Nie je k dispozícii v slovenčine.
Maurice Schmidt
Monetary Policy
- Division
Capital Markets/Financial Structure
- Current Position
-
Monetary Policy Analyst
- Fields of interest
-
Macroeconomics and Monetary Economics,Mathematical and Quantitative Methods
- Education
- 2022-2025
MSc in Money and Finance, Goethe-University, Frankfurt am Main, Germany
- 2019-2022
BSc in Economics and Business Administration, Goethe-University, Frankfurt am Main, Germany
- Professional experience
- 2025-2026
Trainee - Capital Markets and Financial Structure Division, Directorate Monetary Policy, European Central Bank
- 2026
Research analyst - Capital Markets and Financial Structure Division, Directorate Monetary Policy, European Central Bank
- 2024
Intern - Monetary Policy and Analysis Division, Directorate Economics, Deutsche Bundesbank
- Awards
- 2025
Best Master’s Thesis Award 2025 - Goethe University, Economics and Business Administration Department, Frankfurt am Main
- 28 July 2026
- OCCASIONAL PAPER SERIES - No. 395Details
- Abstract
- This paper explores the adoption of artificial intelligence (AI) technologies among euro area firms, using harmonised firm-level data from two dedicated modules of the Survey on the Access to Finance of Enterprises (SAFE) conducted in June and December 2025. Based on responses from around 6,000 firms across 12 euro area countries, the study examines AI adoption rates, drivers, barriers and economic implications. The findings suggest that AI diffusion among euro area firms is progressing rapidly but unevenly, with significant variation across countries and firm characteristics. Approximately 70% of firms report some level of AI use, but only 7% classify their adoption as significant. Adoption is highest in the Netherlands, Finland and Austria, and lowest in Italy and Ireland. Larger and younger firms, particularly in technology-intensive sectors, are leading adopters. Firms identify expected improvements in business processes as the main driver of adoption, while key barriers include skill shortages, data privacy concerns and system incompatibilities. Current AI use and investment are primarily financed through internal funds, complemented by grants and subsidised bank loans. AI adoption is positively associated with firm productivity, turnover growth, fixed investment and own selling price expectations, particularly among intensive users. Survey data show no evidence yet of aggregate labour shedding; instead, AI adoption is positively associated with employment growth. However, firms’ inflation expectations appear largely unaffected by current AI use.
- JEL Code
- C93 : Mathematical and Quantitative Methods→Design of Experiments→Field Experiments
D22 : Microeconomics→Production and Organizations→Firm Behavior: Empirical Analysis
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
L25 : Industrial Organization→Firm Objectives, Organization, and Behavior→Firm Performance: Size, Diversification, and Scope
O33 : Economic Development, Technological Change, and Growth→Technological Change, Research and Development, Intellectual Property Rights→Technological Change: Choices and Consequences, Diffusion Processes
- 26 May 2026
- THE ECB BLOGDetails
- JEL Code
- E50 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→General
G10 : Financial Economics→General Financial Markets→General
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
- 31 March 2026
- ECONOMIC BULLETIN - BOXEconomic Bulletin Issue 2, 2026Details
- Abstract
- This box presents new information about the adoption of, and investment in, artificial intelligence (AI) technologies by euro area firms, based on the Survey on the Access to Finance of Enterprises (SAFE). The findings reveal that large firms, listed or venture capital-backed companies and young firms are adopting AI more frequently. Firms using AI are more likely to expect an increase in turnover and investment in fixed assets compared with firms not using AI. Similarly, they plan to allocate larger shares of their investment to AI compared with non-users, indicating a reinforcing cycle of adoption and innovation. Ownership structure influences investment patterns, with listed or venture capital-backed companies leading early-stage adoption and privately owned firms dominating at more advanced stages.
- JEL Code
- C83 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Survey Methods, Sampling Methods
D22 : Microeconomics→Production and Organizations→Firm Behavior: Empirical Analysis
L25 : Industrial Organization→Firm Objectives, Organization, and Behavior→Firm Performance: Size, Diversification, and Scope
O33 : Economic Development, Technological Change, and Growth→Technological Change, Research and Development, Intellectual Property Rights→Technological Change: Choices and Consequences, Diffusion Processes