Choose your first AI use case in marketing

Start with a task the team already understands

The first AI project should solve an identifiable problem. Does the team spend too much time classifying customer questions, preparing briefs or turning approved notes into first drafts? I would observe the current work: stages, duration, people involved and difficulties. Tool selection comes afterwards. When the process is poorly understood, automation may accelerate an error or move correction work to another person. Understanding the existing workflow is part of the project, not a delay before the interesting technology begins.

The chosen task needs a start, an end and a quality check. “Use AI in marketing” is not a project scope. “Prepare a draft brief from an approved product dossier, then have the owner review it” is testable. This makes the current process and pilot comparable. It also establishes which work the tool may prepare and what a person must still verify. A clear boundary helps participants identify mistakes and avoids expanding the project every time the system produces a plausible new suggestion.

Select a use case by value and verifiability

Consider task frequency, operational cost and how easily the result can be checked. Repetitive work with structured inputs and clear criteria can make a useful first test. Rare work requiring delicate judgement may need more supervision. Value is not only time saved: better organisation of questions can improve briefs even when review remains necessary. Ask who benefits, which decisions become easier and whether the proposed workflow creates additional tasks for another team.

Separate confirmed problems from assumptions. A team may believe drafting is faster without measuring verification time. It may also underestimate a task whose errors cause extensive rework. A simple matrix of frequency, consequence, controllability and effort can support the discussion. Scores should not make estimates look certain. Choose a scope where the team can learn quickly and explain the result to the people affected. Record what would justify continuing and what evidence would suggest the use case is unsuitable.

Prepare the information before writing the prompt

A model does not automatically know the company’s exact conditions. Establish an input dossier covering product, audience, objective, approved facts, tone, constraints and useful examples. Missing information should remain visible. The system can be instructed to flag unconfirmed details, but a reviewer still needs to inspect the output. A plausible sentence is not evidence that a fact is correct or that the company has authorised a particular commercial promise.

Define which information may enter the chosen tool under company rules. An editorial pilot can often use anonymised examples and public or approved documents. Restrict inputs to what the task needs. Dossier preparation is part of the project’s cost and should not disappear from the time comparison. Well-organised inputs may improve human work before AI is involved. This means the pilot can create practical value even if the company ultimately chooses a narrower use of generation than originally planned.

Describe the expected output clearly

A useful prompt specifies the purpose of the output, information to use, questions to cover and required format. For example, request a brief separating reader need, explanation points, available evidence and facts requiring confirmation. Distinct sections help reviewers understand what is supported and what remains uncertain. The goal is not a magical phrase; it is an explicit task that makes the desired result easier to assess and gives people a consistent basis for requesting corrections.

Prepare examples of acceptable outputs. They demonstrate expected depth, precision and wording to avoid. Then test several dossiers, including incomplete information and less common subjects. A prompt that succeeds on an easy example may fail elsewhere. Useful corrections should improve the process rather than be added manually to every output. This can reduce repeated rework without claiming to eliminate review. Maintain a record of important decisions so that an improved prompt does not accidentally remove another requirement the team previously considered necessary.

A decision framework

Separate production from editorial approval

Keep drafting and approval distinct. The person preparing a draft may check structure, while a product owner confirms facts and a reviewer checks meaning. Not every organisation has three people available; the important distinction is between criteria rather than job titles. Fluent writing can still be wrong about a lead time, specification or condition. A small team can perform separate review passes if it makes the different checks explicit and allocates enough time for them.

A review grid can cover accuracy, relevance, original contribution, commercial consistency and language quality. Google’s guidance on helpful, reliable content provides an overarching reference. For the team, that becomes concrete checks and approved sources. Review the final published state too, since errors can arise during formatting, translation or link integration. A draft accepted in a document does not demonstrate that the website presents the same facts, conditions and next steps correctly.

Test representative and difficult situations

Select a small set of tasks using shared acceptance criteria. Some can have complete inputs, while others include ambiguity or missing facts. A pilot should reveal how the workflow responds to difficult situations rather than produce only a successful demonstration. The team can identify where the tool helps and where preparation or specialist judgement remains substantial. Include ordinary tasks, not only examples specially selected because they are easy for the model to handle.

Compare preparation, generation, verification and rework time. Record significant errors even when they are quickly corrected. An apparent speed improvement may disappear if a manager must reconstruct the sources afterwards. Participants should be able to report difficulties without feeling they are undermining the initiative. The objective is an informed decision, including when the sensible result is reducing scope. A successful pilot can show that some work should remain human, provided the evidence and decision are documented clearly.

Measure total time through an illustrative example

Consider a working assumption: a manual brief takes ninety minutes, while assisted work requires twenty minutes of preparation, fifteen of production and thirty of validation. Total assisted time is sixty-five minutes, a difference of twenty-five. These figures are not client data or a promised outcome. They show why measuring generation time alone can exaggerate the benefit and why the whole workflow needs to be included when comparing the two approaches.

If the same task then needs forty minutes of additional corrections, the advantage changes. Observe multiple tasks and quality stability rather than selecting the best demonstration. A planning tool can visualise the time components, but commercial criteria still matter. Does the brief help the writer, cover the right questions and reduce revisions? Saving minutes is useful only when the result remains usable. The business should also consider whether reviewers can reliably provide the required oversight at the intended production volume.

Use AI in research without treating a summary as proof

A tool can organise notes, propose questions or identify themes within an authorised corpus. Its summary should not be treated as independent fact verification. Keep original source links and ask reviewers to check significant claims. A cited page may not support the exact generated statement. Research quality therefore depends on validation, not simply the number of references displayed. Separate confirmed facts, interpretations and suggested questions in the output so each can receive the appropriate review.

For Moroccan topics, local context needs relevant sources and business information. The tool should not invent search volume, buying behaviour or client experience to make the article persuasive. Educational examples can be used when labelled as such. National facts, working hypotheses and company-specific outcomes should remain separate in both briefs and published content. This helps the final article demonstrate judgement and practical understanding without creating an unsupported impression of research or experience that has not actually been documented.

Retain human ownership of language versions

Translation and adaptation can be useful assisted tasks when inputs are approved and a reviewer understands the language and product. Prepare a glossary containing approved terms, promises to preserve and sensitive formulations. Natural wording can change a commercial nuance. Review fidelity and the journey: menus, forms, messages and contact information belong to the language version too. A translated paragraph above an unusable form is not a complete localisation, regardless of how polished the draft appears.

French, Arabic, Darija and English are not interchangeable in every Moroccan situation. Wording must suit the reader and need. An informal expression appropriate to a message may not fit a service page. Compare versions with relevant readers and record misunderstandings. Corrections can then strengthen the glossary. This creates a learning process that improves across publications instead of repeating the same translation mistakes. The team should also know which conditions require specialist review rather than relying on the model’s confidence or fluency.

Avoid large-scale production without a useful contribution

Easy generation does not justify hundreds of pages. Ask what need each piece serves, what reliable information it adds and who will maintain it. Google publishes guidance on generative AI content and spam policies. The operational choice should favour work the team can verify and make useful over volume it cannot responsibly review. Production capacity and approval capacity need to be considered together.

Original contribution can come from product explanations, methods, examples and answers to actual customer questions. It does not require invented research or competitor rewrites. Limit launch to a scope with identifiable value. If multiple pages address the same need without a meaningful difference, reconsider architecture before creating more. Include validation and maintenance in the publishing calendar rather than planning only generation. Otherwise a rapid launch can leave the business with a large collection of outdated or uncertain content that is costly to repair.

Document the workflow and its stopping conditions

A successful pilot leaves instructions for required inputs, stages, roles, quality criteria and escalation situations. Define when production pauses: missing source, uncertain commercial fact, topic outside scope or failed review. These rules let people work without improvising at every step. They make the use of AI more predictable and help transfer the process to another person. Stopping conditions are especially useful when a deadline creates pressure to accept a polished result that has not passed the necessary checks.

Track prompts and examples sufficiently to understand meaningful changes. Avoid heavy administration, but be able to connect an improvement or error to a process revision. Periodic reviews can compare accepted outputs, rework and encountered limits. The team can then expand, change preparation or keep certain tasks entirely human. The project should serve the work rather than become a reason to redesign every activity around one tool. Keep the operational owner involved so decisions remain connected to actual team capacity.

Connect the first use case to a clearer marketing strategy

The first use case can clarify how marketing operates. Research, product knowledge, content and conversion need connections. AI may assist one stage, but does not replace decisions about audience, positioning or promises. I would aim to make that stage more dependable and maintainable. Benefits can include greater consistency, steadier production or more reviewer time for consequential decisions. Explain which benefit is being tested rather than assuming that every assisted workflow automatically produces all of them.

My consulting connects these choices with SEO and organic growth. Read my approach to see how I begin with objectives and constraints. A useful project ends in an explicit decision to continue, adapt or stop. That discipline protects resources and turns an appealing tool into a workflow the team can explain, control and evaluate in real situations. The result should be a practical operating method and an honest assessment of its limits, not simply a successful demonstration shown once in a meeting.

Questions to answer before expanding the pilot

Before expansion, ask whether the team can explain what improved the result. Was it the input dossier, instructions, review or a clearer definition of the task? This avoids assigning every benefit to the model. It also preserves useful practices if the tool changes. The workflow should remain understandable to the people using it and those approving commercial information. A process that nobody can explain becomes harder to maintain and harder to correct when outputs begin to drift.

Check review capacity at the intended volume. Ten drafts may be manageable, while a larger operation creates a queue and pressure to accept inadequate work. Expansion needs to account for that load. Establish who updates product inputs, the glossary and examples when the offer changes. A fast workflow using outdated information can reproduce errors very consistently. Quality ownership should therefore grow alongside production rather than being treated as something the organisation will solve after launch.

Finally, explain value and limitations through concrete tasks. Continue some uses, narrow others and retain human work where appropriate. This nuanced conclusion is more useful than a general declaration that AI works or does not work. It provides a practical direction based on the business’s tasks and criteria actually tested. An expansion decision should identify the next scope, responsible people and review conditions, so the company keeps learning rather than simply increasing output because the first demonstration looked convincing.