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    children: [_jsxs(_components.h2, {
      id: "the-use-cases-for-ai",
      children: [_jsx(_components.a, {
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    }), "\n", _jsx(_components.p, {
      children: "The current AI wave is more like marketing automation 1.0 than we thought. Faster pace and more noise because we’re not talking about under the hood stuff."
    }), "\n", _jsx(_components.p, {
      children: "The biggest similarity is to the early days of marketing automation when we were still searching for concrete use cases. The question was, did the company just end up doing the same email campaigns with a more tech stack and cost base? Today, examples like Klarna are concrete examples of change, but chat based solutions are just the tip of the AI iceberg."
    }), "\n", _jsx(_components.p, {
      children: "In marketing automation it took months or years before companies found their way and got the setups done in a way that started to make an impact. Many are still on this journey."
    }), "\n", _jsx(_components.p, {
      children: "What's different in the GenAI era is we can get on board and start doing things now. GenAI's trial and testing is no longer held back by deployment and technical setup, so we can get started quickly."
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      children: "But don't confuse this with a plug-and-play scenario. AI is a transformation project that requires clear goals and ambition from the management. The technology is here, but integrating it into our workflows takes time and proper experimentation. It also requires companies building AI based services to create intuitive solutions that remove the need for clumsy prompts and go beyond content creation or chatbots."
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      children: "As we've worked with companies from US to Finland we've found the GenAI journey can be broken down into four stages in a simplified way:"
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        children: ["\n", _jsx(_components.p, {
          children: "Learning and mapping: learn to work with GenAI and identify use cases and bottlenecks without heavy IT projects or data integration. Sounds easy, but the winners will be the companies that invest enough time in this and don’t leave it up to the employees. Try out different services and think beyond content generation use cases."
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        children: ["\n", _jsx(_components.p, {
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        children: ["\n", _jsx(_components.p, {
          children: "Fine-tuning for business impact: with a solid understanding and proven use cases, the focus shifts to fine-tuning LLMs. This phase is intense and requires investment, time and experimentation."
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Transformation


## The use cases for AI

The current AI wave is more like marketing automation 1.0 than we thought. Faster pace and more noise because we’re not talking about under the hood stuff.

The biggest similarity is to the early days of marketing automation when we were still searching for concrete use cases. The question was, did the company just end up doing the same email campaigns with a more tech stack and cost base? Today, examples like Klarna are concrete examples of change, but chat based solutions are just the tip of the AI iceberg.

In marketing automation it took months or years before companies found their way and got the setups done in a way that started to make an impact. Many are still on this journey.

What's different in the GenAI era is we can get on board and start doing things now. GenAI's trial and testing is no longer held back by deployment and technical setup, so we can get started quickly.

### The right attitude and investment of time and money will make some companies get an unfair advantage

But don't confuse this with a plug-and-play scenario. AI is a transformation project that requires clear goals and ambition from the management. The technology is here, but integrating it into our workflows takes time and proper experimentation. It also requires companies building AI based services to create intuitive solutions that remove the need for clumsy prompts and go beyond content creation or chatbots.

As we've worked with companies from US to Finland we've found the GenAI journey can be broken down into four stages in a simplified way:

1. Learning and mapping: learn to work with GenAI and identify use cases and bottlenecks without heavy IT projects or data integration. Sounds easy, but the winners will be the companies that invest enough time in this and don’t leave it up to the employees. Try out different services and think beyond content generation use cases.

2. Leveraging data: start using non-sensitive data stored in your CMS and marketing platforms. Plugging it into your GenAI workflows can give you a big boost in analytics and content production.

3. Fine-tuning for business impact: with a solid understanding and proven use cases, the focus shifts to fine-tuning LLMs. This phase is intense and requires investment, time and experimentation.

So the mantra should be: start from use cases. AI's value is not just in the tech but also in the problem solving.

TransformationThe AI journey

Focus on business use cases and invest time and resources in AI and you will be one of the winners

The use cases for AI

The right attitude and investment of time and money will make some companies get an unfair advantage

Read more about
Transformation

Generative Engine Optimization (GEO)—the new search optimization