How to choose AI marketing tools

Choosing an AI marketing tool is not a feature comparison and not a matter of vendor promises. A selection that holds up starts with the task itself: what has to be delivered, with which data, under which rules and with which review. Only then is it worth building a shortlist. What follows is a sequence that a marketing team, IT, data protection and procurement can work through together.

Turn the use case into a task you can test

«We need an AI tool for marketing» is not a requirement you can test. Describe the job as observable work: «From an approved product dossier, produce three text drafts in the specified format and trace every factual claim back to a source.» Or: «Check a campaign table for defined anomalies and mark the findings without changing anything in the advertising account.»

The task names the inputs, permitted sources, output format, quality criteria, excluded actions and the person responsible for review. It also separates must-haves from nice-to-haves. A CRM integration may matter later but is not necessarily a must for a first offline test. Missing data deletion, unclear usage rights or write access that cannot be limited, by contrast, can already be grounds for exclusion.

Compare tool categories, not product names

A general assistant can handle many formats but needs clear templates and controls. A specialised professional tool covers a narrower task, such as image variants, transcription or analysis. An embedded AI function already sits in a marketing or advertising system and partly inherits its identities and permissions. An orchestration platform connects several steps and systems. A self-operated component offers more technical control but requires in-house expertise in models, security and operations.

These categories are not automatically better or worse, but they shift responsibility. An embedded tool can be available faster, yet it remains tied to the platform's data and permission model. An open API may look flexible, but integration, monitoring and error handling then sit with your own team. Record that shift of responsibility in the decision document.

Check the mandatory requirements first

A points score must not hide a fundamental defect. Define the mandatory requirements first. Can the provider explain the intended data purpose and the subprocessors involved? Are retention, deletion and export verifiable? Can the use of inputs for training or product improvement be configured appropriately or clarified in the contract? Can roles and rights be limited to this task? Is there a traceable route for incidents and service changes?

For content and visuals, rights questions need separate attention. What commitments does the provider make about inputs, outputs and indemnities? Which obligations remain with your organisation? The Swiss Federal Institute of Intellectual Property (IPI) points out that with AI use, several technical processes and possible rights in input and output must be assessed separately. A marketing approval does not replace a rights review.

If a tool fails a requirement that is mandatory for the task, good usability does not make up for it. The verdict is «not suitable for this task»; for another, less sensitive task the assessment may turn out differently.

Check quality with a fixed test set

Live demos are useful for exploration but poor for comparison. Build a small, representative test set from synthetic or approved data. It contains straightforward cases, edge cases, missing information, contradictory sources and one deliberately impermissible request. All candidates receive equivalent inputs, data and quality requirements; identical prompts do not make sense across different tools. Record the documented configuration and the version you can actually see. Information you cannot inspect is noted as such and is not an automatic ground for exclusion.

Do not assess only «I like it». For text, criteria can be factual accuracy, source attribution, completeness, tone, format and editing effort. For analysis, what matters is whether the findings are accurate, whether they can be reproduced, how missing values are handled and whether uncertainties are clearly presented. For visuals, brand conformity, artefacts, rights notices and technical formats must be assessed as well. A qualified reviewer assesses blind where that is practicable.

An average alone is not enough. Show the types of error and how widely they vary. A tool that solves nine harmless cases well but publishes an unsupported claim on the critical tenth needs more rigorous human review. Note also when the system correctly refuses a request or makes uncertainty visible. A safe escalation can be worth more than a self-assured answer.

Check data protection and security against the real data flow

The FDPIC states that the Swiss Data Protection Act applies directly to AI-supported processing of personal data. The assessment therefore follows the purpose, data, recipients, transparency and risk of the specific processing, not the «AI» label. Create a data-flow map for each candidate: which fields leave which system, and in which region are they processed? How are requests for access, correction and deletion transmitted to the organisations that process data on the provider's behalf, and how are they carried out there?

Avoid real personal data in the first pilot. Access credentials, tokens and internal secrets do not belong in prompts. OWASP stresses that a system prompt must not be treated as a secret or as an authorisation control. Check real roles, short-lived credentials, logging and the separation of read and write rights. Check whether the recommended controls are actually implemented for each integration.

Even a «read-only» connector can expose large amounts of data. Can objects, fields, accounts and date ranges be limited? Can human approval be enforced technically before any external effect? What happens on prompt injection from a document or a website? These questions belong in a security review and, where relevant, in a technical test; a questionnaire alone proves no security. Authorisations belong in deterministic rules outside the model, not in the prompt.

Integration and operation are cost items in their own right

The licence price does not show the full cost of operation. Configuration, interfaces, identity management, testing, professional review, monitoring, training, incident handling and exit all add to the operating cost. This article deliberately names no market prices. Instead, every offer should state the same scope so that differences become visible. Track two indicators over the same period and the same test population: total cost in Swiss francs per accepted result, and human review time in minutes per accepted result. If you monetise review time, state the hourly rate once and include it in the cost numerator; never count the same review effort twice. With no accepted results, neither ratio can be calculated, and zero known cost must be distinguished from missing cost inputs.

Do not check API documentation merely for its existence. Are the required endpoints, limits, webhooks, versions and error messages documented? Is there a sandbox? Can data and configurations be exported? Who informs you about model or product changes? A tool with good individual output can be unsuitable if operating it does not fit your organisation.

Also name a person responsible for operation. They are responsible not for the model in the abstract but for the concrete use: users, templates, sources, review rate, incidents and the decision to switch the tool off. The business unit, IT, security, data protection and procurement have different review roles. A RACI matrix makes visible who decides, who contributes and who is merely informed.

Test the exit before you sign the contract

A credible exit answers: can your own data, templates, evaluation cases, logs and configurations be exported, and in which format? How are stored data deleted, and how is that documented? Which workflows stop working if the service is discontinued? Is there a manual fallback? Which model or API dependencies must be replaced? Such a test belongs in an agreed, isolated pilot; it is not a call to switch off a running system. New purposes, data or actions require their own review of the affected part.

The current official UK guidance for developing, delivering and procuring GenAI tools treats adoption as an organisational task with training, support, risk management and monitoring. For the pilot described here, also test the exit procedure before signing the contract. In the pilot, carry out at least one export and one deactivation. An export promised only on paper is not a tested way back.

Assess not only the tool but whether the team can use it

A pilot can convince technically and still fail organisationally. Check whether the responsible people understand the results, recognise errors and can run the process without a vendor demo. A test day under ideal conditions says little about cover, holidays, competing priorities or an incident. So simulate a follow-up question, a wrong output, a locked user account and a short-notice switch-off.

Documentation is checked against a concrete task: can a new specialist follow the approved use case, the data sources, the review criteria and the stop procedure? Can IT withdraw rights without blocking other systems? Can procurement recognise a material product change and reassess the contract? A good help-centre article does not replace your organisation's own operating instruction.

Also factor the learning curve into the decision. Record training needs, recurring support questions and the kinds of correction required, without turning them into an invented productivity figure. If only one person can operate the tool safely, there is an operational risk. If the team has to rebuild every output from scratch, the use case or the tool is probably unsuitable. These findings may point to a narrower scope rather than a hasty rollout.

Before any extension, run the fixed test set again. Model, policy or interface changes start a new evaluation round. The original procurement grade is not a permanent seal of quality.

Decide: reject, run a limited pilot or go deeper

In the end it is not only «buy» or «do not buy». A candidate can drop out because of a mandatory requirement. It can pass a limited offline pilot and still need security or contract clarifications. Or it can be approved for exactly one job while integrations and live actions stay blocked. Record the scope explicitly.

How maitiq helps: test the same work package with all candidates

For the selection, use an approved briefing, the same source data and the same expected output. Record factual or task-specific errors, manual rework, exportability, the total cost in Swiss francs per accepted result and the human review minutes per accepted result. All quantities refer to the same period and the same test population. A cheap subscription becomes expensive if the team has to rebuild every result.

maitiq supports the selection from the process side: which task should improve or be automated, what responsibility remains with your team, and how the solution fits into your systems? That turns a tool comparison into a decision you can act on. Only a passed practical test justifies broader adoption. This support is not part of the Google Ads product; it can be agreed as a scoped pilot as described below.

How a first pilot with maitiq begins

In an agreed initial assessment, maitiq can help define the task, identify unresolved data and control requirements, and plan a limited pilot. The deliverables are a clear scope, a pilot plan and the criteria for deciding whether the tool is ready for operational use.

Decision rule: Define the task and the disqualifying grounds first; then compare quality, integration, total cost and review time per accepted result, and the tested exit procedure.

Sources and how to read them

These four sources carry the external references in this article. The FDPIC explains why the Swiss Data Protection Act applies directly to AI-supported processing of personal data; the IPI addresses copyright questions in AI training and use; the UK guidance describes a human-centred approach to adopting AI tools; and OWASP covers the risk of system prompt leakage. Platform documentation describes features and limits, while publications by providers or associations must be read as their own position. Information about how maitiq works is available at maitiq.com.

Have maitiq review your specific case.