Threefold model for AI Readiness: A Case Study with F innish Healthcare SME s (2025)

Abstract: This study examines AI adoption among Finnish healthcare SMEs through semi-structured interviews with six health-tech companies. We identify three AI engagement categories: AI-curious (exploring AI), AI-embracing (integrating AI), and AI-catering (providing AI solutions). Our proposed threefold model highlights key adoption barriers, including regulatory complexities, technical expertise gaps, and financial constraints. While SMEs recognize AI’s potential, most remain in early adoption stages. We provide actionable recommendations to accelerate AI integration, focusing on regulatory reforms, talent development, and inter-company collaboration, offering valuable insights for healthcare organizations, policymakers, and researchers.

## introduction the healthcare industry spans multiple sectors, including pharmaceuticals, diagnostics, medical procedures, and wellbeing services. artificial intelligence (ai) has demonstrated significant potential in transforming healthcare by assisting in tasks such as medical imaging @xcite , speech processing @xcite , and personalized treatment plans @xcite . recent advances in neural models have further enhanced ai's ability to improve diagnostics and patient care while reducing the workload on healthcare professionals (see @xcite . health-tech companies play a crucial role in aidriven innovation, continuously developing new tools and services to enhance medical outcomes and operational efficiency. governments and private enterprises invest heavily in ai-driven medical research, yet adoption remains complex due to challenges such as regulatory restrictions (e.g., gdpr), data privacy concerns (c.f. hämäläinen 2024), and the risks of computational errors in diagnosis (see @xcite . one notable example of ai's impact in healthcare is google's model @xcite , which demonstrated high sensitivity in detecting abnormal chest conditions, including covid-19, from x-ray scans. the model's success, despite not being trained on covid-specific data, highlights ai's potential in identifying unseen diseases-an essential feature for future medical advancements. while finnish health-tech companies acknowledge ai's transformative potential, their adoption remains limited due to data accessibility, compliance barriers, and the need for extensive validation. this study investigates how finnish smes in healthcare integrate ai into their operations, the challenges they face, and pathways to overcoming these obstacles. we introduce a threefold model categorizing ai adoption among smes and provide actionable recommendations to support ai integration in healthcare settings. ai in healthcare extends beyond clinical applications into digital humanities, where natural language processing (nlp) plays a crucial role in analyzing medical texts, patient records, and healthcare policies. understanding ai adoption in healthcare smes contributes to the broader discourse on how ai, nlp, and computational tools shape interdisciplinary research and real-world applications. our main contributions in this paper are: by bridging ai research and practical healthcare applications, this work contributes to the ongoing dialogue on ai's role in healthcare, policy, and digital humanities, offering insights for both researchers and industry practitioners. ## background in recent years, artificial intelligence (ai) has significantly impacted various industries, and the healthcare industry is no exception. @xcite explored the incorporation of ai in healthcare delivery, identifying challenges and opportunities for large-scale use while addressing issues such as medical responsibilities and data access. they utilized a qualitative method based on observations of existing ai technologies and predictions of future developments. in another research, @xcite analyzed the complexity of value-users in healthcare and emerging business models in ai-driven healthcare startups. by examining archetypes of business models used by entrepreneurs worldwide, they conducted a quantitative analysis of 30 healthcare startups that deploy ai. they concluded that designing effective business models is crucial for bringing beneficial technologies to the market. ai definitions and deployment status for medium-sized companies were investigated by ulrich and frank (2021), particularly german smes, and the opportunities of ai in supply chain optimization. they collected both quantitative and qualitative data through an open and closed survey questionnaire from 12,360 german companies' emails via the nexis database. their findings highlighted the relevance of technologies for companies, ai opportunities in smes, and barriers to ai adoption. the research conducted by bettoni et al. (2021) focused on the challenges of applying ai in companies and ai maturity models. they conducted face-to-face interviews and reviewed state-of-theart literature, examining two smes in poland and italy. their research resulted in a conceptual framework to support ai adoption in smes. @xcite studied the application of ai in manufacturing and its utilization in the industrial environment, particularly in measuring the financial impact of ai. they employed a mixed-methods approach, using open-ended online questionnaires to collect data from 441 participants across 68 companies in germany, austria, and switzerland. their research suggested potential strategies to support ai usage in smes and identified two best practice solutions. ## methodology and data this section explains the research strategy and data collection methods used. this work employs a case study research strategy, focusing on healthtech companies in finland as the unit of analysis. the case study methodology allows us to explore complex phenomena and gain insights into the underlying dynamics and mechanisms that drive them @xcite . through semi-structured interviews @xcite , rich and detailed data from various stakeholders can be collected within the health-tech industry. to apply the case study methodology in this research, first, a literature review was conducted to identify relevant theories and concepts that would inform the research questions. then, data was collected through semi-structured interview methods. the data were analyzed using a qualitative data analysis software, following a systematic approach. the results of the analysis informed discussions and recommendations for further research and practice. the research methodology consists of qualitative approaches that include four parts: the first is researching the existing healthcare and wellbeing smes in finland to analyze their products and services offerings to build a general understanding of health-technology applications in the market. then, in the second phase, the companies that have digital products or services have been contacted to request interviews with them. the third part involves analyzing the findings from the conducted interviews with health-tech finnish companies about their ai usage level with a focus on small-and mediumsized enterprises (smes). finally, the development section provides insights and recommendations to support the use of ai in finnish health-tech businesses, based on the interviews conducted, and an overview of the future possible uses of ai in the sector. ## results in this section, the results of interview analysis reports will be presented comprehensively, in a scientific manner that allows understanding and analysis of the results. as well, the most important phrases that were mentioned in the interviews about the research topic. in addition, the common observations, and trends related to the use, benefits, and challenges of embedding ai in the healthcare and wellbeing sector. the key findings of the results are based on the analysis of the data collected through the interviews with the respondents. appendix 1 includes some of the respondents' citations to provide further insight into the themes that emerged. however, not all key findings are represented in the appendix, as some were not explicitly stated by the respondents but were inferred from the overall interview. therefore, it is important to read the entire interview transcript to fully understand the key findings. this section is divided into subsections according to the key findings categories. in each subsection, it will cover the main findings by dividing the answers into groups. also, a synthesis for the findings for each question is provided. ## the threefold model of ai in business to complete the development task of this research, a collaborative brainstorming development method was utilized. this method involves generating a large number of ideas and then selecting the most promising ones to pursue further @xcite . a brainstorming session with two members from the commissioner was organized to generate solutions on what services can pave the way for health-tech smes to adopt and develop the use of ai, and how health-tech companies can cooperate together to elevate the level of ai in the sector. the ideas were then grouped and analyzed to identify the most relevant and feasible ones. this collaborative method allowed the identification of potential gaps in ai utilization in the health-tech sector and to come up with a state-of-the-art framework. based on the findings during the interviews, a threefold model on the use of ai in business has been elaborated. the following three categories have been identified for ai in business: ai curious, ai embracing, and ai catering companies. this section will shed more light on each of these categories and how they differ from each other. the categorization is based on how ai is operationalized in different companies. the model is useful when trying to understand and better analyze the use of ai from a grassroots level. this can help companies better locate themselves in terms of ai and business. one company does not need to fit in only one of the categories either, but a company can, for example, be ai catering in providing a specific solution for healthcare and ai curious when planning on integrating ai into marketing practices. it is important to note that the highest levels of ai maturity are not part of this framework because the interviewed companies would not place themselves that high in the hierarchy. this tells us also something about the paradigm shift in the field of ai, where hardcore ai research and development is in the hands of larger companies such as google, meta, or openai, while the field-specific use of ai is often handled by companies that do not have massive resources for core ai research. revolutionary ai methods such as word embeddings @xcite and the transformer model @xcite have been developed by google, models such as chatgpt and dall-e @xcite by openai, and audio embeddings @xcite by meta. in short, there is no room for a small player to compete in the space of new ai revolutions. ## discussion most companies are still in the early stages of ai adoption, either experimenting or in the initial implementation phase, with none reaching high maturity yet. despite recognizing ai's potential to improve healthcare, they face three major challenges: legal, technical, and financial. all interviewees highlighted that regulatory compliance is the most significant barrier. the legal framework governing ai in healthcare has not kept pace with technological advancements, creating hurdles for innovation @xcite . in finland and the eu, medical licensing is a complex, evolving process, making ai implementation difficult @xcite . for instance, conducting clinical trials for a new ai-powered medical device requires national authority approval, which can take months and necessitate 180,000 tests to ensure safety @xcite . even after approval, further ethical committee clearance is required, adding time and financial strain. these findings underscore the urgent need for regulatory improvements to support ai adoption in finland's health-tech sector. streamlining medical licensing and approval processes would reduce delays and costs, allowing companies to focus on innovation and deployment of ai solutions. two-thirds of interviewees also cited difficulties in finding qualified ai professionals with both medical expertise and programming skills. "there are currently few ai experts in the health-tech industry," stated one company leader. other technical barriers include collecting and cleaning reliable data, integrating ai with hospital systems, and overcoming resistance from medical professionals, who often require years to accept new technologies. financial constraints further limit ai adoption. high upfront costs, delayed return on investment, limited access to training data, and expenses related to regulatory compliance present significant barriers. privacy restrictions and legal requirements further complicate ai implementation for smes in the sector. based on interviews and analysis, this study recommends several measures to enhance ai utilization in health-tech smes, particularly in finland and the eu. regulatory frameworks should be reformed to facilitate ai integration while ensuring data privacy and patient safety. simplifying research processes, streamlining medical licensing, and reducing bureaucratic barriers will allow companies to focus on innovation rather than compliance hurdles. developing ai talent is crucial, as many smes struggle to find qualified professionals. a solution is to invest in continuous training programs tailored to healthcare ai, lowering entry barriers while enhancing expertise. additionally, attracting skilled foreign professionals with competitive salaries and benefits can bridge the talent gap. financial barriers remain a major obstacle for ai adoption, given the high initial investment and delayed returns. establishing funding mechanisms, including government grants and private investments, would provide smes with the resources needed to integrate ai solutions. collaboration platforms can further support ai adoption by connecting smes with research institutions, ai service providers, and industry partners, enabling knowledge-sharing and joint innovation. ensuring data security is another priority. a certification system for ai data privacy should be introduced, verifying that companies handling sensitive medical data comply with strict security standards. this would enforce encrypted storage, controlled access, and detailed audit logs to safeguard patient information while enabling responsible ai deployment. these recommendations offer practical strategies for policymakers and industry stakeholders to foster ai adoption in the finnish health-tech sector, balancing innovation with regulatory compliance and data security. ## conclusion this study examined ai adoption among finnish health-tech smes, identifying key challenges and opportunities. while ai holds immense potential for enhancing healthcare efficiency and patient outcomes, most smes remain in early adoption stages due to regulatory barriers, limited ai expertise, and financial constraints. a more flexible legal framework, improved access to ai talent, and increased funding opportunities are necessary to accelerate ai integration in healthcare. addressing these challenges will enable smes to leverage ai for innovation, ultimately benefiting the healthcare sector and society. future work should explore collaborative ai development models, interdisciplinary training programs, and policy reforms to foster ai adoption. by streamlining regulations and promoting industry partnerships, ai-driven solutions can be more effectively implemented, ensuring sustainable growth and improved patient care.