Artificial Intelligence (AI)
Artificial Intelligence (AI) explained
Which task should work better? That is a useful starting point for AI in marketing. Forecasting demand, classifying feedback and drafting an image are different tasks. They require different data, methods and criteria for a useful result.
Machine learning is an important area within AI. Generative AI produces outputs such as text, images or audio; other methods classify, predict or optimise. Applications can combine these functions. Not every AI system continues learning during use or independently decides which actions to take.
In Creative Engineering, we assess quality and economics across the complete workflow. AI can help explore more directions, organise information or prepare recurring editing steps. A better result still depends on selection, development and suitability for the actual application. We take responsibility for the concept and quality.
Evaluate a use case on representative tasks against a documented baseline. Record correct and incorrect results, plus preparation, review and correction time. A fast draft may require extensive revision; a technically elaborate system may be unnecessary for the task.
Fluent language, convincing images and strong model scores do not establish factual accuracy or operational suitability. Clarify data access, responsibilities and limits of use. Legal requirements depend on the particular deployment; a general AI definition does not replace that assessment.
Examples
Hypothetical application
A team prepares product copy for several formats. An AI system drafts versions from confirmed product data. Editors check facts, brand language and usability. The comparison concerns approved finished copy and total editing effort, not merely the number of generated drafts.
Key Points
- AI includes different methods and tasks.
- Generative output and reliable facts are separate questions.
- Assess quality through usable results.
- Include review and revision in total effort.
- Define responsibilities and permitted actions explicitly.
Practical application
Describe a recurring task, the intended improvement and the criteria for an acceptable result. Trial a suitable solution with appropriate data and a responsible team. Use the findings to decide whether to improve, expand or discontinue it.
Useful measures
Task quality
Accuracy, completeness and suitability against predefined criteria.
Total effort per usable result
Consider preparation, tool costs, editing, review and revision together.
Errors and correction needs
Observed error types and consequences, plus the effort needed to detect and resolve them.
Common mistakes
- Treating a high volume of generated content as evidence of quality.
- Equating model capability with safe integration into a workflow.
- Measuring speed per call while excluding revision and maintenance.
- Publishing unconfirmed AI statements as customer facts.
Sources and context
- OECD: Definition of an AI system (2024)
Definition covering different AI outputs and levels of autonomy.
- NIST: Generative AI Profile
Context on generative AI risks and quality assessment.
Frequently Asked Questions about Artificial Intelligence (AI)
No. A predefined workflow can operate without AI. AI can also perform just one step within a larger automation. The label alone does not describe a specific capability.
No. It can broaden possibilities and support work. Whether it improves quality or economics in a particular workflow needs to be assessed on appropriate tasks, including review, revision and operation.
Choose a bounded task with accessible foundations, assessable results and manageable consequences of errors. Compare a pilot with the existing workflow before expanding its use.
Related links
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