AI frees up time on the marketing team – but for what?

AI has quickly become routine

In 2025, 35 percent of Swedish companies with at least ten employees used some form of AI technology, according to Statistics Sweden (SCB), up from 25 percent the year before. In the information and communications sector, the share was 88 percent. Marketing and sales was also the most common use case among companies using AI.

AI has many applications in marketing, since the work often involves writing copy, generating image ideas, compiling information, creating variations of a message and adapting content for different channels. AI can help with all of this faster than before.

But the fact that it\u2019s possible doesn\u2019t mean every task should always be automated or scaled up. As it becomes easier to produce material, the marketing team needs to get equally better at deciding what material is actually needed.

More content – but not always more impact

A study by Kapero and Brand Marketing Sweden shows that 90 percent of marketers surveyed feel they save time with AI. In their case, the time saved is used above all to produce more content.

Among marketing managers, 74 percent say the amount of content they produce has grown over the past three years. Communication is also spread across many surfaces, with 88 percent of B2C companies active in more than eleven channels. But when AI makes it cheaper to fill even more channels, budget, time and attention get spread thinner across more places at once. The more channels resources are split across, the greater the risk that each one gets too little to be noticed.

This doesn\u2019t have to be a problem in itself. A company may need to produce more content when entering new markets, launching more products, or trying to meet customers at more stages of the buying journey. The problem arises when production runs ahead of priorities.

Kaj Johansson, co-founder of Kapero, describes exactly this when discussing the risks. Without a clear direction, a higher pace can lead to more fragmented communication. More messages across more channels doesn\u2019t necessarily make a company clearer. In some cases it instead becomes harder for the audience to understand what the company offers and why it\u2019s relevant.

The time saved requires follow-up work

The same point comes up in an international survey from Optimizely, where 2,000 marketing managers across seven countries answered questions about automation. According to the survey, a growing share of the time automation was meant to free up is instead spent reviewing, correcting and quality-checking AI-generated material.

That\u2019s not particularly surprising. AI can quickly produce a first draft, but the result often needs to be checked against facts, brand voice, audience and channel. Sometimes the reasoning also needs to be made more precise or less generic.

The time saved is still there, but it doesn\u2019t always show up as fully free capacity. Some of the work shifts from production to editing and judgment. The more material that\u2019s produced, the greater the need for clear criteria for what actually gets published.

Demands for business value are rising

As production increases, expectations of the marketing function change along with it. It\u2019s not always enough to show how many campaigns have run, how much content has been published, or how many clicks an ad has received. Management increasingly wants to understand how marketing contributes to sales, customer value and profitability.

That doesn\u2019t mean reach, traffic and engagement lack value. They can be important indicators of what\u2019s happening along the way. But if reporting stops there, it becomes hard to judge whether marketing is actually making a difference for the business.

When marketing reporting consists mainly of channel data, the discussion can easily get stuck optimizing whatever\u2019s easiest to count. It then becomes tempting to respond to weak results with more content, more campaigns or additional channels, even when the real problem lies in the offer, the audience, the message or the customer journey.

Harder to stand out in a bigger stream

AI doesn\u2019t just change how your own marketing team works. As more companies produce text, images and campaign variants faster, the amount of material competing for customers\u2019 attention also grows.

That applies to newsletters, social media, search results and AI-generated answers. Even accurate content struggles to stick if it relies on familiar phrasing and generic reasoning. Such content often lacks something that makes it relevant specifically to your audience.

What sets a piece of content apart from the crowd doesn\u2019t have to be a brand-new idea. It can be:

• an observation from the company\u2019s own customers
• experience from your own industry
• data or examples others don\u2019t have access to
• a clear stance on an issue where most people say the same thing
• a concrete way of explaining a problem the audience is actually trying to solve

AI can help process and develop such material. But the insight, experience and judgment have to come from somewhere. That\u2019s where your content can gain a clearer voice and greater relevance.

How to give the time saved a direction

AI can\u2019t compensate for unclear definitions, poor tracking, or information scattered across different systems. In Kapero\u2019s study, 43 percent say they lack structure in their data. Before automating more processes, a company therefore needs to know what information exists, how it should be used, and who is responsible for it.

1. 1

   1. Decide what the time should be used for

   Decide concretely where the time saved should go. Should it be spent on customer interviews, better briefs, audience analysis, offer development or follow-up? If the question is left unanswered, the time is often filled with more requests and more material.

2. 2

   2. Prioritize fewer initiatives

   Not every campaign needs to run in every channel. An activity spread across twelve channels today might do better in three, where message, budget and follow-up are connected. Fewer initiatives can also make it easier to see what actually works.

3. 3

   3. Tie measurement to the business

   Keep the metrics you already track per channel, but add metrics that say something about the business. Examples include qualified leads, conversion rate, revenue per customer, customer acquisition cost and long-term profitability. Which ones matter for you depends on your business model and your customers’ buying journey.

4. 4

   4. Use AI where human judgment makes the least difference

   AI often works well for first drafts, sorting and compiling material, producing variations of a text, and recurring adaptations. Your own time is needed more where the work requires prioritization, judgment, customer understanding and responsibility for what your company says.

What happens if the pace keeps increasing?

A lot suggests that parts of marketing work will move even faster with AI going forward. But a higher pace says nothing in itself about what a company should communicate or which efforts actually move it toward its goals.

If the time saved is mainly used to produce more material, there\u2019s a risk that the gap between activity and results widens. But if the time is instead used for better prioritization, clearer follow-up and more relevant content, the technology can give the marketing team greater room to maneuver.

The question, then, isn\u2019t whether you should produce more or less – it\u2019s which parts of the work deserve more attention once production itself speeds up.

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