There's been a severe lack of data available on the state of AI in Europe. We at StageZero decided to remedy this and contribute to the AI community by making available to everyone the data we have gathered from surveying companies in Europe.

In 2022, we asked companies throughout Europe how they generate value through developing and using AI. Here are a few of our key takeaways.

There's been a severe lack of data available on the state of AI in Europe. We at StageZero decided to remedy this and contribute to the AI community by making available to everyone the data we have gathered from surveying companies in Europe.

In 2022, we asked companies throughout Europe how they generate value through developing and using AI. Here are a few of our key takeaways.

"We have identified several correlations between
company practices and how well they perform
when adopting AI.

I was positively surprised to find that most companies seem to be realizing value out of their implementations.”

"We have identified several correlations between
company practices and how well they perform
when adopting AI.

I was positively surprised to find that most companies seem to be realizing value out of their implementations.”

“Managers and technologists recognize a lack of data as the most
significant barrier to providing value through machine learning.”

“High performers are more willing to turn to external data
providers for help and solve problems with training data.”

56% of high performers generate value from partnering with external data
providers, while only 19% of the others do.

56% of high performers generate value from partnering with external data providers, while only 19% of the others do.

“I was surprised to see that the overwhelming
majority of companies considers regulatory
compliance an issue in AI adoption.”

“I was surprised to see that the overwhelming majority of companies considers regulatory compliance an issue in AI adoption.”

“All high performers monitor their models, with 56% monitoring
some deployed models and the rest monitoring all.”

MLOps are practices for maintaining and retraining machine learning models once they are deployed and taken into use.