maitiq guide
How to choose the right AI marketing use cases
maitiq · Published
The best AI use case in marketing is not the most spectacular one but the most clearly scoped one. It describes a recurring task, a current baseline, permitted data, a person responsible and a result that can be verified. Only once these five points are clear is it worth asking which model or tool to use.
A simple rule helps with the shortlist: start with a task where an error is visible and can be corrected. An internal draft is therefore usually a better first test than an automatically sent customer message. Current guidance for Swiss SMEs recommends a needs analysis, small pilot projects and governance. The SECO SME portal also warns against treating AI as just another ingredient; the concrete benefit has to be the focus.
The eight patterns below are neither case studies nor performance promises. They show how a marketing team can formulate an idea as a use case that can be tested.
1. Structure research material
Task: Organise approved internal and public sources by topic, question or contradiction.
Worthwhile when: specialists repeatedly search the same collections of material and review the result anyway.
Evidence: Compare processing time, relevant sources that were missed and wrongly attributed statements against a manual sample.
Risk: A system can invent connections or reproduce sources incorrectly. The source list must be preserved; a human opens and checks every source that supports a claim.
Boundary: A system must not turn a summary into a claim ready for publication. Speeding up research is not claim approval.
2. Prepare briefings and first drafts
Task: Generate a structure, a list of questions or a first draft from an approved fact pack.
Worthwhile when: the team produces many similarly structured briefings and the style, facts and approvals are already defined.
Evidence: Do not measure only the minutes to the first draft. Also record revision cycles, unsupported statements, tone errors and the time to approval.
Risk: Confidential information can reach an unvetted tool, and outputs can reproduce protected third-party elements. The National Cyber Security Centre (NCSC) advises against sensitive inputs, and the Swiss Federal Institute of Intellectual Property (IPI) points to possible rights questions with recognisably protected material.
Boundary: Authorship, originality, the choice of sources and publication approval remain human.
3. Generate creative variants for a controlled test
Task: Propose variants of an already approved message for defined formats.
Worthwhile when: a valid base creative, clear brand boundaries and a real test plan are in place.
Evidence: First check compliance with the relevant rules and rights. Then compare the variants in a pre-defined experiment, not on the basis of personal preference.
Risk: Volume can be mistaken for variety. Variants can contain stereotypical depictions, invented product features or infringements of rights.
Boundary: A system must not invent an offer, a price or any legally relevant statement.
4. Group customer feedback by topic
Task: Sort free text from permitted sources into a prescribed or verifiable topic structure.
Worthwhile when: enough feedback is available and the team currently spends a lot of time on consistent categorisation.
Evidence: A specialist codes a sample independently. Measure agreement, topics that are missing and misclassifications with particularly serious consequences.
Risk: Input can contain personal or sensitive information, so its processing touches data protection. A separate risk is that rare but important complaints are lost during categorisation. Data should be minimised or anonymised as far as this is right for the purpose and the legal basis.
Boundary: A topic frequency does not automatically explain cause, sentiment or representativeness.
5. Flag campaign anomalies
Task: Flag unusual changes in approved performance data and formulate possible review questions.
Worthwhile when: the team monitors several metrics on a recurring basis and can define what counts as a relevant deviation.
Evidence: Use historical periods to test which real incidents are detected and how many false alarms are raised. Document data delays.
Risk: An anomaly can be seasonal, technical or random. An automatic explanation may sound more convincing than the evidence behind it.
Boundary: Flagging is not changing. Budget, bid or campaign changes require separate authorisation and approval; an explicitly scoped standing authority can cover them. Detection-only access authorises no writes.
6. Prepare forecasts for planning scenarios
Task: Estimate a range for demand, volume or resource needs under documented assumptions.
Worthwhile when: enough comparable historical data is available and the team can communicate uncertainty rather than a single number.
Evidence: Compare forecasts against a simple benchmark, such as the previous year’s value or a moving average. Assess errors across several periods.
Risk: Structural breaks, campaign changes and small data sets can invalidate a model. A precise number is not necessarily an accurate forecast.
Boundary: The result is a planning basis, not a commitment about revenue or demand.
7. Support lead or enquiry handovers
Task: Prepare incoming enquiries for human handling using transparent criteria.
Worthwhile when: marketing and sales share a definition of the handover and enough reviewed examples are available.
Evidence: Check misassignments in both directions, differences between groups, response time and whether sales can act on the lead. A high score is not proven sales value.
Risk: Historical decisions can contain bias. Personal data, automated individual decisions and transparency requirements must be reviewed from a professional and a legal perspective.
Boundary: A system must not definitively rule out a person or change a relationship unless an explicitly reviewed basis and human control exist for doing so.
8. Knowledge-based internal assistance
Task: Suggest answers to employees from a limited, versioned set of approved marketing documents.
Worthwhile when: answers recur frequently, sources are maintained and a clear escalation path exists.
Evidence: Assess source referencing, completeness, false confidence and the escalation rate. Outdated or contradictory documents must become visible as a data problem.
Risk: A new interface can unintentionally widen access rights. A correctly quoted statement can still be outdated or misleading; check how current it is, not only whether the source is quoted faithfully.
Boundary: The assistance must not replace a policy, an approval or a binding statement.
A selection matrix instead of a wish list
After the ideas have been gathered, every pattern should be assessed on six dimensions. Business value asks which outcome should improve. Measurability asks about the baseline and the comparison. Data readiness checks availability, lawfulness, quality and access. Consequences of errors cover damage, visibility and correctability. Process maturity assesses responsibility, approval and the fallback path. Implementation effort covers integration, operation, training and ongoing review.
A score from one to five must not pretend to be mathematical truth. A use case with high value and unbearable consequences of errors remains unsuitable, even if the average looks good. That is why you should start with exclusion questions: Are the purpose and the responsibility unclear? Is there no permissible data basis? Can a critical error not be detected in time? Is there no manual fallback? A “Yes” stops or changes the idea.
For each candidate, also add an alternative without AI. A better template, an unambiguous approval step or a classic rule may solve the problem more reliably. This non-AI alternative protects against a distorted selection in which only AI variants compete with one another. Also record which piece of evidence would later trigger a different decision. That turns the matrix from a static ranking into a learning instrument. If the available evidence, the process or the consequences of errors change, the assessment must be reopened. A high priority awarded once is not permanent operational approval.
Two or three candidates can then be compared in a workshop. In its planning guidance, the Microsoft Cloud Adoption Framework recommends prioritising benefit and feasibility together and testing assumptions with a focused proof of concept. Because this guidance comes from a technology provider, it should be understood as a structure, not as a tool decision or proof of success.
How an idea becomes a testable pilot
Formulate the pilot as a contract you can verify: “For task X, team Y uses only data Z. The system delivers A, a human reviews B, and we compare C with today’s process. At D we stop.” This sentence forces the team to clarify scope, data, responsibility, evidence and the stop point at the same time.
Where possible, choose approved anonymised or synthetic examples – even historical or internal data can relate to real people. Define the test cases before you see the result. Pay particular attention to rare errors with serious consequences. Log the model and configuration version, because otherwise a later result cannot be compared with the test.
In the end there are four legitimate decisions: the use case is discontinued, narrowed, tested again or continued with an operating plan. A pilot does not have to go into production to be valuable. A clean “not suitable” prevents long-term costs and risks.
Three confusions to avoid
First, a draft is not a publication. Source, fact, rights, brand and approval checks lie between the two. Second, a recommendation is not a decision. The person responsible must be able to see the reasons and the limits and to object. Third, technical implementation is not business success. Only a comparison against the baseline shows whether the whole process has improved.
This separation keeps the choice of use case level-headed. The team is not looking for “more AI” but for small, measurable progress with clear responsibility. That is exactly the basis for a strategy, a business case and, later, a defensible operational decision.
How maitiq helps: choose the first use case based on a real bottleneck
If leads are answered too late, another image generator brings little benefit. Start with the handover from the form to sales: classify the incoming enquiry, check the information required, propose who is responsible and prepare a draft reply. A human confirms the content and the recipient. The result is a lead that can be worked on, not merely an AI summary.
Compare response time, rework and qualified conversations with the previous process. As part of an agreed project, maitiq helps to capture this baseline, set up the right workflow and support your team in day-to-day operation. That is how you prioritise impact over tool novelty.
How a first pilot with maitiq begins
A limited initial assessment shows which use case should start first, which idea is not yet ready and what limits maitiq recommends for the pilot. You receive a clearly scoped use case, the open data and control questions, a pilot plan and the criteria for later operation.
Sources and how to read them
The sources for this article play different roles: SATW guidance and the SECO interview offer orientation for Swiss SMEs; the NCSC gives cybersecurity advice; the IPI and the Federal Data Protection and Information Commissioner (FDPIC) explain copyright and data-protection questions; the Microsoft Cloud Adoption Framework describes planning from a vendor perspective. They are read as structure and context, not as proof of success. Information about how maitiq works is available at maitiq.com.