Three Words to Cut from Every AI Feature Description

The Three Words Killing Your Feature Descriptions

There are three words you should cut from every feature description you write. They appear in product pages, onboarding tooltips, feature announcements, and sales decks. They feel safe. They feel current. They are almost completely useless.

The words are: Powered by AI.

Look at your current feature page. Count how many times some version of that phrase appears. "AI-powered." "Smart." "Intelligent." "AI-driven." "Machine learning-backed." The number is probably higher than you'd expect.

Now ask the harder question: what does any of that tell your user about what they get?

Nothing. It tells them about your technology stack. It tells them you've made a bet on a particular approach. It tells them approximately nothing about their life before and after using your product.

Users don't hire your product to use AI. They hire it to close more deals, avoid regulatory risk, stop getting caught off-guard by their boss, or get home on time. The job they're hiring your product for is specific. Your feature description should be specific too.

This is the core of the Jobs to Be Done framework — and it's the most practical lens I know for rewriting weak feature copy into copy that converts.

Why "AI-Powered" Fails

There are two reasons "AI-powered" consistently underperforms as a feature description.

1. It describes the mechanism, not the outcome

Think about how you'd describe a car to someone deciding whether to buy one. You wouldn't say "internal combustion-powered transportation." You'd say "gets you from London to Edinburgh in 7 hours without booking train tickets three weeks in advance." The engine type is irrelevant to the decision. The outcome isn't.

Feature descriptions work the same way. "AI-powered email assistant" tells me how your product works. It tells me nothing about what changes for me. Does it save me time? How much? Does it help me write better? In what way? Does it make me look more competent to clients? The mechanism — AI — is not the answer to any of those questions.

2. It's lost all signal value

When every product in a category uses the same descriptor, it stops functioning as a differentiator. AI buzzword saturation in SaaS is now at the point where "AI-powered" reads as noise rather than signal. Product pages from analytics tools, CRMs, legal software, HR platforms, and project management apps all use the same language. It blends into the background.

The phrase has become so overused it now actively damages trust with a segment of buyers — particularly technical evaluators and procurement teams who have been burned by inflated "AI" claims that turned out to mean a regex engine or a simple filter rule. They've learned to discount the claim entirely and look past it for what the product actually does.

This isn't an argument against using AI. It's an argument against leading with it. The technology is table stakes. The outcome is the differentiator.

The JTBD Framework Applied to Feature Copy

Jobs to Be Done is a framework originally developed by Clayton Christensen at Harvard Business School. The core idea: people don't buy products — they hire them to do a job. When someone buys a drill, the job isn't "own a drill." The job is "have a hole in my wall."

Applied to product features, this reframes the question from "what does this feature do?" to "what job does the user hire this feature to complete?"

Christensen's framework identifies three dimensions of every job:

Functional jobs

The practical, task-oriented outcome. What the user needs to get done. "I need to review this contract before the call." "I need to find out why conversion dropped last week." "I need to respond to 40 emails before end of day."

Most feature descriptions stop here — if they get this far at all. They describe a functional outcome but miss the two dimensions that actually close decisions.

Social jobs

How the user wants to be perceived by others as a result of using the product. "I want to be seen as someone who's on top of their numbers." "I want my manager to think I'm proactive, not reactive." "I want clients to see me as thorough and detail-oriented."

Social jobs are particularly powerful in B2B SaaS because most buying decisions involve multiple stakeholders — and the primary user is often trying to look good to someone above them. The product isn't just solving their problem; it's making them look competent in front of their boss.

Emotional jobs

How the user wants to feel (or stop feeling) as a result of using the product. "I want to stop feeling anxious about missing a contract risk." "I want to feel confident walking into that leadership meeting." "I want to stop dreading Monday morning review calls."

Emotional jobs are the ones most product teams are uncomfortable writing. They feel soft. They don't belong in a product UI. That instinct is wrong. The emotional job is often the actual reason someone pays for the product — the functional job is just the justification.

In practice, you won't always have space to address all three in a single line. The art is knowing which job dimension is primary for a given feature and leading with it — then letting the others support it in sub-copy, tooltips, or onboarding flows.

The Original Three Examples: Expanded

The LinkedIn post that sparked this article used three examples. Let's unpack each one in detail.

Example 1: The email assistant

The before version tells you the category. The after version answers the question: "if I use this, what changes?" It leads with a functional job (respond faster), implies a social job (be seen as responsive and effective), and names a specific outcome quantity.

Note what's not in the after version: any mention of AI. Removing "AI" made the description stronger, not weaker. The intelligence of the system is implied by the outcome it delivers.

One thing worth examining: the 67% figure. If that number comes from your own data, use it. If it's borrowed from a competitor's case study or a report, it needs attribution. If it's an estimate, say "up to." Specific numbers add credibility — but only if you can defend them. Fabricated specificity is worse than vague copy because it destroys trust the moment a savvy buyer asks where it came from.

Example 2: The document analysis feature

This rewrite does three things well. It names the specific job (flag contract risks), it gives a timeframe (60 seconds), and it grounds the feature in a real workflow moment — someone has a contract in front of them and needs to review it quickly before a meeting or deadline.

The original says "smart document analysis." Smart compared to what? Analysis of what? For what purpose? Every question a user needs answered to decide if this feature is relevant to them goes unanswered.

The timeframe is crucial. "In under 60 seconds" isn't decoration — it addresses a functional anxiety. Users don't just want contract risk flagged; they want it done before the call starts. The time constraint is part of the job.

Example 3: The insights dashboard

This example is the most instructive because it shows all three job dimensions explicitly before arriving at the final copy.

Let's walk through the three-dimension analysis that produced this rewrite:

• Functional: Identify the root cause quickly when something goes wrong
• Social: Be seen as proactive and informed — not reactive and surprised
• Emotional: Avoid the anxiety of being caught off-guard in front of leadership

The final copy leads with the emotional job ("eliminate anxious scrambling") and names the specific trigger moment ("when leadership Slacks you"). That specificity is what makes it land. Everyone who has ever been mid-task when their VP messages "what's going on with X?" knows exactly what that sentence is describing.

The functional job (identify root cause quickly) is implied — you can't eliminate the scrambling without actually having the answer. The social job (be proactive, not reactive) is the underlying status anxiety that the product resolves. The copy only had to name one dimension explicitly — the emotional one — because it's the sharpest and most recognizable.

Four More Rewrites: Across B2B SaaS Categories

The email, document, and dashboard examples come from the original post. Here are four more rewrites across different SaaS categories — same discipline, different contexts.

HR software: performance reviews

The job here isn't "use AI to write reviews." The job is "complete the performance review cycle without it consuming my entire Thursday afternoon." The time comparison (12 minutes vs. 2 hours) makes the value concrete. "Calibrated and defensible" addresses a secondary anxiety — managers don't just want reviews done, they want reviews that won't blow up in an HR meeting three months later.

Finance software: expense reporting

The job here is getting to zero on the expense backlog without a painful catch-up session on the last day of the month. "Intelligent categorization" is the mechanism. "Stop chasing receipts" is the lived experience. "Expenses file themselves" sounds like a promise — which it is, and one that the feature can keep.

Customer success platform: churn risk

The job is having enough runway to actually do something about churn risk — not just detect it in retrospect. "45 days" is specific, and it's the right specificity because it speaks to the intervention window. A CS team that gets a signal 45 days out can run a QBR, expand the relationship, address the root issue. A team that gets the signal on the last day of the contract cannot.

Developer tooling: code review

This one is interesting because "AI" is more legitimate as a descriptor in developer tooling than in most other contexts — developers want to know what model they're working with, whether output is deterministic, what training data was used. But the feature description still shouldn't lead with it. The functional job (catch vulnerabilities), the social job (not be the person who ships a CVE), and the emotional job (not block the sprint over a review bottleneck) are all present here. The tech can be explained in the expanded feature documentation.

The Objection: "But Our Investors Want to See AI"

This comes up in almost every conversation about outcome-based copy. And it's a legitimate concern — especially for Series A and B companies where the AI narrative is part of the fundraising story and the investor deck needs to demonstrate technical differentiation.

The answer is: investor communications and user-facing product copy are different documents with different jobs.

Your investor deck is making a different argument: that you've built a defensible technical capability, that your approach is differentiated from competitors, that the architecture is hard to replicate. "Powered by AI" — or more specifically, "built on [specific model/approach/architecture]" — belongs in that context because the audience is evaluating technical risk and moat, not deciding whether to complete a workflow.

Your product page has a different job. It's talking to a VP of Sales or a Head of Legal who has 25 browser tabs open and 8 minutes to evaluate your tool. They are not evaluating your architecture. They are deciding whether this product solves the problem they have right now. For them, "AI-powered" is not signal. It's noise that delays the sentence they actually need to read.

You can satisfy both audiences without compromising either message. Put outcome-based copy in the hero and feature descriptions. Put your technical architecture, model choice, and AI credentials in your documentation, your security review pack, and your "How it works" section for buyers who want to go deeper.

The question isn't whether your product uses AI. The question is whether your user needs to know that to decide to buy it.

— Principle applied from Clayton Christensen's Jobs to Be Done framework

The board narrative and the product narrative can coexist. They just shouldn't be the same document.

The Rewrite Process: A Step-by-Step Framework

Here's the process I use when auditing and rewriting feature descriptions. It's five steps and takes about 20 minutes per feature once you have the user research to draw from.

The process works on any feature description. Run your entire feature page through it. You'll find that roughly half your descriptions are technology-first rather than job-first — and those are the ones your users are skimming past.

When AI Is the Right Framing

The argument above is not "never mention AI." It's "don't lead with AI when the outcome is more interesting." There are specific contexts where mentioning AI in a feature description is not just acceptable — it's required.

Developer tools and technical platforms

Developers evaluating a code completion tool, an API, or an embedded model want to know what they're working with. The underlying architecture, model family, context window, and determinism characteristics are all relevant to their technical decision. For this audience, "powered by [specific model]" is a feature, not a buzzword. The job they're hiring the tool to complete includes understanding what's under the hood.

Regulated industries with explainability requirements

In healthcare, financial services, and legal technology, the approach your AI takes can be a compliance question. "AI-assisted" versus "AI-determined" is a meaningful distinction when the output affects a patient, a loan decision, or a contract dispute. In these contexts, describing the AI methodology is part of describing the job the feature performs — because the user's job includes being able to explain and defend the output.

High-trust enterprise procurement

Enterprise security reviews, vendor risk assessments, and procurement checklists often include specific questions about AI usage, training data, data residency, and model governance. For these buyers, the AI description belongs in the security documentation and the enterprise feature matrix — not necessarily in the primary product copy, but definitely somewhere in the buying journey.

The test for all three contexts is the same: does removing "AI" from your description make it less accurate or less useful for the person evaluating it? If yes, keep it. If removing "AI" has no impact on what the buyer understands about the feature, cut it.

How to Test Feature Description Copy

Changing feature copy without measuring the impact is opinion-driven product management. Here's how to treat it as an experiment instead.

What to test and where

The highest-leverage surfaces for feature description testing are, in rough priority order: your pricing page (because it's the closest to a conversion decision), your main features page (because it's where comparison buyers spend most time), and your in-product onboarding tooltips (because first-use framing shapes adoption behavior long after the session ends).

Test one surface at a time. Don't rewrite the pricing page and the features page simultaneously — you won't be able to attribute any change in outcomes.

What to measure

The metric has to be tied to the job you're describing. If you're testing a description for a feature that helps sales teams close deals, your primary metric should be trial starts from visitors who arrived via sales-oriented channels — not generic pageview time or bounce rate. Generic metrics give you generic answers.

For in-product tooltips, measure feature activation rate: the percentage of users who encounter the tooltip and then actually use the feature within their first session. A JTBD-framed tooltip that describes a compelling outcome will produce higher activation than one that describes a mechanism.

Sample size and duration

A common mistake is stopping an A/B test too early. You need at minimum 100 conversions per variant to have any confidence in the result — and preferably 200+. If your features page gets 500 visitors per week and your trial conversion rate is 3%, that's 15 conversions per week, per variant. You need at minimum 7 weeks of data. Run it for 10 to be safe.

Don't run tests through major seasonal events, product launches, or campaign spikes — external factors will contaminate the results.

The minimum viable test

If you don't have the traffic volume for a statistically valid A/B test, there's a cheaper version: user interviews. Show five current users your current feature descriptions and five users your rewritten ones. Ask them to describe what the feature does in their own words. Compare accuracy and comprehension. It's not statistically rigorous, but it will tell you quickly whether your rewritten copy is being understood the way you intended.

FAQ

What does "Jobs to Be Done" mean for feature descriptions?

JTBD is a framework for understanding why users choose a product — not what they do with it, but what they're trying to accomplish. Applied to feature descriptions, it means writing copy that describes the outcome the user gets rather than the technology used to produce it. "Flag contract risks in under 60 seconds" describes the job completed. "AI-powered document analysis" describes the tool.

Why does "AI-powered" fail as a feature description?

It describes your technology stack, not the user's outcome. Users don't hire your product to use AI — they hire it to save time, avoid risk, close deals, or look competent to their boss. "AI-powered" has also become so ubiquitous that it carries no differentiating signal. A description that leads with the specific, measurable outcome is both more informative and more persuasive.

What are the three dimensions of Jobs to Be Done?

Christensen's JTBD framework identifies three job dimensions: Functional (the practical task), Social (how the user wants to be perceived), and Emotional (how the user wants to feel). The strongest feature descriptions address at least two of the three, and usually lead with functional before closing on emotional.

Should I ever mention AI in feature descriptions?

Yes — in specific contexts where the technology is the differentiator, not a commodity. Developer tools, regulated industries with explainability requirements, and high-trust enterprise procurement are the three main cases. The test: does removing "AI" from your description make it less accurate? If yes, keep it. If removing it has no impact on what the user understands, cut it.

How do I A/B test feature description copy?

Run the test on the highest-traffic surface first — usually the features page or pricing page. Test one thing at a time. Use a primary metric tied to the job you're describing. Run until you reach at least 100 conversions per variant. Don't stop early because the numbers look good — they often stabilize in the opposite direction with more data.

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