AI and marketing teams: organising roles, approval and performance

1. Which tasks should you give AI assistance?

Inventory a week’s work: documentary research, briefs, writing, format adaptation, meeting notes and review preparation. For each task, record frequency, time spent and difficulties encountered.

The best first candidates have clear inputs and verifiable results. Reorganising commercial questions or proposing an outline from an approved dossier allows precise checking. Asking the tool to invent a strategy without company information produces a much less defensible foundation.

Exclude from the first pilot tasks whose results nobody can verify. The required checking level is part of the cost of the use.

2. How should you allocate responsibilities?

Plan three functions: the need owner defines the question; the specialist verifies substance; the publication owner authorises the final version. One person may perform several functions, provided the stages remain identifiable.

For a technical article, marketing organises the message, but the subject specialist checks characteristics and limitations. For a campaign, the offer owner checks announced conditions. The tool does not become the author responsible for a promise.

Also state who handles exceptions: missing sources, contradictory figures, confidential data or an out-of-scope request. A “review everything” rule remains too vague if no reviewer is assigned. Our organisation and management advisory service helps clarify these handovers.

3. Which dossier should you provide to obtain a useful proposal?

Prepare a short dossier containing audience, objective, offer scope, approved wording and accessible references. Add what must not be claimed: undocumented results, uncertain availability or absent capabilities.

A brief may specify: “prepare three angles for an article aimed at purchasing managers, using the attached documents; distinguish facts from proposals; flag missing information”. This instruction organises work and makes verification easier.

Keep a dated version of the dossier. If an offer changes, update the references used rather than correct the same errors in every production. Limit shared data to what is necessary, within an environment whose terms of use have been examined.

The content strategy and AI applications should therefore start with company-specific sources rather than a stack of general instructions.

4. How should you build an approval process that catches errors?

Review the deliverable in several passes. First verify facts and sources, then usefulness to the reader, and finally wording, links and publication format. This separation reduces the risk of missing an error because the text reads fluently.

NIST’s generative AI profile describes, among other things, the risk of producing false information presented confidently. For the team, this means a generated reference, quotation or calculation must be checked against its source.

Classify corrections: factual error, omission, lost nuance, tone or formatting. This record shows whether the problem comes from the dossier, instruction or an unsuitable use. A version containing a material error should not be published merely because other passages are satisfactory.

Our guide to the inverted pyramid complements this check by helping make the main answer understandable.

5. Example: preparing a series of industrial articles

Fictional situation. An SME wants to address questions received before quotation requests. Its salesperson provides an anonymised question list and its technical manager prepares verifiable points.

AI assistance groups these questions and proposes outlines. Marketing selects angles matching the offers. The technician corrects two imprecise comparisons; the business leader approves commercial conditions. The writer then develops explanations and examples.

The pilot covers three briefs, without automatic publication. For each, the team retains the dossier, proposal, corrections and approved version. If technical rework remains too heavy, it limits the use to organising questions.

This method preserves professional judgement while testing concrete assistance. It does not allow a productivity percentage to be announced in advance.

6. Which costs and results should you measure?

Compare tasks of similar complexity, with and without assistance. Count preparation, production, checking and rework time. Add subscriptions and integration costs actually attributable to the pilot.

Time simulation. Manual preparation takes 90 minutes. With AI, the brief takes 15 minutes, generation 10 minutes, verification 35 minutes and corrections 20 minutes: the total is 80 minutes. The observed saving is 10 minutes, subject to comparable quality, rather than 80 minutes.

Also track important errors, rejected versions and reusable deliverables. For published content, then examine the questions and enquiries it prepares. Distinguish process performance from acquisition performance: working faster does not prove content attracts the right prospects.

7. How should you move from the pilot to regular operation?

Before testing, define continuation conditions: minimum quality, acceptable full working time, usable data and available reviewers. At the review, choose to continue, change the scope or stop the use.

Then formalise a short procedure: input documents, instruction, checking points, owner and final version location. Plan a manual alternative if the tool is unavailable or produces an unusable result.

A tool or model update may alter answers. Retest affected uses and retain reference examples. The AI, automation and data advisory service addresses this framework and its integration into the organisation.

Should you recruit an AI specialist immediately?

Start with needs and available skills. The pilot reveals necessary responsibilities and training before a recruitment decision.

Can you publish without review?

For content that commits the company, a person must verify substance and authorise publication under an explicit rule.

Which indicator should take priority?

Full working time at comparable quality, accompanied by error and rework tracking. Generated text volume is not enough.