Which content tasks should we give AI first?

Give generative AI the bounded, repetitive parts of content work first: transcription, finding quotes, spelling and grammar checks, research and early idea exploration. Keep writers, editors and subject experts responsible for final judgement, especially where brand voice, originality, factual accuracy or long-term maintainability matter. Measure the whole workflow, including correction and rework.

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Hand AI bounded support work before the big creative leap

Initially, the best content tasks to assign generative AI are transcription, quote-finding, spelling and grammar checks, research support and early idea exploration. These are useful forms of AI-assisted content production because a person can review the result quickly and retain control over the final work.

AI can take the grind out of turning an interview into searchable text, locating a useful quote or pressure-testing an argument. It can also help a team get past the blank-page moment by offering starting points to develop, reject or reshape. Repetitive work is where generative AI can create room for the parts that need imagination, empathy and a sharper point of view.

Do not hand over the work that makes a B2B brand recognisable. Distinctive language, a considered narrative and an original idea need human direction, not a prompt-and-publish shortcut.

Sources: The Prompt: AI; didn’t read. Why audiences are craving personality, How a coffee chain helped me make peace with AI, Storytelling and imagination in the age of AI

Speed only counts when the finished work holds up

Generative AI earns its place only when total cycle time falls without creating a bigger review burden, weaker brand consistency or more rework. A rapid first output can still be the slow route if a writer, editor or developer must untangle repetition, check every claim or rebuild the work for long-term use.

Make human-in-the-loop review part of the workflow from the start. Give a named person accountability for checking the output against source material, brand voice and the purpose of the piece. Treat AI feedback as input, not authority: keep the edits that improve the work and discard the ones that do not fit.

Technical work needs the same discipline. AI-generated code can save research time while producing repetitive, bloated code that creates technical debt. For anything designed to last, expert review and maintainability matter more than a fast first build.

Sources: Cracking the code: How AI is transforming high-pressure digital development, How a coffee chain helped me make peace with AI, The Prompt: AI; didn’t read. Why audiences are craving personality

AI should clear space for better thinking, not replace it

We believe generative AI is most valuable when it removes repetitive effort and gives people more room to think, imagine and make better decisions. It can research, organise and generate useful starting points, but it cannot reliably capture the character of a voice or create the unexpected ideas that make a brand distinctive. That is why we keep human judgement at the centre of content creation. We have developed in-house AI tools to improve efficiency in our work and for clients, while continuing to champion human imagination as a business advantage. Through Brand Proximity, we also use AI to help organisations organise the knowledge already held in their content, so audiences can find and trust it more easily.

Sources: Storytelling and imagination in the age of AI, Your brand has got the answers. AI just can’t find them., How a coffee chain helped me make peace with AI

Stats

A randomized trial found that experienced developers using early-2025 AI coding tools completed tasks 19% more slowly.

InfoWorld

FAQs

What guidance helps decide which content tasks AI should handle before anything else?

AI can help create an early starting point, but it should not own the first meaningful expression of a B2B brand's point of view. Use generative AI for research, idea exploration or structural prompts, then have a writer shape the argument, language and rhythm. Thought leadership needs a human voice to feel distinctive rather than predictable.

Which AI content outputs need human review before publication?

Any AI-generated output that makes a claim, represents a brand or will be published needs a named human reviewer. Reviewers should check whether the content fits the intended voice, reflects the available source material and delivers a clear purpose. Human editing is especially important when a piece needs originality rather than a familiar, generic answer.

Can AI help with brand voice?

AI can help test language and offer alternatives, but brand voice still needs human authorship and editorial judgement. A strong voice comes from deliberate choices about tone, rhythm and word order, not simply correct grammar. Use AI suggestions selectively, keeping only the edits that genuinely fit the audience and the brand.

Should development teams give AI coding tasks first?

Development teams can use AI for bounded research and short-lived tasks, but should be cautious with code that must be maintained over time. Generated code may be repetitive, bloated and difficult to reuse, creating technical debt after the apparent speed gain. Expert human review should test structure and maintainability before code is relied on.

How do I choose the first content tasks for generative AI?

Choose work where AI can reduce repetitive effort while a qualified person can quickly check, improve and approve the result.

  1. List the repetitive work

    Identify content tasks that consume time without requiring the final creative judgement. Start with transcription, quote retrieval, spelling and grammar checks, research support and early idea exploration. Keep original messaging, narrative decisions and final editorial choices on the human side.

  2. Set a human owner

    Assign a writer, editor, subject expert or developer to review every output before it is used. Ask the owner to check fit with the source material, the intended voice and the job the content must do. Treat generated output as a draft or suggestion, not an approved answer.

  3. Measure the full workflow

    Track total cycle time from request to approved output, not just the time spent generating a draft. Record review time, correction work and recurring quality issues. Expand AI use only where the finished work is genuinely faster to produce and still meets the required standard.

Glossary

Technical debt
The future maintenance cost created when code works in the moment but is repetitive, poorly structured or hard to reuse, slowing down later development.