You've heard about AI. You've used ChatGPT. You know it can write things. But you've also run into the wall that everyone who works with data eventually hits: it's great for one item at a time, and breaks completely at 500.
AI batch processing is the answer to that problem. This guide explains what it is in plain English, what marketers are actually using it for, and what the workflow looks like from start to finish — no technical knowledge required.
What batch processing actually means
Imagine you have 300 product pages that all need unique meta descriptions. You could paste each one into ChatGPT individually, but that's hours of manual work. Or you could hire a copywriter, but that's expensive and slow.
Batch processing lets you do this: upload a spreadsheet with all 300 pages, write your instructions once ("you are an SEO copywriter, write a meta description for each page, max 155 characters"), and get a completed spreadsheet back with all 300 descriptions filled in.
One setup. One job. 300 outputs. That's it.
8 marketing tasks it's ideal for
SEO meta tags
Title tags and meta descriptions for every page — consistent keyword targeting, within character limits.
Product descriptions
Unique, on-brand copy for your full catalogue — scaled from a product data spreadsheet.
Ad copy
Google Ads headlines and descriptions for every ad group, in the right format for direct import.
Review analysis
Classify and tag hundreds of customer reviews by theme, sentiment, and whether they need a response.
Email personalisation
Personalised subject lines or opening paragraphs for segmented lists — one instruction, 10,000 variations.
Lead enrichment
Add company descriptions, ICP scores, or messaging angles to a CRM export using company data you already have.
Content localisation
Adapt copy for different markets, audiences, or regional variations — with consistent terminology and tone.
Survey & data coding
Classify open-ended responses, tag support tickets, or code qualitative data against a defined taxonomy.
The common thread: a structured dataset, a consistent task to apply to every row, and a desired output format. If your task fits that description, batch processing is likely a good fit.
What the workflow looks like
The full process has four steps:
- Prepare your spreadsheet. Export from your CMS, CRM, or platform. Clean it up — one row per item, relevant columns filled in. This usually takes 30–60 minutes depending on the size and state of your data.
- Write your batch instructions. This is the single prompt that applies to every row. Define the role, the task, the output format, and any rules. See our guide to writing batch instructions for a full walkthrough.
- Run the job. Upload your CSV, paste in your instructions, choose your model, and submit. Come back when it's done.
- Review and import. Spot-check the output, edit any rows that need it, and import into your CMS, ad platform, or database.
The total time depends on the size of your dataset and how much data prep is involved. For a typical job — 500 rows, clean data, clear instructions — most people are done in an afternoon, including QA and import.
Common concerns, honestly addressed
"Is my data safe?" Your CSV is processed to generate outputs and then deleted. It's not stored, shared, or used to train AI models. Data is transmitted over HTTPS and handled under standard cloud security practices.
"How accurate is it?" For structured tasks with clear instructions — classification, format-constrained copy, data transformation — accuracy is typically very high. Expect to edit around 5–10% of rows. The remaining 90% is import-ready. The accuracy depends heavily on how well-written your batch instructions are and how clean your input data is.
"What does it cost?" Jobs are priced by how much text the model reads and writes (tokens). A typical 500-row product description job costs around £0.25–0.50. A 2,000-row meta tag job costs a similar amount. See our pricing explainer for detailed examples.
"Do I need to know how to code?" No. The only technical skill required is being able to work with a spreadsheet (CSV file). If you can use Excel or Google Sheets, you can use batch processing.
What good results look like
The benchmark for a successful batch job isn't perfection — it's better than the alternative. The alternative for most tasks is: generic supplier copy, blank fields, manual copywriting that takes weeks, or not doing it at all.
A batch job that produces 90% import-ready output and 10% that needs editing is a significantly better outcome than spending three weeks doing it manually. The 10% that needs editing is concentrated in rows with sparse input data — fix the data and rerun, rather than editing the output.
The one thing that consistently separates good results from mediocre ones is the quality of the batch instructions. Precise format requirements, clear rules, and explicit edge case handling produce clean, consistent output. Vague instructions produce variable results. The good news: writing good batch instructions is a learnable skill that gets easier with practice.