From: aidotengineer
Integrating AI into products should go beyond simply adding chatbots, focusing instead on proactive assistance that becomes a seamless part of a user’s natural workflow [00:00:02]. This approach aims to genuinely help users by anticipating their needs rather than waiting for explicit prompts [00:01:11].
The Challenge with Current AI Integration
Many current AI implementations involve “slapping on a chat interface” to existing products, which is an easy solution but often fails to provide genuine assistance [00:00:02]. AI interface design is still evolving, and simply copying chat interfaces is not the optimal solution [00:05:13].
A Proactive Approach to AI
The concept of proactive AI draws inspiration from ideas like Microsoft’s Clippy, which had the right idea of anticipating user needs but lacked the technology and timing for proper execution [00:00:57]. Today, technology allows for effective proactive AI [00:01:03]. The goal is for AI to understand what a user needs without being asked [00:01:14].
Examples from Tigon (AI Issue Tracker)
Tigon, an AI issue tracker, demonstrates proactive AI through different modes:
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Suggestion Mode In suggestion mode, AI tracks user input in real-time, understands the context, and interjects at the precise moment with specific, contextual questions that advance the work [00:01:39]. This eliminates the need for a chat window and provides non-generic “how can I help” questions [00:01:52]. For example, if a user reports a legality issue, the AI immediately asks relevant follow-up questions [00:01:22].
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Action Mode Action mode allows the AI to perceive complexity and suggest better ways to organize work, such as splitting a complex issue into sub-issues [00:01:58]. The AI leverages previous data and general understanding to identify sub-issues effectively [00:02:18]. This AI also considers timelines and resources, acting like a project manager that is always paying attention [00:02:35].
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Question Plus Action Mode This mode integrates asking the right questions with actions to help better manage issues [00:03:05].
A core principle of these modes is that all interactions occur within the natural flow of work, avoiding context switching, extra windows, or chat interfaces [00:02:51]. Users maintain control and can easily revert changes with one click [00:03:11]. This approach guides users seamlessly, helping them create better work without interrupting their flow [00:03:20].
Principles for Proactive AI in Products
To foster proactive AI, 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 [00:03:53].
- AI should be part of the natural workflow, not stop it [00:03:56].
Broader Applications of Proactive AI
The pattern of proactive AI can be powerful across various professional tools, supporting integration of AI in business operations and AI in workflow automation and augmentation.
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Code Editors AI can proactively monitor for common pitfalls and suggest improvements, which is particularly useful for developers learning new languages or frameworks [00:04:02].
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Design Tools Imagine a design tool that suggests accessible design improvements as you work, eliminating the need for post-design checks [00:04:15].
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Communication Tools AI could prepare relevant context before meetings or locate documents mentioned during a call, providing an “adviser in your corner” while keeping the user in control [00:04:24].
Implementing Proactive AI in Your Products
When considering integrating AI agents into existing infrastructure, consider the following for evaluating and optimizing AI agents and workflows for benefits and challenges of using AI in workflow:
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Look for Friction Points Identify areas where users must pause their work to seek help; these are opportunities for proactive assistance [00:04:41].
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Identify User Behavior Patterns Recognize where users consistently need help or what questions they frequently ask; these patterns offer clues for automation of manual workflows with AI web agents [00:04:51].
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Consider Context Determine the specific situations where users get stuck, as these are the most impactful areas for AI intervention [00:05:03].
Ultimately, to improve the impact of AI on development workflow, AI interface design is a developing field, and experimentation with unexpected UI solutions is encouraged over simply replicating existing chatbot models [00:05:13].