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We've traced how onboarding evolved through each technological shift, with each era's solution becoming obsolete as user expectations changed. This brings us to where we are today—and you've likely felt it yourself—we're experiencing the most dramatic shift yet as we transition from digital natives to AI natives.

While companies rush to integrate AI into every workflow, their marketing and growth tactics remain frozen in time. We're still clinging to SaaS-era tooltips even as our users increasingly expect conversational, intelligent assistance. Here's why this approach is fundamentally broken:

Band-Aids for Bad UX

Tooltips are designed to solve product inadequacies rather than help users achieve their goals. They're excellent at reducing those "what is this...?" support tickets, but every tooltip adds cognitive load that forces users through an unnecessary mental process:

• "Oh, so this feature does this..."
• "Hmm... how does this impact me?"

Feature explanations can only appeal to a general audience instead of providing contextual problem-solving. They're more likely to interrupt workflows—pushing features when users don't need them—while being absent when users need help most.

“If this, then that” is not enough

Personalization has always been a key goal for every onboarding solution. We've implemented common workflows: asking users for their role, offering templates, and segmenting by use case. But these approaches still fail to connect individual goals to the features being shown. They rely on simple "if this, then that" product logic, when everyone's context is far more nuanced.

A tooltip that treats a seasoned developer the same as a first-time user feels antiquated and frustrating. Onboarding surveys attempt to address this issue, but I can't recall when they've helped with anything beyond selecting a template workspace. Modern users have been trained by AI assistants that understand their context and adapt to their knowledge level in real-time.

Obsolete in Modern AI Interfaces

Many companies now focus on chat-based and canvas-based interfaces where conversations drive design, not button placement. The emphasis has shifted from "where to click" to "what to say" or "what to create."

Adaptive UIs—interfaces that reorganize based on user behavior—eliminate predictable locations tooltips depend on. Notion's interface changes based on your task; Figma's toolbar adapts to your mode. There's nothing consistently static to highlight.

Most users aren't asking "What does this button do?" but "How can I get the most out of this tool?" Tooltips were built for the former question, not the latter.

Cannot Solve the Cross-Product Problem

Users work across multiple tools, with your software being just one part of a bigger workflow. Tooltips cannot help users understand how your product fits into their broader objectives.

AI-powered integrations make this worse. Context now spans multiple applications, making single-product onboarding even more disconnected from real needs. The combination of screen-sharing and AI opens possibilities for seamless cross-product experiences that understand entire work contexts.

Scale Limitations in Complex Software

User needs have expanded from limited use cases to virtually unlimited scenarios. Rules-based tooltip systems can't scale to handle this complexity—personalization cannot scale through predetermined paths.

Global users bring cultural diversity that expands possible contexts. Enterprise software has become increasingly powerful, making it difficult to discover relevant use cases through simple overlays. Users ask complex questions like "How can I rally my team around a mission?" rather than "How do I create an issue?" Tooltips are equipped for the latter, not the former.

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