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What AI Is Good and Bad at

A plain-language breakdown of where AI tools reliably help ecommerce teams, where they fall short, and how to calibrate your expectations.

Last updated 2026-02-24

Summary

AI is strong at writing, rewriting, summarizing, and extracting structured data from messy text—high-volume language work.

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  • It's weak at math, real-time data, brand judgment, and factual accuracy. It can confidently state things that aren't true.
  • Think of it as a fast, tireless text worker who needs clear instructions and doesn't know your business unless you tell it.

AI tools can feel like magic when they work and frustrating when they don’t. The difference usually comes down to whether you’re asking them to do something they’re actually good at.

Here’s a practical breakdown for ecommerce teams.

What AI does well

Writing, rewriting, and reformatting

This is AI’s strongest area. Give it text and tell it what to do with it—make it shorter, change the tone, adapt it for a different channel, expand a bullet into a paragraph—and it executes reliably. Volume doesn’t matter much: whether you need one rewrite or a hundred, the effort is the same on your end.

Summarizing large amounts of text

Customer reviews, survey responses, support tickets, competitor descriptions—AI reads and summarizes faster than any human. Ask it to pull out the top five complaints from 200 reviews, and it will do a credible job in seconds.

Generating variations

Subject line A/B tests, product title variants, headline options—AI can produce a dozen variations from one brief. Most won’t be perfect, but having options to evaluate is faster than writing each one from scratch.

Extracting structured information from unstructured text

Ask it to pull size, color, material, and care instructions from a pile of inconsistent supplier descriptions and return them in a table. This is tedious work for humans and easy work for AI.

Following complex, detailed instructions

Well-written prompts produce reliably structured output. If you tell it exactly what format you want, what to include, what to avoid, and what your audience cares about, it will follow those instructions consistently.

What AI does badly

Knowing what just happened

AI tools have a training cutoff—a point in time after which they don’t know anything. Claude’s knowledge doesn’t include last week’s industry news, your latest inventory, or your current pricing. Always verify anything time-sensitive.

Making judgment calls about your brand

AI doesn’t know that your brand voice is “direct but warm, never salesy” unless you tell it. It doesn’t know that you avoid the word “premium” because your CEO hates it. Without that context, it defaults to generic. The more context you provide, the better the output.

Arithmetic and financial calculations

Language models are not spreadsheets. They can reason about numbers conversationally, but for actual calculations—margin analysis, pricing tables, forecasting—use a spreadsheet and feed the results to AI for narrative interpretation. Don’t rely on it to multiply or percentage-calculate accurately.

Real-time or proprietary data

AI doesn’t have access to your catalog, your analytics, your customer list, or your supplier portal unless you paste that data into the conversation. It can work with data you provide—it just can’t fetch it.

Consistent factual accuracy

AI can confidently state things that aren’t true. This is called hallucination. It’s more likely to happen with specific facts, statistics, or anything where there isn’t a lot of training data. Always review outputs before they go anywhere a customer might see them.

The right mental model

Think of an AI tool as a very capable text worker who:

  • Can write, edit, summarize, and format anything
  • Works fast and doesn’t get tired
  • Needs clear instructions—vague requests produce vague results
  • Doesn’t know anything about your business unless you tell it
  • Can be wrong, and won’t always flag when it is

That framing makes it easier to know when to reach for AI (any high-volume, language-heavy task with clear criteria) and when not to (strategic decisions, anything requiring proprietary data you haven’t provided, numerical calculations).

What this means for ecommerce

The highest-value uses for ecommerce teams stay squarely in AI’s strengths:

  • Product content creation and reformatting (writing, rewriting, adapting)
  • Customer feedback analysis (summarizing, extracting themes)
  • Email and campaign drafting (variations, personalization copy)
  • Data cleanup and standardization (normalizing inconsistent attributes)
  • Process documentation (turning rough notes into SOPs)

The lowest-value uses are the ones that rely on AI’s weaknesses: up-to-date competitor pricing, accurate margin calculations, or anything that requires accessing your actual store data without providing it first.

Ready to get better results? Read Getting Better Results from AI next.