Generative AI in marketing: How should businesses use it?

Norwegian companies are spending more and more money on artificial intelligence, and with good reason. A survey by Abelia and NHO showed as early as 2024 that over half of Norwegian businesses were experimenting with generative AI. By 2026, the question is no longer whether you should use generative AI in your marketing, but what you should actually use it for to achieve a real impact. For many companies, initial attempts have yielded mixed results: some have saved hundreds of hours, whilst others are left with generic content that appeals neither to customers nor to Google. The difference almost always lies in the strategy behind it, not in the technology itself.

The new landscape for generative AI in marketing

What is generative AI and why is it relevant now?

Generative AI refers to systems that can create new content based on patterns they have learnt from vast amounts of data. This includes text, images, video, audio and code. Models such as GPT-4o, Claude, Gemini and Midjourney have become everyday tools for marketers worldwide, and the possibilities for Norwegian-language content have improved significantly in the last year alone.

The reason this is relevant right now is down to the technology maturing. In 2023 and 2024, there was a great deal of hype and experimentation. By 2026, the tools have reached a level where they actually deliver consistent quality, particularly for tasks such as text generation, image processing and data analysis. At the same time, the cost of using these tools has fallen dramatically, making them accessible to everyone from start-ups to large corporations.

What makes generative AI particularly interesting for marketing is its speed. A campaign that previously took three weeks to plan, write and produce visual content for can now be carried out in a matter of days. But speed without strategy simply creates more noise. This is where Norwegian companies need to think carefully about which applications actually add value.

From manual processes to automated workflows

Consider the typical workflow for a marketing department in a medium-sized Norwegian company. Someone writes a blog post, sends it for proofreading, waits for feedback, makes adjustments, creates an image, adapts the format for LinkedIn, Facebook and newsletters, and schedules publication. The whole process can take a week.

With generative AI, large parts of this workflow can be automated. A first draft can be generated in minutes, images can be created without waiting for an external photographer, and the content can be adapted for different channels with just a few instructions. However, it is important to understand that AI-generated content is typically around 80 per cent complete: this provides a huge head start, but still requires human quality control and fine-tuning.

The transition from manual processes to automated workflows is not just about technology. It is about change management. Staff need training in prompt engineering – that is, the art of giving AI tools precise instructions. Companies that have been most successful in this transition have invested time in training their teams, rather than simply buying licences and hoping for the best.

Streamlining content production and creativity

Copywriting for social media and blogs

Content production is the area where most Norwegian companies are already using generative AI, and it is easy to see why. Writing three LinkedIn posts a week, a blog post, newsletters and ad copy requires considerable capacity. AI tools can generate drafts for all of this in a fraction of the time.

What distinguishes good results from poor ones is how specific the instructions are. A vague prompt such as ‘write a post about sustainability’ produces generic output. A detailed prompt describing the target audience, tone, desired length, specific points and a clear CTA yields something that can actually be used. At Mediabooster, we’ve seen that companies which invest in building prompt libraries tailored to their brand achieve dramatically better results than those who improvise each time.

Blog posts are a particularly interesting area of application. AI can help with research, structuring and first drafts, but the human editor remains crucial for adding industry insight, personal experience and an authentic voice. Google rewards content that demonstrates genuine expertise, and that is something AI alone cannot deliver.

Visual content creation and image generation

Image generation has made enormous strides forward. Tools such as Midjourney, DALL-E 3 and Adobe Firefly can now produce images that are difficult to distinguish from professional photographs. For marketing departments, this means you can create unique visual elements for campaigns, social media and websites without organising photoshoots or buying expensive stock images.

Norwegian businesses are using this in creative ways. An estate agent can visualise what a flat in need of refurbishment will look like after renovation. An online shop can generate product images in different settings without having to set up physical backdrops. A travel company can create atmospheric images that match the season and the campaign.

But there are pitfalls. Generated images can contain subtle errors: an extra finger, illogical shadows, or text that looks nonsensical. Brands that use AI-generated images without quality control risk appearing unprofessional. It is also important to have a clear policy for labelling AI-generated content, something that an increasing number of consumers and regulatory authorities now expect.

Large-scale video production and personalisation

Video is the fastest-growing format across all digital platforms, and generative AI has made video production accessible to businesses that previously lacked the budget for it. Tools such as Runway, Sora and HeyGen make it possible to create everything from short advertising videos to explanatory videos featuring AI-generated avatars.

The most exciting area of application is personalisation on a large scale. Imagine being able to create a hundred variations of a product video, each tailored to a specific customer segment, with different angles, language and visual style. What previously required a production team and weeks of work can now be done in a matter of hours.

Norwegian companies in the B2B sector are using this to create personalised sales videos in which an AI avatar presents a solution tailored to the specific recipient. According to industry figures, conversion rates for such videos are 30–40 per cent higher than for generic sales presentations. It is worth emphasising that the quality of AI-generated video still varies, and that the best results come from combining AI-generated material with professional editing.

Data-driven insights and personalisation

Analysing customer data for better segmentation

Most Norwegian businesses hold far more customer data than they actually utilise. CRM systems, online shop data, email statistics and behavioural data from websites contain valuable information that often remains unused because it takes too long to analyse manually.

Generative AI is fundamentally changing this. By feeding AI models with customer data (anonymised and in line with the GDPR), companies can identify patterns and segments they would never have discovered on their own. Perhaps the data shows that customers who purchase product A within 14 days of their first visit have a lifetime value 70 per cent higher than the average. Such insights make it possible to prioritise marketing efforts where they yield the greatest return.

It is important to distinguish between AI projects and traditional IT projects here. An AI project for customer segmentation is not something you simply set up and forget about. It requires continuous fine-tuning, updating of the data set and evaluation of the results. The quality of your data is absolutely crucial: poor input data leads to poor analysis, no matter how advanced the AI model is.

Enhancing the customer journey with intelligent assistants

Next-generation chatbots and customer service

Forget the frustrating chatbots of 2020 that could only answer pre-defined questions. Today’s AI-powered assistants understand context, remember previous conversations and can handle complex enquiries in natural Norwegian. They can draw on information from product databases, order histories and FAQs in real time to provide accurate answers.

For Norwegian businesses where customer service is a key function, this represents a significant opportunity. An AI assistant can handle 60–80 per cent of all incoming enquiries without human intervention, freeing up customer service staff to focus on the complex issues that truly require human empathy and judgement.

What makes these solutions particularly valuable for marketing is that they collect data on what customers are actually wondering about. This insight can be used directly to improve product descriptions, create better FAQs, refine marketing messages and identify new content opportunities. The chatbot becomes not just a customer service channel, but a continuous source of customer insight.

Interactive and dynamic user experiences

Generative AI opens the door to entirely new types of user experiences on websites and in apps. Instead of static product pages, you can offer interactive advisors that help customers find the right product based on their specific needs. A sports shop could have an AI-powered fitness advisor that recommends equipment based on activity level, goals and budget.

Dynamic content personalisation is another powerful application. Your website can display different content to different visitors based on their behaviour, industry or where they are in the buying journey. A first-time visitor sees introductory content, whilst someone who has visited three times sees more detailed product information and a clear call to action.

Mediabooster has helped several Norwegian businesses implement such dynamic experiences, and experience shows that personalised user journeys typically result in 15–25 per cent higher conversion rates compared with static websites. The key is to start simply: choose one part of the customer journey where personalisation will have the greatest impact, and build on that.

Ethical considerations and strategic implementation

Safeguarding the brand’s voice and quality

One of the biggest risks of using generative AI in marketing is that the content loses the brand’s unique voice. AI models tend to produce text that sounds the same regardless of who uses them, unless you are very mindful of how you instruct them.

The solution is to develop detailed guidelines for AI use that include the brand’s tone of voice, choice of words, values and taboo words. Some companies create their own finely tuned models trained on their own content, whilst others use detailed system prompts to ensure consistency. Whichever approach is taken, it is crucial that someone with a good understanding of the brand always reviews AI-generated content before publication.

Quality control is also about fact-checking. AI models can produce what are known as ‘hallucinations’: claims that sound credible but are incorrect. In marketing, this can range from incorrect product specifications to fabricated statistics. A robust quality assurance process is not optional; it is a necessity.

The way forward: How to get started with your AI strategy

Adopting generative AI in marketing isn’t about buying the most expensive tool and hoping for magic. It’s about starting with a clear problem you want to solve. Perhaps your team spends too much time writing social media posts, or perhaps you’re struggling to personalise your communications for different customer segments. Identify the biggest pain point, and start there.

A practical approach is to define three to five specific use cases, set measurable KPIs for each of them, and run a pilot period of 8–12 weeks. Measure the results, adjust your approach, and scale up what works. Remember that AI projects are iterative by nature: you won’t get it right first time, and that’s perfectly fine. The important thing is to learn quickly and make adjustments as you go along.

The difference between an AI agency and an IT company is comparable to the difference between a specialist and a general practitioner. Both have their place, but when you’re implementing AI in marketing, you need someone who understands both the technology and the business value it is intended to create. Mediabooster acts as a partner and colleague in this process: not just an external supplier who delivers and then disappears, but a team that works closely with you to ensure that your AI strategy actually delivers measurable results. Would you like to explore what generative AI can do for your business? Contact us for a no-obligation chat about the possibilities.