The Nordic State of AI
The annual Nordic State of AI report provides an overview of the use of artificial intelligence in the Nordic region, with the goal of offering business leaders, academics, policymakers, as well as anyone interested, a comprehensive view of the latest changes and developments in Nordic AI.
In collaboration with
Industrial incumbents embrace AI but
but face challenges in large-scale adoption
Full-scale integration of AI into the core of companies continues to be an evolving challenge, increasingly an area requiring more investment.
Talent is a growing bottleneck to AI adoption. Recruitment of AI talent is becoming increasingly critical as companies strive to scale their AI capabilities.
For Europe and its forward-thinking companies, prioritizing investment in a flexible AI infrastructure is key, ensuring strategic readiness to harness AI's full potential.
The Nordic countries, and Europe at large, score high on AI readiness on a general level, but lag behind in the technology sector. This is why Europe needs software companies.
AI integration at the core still a challenge
Companies are primarily targeting shorter term efficiency improvements rather than deep integration of AI into the core of their business and products.
80% of respondents have started experimenting with ChatGPT-type LLM-products, and ¾ say that they use AI as part of their product or service, only 20% of respondents expect to invest more than €10 million in AI in 2024.
Talent is a growing bottleneck to AI adoption
The recruitment of AI talent is becoming increasingly critical as companies strive to scale their AI capabilities.
A majority of firms have identified a pressing shortage of skilled professionals, with 51.4% citing a lack of talent as a primary challenge, up from 35.3% in the previous year. This talent gap is most acute in high-demand roles such as data scientists, data engineers, and machine learning engineers.
Prioritizing investment in AI infrastructure is key
Building a versatile and adaptive AI infrastructure in Europe is crucial.
AI infrastructure is key to developing AI-enhanced products and services tailored to European needs, embodying more than just computing power to include vital cultural, value-based and linguistic nuances.
Businesses will have to invest significantly not only in data quality and management, but also in the integration and running of AI models.
Europe lags behind in the technology sector
Europe should draw conclusions from earlier technological shifts like cloud computing and mobile technologies, where U.S. dominance led to missed strategic and economic opportunities.
To avoid a similar scenario in AI, Europe needs to actively shape its trajectory in line with its values and objectives, securing a competitive edge in the global AI arena and fostering technological progress that aligns with European strengths and market needs.
Oxford Insights Government AI Readiness Index lists all Nordic countries, except Iceland, in the top 15, with Finland as the only one in the top 10 with its 4th position. The index looks at three pillars: Government, Data & Infrastructure, and Technology Sector.
Read more unique interviews and all the key findings in the report.
AI Insights from the Nordics
Director of Science and Technology, CSC
Companies that need large language models can use foundation models that have been trained using publicly-funded resources, and fine-tune their own AI on top of it. The open-source foundation models add to the transparency of the tools and democratize access to these new technologies.
Lead AI Scientist
The low priority of “Explainable AI”, especially compared to the previous year, is interesting. With the significant increase in the use of Generative AI/LLM technologies, this indicates a strong prioritization of productivity increases. The lack of adoption of Bayesian learning can be explained by two factors: concerns over the practicality of Bayesian counterparts of cutting edge neural network methods, and a lack of understanding of the advantages of the Bayesian methods, such as better data exploitation especially in lower size datasets and a natural expression of uncertainties in predictions. The rest of figure 10 exhibits predictable changes in the use of technologies, going hand in hand with the overall industry and academic trends.
The trend now is that foundation models are being fine-tuned for domain-specific areas. With high quality data, you could have smaller models that have the same performance as larger models.
Research Lead, SEBx
I think it's important to have knowledge internally, and not to do everything from scratch. You need to be able to choose external vendors, to know what tools to buy.
Head of Expert Development and Community, Silo AI
AI is practically an extension to conventional software. The development and utilization of trustworthy AI requires foundational understanding of software development but also of data processing and machine learning. The AI landscape will continue to evolve a lot in the foreseeable future, which emphasizes the need for continuous talent development. Recruitment and outsourcing are probably both needed, given the high demand for senior talent required to guide less experienced professionals. A necessity, along with recruiting and outsourcing, is significant investments into talent development of both seniors and juniors.
Head of SEBx
There are companies in the financial industry who are still debating if AI is something they should do or not. I think that is a strategic mistake. There are ethical concerns, but not trying is a choice that puts you in the backseat when it comes to adoption.
Adrian Yijie Xu
Senior AI Scientist & Expert Lead, Silo AI
As the AI landscape matures, increasing attention is being paid to data-centric AI. Focusing on obtaining and engineering high-quality data that is consistently labeled would unlock the value of AI across a variety of domains. As such, developing shared, systematic practices around data is key to ensuring a high chance of success of new projects, while ensuring a high level of operational readiness and minimized turnaround time.
Senior AI Scientist, Silo AI
The skills required in an AI project depend on at which stage the project is. At the beginning it’s important to have experts that can scope the project well, and at the later stages more technical expertise is required. For this reason it’s important to have diversity in the project team and to utilize everyone’s strengths to get the best outcomes.
Managing Director, AI Sweden
I believe that successful adoption of AI starts with the leadership, at a level where there is a solid understanding of the big picture; the business objectives and the resources needed. That way there is a strong motivation and commitment to take AI to production.
Trends over time
When looking at which AI technologies companies use, the number of options have doubled from the first to the third report, reflecting the evolution, adoption and maturity of different AI technologies.
The most common answer for where AI is used has remained the same over the years; as part of a product or service. This year using AI as part of production of manufacturing processes has started to catch up.
For the first time, investing in training and competence development surpasses investing in recruiting new AI talent.
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