From: aidotengineer
The current landscape of AI in products often sees companies rapidly adding chatbots, which, while an easy solution, may not always be truly helpful [00:00:02]. Arthur, a product designer at Evil Marti, challenges this approach, advocating for a different perspective on how AI can be integrated into products [00:00:14].
Challenging the Status Quo: Beyond Reactive Chatbots
Inspired by the concept behind Clippy—which had the right idea but terrible execution and timing—modern technology now allows for the proper implementation of proactive AI [00:00:46]. Instead of AI assistants waiting to be asked, the focus shifts to an AI that anticipates user needs [00:01:09].
The development of Tigon, an AI issue tracker, exemplifies this approach by doing the complete opposite of simply slapping on a chat interface [00:00:31]. This proactive AI guides users seamlessly, helping them create better work without breaking their flow [00:03:20].
Types of Proactive AI Assistance in Tigon
Proactive AI in Tigon operates within the natural flow of work, eliminating the need for context switching, extra windows, or chat interfaces [00:02:51]. Users retain control, with changes easily reversible with a single click [00:03:08].
- Suggestion Mode This mode tracks user input in real-time, understands the context, and intervenes at the precise moment with specific, contextual questions that advance the work [00:01:39]. For example, if a user reports a “legality share,” the AI immediately asks relevant, non-generic questions [00:01:22].
- Action Mode When the AI detects complexity, such as an issue that can be divided into sub-issues, it immediately suggests a more organized approach [00:01:55]. This AI leverages historical data and a general understanding of best practices to identify and propose sub-issues, demonstrating true comprehension [00:02:18]. It can even consider timelines and resources, much like an attentive project manager [00:02:35].
- Question Plus Action Mode This combines the proactive questioning and action-oriented suggestions within the existing workflow, helping users better manage issues [00:03:00].
Core Principles for Fostering Proactive AI
To cultivate proactive AI in products, three simple rules are followed [00:03:41]:
- AI should supplement user agency, not replace it [00:03:47].
- AI should offer recommendations, never force them [00:03:50].
- AI should be part of the natural workflow, not stop it [00:03:56].
Applications of Proactive AI Across Professional Tools
This proactive pattern can be incredibly powerful across various professional tools [00:03:36]:
- Code Editors: AI can proactively monitor for common pitfalls and suggest improvements, particularly beneficial for developers learning new languages or frameworks [00:04:02].
- Design Tools: Imagine a tool that suggests accessible design improvements as you work, eliminating the need for post-design checks [00:04:12].
- Communication Tools: AI could prepare relevant context before meetings or find documents mentioned during a call, acting as an adviser while the user retains control [00:04:24].
How to Start Thinking About Proactive AI for Your Products
To begin developing proactive AI for your own products, consider the following [00:03:36]:
- Look for Friction Points: Identify where users typically stop their work to ask for help; these are prime opportunities for proactive assistance [00:04:41].
- Identify User Behavior Patterns: Observe where users consistently need help or what questions they frequently ask; these patterns are clues for automation [00:04:51].
- Think About Context: Determine where users commonly get stuck, as this is where AI can provide the most significant help [00:05:03].
Conclusion
AI interface design is still in its nascent stages, meaning there isn’t a fully formed playbook of best practices yet [00:05:13]. Simply copying and pasting chat interfaces is not the answer [00:05:20]. Therefore, it is crucial to embrace experimentation in products, challenge the status quo, and propose unexpected UI solutions [00:05:26].