Transformation


## Use cases rule them all

The current AI wave is in many ways similar to the coming of marketing automation 1.0 more than a decade ago. The pace is just faster, and the buzz is bigger as we are not talking about some processes that affect under the hood.

The biggest similarity relates to the early days of marketing automation when the hunt for concrete use cases was still on. The question for getting bang for the buck for an individual company was, did the company just end up doing the same email campaigns with a heavier tech stack and cost base? Today, while examples like [Klarna](https://forbes.com/sites/jackkelly/2024/03/04/klarnas-ai-assistant-is-doing-the-job-of-700-workers-company-says/) are concrete examples of change, it's clear that chat-based solutions are just the starting point of AI's potential.

In marketing automation, it could take months or years before organizations found the way and managed to complete the setups in a way that began to make an impact. Many are continuing on this journey.

What sets the GenAI era apart is the immediacy we can now get on board and start making things happen. GenAI's trial and testing are no longer slowed by extensive deployment and technical setup, providing a quick way to get started.

### The right attitude and investments in time and money will help some companies achieve an unfair competitive advantage

However, distinguish this from a mere plug-and-play scenario. AI is a transformational project requiring clear objectives and ambition from the management. The technology is here, but integrating it into our workflows requires time and proper experimentation. It also involves ambition from companies developing AI-based services to create intuitive solutions that remove the need for clumsy prompting and that go beyond content creation or chatbots.

As we've worked with all kinds of organizations from US to Finland, we have learned the GenAI transformation journey proceeds in 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. Test different services and think outside of narrow content generation use cases.

2. Leveraging data: Start by using non-sensitive data stored in your CMS and marketing platforms. Incorporating it into your GenAI processes can significantly improve analytics and content production results.

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

 The guiding principle should be clear: Use cases rule them all. The true measure of AI's value is not only in its technological power but also in its ability to solve real-world problems.

Blog/Transformation

The different stages of AI transformation