Not all AI is created equal: Why this difference determines the success of your advertising
The picture is shaped primarily by generative AI—that is, systems like ChatGPT or Claude that generate text, images, or code. But there are also other forms of artificial intelligence with entirely different tasks. Using the wrong AI for the wrong task is like hammering a nail into the wall with a screwdriver: the tool isn’t bad, but it’s designed for a different task.
When it comes to data-driven decisions like advertising planning, it’s worth taking a closer look: What types of AI are there—and which one is designed for what?
The Three Main Types of AI
In a business context, we encounter three main types of technology that are often grouped under the umbrella term “AI,” even though they do fundamentally different things.
Generative AI – The Creative One
Best known for tools such as ChatGPT, Midjourney, and Gemini, generative AI is designed to create new content: text, images, videos, or code. It uses large AI models and generates content based on probabilities and patterns—not on verifiable facts.
Analytical AI – the Data Analyst
The down-to-earth counterpart: Analytical AI sifts through large amounts of data, identifies patterns, and provides the basis for informed decisions. It uses machine learning methods and other data-driven analytical techniques. Its goal is not creativity, but gaining insights.
RPA – The Digital Clerk
Robotic Process Automation, or RPA for short, is not strictly speaking artificial intelligence in the traditional sense, but it is often mentioned in the same context. RPA handles rule-based, repetitive tasks —quickly, reliably, and without errors. It is less about “thinking” and more about automatically executing clearly defined processes.
| Characteristic | Generative AI The Creative One | Analytical AI the data analyst | Robotic Process Automation (RPA) the digital clerk |
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| Goals | Create new content (text, image, audio, video) | Analyze data and patterns, Make forecasts | Automate Processes |
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AI works best when used in combination with other technologies
Generative AI, analytical AI, and RPA aren’t competitors—they work as a team. The key is to use the right technology for each specific task. Especially in local marketing, a smart mix can make all the difference.
A real-world example:
| Task | Appropriate AI Type |
|---|---|
| Analytical AI evaluates market potential, search behavior, and location data, thereby helping to identify the appropriate measures. | Analytical AI |
| Generative AI then helps with brainstorming and creating appropriate ad copy, images, and videos. | Generative AI |
| Automation then ensures that campaigns are launched, reports are generated, and recurring tasks are handled efficiently. | RPA |
Where AI Creates Value
This is precisely where the problem often lies in practice: Many companies are already using AI in marketing, but not always where it makes the biggest difference. Generative AI is good at generating text and drafting emails. However, the real value is created in core processes such as media planning, budget allocation, and target audience analysis. Those who apply AI where the most important decisions are made create the ideal conditions for measurable results and a clear ROI.
AI expert Jan Schoenmakers from Hase & Igel explains why this is the case and what analytical AI can actually achieve in practice in the latest episode of our Local Branding Heroes podcast.
How Analytical AI Is Used in Advertising Planning
For many years, marcapo has been exploring how AI can make local marketing more successful. In the campaign cockpit—developed in-house as part of the marcapo marketing platform—we use analytical AI exactly where it can best demonstrate its strengths: in AI-powered advertising planning.
The AI analyzes a wide range of internal and external data sources—from historical performance data and brand data to local search queries and purchasing power patterns, as well as seasonal trends and weather data.
Based on this, the AI determines the following for each location individually:
- Which marketing channels make sense
- How to Allocate Budgets More Efficiently
- When Campaigns Should Start
- Which local measures have the greatest potential
The result is not a one-size-fits-all marketing plan, but rather a location-specific, data-driven recommendation. After all, what works in a big city won’t necessarily be successful in rural areas. The Campaign Cockpit goes one step further: Not only does the AI provide specific recommendations on what a partner should do, but those recommendations can be implemented directly with just a few clicks.
Conclusion
Advertising planning shouldn’t be a game of chance. Generative AI can generate creative ideas, and RPA can accelerate processes. But when it comes to setting the right course, analytical AI is essential. It creates transparency, identifies opportunities, and ensures that marketing decisions are based on a solid foundation of data. This forms the basis for measurable impact and can contribute to a better ROI.
Learn more about this topic in the podcast
In the latest episode of the Local Branding Heroes podcast, marcapo CEO Thomas Ötinger speaks with Jan Schoenmakers, founder and CEO of Hase & Igel, about precisely this topic: the difference between generative and analytical AI, why many AI projects fail to deliver a return on investment—and why the future of marketing lies closer to brick-and-mortar retail than many might think.
