## 1. AI Preparedness, Digital Indicators and Electricity Prices

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### Context and scope
- Assesses Finland’s AI preparedness using the IMF’s AI Preparedness Index (AIPI) and other indicators; maps exposure and complementarity to AI across occupations; discusses policy implications.
- The IMF’s AIPI assesses AI preparedness as of 2023 across 174 countries, based on indicators covering digital infrastructure; innovation and economic integration; human capital and labor market policies; and regulation and ethics. The index incorporates perceptions-based indicators and should be seen as an indicative measure.

### Infrastructure, human capital, and digital preparedness
- Finland ranked among the most prepared countries for the AI transition by the IMF’s AI Preparedness Index.
- Finland hosts LUMI, described as Europe’s most powerful supercomputer.
- Labor force characteristics:
  - Well-educated and tech savvy; higher digital skills than other European countries for both simple and complex tasks.
  - A majority of Finns view the use of AI in the workplace positively; most companies report satisfaction with AI outcomes.
- AI Preparedness Index (IMF staff calculations):
  - Finland = 0.76
- Digital skills (share of adults, 16-74 years, 2023): Finland shows higher percentages across Information and data literacy skills; Communication and collaboration skills; Digital content creation skills; Safety skills; Problem solving skills (figures shown in source).

### Use of AI in companies (adoption and barriers)
- Adoption (2024):
  - 25 percent of Finnish companies with 10 or more employees were using at least one AI technology.
  - The 25 percent figure nearly doubled in one year, indicating rapid adoption.
- AI usage by task (2024 examples):
  - Text mining 14 percent (2024)
  - Production of written or spoken language (LLMs) 13 percent (2024)
  - Other tasks (machine learning for data analysis; automate workflows or assist in decision-making; convert spoken language to machine readable code; image recognition & image processing; autonomous robots, self-driving vehicles, autonomous drones) are shown in source figures.
- AI usage by sector (2024):
  - Information & Communication 70 percent
  - Professional, Scientific & Technical 50 percent
  - Real estate 33 percent
  - Manufacturing 21 percent
  - Other sectors shown with lower shares in source figures.
- AI usage by company size (2024):
  - Firms with 100 or more employees: 60 percent using AI
  - Medium-sized firms: 30 percent using AI
  - Small firms (less than 20 employees): 20 percent using AI
- Reasons for enterprises NOT using AI (2023, Percent):
  - Lack of relevant expertise 11.58
  - Incompatibility with existing equipment, software or systems 9.39
  - Availability or quality of the necessary data 7.13
  - Concerns regarding violation of data protection and privacy 6.27
  - Lack of clarity about legal consequences 5.19
  - Costs 5.00
  - Not useful for Enterprise 2.72
  - Ethical considerations 1.85

### Labor market exposure and complementarity to AI (method and high-level results)
- Occupational classification framework:
  - Uses three categories: “High Exposure and High Complementarity” (HEHC), “High Exposure and Low Complementarity” (HELC), and “Low Exposure” (LE), based on Felten et al. (2021) and Pizzinelli et al. (2023).
  - Applied to the 2023 EU Labor Force Survey across 119 occupations, 19 economic activities, three age groups, gender, three educational attainment groups, and two country-of-birth groupings.
- National and comparative shares:
  - Around 40 percent of Finnish workers hold jobs with high exposure and high complementarity to AI.
  - About 20 percent of the workforce occupy jobs with high exposure but low complementarity to AI.
  - Around 40 percent of the Finnish workforce is classified as unlikely to be affected by AI (low exposure).
  - Finland’s share of HEHC is slightly higher than in other European countries but 8½ percentage points lower than in other Nordic countries.
  - Approximately one-fifth of Finland’s workforce is at risk of job displacement.

### Sectoral and occupation patterns
- HEHC concentrated in business, teaching, and science and engineering professions.
- HELC examples include software developers, finance professionals, and administrative staff.
- Low exposure concentrated in personal care, personal services, and drivers.
- Sectoral exposure/complementarity breakdowns are provided in source figures for: Information and Communication; Financial and insurance activities; Electricity, gas, steam and ...; Professional, scientific and technical; Public administration and ...; Arts, entertainment and ...; Real estate activities; Manufacturing; Water supply; Transportation and storage; Administrative and support services; Wholesale and retail trade; Accommodation and food; Other service activities; Mining and quarrying; Construction; Education; Human health and social work; Agriculture, forestry and ... (percent shares shown in source figures).

### Distributional patterns: gender, education, age, and immigrants
- Gender (Employment Exposure and Complementarity to AI — Percent, figure values):
  - Female: HEHC 54.9, HELC 32.1, LE 20.0
  - Male: HEHC 18.0, HELC 25.1, LE 49.9
  - Nearly 75 percent of the female workforce is highly exposed to AI, with a substantial majority in jobs with high complementarity.
  - Half of the male workforce has low exposure to AI.
- Education (Employment Exposure and Complementarity to AI — Percent, figure values):
  - Tertiary education (levels 5-8): HEHC 69.6, HELC 62.2, LE 17.8
  - Upper secondary and post-secondary non-tertiary (levels 3-4): HEHC 11.8, HELC 13.3, LE 25.9
  - Less than primary, primary and lower secondary (levels 0-2): HEHC 18.7, HELC 24.5, LE 56.3
  - Highly educated workers are anticipated to benefit most, although ¼ of these workers could be at risk of job displacement.
  - Low-educated workers generally have low exposure to AI and hence lower displacement risk.
- Age:
  - Impact of AI is not expected to differ significantly across age groups (Age — Employment Exposure and Complementarity to AI percent breakdowns for Less than 30 / 30-49 / More than 50 shown in source figures).
- Immigration status (Employment Exposure and Complementarity to AI — Percent, figure values):
  - National: HEHC 39.7, HELC 24.9, LE 19.0
  - Foreign: HEHC 19.8, HELC 41.3, LE 55.3
  - Finnish nationals are anticipated to benefit more than immigrants; immigrants tend to occupy jobs with low exposure to AI.

### Employment structure and sectoral composition (key figures)
- Public sector employment:
  - Approximately 30 percent of Finnish employees work in the public sector, namely:
    - Public administration 5 percent of total employment
    - Education 8 percent of total employment
    - Healthcare 16 percent of total employment
  - The share of workers in the health sector is higher than in other European countries, but lower than in other Nordics.
- Manufacturing represents 13 percent of total employment.
- Common occupations highlighted include: Personal Care Workers; Physical and Engineering Technicians; Administration Professionals; Primary School and Early Education Teachers; Sales and Purchasing; Software and Applications; Child Care Workers; Shop Salespersons; Nursing and Midwifery; Finance Professionals; Transport and Storage; General Office Clerks; Building Frame and Related; Domestic, Hotel and Office; Administrative and Financial and Mathematical; Engineering Professionals; Heavy Truck and Bus Drivers; Food Preparation Assistants; Information and... (full lists and percentages shown in source figures).
- Sectoral list with emphasis (2023) includes: Human health and social work; Manufacturing; Wholesale and retail trade; Education; Professional, scientific and technical activities; Construction; Information and communication; Transportation and storage; Administrative and support service activities; Public administration; Accommodation and food service activities; Other service activities; Agriculture, forestry and fishing; Arts, entertainment and recreation; Financial and insurance; Real estate activities; Electricity, gas, steam and air conditioning supply; Water supply; sewerage; Mining and quarrying.

### Conclusions and policy considerations
Findings:
- Finland is well-prepared to reap AI benefits due to a highly educated, tech-friendly workforce and higher share of workers in high-complementarity occupations compared to other European countries.
- Approximately one-fifth of the workforce is at risk of job displacement.
- So far AI adoption had little impact on Finnish employment, but rapid adoption could lead to faster and larger-than-expected job displacement.
- Few Finnish workers are receiving AI training at their workplace (Federation of Finnish Enterprises, 2025).

Policy recommendations and considerations:
- Closely monitor the impact of digital technologies on labor markets.
- Provide high-quality courses in AI and enhance vocational training and lifelong learning for the Finnish workforce.
- Make tertiary education more agile to facilitate upskilling and reskilling and to better align with employer needs.
- Support AI adoption in the private sector, especially among SMEs; Finland launched an Artificial Intelligence Programme in 2022 aimed at accelerating AI adoption among SMEs (program noted in source).
- Address obstacles to scaling AI startups, including insufficient investment and difficulties scaling up despite access to early-stage public funding.
- Promote further European integration and strengthened Nordic cooperation to reduce internal barriers and help Finnish companies access larger markets and attract foreign capital.
- Implement policies to attract and retain foreign talent, given firms report a shortage of relevant expertise as an obstacle to AI adoption.
- Accelerate AI adoption in the public sector to free up resources, reduce administrative costs, and improve public services; address siloed implementations and inefficiencies.

*Source: IMF staff chapter "AI Preparedness, Digital Indicators and Electricity Prices" (Finland), December 10, 2025.*

### 1. AI Preparedness, Digital Indicators and Electricity Prices _____________________________ 3

### 1. AI Preparedness, Digital Indicators and Electricity Prices

### Context
- Artificial Intelligence (AI) and Generative AI models have rapidly advanced, performing advanced cognitive functions, processing vast amounts of data, identifying patterns, and making decisions.
- The speed of AI adoption by workers and firms is described as unprecedented.
- The chapter assesses Finland’s AI preparedness (using the IMF’s AI Preparedness Index (AIPI) and other indicators), maps exposure and complementarity to AI across occupations, and discusses policy implications.
- The IMF’s AIPI assesses AI preparedness as of 2023 across 174 countries, based on indicators covering digital infrastructure; innovation and economic integration; human capital and labor market policies; and regulation and ethics. The index incorporates perceptions-based indicators and should be seen as an indicative measure.

### Infrastructure, Human Capital, and Digital Preparedness in Finland
- Finland is ranked among the most prepared countries for the AI transition by the IMF’s AI Preparedness Index.
- Finland hosts Europe’s most powerful supercomputer, LUMI.
- Finland’s labor force is described as well-educated and tech savvy, with higher digital skills than other European countries for both simple and complex tasks.
- A majority of Finns view the use of AI in the workplace positively; most companies report satisfaction with AI outcomes.
- Finland = 0.76 (AI Preparedness Index by IMF staff calculations; index shown in figure).

Key statistics and indicators:
- Digital Skills - Basic or Above Basic Skills, 2023 (share of adults, 16-74 years) — Finland shows higher percentages across: Information and data literacy skills; Communication and collaboration skills; Digital content creation skills; Safety skills; Problem solving skills (figures shown in source).

### Use of Artificial Intelligence (AI) in Companies
- In 2024, 25 percent of Finnish companies with 10 or more employees were using at least one AI technology.
- The 25 percent figure nearly doubled in one year, indicating rapid adoption.
- AI usage by task (2024 vs 2023): Text mining 14 percent (2024), Production of written or spoken language (LLMs) 13 percent (2024), Automate workflows or assist in decision-making (2024 shown), Machine learning for data analysis (2024 shown), Convert spoken language to machine readable code (2024 shown), Image recognition & image processing (2024 shown), Autonomous robots, self-driving vehicles, autonomous drones (2024 shown).
- AI usage by sector (2024): Information & Communication 70 percent; Professional, Scientific & Technical 50 percent; Real estate 33 percent; Manufacturing 21 percent; plus other sectors listed with lower shares.
- AI usage by company size (2024): 60 percent of firms with 100 or more employees were using AI, compared to 30 percent of medium-sized firms and 20 percent of small firms with less than 20 employees.
- Reasons for enterprises NOT using AI, 2023 (Percent): Lack of relevant expertise 11.58; Incompatibility with existing equipment, software or systems 9.39; Availability or quality of the necessary data 7.13; Concerns regarding violation of data protection and privacy 6.27; Lack of clarity about legal consequences 5.19; Costs 5.00; Not useful for Enterprise 2.72; Ethical considerations 1.85.

### Labor Market Exposure and Complementarity to AI
- Occupational classifications used: “High Exposure and High Complementarity” (HEHC), “High Exposure and Low Complementarity” (HELC), and “Low Exposure” (LE), based on frameworks by Felten et al. (2021) and Pizzinelli et al. (2023).
- The analysis applies these classifications to the 2023 EU Labor Force Survey across 119 occupations, 19 economic activities, three age groups, gender, three educational attainment groups, and two country-of-birth groupings.

Key findings (national and comparative):
- Around 40 percent of Finnish workers hold jobs with high exposure and high complementarity to AI.
- About 20 percent of the workforce occupy jobs with high exposure but low complementarity to AI.
- Around 40 percent of the Finnish workforce is classified as unlikely to be affected by AI (low exposure).
- Finland’s share of HEHC is slightly higher than in other European countries but 8½ percentage points lower than in other Nordic countries.
- Approximately one-fifth of Finland’s workforce is at risk of job displacement.
- Private vs public sector:
  - Approximately 25 percent of the Finnish private sector workforce faces a risk of job displacement.
  - Approximately 10 percent of the public sector workforce faces a risk of job displacement.
  - More than half of public sector employees are anticipated to benefit from AI, particularly in education and healthcare.

Occupations and sectors with exposure/complementarity patterns (examples shown in figures):
- HEHC concentrated in business, teaching, and science and engineering professions.
- HELC examples include software developers, finance professionals, and administrative staff.
- Low exposure concentrated in personal care, personal services, and drivers.

### Sectoral Differences; Gender, Education, Age and Immigrants
- Exposure and complementarity by broad sectors (percent shown in figures): breakdowns provided for Information and Communication; Financial and insurance activities; Electricity, gas, steam and ...; Professional, scientific and technical; Public administration and ...; Arts, entertainment and ...; Real estate activities; Manufacturing; Water supply; Transportation and storage; Administrative and support services; Wholesale and retail trade; Accommodation and food; Other service activities; Mining and quarrying; Construction; Education; Human health and social work; Agriculture, forestry and ... (percent shares shown in source figures).
- Gender:
  - Nearly 75 percent of the female workforce is highly exposed to AI, with a substantial majority in jobs with high complementarity (women’s predominance in education or healthcare).
  - Half of the male workforce has low exposure to AI (men more likely in construction or transport).
  - Gender - Employment Exposure and Complementarity to AI (Percent) example breakdowns: Female: HEHC 54.9, HELC 32.1, LE 20.0 (numbers shown in figure); Male: HEHC 18.0, HELC 25.1, LE 49.9 (figure values).
- Education:
  - Tertiary education (levels 5-8): HEHC 69.6, HELC 62.2, LE 17.8 (values shown in figures).
  - Upper secondary and post-secondary non-tertiary (levels 3-4): HEHC 11.8, HELC 13.3, LE 25.9.
  - Less than primary, primary and lower secondary (levels 0-2): HEHC 18.7, HELC 24.5, LE 56.3.
  - Highly educated workers are anticipated to benefit most, although ¼ of these workers could be at risk of job displacement.
  - Low-educated workers generally have low exposure to AI and hence lower displacement risk.
- Age:
  - Impact of AI is not expected to differ significantly across age groups (Age - Employment Exposure and Complementarity to AI—Percent: Less than 30 / 30-49 / More than 50 breakdowns shown in figures).
- Immigration status:
  - Finnish nationals are anticipated to benefit more than immigrants; immigrants tend to occupy jobs with low exposure to AI.
  - Immigrant - Employment Exposure and Complementarity to AI (Percent) example values: National HEHC 39.7, HELC 24.9, LE 19.0; Foreign HEHC 19.8, HELC 41.3, LE 55.3 (values shown in figures).

### Conclusions and Policy Considerations
Findings:
- Finland is well-prepared to reap AI benefits due to a highly educated, tech-friendly workforce and higher share of workers in high-complementarity occupations compared to other European countries.
- However, approximately one-fifth of the workforce is at risk of job displacement.
- So far AI adoption had little impact on Finnish employment, but rapid adoption could lead to faster and larger-than-expected job displacement.
- Few Finnish workers are receiving AI training at their workplace (according to Federation of Finnish Enterprises, 2025).

Policy recommendations and considerations (from staff comments and assessments):
- Closely monitor the impact of digital technologies on labor markets.
- Provide high-quality courses in AI and enhance vocational training and lifelong learning for the Finnish workforce.
- Make tertiary education more agile to facilitate upskilling and reskilling and to better align with employer needs.
- Support AI adoption in the private sector, especially among SMEs, to harness AI benefits fully; note Finland launched an Artificial Intelligence Programme in 2022 aimed at accelerating AI adoption among SMEs.
- Address obstacles to scaling AI startups, including insufficient investment and difficulties scaling up despite access to early-stage public funding.
- Promote further European integration and strengthened Nordic cooperation to reduce internal barriers and help Finnish companies access larger markets and attract foreign capital.
- Implement policies to attract and retain foreign talent, given firms report a shortage of relevant expertise as an obstacle to AI adoption.
- Accelerate AI adoption in the public sector to free up resources, reduce administrative costs, and improve public services; public sector adoption can address siloed implementations and inefficiencies.

*Source: IMF staff chapter "AI Preparedness, Digital Indicators and Electricity Prices" (Finland), December 10, 2025.*

### References

### 1finea2026002-source-pdf - References

### References
- AI Finland and Business Finland, The Finnish AI Landscape, 2025
- AI Finland, The Nordic State of AI, 2025
- Brynjolfsson, E., Chandar, B., and Chen, R., 2025, “Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence”. Stanford University.
- Cazzaniga M., F. Jaumotte, L. Li, G. Melina, A. J. Panton, C. Pizzinelli, E. Rockall, and M. M. Tavares, 2024, “Gen-AI: Artificial Intelligence and the Future of Work”, IMF Staff Discussion Note SDN2024/001, International Monetary Fund, Washington, D.C.
- Felten E., M. Raj and R. Seamans, 2021, “Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses,” Strategic Management Journal, 2021;1–23.
- Kauhanen, A., & Rouvinen, P. (2025). Assessing early labour market effects of generative AI: evidence from population data. Applied Economics Letters, 1–4.
- Mish F., B. Park, C. Pizzinelli, and G. Sher., 2025 AI and Productivity in Europe, IMF Working Papers 2025/067, International Monetary Fund, Washington, D.C.
- Ministry of Economic Affairs and Employment of Finland, 2022, “Artificial Intelligence 4.0: Finland as a leader in the twin transition” Helsinki, Finland.
- Pizzinelli C., A. Panton, M. M. Tavares, M. Cazzaniga, and L. Li. 2023. "Labor Market Exposure to AI: Cross-Country Differences and Distributional Implications." IMF Working Paper 2023/216, International Monetary Fund, Washington, D.C.

### Annex I. Measuring Exposure and Complementarity to AI
- Measuring Exposure to AI:
  - Felten et al. (2021) link 10 applications of AI (e.g., image recognition, text creation) to 52 occupational abilities (e.g., oral comprehension, inductive reasoning), using U.S. occupational characteristics from the O*NET database.
  - Each occupation is modeled as a combination of the 52 abilities, weighted by the degree of importance and complexity of such skills in each job.
- Measuring complementary to AI:
  - Pizzinelli et al. (2023) develop an index of complementarity using two additional O*NET dimensions: ‘work contexts’ and ‘job zones.’
  - They identify 11 contexts relevant work contexts for AI, grouped together with job zones into six components: communication, responsibility, physical conditions, criticality, routine, and skills.
  - The framework determines how AI can complement or substitute specific occupations.
- Bringing exposure and complementary together conceptually:
  - The measures form a conceptual matrix of three dimensions: ‘High Exposure and High Complementarity’ (HEHC), ‘High Exposure and Low Complementarity’ (HELC), ‘Low Exposure’ (LE).
  - Thresholds use the medians across all exposure and complementarity values.
  - HE-LC (High Exposure and Low Complementarity) is interpreted as occupations at higher risk of job displacement.

### Annex II. Employment by Occupation and by Sector
- Key employment structure findings:
  - Approximately 30 of Finnish employees work in the public sector, namely in public administration (5 percent of total employment), education (8 percent), and healthcare (16 percent).
  - The share of workers in the health sector is higher than in other European countries, but lower than in other Nordics.
  - Common occupations include personal care workers, primary school teachers and childcare workers.
  - Manufacturing represents 13 percent of total employment, with physical and engineering science technicians being Finland’s second-largest occupation.
  - Administrative professionals, working in both private and public sectors, constitute a significant share of the workforce.
- Main occupations and sectoral composition (2023):
  - Top occupations highlighted include: Personal Care Workers; Physical and Engineering Technicians; Administration Professionals; Primary School and Early Education Teachers; Sales and Purchasing; Software and Applications; Child Care Workers; Shop Salespersons; Nursing and Midwifery; Finance Professionals; Transport and Storage; General Office Clerks; Building Frame and Related; Domestic, Hotel and Office; Administrative and Financial and Mathematical; Engineering Professionals; Heavy Truck and Bus Drivers; Food Preparation Assistants; Information and...
  - Sectors listed with percentage emphasis include: Human health and social work; Manufacturing; Wholesale and retail trade; Education; Professional, scientific and technical activities; Construction; Information and communication; Transportation and storage; Administrative and support service activities; Public administration; Accommodation and food service activities; Other service activities; Agriculture, forestry and fishing; Arts, entertainment and recreation; Financial and insurance; Real estate activities; Electricity, gas, steam and air conditioning supply; Water supply; sewerage; Mining and quarrying.
  - Sources: Eurostat Labor Force Survey; and IMF staff estimates.

*Source: 1finea2026002-source-pdf - References*

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_Source: https://www.imf.org/-/media/files/publications/cr/2026/english/1finea2026002-source-pdf.pdf_