Mean values of all structured response options (T: Technological, O: Organizational, E: Environmental)
| Order | Challenge | Mean of responses |
|---|---|---|
| #1 | O: Financial resources | 2.6 |
| #2 | T: Technical AI competence in social and healthcare organizations | 2.4 |
| #3 | O: Availability of AI experts in social and healthcare organizations | 2.4 |
| #4 | O: Procurement expertise for AI solutions | 2.4 |
| #5 | O: Staff time availability for AI adoption and training | 2.4 |
| #6 | O: Change management in processes and projects | 2.3 |
| #7 | O: Understanding and support from management and decision-makers | 2.3 |
| #8 | O: Competence in applying legislation within social and healthcare organizations | 2.3 |
| #9 | E: Availability of AI experts in the workforce | 2.3 |
| #10 | E: National AI funding | 2.3 |
| #11 | O: Management commitment and securing resources for AI adoption | 2.2 |
| #12 | E: Uniform practices for AI utilization in public social and healthcare services | 2.2 |
| #13 | E: Challenges related to the use of the Findata service (national health data permit authority) | 2.2 |
| #14 | T: Information security and data protection of AI solutions | 2.2 |
| #15 | O: Challenges in assessing and measuring the impacts of safe AI use | 2.2 |
| #16 | O: Reconciling current working methods of different professional groups with AI | 2.2 |
| #17 | E: Restrictions related to the processing and use of personal data | 2.1 |
| #18 | T: Transparency of AI algorithms (the so-called black box problem) | 2.1 |
| #19 | T: Ensuring patient and client safety in AI solutions | 2.1 |
| #20 | E: Limitations of national legislation in AI utilization | 2.1 |
| #21 | T: Technical costs of AI solutions (implementation and maintenance) | 2.1 |
| #22 | T: Challenges in transitioning AI solutions to production after the pilot phase | 2.0 |
| #23 | O: Digital skills of healthcare and social welfare staff | 2.0 |
| #24 | O: Building a technology-positive organizational culture | 2.0 |
| #25 | E: System vendors’ understanding of public sector needs | 2.0 |
| #26 | O: Identifying needs and utilization opportunities | 2.0 |
| #27 | O: Developing IT management collaboration within social and healthcare organizations | 2.0 |
| #28 | E: Impacts of the EU AI Act | 2.0 |
| #29 | O: Systematic planning and phasing of AI adoption | 2.0 |
| #30 | E: Co-development with private healthcare and social welfare actors | 1.9 |
| #31 | T: Accuracy and reliability of AI solutions | 1.9 |
| #32 | T: Ethics of AI solutions (e.g., ensuring non-discrimination and accountability) | 1.9 |
| #33 | E: Cooperation between wellbeing services counties | 1.9 |
| #34 | T: Technical customizability and compatibility of AI solutions with the current technological infrastructure of social and healthcare organizations | 1.9 |
| #35 | T: Suitability of international AI solutions for Finnish social and healthcare services | 1.9 |
| #36 | E: National coordination and guidelines | 1.9 |
| #37 | E: Limitations of tendering and procurement legislation | 1.9 |
| #38 | E: Citizens’ trust in AI-assisted social and healthcare services | 1.8 |
| #39 | O: Developing an AI strategy | 1.8 |
| #40 | O: Staff resistance to change regarding AI | 1.8 |
| #41 | T: Trust in cloud-based AI solutions | 1.7 |
| #42 | E: Citizens’ readiness to utilize AI-based solutions | 1.7 |
| #43 | T: Suitability of AI solutions for the public social and healthcare sector | 1.7 |
| #44 | T: Awareness of AI solutions and applications available on the market | 1.7 |
| #45 | T: Suitability of AI solutions for different client and patient situations | 1.7 |
| #46 | T: Availability or sufficiency of digital materials (e.g., patient or client data) | 1.6 |
| Order | Challenge | Mean of responses |
|---|---|---|
| #1 | O: Financial resources | 2.6 |
| #2 | T: Technical AI competence in social and healthcare organizations | 2.4 |
| #3 | O: Availability of AI experts in social and healthcare organizations | 2.4 |
| #4 | O: Procurement expertise for AI solutions | 2.4 |
| #5 | O: Staff time availability for AI adoption and training | 2.4 |
| #6 | O: Change management in processes and projects | 2.3 |
| #7 | O: Understanding and support from management and decision-makers | 2.3 |
| #8 | O: Competence in applying legislation within social and healthcare organizations | 2.3 |
| #9 | E: Availability of AI experts in the workforce | 2.3 |
| #10 | E: National AI funding | 2.3 |
| #11 | O: Management commitment and securing resources for AI adoption | 2.2 |
| #12 | E: Uniform practices for AI utilization in public social and healthcare services | 2.2 |
| #13 | E: Challenges related to the use of the Findata service (national health data permit authority) | 2.2 |
| #14 | T: Information security and data protection of AI solutions | 2.2 |
| #15 | O: Challenges in assessing and measuring the impacts of safe AI use | 2.2 |
| #16 | O: Reconciling current working methods of different professional groups with AI | 2.2 |
| #17 | E: Restrictions related to the processing and use of personal data | 2.1 |
| #18 | T: Transparency of AI algorithms (the so-called black box problem) | 2.1 |
| #19 | T: Ensuring patient and client safety in AI solutions | 2.1 |
| #20 | E: Limitations of national legislation in AI utilization | 2.1 |
| #21 | T: Technical costs of AI solutions (implementation and maintenance) | 2.1 |
| #22 | T: Challenges in transitioning AI solutions to production after the pilot phase | 2.0 |
| #23 | O: Digital skills of healthcare and social welfare staff | 2.0 |
| #24 | O: Building a technology-positive organizational culture | 2.0 |
| #25 | E: System vendors’ understanding of public sector needs | 2.0 |
| #26 | O: Identifying needs and utilization opportunities | 2.0 |
| #27 | O: Developing IT management collaboration within social and healthcare organizations | 2.0 |
| #28 | E: Impacts of the EU AI Act | 2.0 |
| #29 | O: Systematic planning and phasing of AI adoption | 2.0 |
| #30 | E: Co-development with private healthcare and social welfare actors | 1.9 |
| #31 | T: Accuracy and reliability of AI solutions | 1.9 |
| #32 | T: Ethics of AI solutions (e.g., ensuring non-discrimination and accountability) | 1.9 |
| #33 | E: Cooperation between wellbeing services counties | 1.9 |
| #34 | T: Technical customizability and compatibility of AI solutions with the current technological infrastructure of social and healthcare organizations | 1.9 |
| #35 | T: Suitability of international AI solutions for Finnish social and healthcare services | 1.9 |
| #36 | E: National coordination and guidelines | 1.9 |
| #37 | E: Limitations of tendering and procurement legislation | 1.9 |
| #38 | E: Citizens’ trust in AI-assisted social and healthcare services | 1.8 |
| #39 | O: Developing an AI strategy | 1.8 |
| #40 | O: Staff resistance to change regarding AI | 1.8 |
| #41 | T: Trust in cloud-based AI solutions | 1.7 |
| #42 | E: Citizens’ readiness to utilize AI-based solutions | 1.7 |
| #43 | T: Suitability of AI solutions for the public social and healthcare sector | 1.7 |
| #44 | T: Awareness of AI solutions and applications available on the market | 1.7 |
| #45 | T: Suitability of AI solutions for different client and patient situations | 1.7 |
| #46 | T: Availability or sufficiency of digital materials (e.g., patient or client data) | 1.6 |
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