Drop-In Clarity: Designing for Non-Linear Paths in AI Products
Jul 6, 2025
If you walked into a movie halfway through, would you still understand what’s going on?
In great films, the answer is often yes. You may not know how the characters got where they are, but you understand what they are dealing with. The tension is clear. The objective is visible. You can follow the thread.
That kind of clarity is not accidental. It is the result of structure, pacing, and repetition. Every scene carries its own narrative weight. Even if the viewer misses the setup, the story still makes sense.
In product design, we rarely design with that assumption. Interfaces are often built around linear flows. We imagine users entering at step one and proceeding forward. But that is not how AI-powered tools are used.
AI introduces a new kind of interaction where users no longer follow predefined paths. They ask for an outcome and land directly at the result. It's a non-linear path.
Consider a CRM assistant. A user might type, “Draft a follow-up email to all leads from last week’s webinar.” The system then responds with a polished email. The user never applied filters, they did not review the list, and they may not even remember what the webinar covered. They are dropped into the final screen without seeing the steps that got them there.
This type of interaction is becoming the norm across AI systems. In GitHub Copilot, code is written on behalf of the user before they think through the structure. In Notion AI, summaries appear without reviewing the source text. Even in productivity tools like Superhuman or Salesforce Einstein, AI-driven suggestions increasingly bypass traditional UI workflows.
These shortcuts are not flaws. They are the promise of AI. But when the interface jumps ahead, it often leaves the user behind.
The result is a quiet form of disorientation. The user gets what they asked for, but not the context that makes it feel trustworthy or editable. When this happens repeatedly, confidence in the system declines, even if the outputs are technically accurate.
At Human Intervention, we are exploring what it means to design for this new kind of path. We call it Drop-In Clarity.
The idea is simple. Even if a user skips the steps that usually build context, the product should still make sense. It should provide clarity around what the AI did, what it used, and what assumptions it made. It should make it obvious what the user can do next. It should support editing, reviewing, or rewinding the process without requiring a full restart.
This is not about making AI explain itself in detail. It is about restoring narrative integrity within the interface. When AI helps users jump ahead, Drop-In Clarity ensures they land in the right place, with their bearings intact.
As we build this framework, we are asking a few core questions:
How can we let users see what the AI knows and what it’s assuming?
How can we reveal the decisions AI skipped?
How can we make the next step obvious?
How can we let the user audit, edit, or rewind?
These are not easy questions, but we believe they are timely. AI UX is maturing quickly, and much of the friction lies at the point of arrival. Interfaces are no longer structured around clear beginnings. Users arrive in the middle, and the system must meet them there.
We will share more as the Drop-In Clarity framework takes shape. For now, we are inviting teams building AI products to think differently about what happens after the jump.