AI should provide suggestions without imposing them on the user [00:03:50].
- Part of Natural Workflow, Not a Stop: AI should integrate seamlessly into the user’s tasks without causing interruptions [00:03:56].
Applications of Proactive AI
This proactive pattern can be applied across various professional tools:
- Code Editors: AI could proactively monitor for common pitfalls and suggest improvements, especially beneficial for developers learning new languages or frameworks [00:04:02].
- Design Tools: Tools could make real-time suggestions for accessible design, eliminating the need for post-design checks [00:04:14].
- Communication Tools: AI could prepare relevant context before meetings or locate documents mentioned during calls, acting as a personal advisor [00:04:24].
Shifting Towards Better AI Interface Design
To implement proactive AI, consider the following:
- Identify Friction Points: Look for areas where users stop to ask for help; these are opportunities for proactive assistance [00:04:41].
- Analyze User Behavior Patterns: Understand where users frequently need help or what questions they consistently ask to identify automation clues [00:04:51].
- Focus on Context: Determine where users get stuck; this is where AI can provide the most valuable help [00:05:03].
The field of AI interface design is still evolving, and simply copying existing chatbot interfaces is not the optimal solution [00:05:13]. Experimentation and challenging the status quo with unexpected UI solutions are encouraged [00:05:26].