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No: more AI features do not automatically make a product better. An AI feature earns its place when it addresses a real user need and improves the experience enough to justify its costs in complexity, errors, and user control. The title’s “ideas I removed” framing implies a personal product story, but no verified examples or outcomes are available here; rather than invent them, this article offers a practical way to decide whether a feature should ship, change, or go.
When should you add AI to a product?
Start with the task, not the technology. Identify who has a problem, what they are trying to do, and how often or how seriously the problem gets in their way. Then ask whether AI is necessary or materially improves the solution over a rule, search, template, or manual choice.
Google’s People + AI Guidebook recommends checking whether a feature “requires AI, or would be enhanced by it” before building. It also cautions: “Don’t use AI just because you can.” Its examples include recommendations, predictions, natural-language understanding, and image recognition, but those capabilities are not a reason by themselves to add a feature. Google’s People + AI Guidebook
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitches- Define the need: Name the user, task, and specific obstacle the feature addresses.
- Identify AI’s advantage: Explain what AI can do better than a simpler approach, and how that difference matters to the user.
- Set a success measure: Choose an outcome tied to the task, such as fewer steps or easier completion. There is no universal threshold that makes an AI feature worthwhile; set one for the context and measure it.
When is a simple or manual option better?
AI can be a poor fit when users need consistent, transparent results or want to make the decision themselves. A predictable rule or manual control may be easier to understand and correct than a system that infers what someone wants. This is not a case against AI in general: it is a reason to match the method to the task and the user’s preferences.
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Consider whether a person needs to inspect the result, choose among options, or retain control over a consequential action. Google’s guidance identifies cases where heuristics or manual choices may be preferable. Microsoft Research’s human-AI interaction guidelines offer additional design prompts, including enabling efficient correction and providing global controls; they help teams shape an interaction, but do not prove that a feature should exist. Microsoft Research’s human-AI interaction guidelines
How do you know if an AI feature is useful?
Presence is not the same as value. In an article published around 2024, Figma reported that 72% of respondents whose products included AI said it played a minor or non-essential role. That is a survey result, not a rate that can be applied to every product or user. The same article reported that 89% of respondents expected AI to affect their company’s products and services in the following 12 months, while 37% expected a significant or transformative impact. Those figures describe expectations, not realized impact. Figma’s AI and design article
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The practical lesson is to evaluate each feature by its contribution to a user’s task, rather than by how many AI capabilities a product contains. A feature can be available without being central to the product’s value.
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Uncertainty and error recovery
AI systems are probabilistic: they can produce incorrect or unexpected results. Users need to know what the feature can and cannot do, especially when errors have serious consequences. Plan how a user will notice a problem, correct the output, and recover without starting over. Google’s People + AI Guidebook recommends setting expectations and communicating limitations.
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Extra interaction and attention
A feature may add prompts, review steps, settings, or decisions to an existing workflow. Figma’s article describes concern about integrating AI coherently and cites indifference to “yet another AI feature.” That does not establish that users broadly reject AI; it is a reminder that an added capability can become extra work if it does not fit the task.
Effects on judgment and skill over time
Automation can shape what users practice, not just how they complete a single task. A 2026 exploratory paper by Christopher Noessel raises concerns about deskilling and overreliance and proposes sequencing interactions so a person thinks before seeing an AI suggestion (“Human Goes First”). Its simulated-navigation study included 15 participants per condition, and the author describes the results as approximate and too small for statistical significance. Treat it as a design question worth evaluating, not proof of a general effect or a universal prescription. AI Magazine
Should you remove an AI feature users don’t need?
Removal is one option, but not the only one. First determine whether the underlying user need is absent or whether the current design makes a useful capability hard to control, trust, or access. Then choose a response that fits the problem:
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- Remove it if it solves no meaningful need or adds more burden than benefit.
- Simplify it if the task is useful but the AI interaction is needlessly complicated.
- Make it optional if some users benefit while others prefer the manual path.
- Show it when relevant if a persistent feature interrupts workflows where it is rarely needed.
- Add correction and control if users need a better way to review, edit, undo, or turn off its output.
- Let the user think first if seeing a suggestion immediately could crowd out their own judgment. This is a proposed design approach with preliminary evidence, not a rule for every product.
How to decide: add, reshape, or remove
Use these questions to assess a candidate feature before committing to it. They combine user value, interaction design, reliability, and delivery constraints; they are decision prompts, not a universal scoring formula.
Best Value
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- User need: What task or pain point does it address, who encounters it, and how often or consequentially?
- AI advantage: What can AI do better than a simpler rule, search, template, or manual choice?
- Predictability and control: Does the user need a consistent result, transparency, or the ability to decide personally?
- Quality and recovery: How might the system fail, how will users notice, and how can they correct the result or recover?
- Interaction cost: Does the feature make the existing flow easier, or add steps, attention demands, and decisions?
- Long-term effect: Does it support a person’s judgment and skill, or routinely displace practice that matters?
- Feasibility: Can the team deliver and maintain it at the quality the task requires?
Feasibility belongs alongside user desirability. A January 2026 JetBrains Research and TU Delft study focused on 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 study, alignment between user need and feasibility was stronger for implementation and context awareness than for proactivity and maintenance support. These findings can inform prioritization, but they do not establish rankings for other kinds of products. JetBrains Research publications
Decide feature by feature. If AI does not improve a meaningful task, do not add it for novelty. If the task matters but the implementation gets in the user’s way, reshape it. If the value is clear and the risks and costs can be handled, the feature may earn its place.
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