AI Search 101
How is AI used in marketing in Finland?
Marketing teams use AI in three distinct ways: producing content, analysing data, and reaching buyers through channels that are themselves AI systems. Adoption is heaviest in production, most established in analysis, and least understood in distribution, which is where it now matters most.
The basics
| Heaviest use | Written content, at 84.3% of surveyed teams |
| Main benefit reported | Speed, at 90% |
| Underlying technology | Generative AI and machine learning |
| Newest dimension | AI search and answer engine optimisation |
| Evidence base | Survey of 70 marketing leaders in Finland |
Production: making things
This is where adoption started and where it remains concentrated. In a survey of 70 marketing leaders, 84.3% of teams used AI for blog and website copy, 80% for creative thinking, 60% for social media, 48.6% for online advertising, 42.9% for images and 18.6% for video and audio.
The ordering follows how cheaply the output can be checked. A paragraph is judged in seconds; a generated video takes real time to review and fails more visibly. That gap has narrowed but it still shapes where teams are strongest.
The reported benefits skew operational rather than commercial. Ninety percent said AI made work faster. Sixty percent said it made work better. That difference is the most instructive number in the whole survey: volume rose first, and whether standards followed depended entirely on what teams did with the time they recovered.
Analysis: understanding things
The oldest and least discussed use. Segmentation, propensity and churn scoring, lifetime value estimation, attribution modelling and forecasting are all machine learning, and most predate the current wave by a decade. Teams running them rarely describe the work as AI.
What generative models added is the ability to work with unstructured text at volume: clustering open-ended survey responses, classifying inbound enquiries, summarising research, and reading long documents for the parts that matter. Work that was previously skipped because nobody had time now gets done.
Distribution: reaching people
The part that has changed most and is reflected least in how teams organise themselves.
Advertising and social platforms have run on machine learning for years; bidding and audience selection are model decisions, not settings. That is familiar.
What is new is that buyers now ask a language model what a company does, which vendors serve a category, and how two products compare. The assistant composes an answer from what it has read. For a growing share of buyers this is the first description of a company they encounter, and no one at the company wrote it.
This makes visibility in AI search a distribution problem rather than a content problem, and it is covered in what AI search is, answer engine optimisation and AI citations.
What consistently goes wrong
Volume without a check. The most common failure is using generative tools to produce more of what the team already produced, with nobody reviewing the result. This creates material that describes the company the way it describes everyone else, which is worse than publishing less.
Measuring the wrong thing. Output per week is easy to measure and tells you nothing. Whether the work changed what a buyer believes is hard to measure and is the actual question.
Tool sprawl. The same survey found 40 different AI applications across 70 companies. That is not a rich toolkit, it is an absence of decisions.
Ignoring the third category. Most teams have a production strategy and no view at all on what assistants say about them, which is the dimension with the least competition.
How this impacts your business
A language model does not look a company up in a register. It composes a description from everything it read during training and whatever it retrieves at the moment of the question. Trade press, directories, forums, competitor comparison pages, and somewhere in that mix the company's own website too.
So the description a buyer sees is assembled, not published. No company controls it, but every company supplies a share of the material it is built from.
The traffic impact is still small, roughly one percent of sessions for most companies. The influence is not small, because the reader is often a buyer forming a first impression of what a company does and who it serves, before any page gets a click.
Checking takes ten seconds. Type the company name into ChatGPT and read the answer.
Frequently asked questions
What do marketing teams use AI for most?
Written content. In a survey of 70 marketing leaders, 84.3% used it for blog and website copy, ahead of creative thinking at 80% and social media at 60%.
Does AI improve marketing quality or only speed?
Mostly speed, on the reported evidence. Ninety percent of surveyed leaders said their teams worked faster; sixty percent said the work was better.
Should a marketing team have an AI policy?
It needs answers to three practical questions: which tools are approved, what data may be entered into them, and who checks output before it is published.
Does AI-generated content rank in search?
Search engines judge usefulness rather than authorship. Generated content that answers a question well can rank; generated content produced for volume generally does not.
What is the biggest missed opportunity?
Distribution. Most teams have adopted AI for making things and have no view of what AI systems say about them to buyers.
Related definitions
- What is AI search?
- What is answer engine optimisation?
- What is generative AI?
- What is machine learning?
- AI Search 101
Written by Lari Numminen, Generate More. Also available in Finnish. Updated 31 August 2026.