How to choose an AI writing tool

Most AI writing demonstrations look similar: a tidy paragraph, a confident headline and a polished list. That first impression is a poor measure of how a tool will perform repeatedly, especially when the brief is incomplete or the deadline is close. A good first draft can still become expensive once fact-checking, rewriting and approval are included.

The tool worth paying for is the one that gets you to an approved article with less total effort than your current process, without weakening accuracy, originality, privacy or editorial control. Test candidates on the same realistic assignment and judge the entire path from brief to publication, not a single prompt.

Start with the job, not the tool

“Write faster” is not something you can test. Decide what the tool should actually help with:

• turning interview notes into a first structure;
• producing alternative headlines;
• rewriting approved copy for another channel;
• checking clarity and consistency;
• summarizing internal material;
• maintaining terminology across a team.

Each of these is a different job, and a product may handle some much better than others.

Now document the restrictions. The tool must not invent customer evidence, publish without human approval or send confidential material to an unapproved service. Restrictions are often documented less carefully than desired features, even though a single failure may eliminate a product from consideration.

Establish a baseline before testing. Record how long the task takes today, who participates and where corrections usually happen. Without that information, “saved time” is only an impression.

Build a test assignment that resembles your work

Do not test a writing product with a generic request such as “write a blog post about AI.” Use the kind of brief you would give a paid writer.

A useful test pack contains:

• a one-page brief with audience, purpose and desired action;
• three approved primary sources;
• five facts that must be included;
• two claims the draft must not make;
• a 300-word sample of your house style;
• the required structure, length and output format;
• a definition of what “ready for approval” means.

Example assignment

Produce an 800-word guide for a small marketing team choosing an AI writing tool. Use only the three supplied sources for factual claims. Include a seven-criterion comparison framework and one worked cost example. Do not claim that any product was tested or is “best.” Flag missing evidence rather than filling the gaps. Use British English and return Markdown.

Run the assignment three times in each tool. One run can be unusually good or bad; repetition reveals whether instructions, facts and tone remain stable.

Score every tool the same way

Loose impressions are difficult to defend later. Score every criterion from 1 to 5, multiply it by the weight and use the same reviewer and scoring definitions for each product.

Calculate the result as:

Weighted score = sum of (criterion score ÷ 5 × criterion weight)

Treat 70/100 as a rough shortlist threshold rather than a universal rule. Accuracy, privacy and consent can remain pass-or-fail requirements regardless of the total. A high overall score cannot compensate for invented quotations or unacceptable data handling.

Examine the areas behind the score

1. Brief and context controls

Can you provide an audience, purpose, source material, required facts, forbidden claims and a style guide? More importantly, can the product distinguish instructions from reference material? If it regularly confuses the two, it may create more review work rather than less.

2. Evidence and citations

If a product offers web research or citations, open every source used for an important claim. Confirm that it supports the sentence and record when it was checked. A clickable link is useful, but it does not prove that the generated claim is accurate.

3. Editing and collaboration

Draft quality is only part of the decision. A second person should be able to see what changed, understand the correction and approve it without rebuilding the article. Test comments, version history, export formats, shared templates and approval controls.

4. Privacy and data handling

Read the vendor’s current terms and privacy documentation. Identify what is stored, how long it is retained, whether submitted material may be used to improve models, which administrative controls exist and how deletion works. Do not place client secrets or personal data into a product until that use has been approved.

5. Original value

Fluent paraphrasing is not the same as analysis. Check whether the workflow can incorporate an interview, first-party data, a worked example or a comparison based on explicit criteria. If every paragraph could appear on a dozen competing sites, faster generation has produced more copy, not a more useful article.

6. Cost of the completed article

Subscription price is only one component. Measure briefing, generation, fact-checking, editing, formatting and approval time. A cheaper plan can be more expensive when every draft requires extensive repair.

7. Consistency under repetition

Across the three runs, look for unstable facts, lost constraints, repetitive phrasing and changes in tone. Record failures rather than averaging them out silently. A production tool needs predictable controls, not one impressive sample.

Calculate the cost of the finished article

Use this formula:

Completed cost per article = allocated subscription cost + human time + usage charges + rework

Suppose Tool A costs €69 per month and supports 20 completed articles. Its allocated subscription cost is €3.45 per article. If briefing, verification, editing and approval take 55 minutes at an internal rate of €30 per hour, the completed cost is approximately €30.95.

Tool B may cost only €20 per month, or €1 per article at the same volume. But if the draft requires 95 minutes of human work, its completed cost is €48.50. The cheaper subscription is the more expensive production system.

Use your actual labour rate and realistic publication volume. Planned articles that never reach approval should not make the subscription appear cheaper.

Keep an evidence record

For each run, save:

• tool, plan, model or mode and date;
• the exact brief and attached sources;
• raw output;
• factual corrections;
• sections substantially rewritten;
• generation and human editing time;
• reviewer and final decision;
• screenshots of settings or failures that affect the verdict.

This record replaces “we liked it better” with evidence that can be examined again when the product, price or underlying model changes.

Know when to stop the trial

Reject or pause the evaluation if the tool:

• invents quotations, sources or customer evidence;
• changes a material fact after a rewrite;
• ignores explicit exclusions;
• cannot separate instructions from reference material;
• makes review or export unnecessarily difficult;
• provides unacceptable controls for sensitive information;
• encourages direct publication without accountable approval.

Treat promises such as “SEO optimized,” “human-quality” or “ready to publish” as marketing claims until the output passes your own checks.

Google’s guidance focuses on accuracy, quality and relevance, including when generative AI is used. Producing many pages without additional value can fall under scaled content abuse. That principle is useful when selecting a tool: reward better research and editing, not simply a higher output volume.

Make the final decision

1. Eliminate candidates that fail a non-negotiable gate.
2. Compare weighted scores and completed cost.
3. Review the evidence behind the two leading results.
4. Select the smallest plan that supports the required workflow.
5. Document approved use cases, prohibited inputs and final approver.
6. Re-test when the model, product, pricing or terms materially change.

A tool worth keeping should improve the finished article and reduce the work required to approve it. If it only produces more text, it has not solved the publishing problem.

Sources

• Google Search Central: Guidance on using generative AI content — accuracy, quality, relevance and scaled content; checked 1 August 2026.
• NIST AI Risk Management Framework — governance and risk-management framework; checked 1 August 2026.
• U.S. Copyright Office: Copyright and Artificial Intelligence, Part 2 — human authorship and AI-assisted work; checked 1 August 2026.