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No: adding AI features does not automatically make a product better. An AI feature earns its place when it addresses a real user need and makes the experience meaningfully better than a simpler option. The title’s reference to “ideas I removed” is author-specific; without verified details about those ideas or the product, this article focuses on how to assess an AI feature rather than inventing a removal story.
When should you add AI to a product?
Start with the user’s task, not the technology. Google’s People + AI Guidebook advises checking whether a feature requires AI or would be enhanced by it. AI can suit tasks such as recommendations, prediction, natural-language understanding, and image recognition, but those capabilities alone do not show that a particular product needs them.
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Ask what problem the feature solves, who encounters it, and how often or consequentially it occurs. Then compare AI with a rule, search tool, template, or manual control. If a simpler approach serves the user just as well—or better—adding AI may introduce complexity without adding value. Google’s concise guidance is: “Don’t use AI just because you can.”
How do you know if an AI feature is useful?
Evaluate the feature against the task and the existing workflow, not against a general desire to have more AI. Useful questions include:
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- Need: Is the problem frequent or consequential enough to warrant a new feature?
- AI advantage: Does AI materially improve the result compared with a simpler solution?
- Reliability: How likely are incorrect or unexpected results, and what happens when they occur?
- Control: Can users understand, correct, override, or turn off the behavior?
- Workflow fit: Does the feature remove effort, or add steps, interruptions, and decisions?
- Long-term effect: Does it support users’ judgment and skills, or routinely replace practice?
- Feasibility: Can the team build and maintain it at a quality level appropriate to the task?
There is no universal numeric score or threshold that makes an AI feature worthwhile. Teams need success measures tied to their users and task—for example, whether the feature helps complete the intended task, reduces avoidable effort, and lets people recover when the result is wrong.
When is a manual option better than a “smart” one?
Manual controls and predictable rules can be better when users need consistency, transparency, or personal choice. A system that guesses what someone wants may be less helpful than one that lets them choose directly, particularly when an incorrect guess is costly or hard to undo. Google’s design guidance explicitly identifies cases where heuristic behavior or manual choice may be preferable.
This does not mean manual systems are always better. The question is whether the AI’s added capability is valuable enough to justify giving up some predictability or control. A practical design may keep both paths: automate the repetitive part while making it easy to inspect, adjust, or do the task manually.
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What costs can an AI feature add?
Unexpected results and recovery work
AI systems are probabilistic, so they can produce incorrect or unexpected outputs. Google recommends setting expectations about capabilities and limitations, especially in high-stakes situations. The product should also make it clear how users can notice a problem, correct the result, or recover without starting over.
Microsoft Research’s human-AI interaction guidance offers useful design prompts, including supporting efficient correction, adapting cautiously over time, and providing global controls. The guidelines were evaluated through multiple rounds, including a study in which 49 design practitioners assessed 20 AI-infused products. They are interaction-design guidance, not proof that any particular feature should ship.
More interaction without more value
A feature can make a product feel busier without making its core task easier. Figma’s AI + Design article reported that 72% of surveyed respondents whose products included AI said it played a minor or non-essential role. That figure describes respondents in that survey; it is not a rate for all products or users. The article also discusses concerns about integrating AI coherently and about adding “yet another AI feature.”
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Effects that emerge over time
Automation can affect how people work beyond a single task. A 2026 exploratory article by Christopher Noessel raises concerns about deskilling and overreliance and discusses the design proposal “Human Goes First”—letting people think before seeing an AI suggestion. Its simulated-navigation study included 15 participants per condition; the article describes the findings as approximate and the sample as too small for statistical significance. Treat this as a reason to consider long-term human effects, not as a general estimate of skill loss or a universal rule for feature design.
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Use the answers above to choose among more than just “ship” and “delete.”
- Add it when a defined user need is strong, AI offers a meaningful advantage, and the team can deliver appropriate quality and recovery controls.
- Reshape it when the underlying need is real but the current interaction adds friction. Consider making the feature optional, showing it only when relevant, preserving a manual path, or adding correction and feedback.
- Remove it when the need is weak, a simpler option serves users better, or the likely errors and interaction costs outweigh the benefit.
Prioritize user need alongside technical and organizational feasibility. A January 2026 JetBrains Research and TU Delft study examined AI assistants in development environments: researchers interviewed 35 professional developers and used an internal prediction market with 102 practitioners to assess 12 user-desired features. In that IDE-specific context, alignment between need and feasibility was stronger for implementation and context awareness than for proactivity and maintenance support. Those findings are useful for thinking about prioritization, but they do not establish a ranking for every kind of product.
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