Hossameldin Faraj 

Unleashing potential of Artificial Intelligence and 
Digital Servitization: Investigating the role of 

Dynamic Capabilities on Finnish Small and Medium 
Sized Enterprises. 

 

 

School of Management 

Master’s thesis in strategic business development 

 

 

 

Vaasa 2024  



 
 

 
 

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UNIVERSITY OF VAASA 
School of Management 
 
Author:   Hossameldin Faraj 
 
Title of the Thesis: Unleashing potential of artificial intelligence and digital servitization: 
Investigating the role of dynamic capabilities on Finnish small and medium sized enterprises. 

 
Degree:   Master of science 
Program:  Strategic Business Development 
Supervisor:  Tuomas Huikkola 
Year:   2024 Sivumäärä: 75 

Abstract 

A unique and interesting fact about small and medium-sized enterprises (SMEs) that adopt 
artificial intelligence (AI) and become fully servitized is that they can significantly shift from 
product-based revenue models to outcome-based ones. In this transition, rather than simply 
selling products or one-off services, SMEs using AI can offer continuous, predictive services that 
directly enhance customer outcomes. When there is this potential to move from transactional 
to relational and outcome-based services, SMEs can build long-term customer relationships as 
well as generate recurring revenues and leverage AI-driven insights to continuously improve 
their offerings. In Finland, where sustainability and innovation are key priorities, SMEs that 
adopt AI can integrate environmental monitoring and optimization services into their offerings. 
If, for instance, a company develops wearable devices that monitor patients’ vital signs, such as 
heart rate, oxygen levels, and glucose levels, and then simply transmits this data to healthcare 
providers in real-time. However, instead of simply selling the wearable devices, this company 
uses AI-driven analytics to offer “Health Efficiency as a Service.” 

This thesis investigates the potential of AI on the digital servitization journey of Finnish SMEs 
within the healthcare industry. The findings reveal a disparity among three Finnish SMEs, which 
shows a spectrum of AI adoption. Some SMEs depend heavily on AI for their product offerings, 
while others are only starting to explore the capabilities of this new technology. Notably, none 
of the SMEs included AI in their administrative tasks. Drawing on dynamic capability theory 
(DCT), this thesis delineates three factors: sensing, seizing, and reconfiguring. Under these 
factors, specific steps are outlined for Finnish SMEs to achieve digital servitization. Additionally, 
the concept of AI maturity level is introduced. The interviewed companies were ranked 
according to Microsoft's AI maturity model. Level 4 is identified as the optimal level for SMEs to 
leverage AI effectively for digital servitization. 

     Given the scarcity of research on the impact of AI on digital servitization, this study employs a 
qualitative, multiple case study, mono-method approach, utilizing cross-sectional data from 
structured interviews. The observations indicate a general hesitation among Finnish SMEs 
regarding the full adoption of AI in their journey towards digital servitization. 

 

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KEYWORDS: AI, SME, Dynamic Capability theory, AI maturity model, digital servitization 



 
 

 
 

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Contents  

1 Introduction 6 

2 Theoretical background 11 

2.1 Digital servitization and AI 12 

         2.1.1 Defining AI Maturity model                                                                                 15 

          2.1.2 AI maturity model in relation to digital servitization.                                     16 

2.2 Literature review 20 

2.3 Dynamic capability theory (DCT) 23 

2.4 Theoretical framework 26 

3 Methdology  33 

3.1 Research apprach                                                                                                         33 

3.2 Sampling 35 

3.3 Data collection 37 

     3.4     Data analysis                                                              38 

     3.5      Assessment of the quality of the data                         39 

4 Findings 42 

     4.1 Digital servitization journey limited by resources and skills gap yet high 

      alignment, adaptability and market responsiveness for future integration         45 

4.2 AI integration in operations 47 

4.3 Customer centricity vs market approach 48 

4.4 Use of supporting technologies 49 

4.5 Recommendations based on expert interview 51 

5 Discussion 53 

       5.1    Theoretical contribution                                                                                                53 

       5.2.    Managerial implications                              57 

       5.3     Limitation / future research                                                                                     58 

 



 
 

 
 

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References 60 

Appendices  

Appendix 1. Interview questions 74 

Appendix 2. Companies’ description 75 

  



 
 

 
 

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Figures 

 

Figure 1. Microsoft RAI MM (Vorvoreanu et al., 2023). 
Figure 2. Link between Dynamic Capabilities, Digital Servitization, and AI. 

Figure 3. Illustration of digital servitization journey towards fully servitized firm. 

Figure 4. Showing Finnish SMEs' usage of AI measured against Microsoft's RAI MM in 
conjunction with DCT to determine the maturity level leading to servitization. 
 
Figure 5. Research methodologies of thesis (Saunders et al. 2003). 

Figure 6. AI adoption level for a Finnish SME to digital servitization. 

 

Tables 
 

Table 1.  Illustrative figure showing stages of AI maturity in a firm  
Table 2.  Showing how DCT components can be incorporated to study the servitization 
of Finnish SMEs 

 

 

Abbreviations 
 

SME: Small and Medium Size Enterprise 

AI: Artificial Intelligence 

DCT: Dynamic Capability Theory 

TOEH: Technologies, Organizations, Environments, and Humans 

PSS: Product Service Systems 

RPA: Robotic Process Automation 

 

 

 

 

 

 

 



 
 

 
 

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1 Introduction 
 

Artificial Intelligence (AI) represents the next generation and most advanced form of 

industrial digitalization and digital servitization (Parida et al., 2019; Kohtamäki et al., 

2022). Its systems and technologies have become more pervasive and efficient (Iansiti 

and Lakhani, 2020), leading to a reduction in costs and an increase in effectiveness 

through reduction of re-work when addressing business, societal, and sustainability 

challenges (Phan et al., 2017; Chauhan et al., 2022). Moreover, the value of AI solutions 

can be significantly enhanced by incorporating novel ecosystem partners with diverse 

resources and applications (Kolagar et al., 2022). 

According to Kohtamäki et al. (2019) and Bustinza et al. (2018), the shift towards a 

service-oriented business models through digital servitization contributes to enhanced 

profitability. Firms that have implemented digital service solutions report being better 

positioned in competitive markets (Coreynen et al., 2017; (Sjödin et al., 2020). 

Consequently, the result is lower operational costs and increased customer retention 

(Huikkola et al, 2022). Companies can also optimize product management and 

maintenance through digital technologies, which reduces downtime and enhances 

product life cycles, thereby contributing to profitability (Gebauer et al, 2022). 

As reported in AI-TIE (2024), AI adoption in Finnish small and medium sized enterprises 

(SMEs) has led to direct cost savings through the automation of processes. For instance, 

AI implementation in production was noted in 17.5% of cases, while back-office functions 

utilize AI in 16.2% of cases. This automation reduces the need for manual input, thus 

decreasing operational costs (AI-TIE, 2024). According to Jafarzadeh et al. (2024) AI has 

gained attention in many SMEs, leading to improved operational efficiency and 

enhanced overall business performance. Specifically, AI has been integrated into 

production processes in approximately 17.5% of cases reported, while back-office 

functions also utilize AI in around 16.2% of cases. For instance, several SMEs have 

employed AI for automating production lines and enhancing logistics.  



 
 

 
 

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The result is significant efficiency gains. Furthermore, many SMEs have adopted AI-

powered customer service solutions like intelligent chatbots leading to improved 

customer engagement and satisfaction (AI-TIE, 2024). 

 

Today, the rapidly evolving landscape as well as the integration of AI with servitization 

are revolutionizing industries, especially within Finnish SMEs, which alters the value 

generated (Sjödin et al. 2020). The advancement of AI and machine learning (ML) 

technologies, as a part of AI, significantly transformed various sectors. In healthcare, AI-

powered diagnostic tools such as deep learning algorithms have demonstrated 

remarkable performance in detecting diseases from medical images, thereby aiding 

physicians in making more accurate diagnoses (Rajpurkar et al., 2017). Similarly, ML 

techniques such as neural networks have been employed to analyze complex financial 

data and predict market trends with higher precision, enabling investors to make more 

informed decisions (Vanzara & Azevedo, 2023; Chen et al., 2015). In the same manner, 

prior studies confirmed that SMEs play an important role in the economic development 

of countries (Glonti, Manvelidze & Surmanidze, 2021; Yang, Chen, Visnjic, Parida, and 

Zhang, 2024). For this reason, Finland holds a positive view of SMEs (Finnish Ministry of 

Economic Affairs and Employment, 2017). Considering them a crucial driver of economic 

growth, innovation, and job creation. Noticeably, SMEs in Finland are increasingly 

implementing AI to enhance decision-making, optimize business operations, and 

become more competitive (Ministry of Economic Affairs and Employment of Finland, 

2021). 

However, according to (Holgersson et al., 2022) the influence of AI on service delivery 

remains relatively new, as enhancing a company's AI capabilities necessitates gradual 

investment in sophisticated technology. This process also demands a careful equilibrium 

between forces that encourages the integration of manufacturers' activities and those 

that drive the value proposition towards customer-driven market dynamics. 

Consequently, this evolution in the services ecosystem is still in progress.  



 
 

 
 

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On the same note, Gebauer et al. (2021) and Kohtamäki et al. (2020) indicated that the 

scholarly discourse regarding digital servitization is currently in its early stages of 

development. According to Wirtz et al. (2018), academic inquiry into the impact of AI on 

servitization remains in its early stages and lacks adequate empirical validation. This 

observation highlights the research gap identified by Chen, Visnjic, Parida, and Zhang 

(2021), who noted that the existing literature fails to provide a clear understanding of 

how companies can effectively leverage AI to generate value through digital servitization, 

a factor that is crucial for the sustainability of SMEs. Furthermore, as pointed out by 

Peillon and Dubruc (2019), the focus on the digital servitization journey of SMEs has been 

limited, often neglecting the perspectives and experiences of SMEs. 

Quoting the thesis of Tikkanen et al. (2022, p.14): “Even though Finland is generally 

technologically advanced, including in the deployment of AI, only 3,15 percent of Finnish 

companies with over 5 employees utilize AI on daily basis”. They further mentioned: 

“Companies who do not utilize any AI are expected to face hard competition in the future, 

according to many sources”. According to Tikkanen et al. (2022): most SMEs need to 

embrace digitalization and employ AI in a revolutionary manner.  Hence, there is a need 

for new approaches to boost the integration of AI and other tools within the broader 

framework of digitalization in SMEs within the region. This study adds to the deficiency 

in research on AI and service delivery by empowering Finnish SMEs with a 

scientific/theoretical method to measure their position vis-à-vis the adoption of AI on 

their journey to achieve fully digital servitization. 

As qualitative research, this study adopts a multiple-case approach utilizing a mono-

method, with observations drawn from cross-sectional data. The purpose of this study 

is to determine the extent to which Finnish SMEs can be classified as digitally servitized 

entities through the integration of AI. Hence, answering the research question: “How 

Finnish SMEs employ their dynamic capabilities when implementing AI-driven digital 

servitization?”. Drawing on Dynamic Capability Theory (DCT), the study examines three 

key elements: sensing, seizing, and reconfiguring. Each stage involves assessing the 

SME's current operational level.  



 
 

 
 

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The pre-established interview questions will enable a preliminary assessment of the 

company's current use of AI based on the responses provided by participants. 

Additionally, these interview questions aim to offer a comprehensive insight into how 

Finnish SMEs navigate the complexity of AI implementation and its results on the digital 

servitization journey.  

 

Corley and Gioia (2011) advocate for a perspective on theory development that 

emphasizes theories with broader applicability in both scientific and practical contexts. 

They examine two aspects of theoretical contributions: originality and usefulness. The 

originality of theoretical contributions may vary from small advancements to significant 

breakthroughs, while usefulness can be assessed in terms of scientific value and practical 

applicability. Also, quoting the article of Abou-Foul, Ruiz-Alba and López-Tenorio (2023, 

p.09, p.11): “… we are still in the first stages of understanding AI developments and their 

impact on business contexts” and also: “Studies that examine the relationship between 

AI capabilities and servitization are still scarce and inconclusive, and businesses still 

struggle to capture the value of digital transformation due to bad measures and 

untargeted technology investment”.  Therefore, the primary contribution of this paper 

lies in its original approach in understanding AI impact on business context. Whereby 

Finnish SMEs can support their adoption of AI within the framework of digital 

servitization via a methodological process rooted in established theory. Furthermore, its 

utility is underscored by the systematic delineation of steps that facilitate a coherent 

theoretical engagement with digital servitization considering contemporary 

technological advancements. The practical significance of these findings is evident in 

enabling CEOs of SMEs pinpoint areas for enhancement, thereby fortifying their strategic 

endeavors toward digital servitization. 

 

 

 

 

 
 



 
 

 
 

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This paper is structured as follows. The introduction established the context for the 

primary incentive that influenced the selection of the topic, alongside addressing the 

gap identified in the literature and providing a brief description of the methods. 

Subsequently, the following section presents the theoretical background, decoding the 

significance of the chosen theory and its relevance to the topic. It also examines the scale 

of Microsoft’s Responsible Artificial Intelligence Maturity Model (RAI MM) utilized to 

assess the level of AI adoption among SMEs in Finland. Moving on to the third part, the 

methodology is clarified, with a focus on the significance of the research approach 

"Abduction" employed in this thesis. Finally, the last two parts, namely: the findings and 

discussion. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 



 
 

 
 

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2 Theoretical background 

In the context of this paper, it is imperative to first introduce key definitions to establish 

foundational parameters. 

 

Artificial intelligence (AI) refers to how computers or robots can perform tasks typically 

associated with intelligent beings, such as reasoning, discovering meanings, generalizing, 

and learning from past experiences (Sheikh, Prins, & Schrijvers, 2023). Although there 

isn't a single definition that everyone agrees on, various interpretations of this concept 

can be found in literature. According to The High-Level Expert Group on Artificial 

Intelligence (AI HLEG) of the European Commission, AI is seen as “systems that display 

intelligent behavior by analyzing their environment and taking actions – with some 

degree of autonomy – to achieve specific goals” (Encyclopedia Britannica, n.d). This 

definition of AI HLEG is chosen because it emphasizes both environmental analysis and 

goal-oriented actions, providing a more comprehensive understanding of AI. 

 

Small and Medium Enterprises (SME) in Finland are sorted out using specific rules 

described by the government, as explained by Finnvera (2022). According to the general 

definition, these companies must have fewer than 250 paid workers, make annual sales 

below EUR 50 million, or have a balance sheet total under EUR 43 million (Finnish 

Statistical Office, n.d.). Also, they need to meet the independence requirement, which 

means that they should operate independently from larger organizations (Finnish 

Statistical Office, n.d.). This rule states that SMEs should not have one entity holding 

25% or more of their ownership or voting rights, which is in line with how SMEs are 

classified regardless of ownership structures (Finnvera, 2022; European Commission, 

2020). 

 
 
 
 
 



 
 

 
 

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2.1 Digital servitization & AI 

Digital servitization based on the views of Kolagar, Reim, Parida, and Sjödin (2022) 

involves the integration of digital services into traditional product offerings. Which 

allows SMEs to develop digital service offerings alongside physical products to enhance 

service maturity and create a process framework for digital-servitization-enabled model 

(Kolagar, Reim, Parida, & Sjödin, 2022). On the same note, Coreynen et al. (2017) 

examine how digital servitization transforms traditional business models by integrating 

advanced digital technologies, such as the Internet of Things (IoT), big data analytics, and 

AI, into product and service offerings. Digital servitization shifts businesses from merely 

selling products to delivering smart, connected services that add value through real-time 

data, automation, and predictive capabilities (Coreynen et al. 2017). Coreyman et al. 

(2017) argue that these digital tools enable companies to move from reactive service 

models, where firms respond to customer needs, to proactive data-driven models that 

anticipate customer requirements, such as automated service delivery. Thereby, firms 

can offer enhanced value propositions that impacts customer relationships and allows 

for longer-term commitment. 

Following the fore-mentioned definition of digital servitization, it is important to make a 

distinction between AI and digital servitization: one specific enabler of digital 

servitization is AI (Ordoñez de Pablos, 2023). Studies by Kindström et al. (2013), 

Coreynen et al. (2020), and Huikkola et al. (2022) provide significant insights into how AI 

serves as a digital servitization enabler for manufacturing companies. Even though AI 

and digital servitization are distinct concepts, AI technologies can enhance services 

offered within digital servitization initiatives. For example: predictive maintenance and 

personalized user experiences (IABAC, 2024; Porter & Heppelmann, 2014). Also, AI can 

enhance data analytics and machine learning algorithms (Li et al., 2023).  In addition to 

the delivery of more advanced, personalized, and value-added digital services to 

customers which constitutes some usages of AI in business (Data Science Dojo, 2023; 

Squirro, 2023; Porter & Heppelmann, 2014). 



 
 

 
 

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However, when it comes to SMEs, previous research has not sufficiently focused on 

digitalization (Coreynen et al., 2017; Kohtamäki et al., 2019), despite the significant 

potential offered by advanced digital tools and technologies (Porter & Heppelmann, 

2015) such as IoT (Porter & Heppelmann, 2014), augmented reality (AR) (Porter & 

Heppelmann, 2017), and AI, which facilitate system connectivity (Huikkola et al. (2022). 

In their study, Baines et al. (2017) emphasize that digital servitization empowers SMEs 

to offer tailored and individualized solutions that resonate with diverse customer 

segments. Through the utilization of advanced digital technologies such as IoT sensors, 

AI algorithms, and big data analytics, SMEs can gather real-time insights into customer 

preferences and behavior patterns, thereby they can adapt their offerings effectively to 

meet specific customer needs (Kumar, 2023; Baines et al., 2017). Tim Baines, 

additionally, categorizes services into three distinct tiers: base, intermediate, and 

advanced services (Baines et al., 2013). These classifications reflect the degree of value 

added and strategic complexity incorporated in service offerings. The classification of 

services into base, intermediate, and advanced categories, as established by Tim Baines 

in his paper: “What is servitization of manufacturing? A quick introduction. (2024)”, 

serves as a critical framework for understanding servitization. Each type of service aligns 

with specific examples and AI capabilities that enhance operational efficiency and 

customer value, as described next:  

- Base services are the most fundamental service offerings, often it revolves 

around reactive customer support such as warranties and spare parts supply. 

Hence, AI enhances these services through predictive maintenance and the 

optimization of inventory management, which leads to improvement in 

customer satisfaction and reduction in operational costs. 

- Intermediate services offer additional value that focuses on maintenance and 

operational support. Some examples include regular maintenance and 

helpdesks. Thus, AI through condition monitoring and chatbots, permits real-

time diagnostics and efficient customer service, which reduces response times 

and enhances support. 



 
 

 
 

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- Advanced services are outcome-based. This type of services is extremely crucial 

because it refers to a fully servitized firm (Baines, Lightfoot, Peppé, & Braganza, 

2009). As in the case of Outcome Base Contracts (van der Meer-Kooistra & 

Vosselman, 2012), which is a service model that prioritize the delivery of defined 

results or outcomes instead of merely providing fixed products or services. For 

instance, within the healthcare industry, a hospital may enter into an agreement 

with a medical technology provider where remuneration is contingent upon 

achieving enhanced patient outcomes, such as lower readmission rates, rather 

than being based on the purchase of specific medical equipment. Another 

example is the Product-as-a-Service (PaaS) model (Baines, Lightfoot, Peppé, & 

Braganza, 2009), which enables customers to utilize products without outright 

ownership, requiring them instead to pay based on their usage or the specific 

services provided, thereby transforming traditional consumption patterns into a 

service-oriented approach that emphasizes access over ownership. This category 

of services allows manufacturers to meet contractual outcomes, thus 

transforming customer engagement from product-based to performance-based 

models (Copperberg, 2023). 

Peter Drucker (1954) once said: it is not possible to improve what you cannot measure. 

That is why it was essential to establish a clear classification of services in relation to AI 

capabilities and the processes involved in transitioning between them. Consequently, 

the concept of AI maturity is introduced. This model is valuable and serves as a “scale” 

that permits an organization measure where it stands in its AI adoption journey. Hence, 

it can move from one type of service to the other. The AI maturity model consists of four 

stages—Awareness, Developing, Aspirational, Mature, and Optimization—which provide 

a structured framework for organizations aiming to enhance their service offerings 

(Servitly, 2024). As companies progress from basic to more advanced services, they can 

utilize AI to automate tasks, improve customer interactions, and tailor their offerings: 

 

 



 
 

 
 

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- At the Awareness Level, organizations engage with basic services. They 

occasionally employ AI tools to aid in product maintenance, even if there are no 

structured applications or comprehensive strategies. 

- Progressing to the Developing Level, companies can adopt intermediate services. 

They incorporate fundamental AI applications into their service operations. This 

is where they enhance areas like training and technical support. At the same 

time, they begin to experiment with dedicated AI strategies. 

- Upon attaining the Aspirational Level, firms become more confident in their AI 

capabilities. That is where they advance toward sophisticated services that 

deliver significant value to customers. At this stage, companies may use AI tools 

to enhance customer insights and improve service delivery, which encourages a 

more extensive incorporation of AI into their core business strategies. 

- At the Mature Level, organizations embed AI comprehensively across various 

functions. Which creates a foundational element of their service innovations and 

strategic decisions, that results in high-performance service outcomes. 

- When firms reach the Optimization Level, they capitalize on already existing 

capabilities and leverage the use of AI. 

 

 

2.1.1 Defining AI Maturity model 

AI maturity model refers to an organization's capacity to effectively adopt and implement 

AI technologies (Sivek, 2024). It encompasses the extent to which a firm has 

incorporated AI into its operational frameworks, strategic planning, and organizational 

culture (Sivek, 2024). Firms at different stages of maturity demonstrate varying levels of 

capability, ranging from limited, random application of AI to full-scale integration across 

business processes. The recognition of this developmental continuum is essential for 

determining the firm's current position within its AI adoption journey. 

 

 



 
 

 
 

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2.1.2 AI maturity model in relation to Digital Servitization 

To better comprehend the concept of AI maturity model, the below illustrative table is 

provided (see Table 1). It simulates the levels a firm goes through until fully adopting AI 

and it is inspired by ElementAI’s model (Eggers et al., 2017): 

 

Dimension Awareness Developing Aspirational Mature Optimized 

Strategy No strategy Limited 
inclusion of 
AI in the 
firm’s 
strategy 

Strategy is 
defined in 
alignment 
with AI 

Strategy is 
integrated in 
the firm 

The firm is AI-
driven 

Data 
management 

Limited 
data 
available 

In progress 
usage of 
data 
compiling 

Data 
management 
is centralized 

Data is well 
governed in 
the 
organization 

Comprehensive 
data strategy in 
alignment with 
AI 

Technology Limited 
usage of AI 

Only trials  There are 
some AI tools 

AI tools 
integrated in 
most 
processes 

Advanced 
technology and 
advanced 
systems using 
AI 

Talent No specific 
skills  

Initiating 
talent 
building in 
the field 

Recruiting 
personnel in 
the field 

There are 
units/teams 
specialized 
in AI 

AI talent is 
present across 
the firm 

Culture Firm not 
fully aware 
of AI usage 

Open to 
accept AI 

AI expertise 
is supported 
across the 
organization 

The AI 
culture is 
absorbed 
correctly 

AI mindset 
driven by 
continuous 
learning in the 
field 

Table 1: Illustrative figure showing stages of AI maturity in a firm. 

 

For this study, the evidence is derived from industry reports rather than peer-reviewed 

scientific literature. The term AI maturity level is used here to denote the degree of AI 

integration and adoption within an organization. Specifically, it refers to the extent to 

which AI technologies and methodologies are embedded in the organization's 

operational processes and strategic objectives (WeSoftYou, 2023; Maturity Model Guy, 

n.d.).  



 
 

 
 

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This progression through various stages of AI maturity is necessary for companies aiming 

to cultivate the full potential of artificial intelligence in enhancing decision-making 

processes, optimizing workflows, and fostering innovation (Deepchecks, 2023). Each 

stage represents a distinct level of AI utilization, ranging from mere awareness of AI's 

existence to its integration into every component of organizational operations 

(WeSoftYou, 2023; Maturity Model Guy, n.d.; Deepchecks, 2023): 

At the initial level, organizations recognize the existence of AI but have not yet fully 

implemented its capabilities into their operations (WeSoftYou, 2023). Ideas regarding AI 

utilization may exist, but concrete strategies are lacking (WeSoftYou, 2023; Maturity 

Model Guy, n.d.). As companies progress to the following level they begin experimenting 

informally with AI technologies, exploring basic models and tools such as Jupyter 

notebooks (WeSoftYou, 2023; Maturity Model Guy, n.d.). However, AI adoption remains 

constrained, with limited integration into daily functions (WeSoftYou, 2023; Maturity 

Model Guy, n.d.; Deepchecks, 2023). Advancement to the following level represents a 

significant milestone, where organizations have integrated machine learning into their 

day-to-day functions: that is establishing data pipelines and managing Machine Learning 

models (WeSoftYou, 2023; Deepchecks, 2023). At this stage, AI assists in information 

processing tasks, which leads to operational efficiency (WeSoftYou, 2023; Deepchecks, 

2023). In later levels, strategic deployment and transformational integration of AI within 

the organization are witnessed. That is when AI becomes a core element of business 

strategy and drives innovation and creates value (WeSoftYou, 2023; Maturity Model Guy, 

n.d.; Deepchecks, 2023). 

There are various models of AI maturity level, each providing a unique framework to 

evaluate the level of maturity of AI adoption and integration inside a business. Several 

popular models of AI maturity exist, for example: Microsoft’s RAI MM by Microsoft 

(2023), The AI Maturity Model from Gartner, The AI maturity Model from WeSoftYou 

(2023), The Maturity Model of MITRE AI (n.d), Maturity Guy's Model of AI Maturity (n.d), 

The AI Maturity Model by Deepchecks(2023).  



 
 

 
 

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The phases and criteria for each of these models are different. However, they have the 

same goals and themes when it comes to helping firms in assessing their adoption level 

of AI. 

For this thesis, Microsoft’s Responsible AI Maturity Model (RAI MM) is employed. The 

RAI MM (Vorvoreanu et al., 2023) provides a structured approach for organizations to 

enhance their AI practices. It outlines the journey from basic to advanced maturity levels, 

the model helps organizations plan their development strategically. In essence, the RAI 

MM guides organizations in integrating AI principles into their operations and making 

positive changes in the AI field. 

 

 

Figure 1 : Microsoft RAI MM (Vorvoreanu et al., 2023). 

 

Microsoft's RAI MM (Figure 1) provides a vital framework for SMEs seeking to enhance 

their digital servitization initiatives. This evaluation process is crucial for refining service-

focused business models and improving operational effectiveness. Additionally, the 

model promotes a culture of ongoing improvement. This leads to responsible AI 

integration and ethical considerations that are integral to achieving lasting success in 

digital transformation. 

The implementation of RAI MM permits SMEs to acquire the essential tools to manage 

the challenges associated with AI technologies. Where technological progress aligns with 

their strategic goals. This alignment also allows them to innovate their product service 

systems and establish strategic partnerships, eventually achieving a competitive edge in 

the market. The framework not only aids in facilitating smoother transitions toward 

digital servitization, but also helps SMEs adapt to changing customer needs and 

encourages sustainable growth. 

 



 
 

 
 

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2.2 Literature review 

Kohtamäki et al. (2019) elaborate on how digital servitization allows SMEs to provide 

personalized and responsive services, thereby enhancing customer satisfaction and 

loyalty. Using real-time data analysis, SMEs can anticipate customer needs and offer 

tailored solutions that enhance the overall customer experience. Additionally, the agility 

afforded by digital technologies enables SMEs to respond to market changes and 

evolving customer demands (Kohtamäki et al., 2019).  

Similarly, Le-Dain, Benhayoun, Matthews, and Liard (2023) highlight the role of digital 

servitization in the development of smart Product-Service Systems (PSS) by SMEs. This 

approach integrates products and services to create innovative solutions that cater to 

the specific demands of customers. Consequently, competitiveness will be enhanced, 

and more revenue will be generated. Additionally, digital servitization enables SMEs to 

establish stronger relationships with customers by providing value-added services (Le-

Dain et al., 2023). Moreover, Bakić (2024) underscores the multiple benefits of digital 

servitization for SMEs: in addition to reducing operational costs and increasing 

productivity, digital servitization leads to higher profits, a more satisfied workforce, and 

improved sustainability and flexibility. Embracing digital transformation can enhance 

SMEs’ competitiveness, drive innovation, and create sustainable growth opportunities 

(Bakić, 2024). In conclusion, it can be affirmed that the leveraging of digital tools enables 

SMEs to create a sustainable competitive advantage through continuous improvement 

and the ability to offer unique, high-value services that are difficult for competitors to 

replicate (Kohtamäki et al., 2019). 

Given the fact that it was not possible to find any research papers explicitly addressing a 

specific research gap in the adoption of AI in Finnish healthcare SMEs on their way to 

achieve digital servitization, alternatively, the following papers by Abou-Foul, Ruiz-Alba, 

and López-Tenorio (2023); Alnajjar, Muna (2023);  Kolagar, Reim, Parida, and Sjödin 

(2022) discuss the relationship between AI and digital servitization from different 

perspectives. 



 
 

 
 

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In their paper, Abou-Foul, Ruiz-Alba, and López-Tenorio (2023) demonstrate that AI 

capabilities have a positive effect on servitization in manufacturing firms. The research 

investigates how AI capabilities influence the shift from a product-centric to a service-

centric business model, known as servitization. It highlights the role of absorptive 

capacity: a firm's ability to recognize and apply new information, which enhances the 

positive impact of AI on servitization. Grounded in dynamic capabilities literature, the 

study suggests that AI capabilities are essential for firms to adapt and remain competitive 

in rapidly changing environments. The findings indicate that AI capabilities significantly 

boost servitization. The path to effective servitization involves advancing AI for 

optimizing internal processes as well as resource management. The study concludes with 

theoretical and practical implications that emphasize AI as a critical business capability 

beyond mere technological advancement. 

In a remote but somehow similar manner, the article by Alnajjar (2023) discusses how 

SMEs are adopting AI to achieve servitization. He emphasized that many SMEs are 

integrating AI as a tool for data analysis, future solutions, or core products. Most of them 

are in the early stages of AI adoption. They experiment with AI’s potential benefits before 

making significant investments. He further adds that the integration of AI offers 

advantages such as increased speed, accuracy, and indispensability in operations, 

enhancing both competitiveness and outcomes. However, SMEs face several challenges, 

including legal, technical, and financial obstacles that slow down further advancement 

in AI utilization. The paper also discusses AI adoption within the context of servitization, 

highlighting how AI can transform products into services through enhanced data analysis 

and customer insights. It emphasizes the need for a regulatory framework that allows 

flexible AI integration while ensuring data privacy. His recommendations include 

developing talent, creating funding mechanisms, and establishing a platform to match 

SMEs with AI expertise. The paper suggests that while SMEs recognize AI's potential 

benefits, they are still navigating the early stages of adoption and must address various 

challenges to fully realize AI's capabilities in servitization. The research concludes by 

identifying gaps in AI adoption among SMEs. 



 
 

 
 

21 

Equivalently, Kolagar, Reim, Parida, and Sjödin (2022) highlight that AI capabilities can 

significantly enhance the development of digital service offerings and ecosystem 

partnerships. SMEs that can integrate digital servitization with ecosystem partnerships 

are likely to achieve significant growth. The study presents a detailed view of how SMEs 

can use digital servitization to expand globally. AI is viewed as a crucial tool in facilitating 

effective innovation and scaling of digital services.  

In line with cited findings from the previous papers, the state of adoption of AI within 

Finnish SMEs can be outlined according to the study of Törnroth et al. (2020). The study 

investigates how Finnish SMEs are currently adopting AI and Robotic Process Automation 

(RPA). It was conducted with an organization aiming to advance the digital 

transformation of SMEs through a collaborative knowledge hub. The research covers the 

use and adoption of AI by identifying common trends found in research papers, other 

publicly available publications, surveys, expert interviews, and workshops. The research 

revealed that most Finnish SMEs are still in the early stages of using AI and RPA. Many 

SMEs do not yet see the benefits of these technologies but are interested in training to 

improve their knowledge and skills. The research sheds light on the state of AI and RPA 

adoption in Finnish SMEs and identifies the competencies needed for effective adoption. 

It adds to existing literature by providing detailed insights into the current and future 

trends of AI and RPA use in these businesses. This aligns with Finland’s current AI 

strategy, which emphasizes promoting AI and digital technologies in SMEs to boost 

business competitiveness, improve public services, enhance public sector efficiency, and 

ensure societal well-being (Ministry of Economic Affairs and Employment of Finland, 

2021). 

 

 

 

 

 



 
 

 
 

22 

In their article, Ruohonen, A. and Kallasvaara, H. (2022) offer a comprehensive overview 

of the opportunities and advancements in the AI and MedTech ecosystem in Finland. 

According to this study, Finland has been a pioneer in making AI available for SMEs, 

which make up 99% of all businesses in Europe. While larger organizations are the 

primary benefactors of AI technologies, SMEs also hold significant potential for 

incorporating AI into their operations. The health and MedTech sectors have been a 

focus, contributing nearly €14 billion to Finland’s foreign trade over the past 20 years. In 

an industry-specific AI initiative, the AI-TIE accelerator launched in April 2022 exemplifies 

this effort. It brought together 16 Finnish SMEs from the health and MedTech sectors to 

explore AI-related business opportunities and develop solutions. The findings they 

presented offer a comprehensive overview into the current state of AI adoption within 

Finnish SMEs, along with the associated opportunities. They mention the necessity for 

additional support and resources to assist these organizations in fully capitalizing on AI's 

potential.  

This thesis aims at providing a theoretical framework for assessing AI adoption levels 

within Finnish SMEs operating in the healthcare sector. Consequently, the acceleration 

of dynamic capabilities as well as the drivers of digital transformation, and the 

integration of AI into innovation strategies. The mentioned studies collectively illustrate 

an all-encompassing approach to leveraging AI in enhancing service-oriented business 

models. Drawing from prior research, dynamic capability theory (DCT) emerges as the 

most suitable framework for evaluating a company's digital servitization enabled by AI. 

Therefore, the subsequent section elaborates on this theory and delineates the elements 

applicable for assessing an organization's AI adoption. 

 

 

 

 

 



 
 

 
 

23 

2.3  Dynamic Capability Theory (DCT) 

Dynamic capability theory (DCT) is a well-established concept in strategic management. 

It delves into how organizations can cultivate and adjust their abilities to succeed given 

the evolving technology (Teece, Pisano, & Shuen, 1997). It emerged in the late 1990s 

through the research of David Teece, Gary Pisano, and Amy Shuen and has since emerged 

as an important framework for understanding organizational adaptability and 

competitiveness (Teece, Pisano, & Shuen, 1997). It highlights the significance of an 

organization’s capacity to detect, capitalize on, and adapt its resources and capabilities 

in the face of evolving market dynamics, technological advancement, and competitive 

challenges. According to this theory, organizations equipped with these dynamic 

capabilities are better positioned to drive innovation, generate value, and maintain a 

competitive edge over the long term (Denrell & Powell, 2016; Teece, Pisano, & Shuen, 

1997). 

Eisenhardt and Martin’s (2000) dynamic capability theory highlights the critical role of 

an organization's ability to adapt and reconfigure its resources in response to fluctuating 

market conditions. They argue that dynamic capabilities consist of processes that help 

firms maintain competitiveness while continuously adjusting their strategies. This 

interpretation is particularly useful to help understand how organizations can keep a 

competitive advantage through the leveraging of both internal and external resources. 

They define it as follows: organizational processes that allow firms to modify and 

reconfigure their resource base to remain competitive in rapidly changing environments. 

These processes, that the above-mentioned transformation described, are crucial for 

firms operating in industries with frequent innovation and dynamic shifts. This view of 

dynamic capability highlights that success in volatile environments depends on a 

company’s capacity to realign its resources in response to external pressures. Eisenhardt 

and Martin (2000) further identify key processes that form the foundation of dynamic 

capabilities. For instance, the ability to integrate new information, cultivate 

relationships, and learn from past experiences. Such processes enable organizations to 

successfully navigate risky markets and adjust their strategic assets accordingly.  



 
 

 
 

24 

In sum, the development of these capabilities enhances a firm's ability to sustain 

competitive advantage. 

Additionally, Coreynen et al. (2017) make several important contributions to the 

understanding of how digital technologies, particularly in the context of digital 

servitization, affect firms' performance and capabilities. They emphasize that digital 

servitization is reshaping the way companies deliver value by enabling more connected, 

proactive, and personalized service. The authors argue that firms with strong dynamic 

capabilities are better positioned to capitalize on digital servitization. Without the ability 

to sense opportunities, seize them, and adapt continuously, companies risk falling 

behind in markets increasingly dominated by digital technologies and smart services. 

These dynamic capabilities are, thus, essential for success in the digital age. 

Similarly, as discussed by Kindström et al. (2013), the concept of dynamic capabilities 

plays an important role in the servitization process. They refer those capabilities to an 

organization’s ability to integrate, build, and reconfigure its resources and competencies 

to adapt to a rapidly changing environment. In the context of servitization, firms must 

cultivate these capabilities to shift from traditional product-centric models to more 

service-oriented business frameworks. This transformation requires SMEs to sense 

market opportunities, align their service offerings with customer needs, and engage in 

continuous innovation. Eventually, the development of service-oriented competencies 

permits organizations to offer greater value to customers.  

One unique fact about dynamic capabilities is mentioned in the paper of Huikkola et al. 

(2022). It discusses dynamic capabilities in the context of smart solution services with a 

particular focus on how organizations can effectively adapt to rapid technological and 

market changes in industrial ecosystems. The unique insight presented in the study is 

the emphasis on the co-alignment of processes, routines, and practices as a crucial 

element for enhancing dynamic capabilities within manufacturing firms. This co-

alignment enables organizations to integrate product, service, and software 

development more seamlessly, which is vital for effective smart solution development 

(SSD).  



 
 

 
 

25 

Furthermore, the paper underscores that the development of dynamic capabilities is not 

solely about possessing resources but also about the ability to leverage these resources 

through collaborative efforts and continuous learning. This approach highlights the 

strategic importance of fostering a culture that promotes agility and innovation as 

organizations strive to remain competitive in an increasingly complex industrial 

environment. At the end, the study reveals that establishing dynamic capabilities 

through intentional design practices and robust management processes allows firms to 

respond proactively to unforeseen challenges. This dynamic responsiveness is framed as 

a vital organizational trait necessary for achieving sustained innovation and performance 

in the context of growing service models. 

Employing dynamic capability theory in the analysis enabled a deeper understanding of 

how Finnish SMEs address AI adoption and its impact on the digital servitization process 

(Sjödin, Parida, & Kohtamäki, 2023). This theory facilitated the examination of both the 

immediate effects of AI technology and the organizational abilities needed to sense, 

seize, and reconfigure resources in response to technological changes (Sjödin, Parida, & 

Kohtamäki, 2023). 

To break it down, the following components (Teece, Pisano, & Shuen, 1997) are essential 

to measure: first ‘Sensing’, here the ability of an organization to perceive changes and 

opportunities in its external environment is measured. It involves gathering information, 

trend watching, and understanding customer needs, as well as emerging technologies. 

Second ‘Seizing’, once changes and opportunities are identified, organizations must be 

able to act quickly and effectively to benefit from them. This stage involves making 

strategic decisions and allocating resources to exploit new opportunities or respond to 

threats. Third ‘Reconfiguring’, given the fact that the competitive landscape in a dynamic 

environment is constantly changing, which requires organizations to adapt and evolve 

their internal capabilities. Reconfiguring capabilities involves the ability to reallocate 

resources, redesign processes, and develop new competencies to maintain 

competitiveness. 



 
 

 
 

26 

In the next section “theoretical framework” many reflections exist. Among them: the 

Resource-Based View (RBV) (Barney, 1991), Service-Dominant Logic (SDL) (Vargo & Lusch, 

2004), and Absorptive Capacity (Cohen & Levinthal, 1990). They all provide essential 

insights into the role of AI in enhancing digital servitization and dynamic capabilities. For 

example, RBV emphasizes the significance of a firm’s internal resources (Barney, 1991). 

It regards AI as a rare, valuable, and inimitable asset that can contribute to competitive 

advantage by mixing both digital servitization and dynamic capabilities (Barney et al., 

2021). SDL, on the other hand, shifts the focus from tangible products to intangible 

services (Bustinza, Vargo, & Kohtamäki, 2024). This will help in the correct alignment 

with digital servitization given that AI facilitates the co-creation of value with customers 

through smart services. Finally, Absorptive capacity, such as AI-driven insights that play 

an important role in strengthening both dynamic capabilities and digital servitization 

(Vigren, Kadefors, & Eriksson (2022). When reflecting on these frameworks, they all 

emphasize how firms can continuously adapt to changing environments and leverage 

emerging opportunities.  

Nevertheless, Dynamic Capabilities Theory (DCT) is considered the most suitable 

framework for examining the phenomenon in this thesis. Since AI plays a crucial role in 

strengthening dynamic capabilities and enhancing an organization's ability to sense, 

seize, and transform. This eventually leads to a fully servitized firm. 

 

 

2.4  Theoretical framework 
 

Simply put digital servitization is the transformation of a business from selling products 

to offering integrated services. This journey involves digitalizing products, adding digital 

services, and eventually providing comprehensive solutions. AI enables this by analyzing 

data, predicting needs, and optimizing operations. Dynamic capabilities ensure firms can 

adapt, seize opportunities, and effectively use AI, driving the shift toward service-

oriented models and enhanced customer value 

(Coreynen et al., 2017; Teece et al., 1997). 



 
 

 
 

27 

 

Figure 3: Transition to a fully servitized firm with AI as an enabler. 

 

 

 

 

Figure 2: Representation of the framework of this thesis showing the link between Dynamic 
Capabilities, Digital Servitization, and AI. 

 

 

 

 

Dynamic Capabilities of a firm 

↓ 

Digital Servitization  

↓ 

AI as an Enabler 

↓ 

Fully Servitized Firm 

D
igital servitization journey 



 
 

 
 

28 

Figure 2 illustrates the link between Dynamic Capability, Digital Servitization, and AI. It 

highlights how AI acts as a catalyst for both, enhancing dynamic capabilities, while 

supporting a firm in its digital servitization journey through AI-driven services. AI acts as 

an incentive for firms, that want to enhance their dynamic capabilities and facilitate the 

process of digital servitization. Dynamic capabilities, as mentioned by Teece (2018), 

empower organizations to navigate rapidly changing environments. This empowerment 

can be achieved through sensing opportunities, seizing them, and transforming 

resources. For instance, AI-driven analytics enable firms to anticipate customer 

preferences and market trends with unprecedented speed and accuracy as confirmed 

by (A. I. Talwar, A. R. Talwar, & R. B. Sharma, 2023). This awareness equips organizations 

to seize emerging opportunities. Therefore, they can create innovative, AI-driven service 

offerings that are integral part of their digital servitization strategies (A. I. Talwar, A. R. 

Talwar, & R. B. Sharma, 2023). 

As defined above, the shift towards digital servitization involves transitioning from 

traditional product offerings to a focus on providing digital services, with AI at the front 

line of this transformation (Paschou et al., 2020). The implementation of AI facilitates 

various service innovations, among them: predictive maintenance and autonomous 

operations. Which require firms to develop robust capabilities for gathering, processing, 

and utilizing data effectively. As depicted in the theoretical framework diagram above, 

AI is crucial not only in enhancing service capabilities but also in automating service 

delivery, which enables the generation of significant value for customers, eventually 

leading to a fully servitized firm. Figure 3 illustrates that digital servitization is viewed as 

a journey. Starting by dynamic capabilities of the firm, AI acts as an enabler of digital 

servitization, the firm can eventually reach a point of full servitization. Which leads to 

competitive advantage (Baines, Lightfoot, Peppé, & Johnson, 2009). 

The interplay between dynamic capabilities and AI is essential for the successful 

execution of digital servitization strategies. As organizations leverage AI to refine their 

dynamic capabilities, they become capable of adapting their business models to the 

evolving digital transformation. 



 
 

 
 

29 

In the context of Finnish SMEs wanting to adopt a digital servitization strategy, dynamic 

capability theory suggests that the successful implementation of AI technologies can 

significantly influence their ability to innovate and transform their business models 

(Liu, Cui, Han, & Zhang, 2024). 

Firstly, the adoption of AI technologies enables SMEs to enhance their operational 

efficiency and effectiveness by automating repetitive tasks, optimizing resource 

allocation, and improving decision-making processes 

(Jalil, Lynch, Awang Marikan, & Md Isa, 2024). These operational improvements 

contribute to the firm's dynamic capabilities by encouraging agility and responsiveness 

to market changes (Teece, 2007). Secondly, AI implementation can facilitate the 

development of new service offerings and business models within the digital 

servitization process (Kohtamäki et al., 2022).  

When Finnish SMEs leverage AI-driven analytics and insights, they can better understand 

customer needs, preferences, and behaviors (Westenberger & Jafarzadeh, 2024). This 

would help in the creation of personalized and value-added services (Westenberger & 

Jafarzadeh, 2024). Moreover, AI-enabled predictive maintenance and remote 

monitoring capabilities can enhance the reliability and performance of products-as-a-

service offerings, thereby customer satisfaction and loyalty increase 

(Mourtzis, Angelopoulos, & Panopoulos, 2020). Furthermore, dynamic capability theory 

suggests that the successful integration of AI technologies into the digital servitization 

process requires ongoing learning, experimentation, and adaptation (Abou-Foul, Ruiz-

Alba, & Lópe-Tenorio, 2023). Finnish SMEs need to develop organizational routines and 

processes that enable them to continuously acquire and apply new knowledge and 

capabilities related to AI. This may involve investing in employee training and 

development, adopting a culture of innovation and experimentation, and forming 

strategic partnerships with technology providers and industry experts (Teece, D. J. 2018). 

 

 



 
 

 
 

30 

To address the research question “How Finnish SMEs employ their dynamic capabilities 

when implementing AI-driven digital servitization?”, Dynamic Capability Theory is 

employed to support in the journey of digital servitization of Finnish SMEs enabled by 

AI. Subsequently, the following table 2 delineates the components evaluated across 

various categories to ascertain the degree of servitization, facilitating its comparison 

with Microsoft's maturity model. 

 

Sensing Seizing Reconfiguring  
First, it is important to 
explore how Finnish SMEs 
perceive the opportunities 
and challenges presented by 
AI technologies in the 
context of digital 
servitization 

First, analyse the strategic 
decision-making process of 
Finnish SMEs as they adopt 
and integrate AI 
technologies to improve 
their efforts in digital 
servitization. 

First, it is crucial to explore 
how the implementation of 
AI technologies necessitates 
changes in the organizational 
structure, processes, and 
capabilities of Finnish SMEs. 

Then, investigate how SMEs 
identify potential areas for AI 
integration within their 
existing service offerings and 
business models. 

 

Then, investigate the 
processes through which 
SMEs allocate resources, 
invest in technology 
infrastructure, and develop 
partnerships to leverage AI 
capabilities. 

Then, investigate how SMEs 
adapt their business models, 
service delivery mechanisms, 
and customer interactions in 
response to the introduction 
of AI-enabled services. 

 
Lastly, assess the ability of 
SMEs to monitor market 
trends, customer 
preferences, and 
technological advancements 
related to AI and digital 
servitization. 

Lastly, assess the agility and 
responsiveness of SMEs in 
seizing opportunities to 
innovate and differentiate 
their service offerings 
through AI integration. 

 

Lastly, assess the ability of 
SMEs to continuously 
reconfigure their resources 
and capabilities to maintain 
competitiveness in the 
evolving landscape of digital 
servitization. 

 

Table 2:  Showing how DCT components can be incorporated to study the servitization of 
Finnish SMEs. 

 

Table 2 represents a framework that allows the examination not only of the direct 

effects of AI technologies but also the organizational capabilities required to effectively 

sense, seize, and reconfigure resources in response to technological change. 



 
 

 
 

31 

To apply the Responsible AI Maturity Model (RAI MM) from Microsoft in understanding 

how dynamic capability theory relates to the servitization of Finnish SMEs, several steps 

can be taken. Firstly, measuring the level of AI adoption, which emphasizes AI 

implementation, with the fundamental principles of dynamic capability theory. This 

involves recognizing how aspects like AI usage correspond to the principles of dynamic 

capability theory. This alignment highlights an organization's capacity to sense, seize, 

and reconfigure resources for competitive advantage. Next, utilizing the RAI MM to 

evaluate the current level of AI integration and maturity within the chosen sample of 

Finnish SMEs undergoing servitization. Which provides insights into how effectively 

Finnish SMEs are adopting AI practices. Assessing whether these efforts align with the 

principles of dynamic capability theory can reveal areas of strength and areas needing 

improvement concerning AI integration and dynamic capabilities. Lastly, the findings 

from the RAI MM assessment simultaneously with the principles of dynamic capability 

theory are compared according to the perception of each interviewed SME. The aim is 

to identify a measured variable, that serves as a benchmark, for enhancing the 

servitization process of Finnish SMEs. Determining this measured variable of the 

implementation of AI can contribute to strengthening dynamic capabilities within the 

servitization context. 

 

Figure 4: Showing Finnish SMEs' usage of AI measured against Microsoft's RAI MM in relation 
with DCT to determine the maturity level leading to servitization. 

 

 



 
 

 
 

32 

The flowchart above depicts the process to reach servitization. In a trial to show the 

importance of the strategic management of resources and capabilities in shaping the 

impact of AI technology measured against Microsoft’s RAI MM and its implementation 

on the digital servitization process of Finnish SMEs. The focus on AI capabilities can 

enhance operational efficiency and drive innovation, hence SMEs can position 

themselves for long-term competitiveness and growth (Teece, D. J. 2018). 

The proposed framework initially presents digital servitization as an independent 

journey designed to empower organizations aiming to attain a competitive advantage. 

Subsequently, it highlights AI as a digitalization enabler capable of enhancing an 

organization’s capabilities, potentially leading to a fully servitized state over time. Table 

2, shown above, presented each phase of the dynamic capability theory, detailing its 

connections to AI applications throughout the digital servitization process. Microsoft's 

suggested maturity model aids SMEs in progressing from one phase to the next, as 

described in the AI maturity model section. This model aims to support Finnish SMEs in 

evaluating their current position, thus enabling them to identify AI capability needs 

essential for achieving a fully servitized firm. 

 

 

 

 

 

 

 

 

 

 



 
 

 
 

33 

 

  3 Methodology 

3.1 Research approach 

 

The figure below is the research onion, and it depicts the research approach employed 

during this thesis.  

 

 

Figure 5: Research methodologies of the thesis (Saunders et al. 2003). 

 

 

Secondary data 

& Interviews 

Cross-

sectional 

Multi-case 
study 

Abductive 

Interpretivism 

Realism 



 
 

 
 

34 

Figure 4 represents the research onion where the chosen approach to conduct this 

research is highlighted. Interviews and secondary data as a collection method, cross 

sectional pattern as a research design, multi-case study as a research strategy, abduction 

as a research approach, and interpretivism as a research philosophy. The analysis of the 

cases in this research paper is mono method. This representation of the research onion 

was important because it provided a systematic framework that enhanced the 

consistency and coherence of the research process (Saunders et al., 2003). Its value lied 

in its flexibility (Saunders et al., 2003). It comprehensively covered all aspects of the 

research process. This model helped in ensuring that each critical element is considered, 

from philosophical foundations to specific data collection and analysis techniques. This 

structured approach also helped improve the rigor and validity of the study (Saunders et 

al., 2003).  

The research onion was developed by Saunders et al. (2003). It is a model that helps in 

structuring the methodologies effectively. The model consists of six layers, each layer 

representing different aspects of the research process. The outermost layer addresses 

research philosophies, which include positivism, realism, interpretivism, and 

pragmatism. These philosophies reflect the foundational belief systems that form the 

research approach (Saunders et al., 2003). Moving inward, the research approaches layer 

involves choosing between a deductive approach, which tests a hypothesis, and an 

inductive approach, which develops a theory from data, and an abductive approach, 

which is a combination between both but more resembling to inductive approach 

(Dubois & Gadde, 2002). The research strategies layer includes various methods such as 

experiments, surveys, case studies, grounded theory, ethnography, and action research, 

each offering a distinct way to collect and analyze data. Choices within the model refer 

to the selection of mono-method, mixed-method, or multi-method approaches for data 

collection and analysis. Time horizons can be either cross-sectional, studying 

phenomena at a specific time, or longitudinal, examining changes over time. The 

innermost layer focuses on the techniques and procedures for data collection and 

analysis, such as questionnaires, interviews, observations, and statistical or thematic 

analysis. 



 
 

 
 

35 

The reasoning behind the chosen research approach stems from the quote of Dubois 

and Gadde (2002, p.556): 

“[..] abductive matching requires more, and has the potential to yield more, than 

inductive fit [..]” 

Additionally, in their work on systematic combining and abductive approach to case 

research, they acknowledge the value of qualitative research methodologies (Dubois & 

Gadde, 2002). They emphasize the importance of qualitative methods in generating rich, 

context-specific insights and understanding complex phenomena in depth. They view 

qualitative research as essential for capturing the nuances, meanings, and complexities 

of an organizational phenomena, particularly in the context of case studies. Moreover, 

by advocating for a systematic combining approach, they suggest that qualitative 

research, when combined with other methods and theoretical perspectives, can 

enhance the rigor and comprehensiveness of research findings. Furthermore, Charmaz 

(2014); Creswell and Creswell (2017); as well as Tavory and Timmermans (2014) confirm 

that such an approach permits revealing the relationships, and potential explanations 

that emerge from the collected data.  Therefore, an “Abductive”, “Qualitative” research 

approach is the chosen approach for studying the impact of AI on the servitization of 

Finnish SMEs. As for the research philosophy “Interpretivism” asserts that each observer 

has a distinct perception and understanding of reality (Saunders, Lewis, & Thornhill, 

2007). Accordingly, “interpretivism” was chosen as the research philosophy because the 

collected data is analyzed through the lens of personal views and past experiences.  

 

 

3.2 Sampling 

Many studies enumerated the pros of multiple case study research. For example, 

Eisenhardt (1989) determined that 4 to 10 cases are the optimal number of cases to look 

at when conducting multi case study research. 

 



 
 

 
 

36 

Yin (1994) suggests that utilizing multiple sources enables researchers to explore a wider 

array of historical and behavioral factors. Additionally, Dubois and Gadde (2002) 

reported on behalf of Yin (1994) that findings or conclusions drawn from case studies 

are more compelling and precise when supported by various sources of information, 

validating the results through corroboration. 

“Purposive sampling” was the employed method for the selection of the cases. The 

participants were chosen according to specific criteria that aligned with the research 

question, such as particular experiences, expertise, demographic characteristics, and / 

or length of employment in that specific company.  

Unlike random sampling methods, purposive sampling is non-random, and purpose 

driven. This way it was ensured that participants possess the relevant characteristics or 

experiences necessary to provide meaningful insights related to the research question 

(Etikan, Musa, & Alkassim, 2016). This method facilitated the collection of rich, detailed 

data from the targeted group. Consequently, it enhanced the depth and quality of the 

findings (Palinkas et al., 2015). Additionally, purposive sampling was found to be more 

efficient and practical compared to random sampling, especially for this research paper 

where in-depth understanding is prioritized over generalizability (Patton, 2002). 

Given the researcher's keen interest in the healthcare industry, specific attention was 

directed towards three Finnish SMEs operating within this sector. Initially, emails were 

sent to 40 SMEs operating in Finland in the healthcare sector. Based on the responses 

received, a pool of 10 SMEs was selected. Due to time constraints and unforeseen 

circumstances with certain chosen SMEs, the last pool of cases remaining totaled three.  

These selected companies have demonstrated varying competence in adopting and 

effectively utilizing AI technologies to push their businesses forward, particularly in 

advancing towards digital servitization.  

Notably, these SMEs have integrated AI into various aspects of their operations, such as 

disease diagnosis and infrared cameras for continuous health monitoring. Which 

demonstrated their commitment to innovation and leveraging technology within the 

Finnish healthcare system.  



 
 

 
 

37 

Their approaches not only improved patient outcomes but also enhanced the overall 

quality of service provided by healthcare SMEs (Reddy et al., 2022). 

 

 

 3.3 Data collection 

Collected data was a combination of primary and secondary data using a qualitative 

approach based on cross-sectional observation. Primary data was collected through 

virtual interviews with representatives of Finnish SMEs in the healthcare sector. Using 

Microsoft Teams as a platform, the interviews were structured interviews (see appendix 

1 for questions). The point of using structured interviews was to ensure consistency and 

reliability in data collection. The incorporation of a structured format, led to standardized 

responses gathering from all participants. This allowed an easier approach for 

comparison and analysis. In addition, structured interviews also helped in minimizing 

interviewer bias and ensured that all relevant topics were covered. Moreover, they 

helped to provide a clear framework for both the interviewer and the interviewee.  

Which enhanced the efficiency and accuracy of data collection. Interviews were held 

during the month of May 2024. The language used for the interviews was English, 

however, some interviews were a combination of English and Finnish to make it easier 

for the interviewee to express all their ideas in the native language. All interviews were 

not recorded, and the results are reported anonymously. Notes and important facts were 

documented during the interview manually. The notes were subsequently reviewed to 

extract the most relevant facts to answer the research question. The respondents were 

senior executives in their companies, possessing pertinent knowledge of new 

technologies and their applications for enhancing customer offerings.  

It is equally important to mention that the respondents expressed a certain level of 

reluctance to provide in-depth information and to elaborate on certain technologies that 

they were using in their product offerings. Many of them were also interested in 

receiving the submitted version of this thesis. Which indicated the importance of the 



 
 

 
 

38 

researched subject. The hesitation expressed in the pre-interview imposed a 

considerable level of cautiousness while conducting the interview.  

Next, the secondary data was based on online content from Finnish websites and news 

portals heavily discussing AI achievements and projects in business fields and in Finnish 

universities, specifically in the healthcare sector.  

The collection of secondary data in the analysis optimized the utility of previously 

gathered data (Ruggiano & Perry, 2019). Thus, it confirmed primary data findings 

(Ruggiano & Perry, 2019).  It also gave a comprehensive understanding and served as a 

way for validation and comparison. It also enhanced the reliability and validity of the 

research outcome. 

 

  3.4 Data analysis 

Integrating various sources of evidence and transitioning between analysis and 

interpretation typically signifies triangulation (Yin, 1994; Denzin, 1978). Yin (1994) 

suggested that the primary benefit of triangulation is the emergence of converging lines 

of inquiry. Another assumed benefit suggests that incorporating interviews, 

observations, documents, and archival records as data sources can deepen and broaden 

the collected data. This enhances the validity of the findings (Quintão et al., 2020). In 

1994, Huberman and Miles described this process as an attempt to gather and 

authenticate findings.  

Triangulation is commonly advocated in textbooks on case study research methods 

(Dubois & Gadde, 2002). In systematic integration, the focus is not solely on verifying data 

accuracy, rather multiple sources have the potential to unveil aspects unknown to the 

researcher, thus revealing new dimensions of the research problem (Dubois & Gadde, 

2002). While most data collection ventures were aligned with the existing framework, 

additional efforts were necessary to uncover new insights (Dubois & Gadde, 2002). That 

is why a recursive process was necessary: data and theory were iteratively alternated to 

ensure the theoretical framework closely aligned with the empirical evidence.  



 
 

 
 

39 

This approach aimed to improve the accuracy and reliability of the resulting theory 

(Eisenhardt & Graebner, 2007; Eisenhardt, 1989). 

In summary, triangulation was used for the analysis and AI maturity level considered a 

unit of analysis. Then a rough estimation of the level of adoption of AI was concluded 

based on the interviews and based on the perception of the interviewees. 

 

 

 3.5  Assessment of the quality of the data 

One significant issue in case-based research is the common lack of selectivity, as noted 

by Siggelkow (2007). To address this, it was crucial to provide readers with enough 

information to evaluate the appropriateness of the research methods and results, a point 

emphasized by Eisenhardt (1989). Also, addressing potential biases was essential to 

maintain the integrity of the research. Dubois and Gadde (2014) highlighted the 

importance of re-interviewing and re-observation, as well as triangulation with written 

documents, to cross-verify findings and reduce the influence of individual biases. This 

approach helped in obtaining a more objective and balanced perspective. Some setbacks 

occurred such as the cancellation of interviews by some managers from certain SMEs. 

Therefore, there was a need for re-observation and triangulation to mitigate the risk of 

missing important findings as well as potential biases. 

To ensure the validity of the findings, the cases were clearly and thoroughly defined, 

following Yin's (2018) recommendations. A variety of data sources, including interviews, 

observations, documents, and archival records, were utilized. The findings were then 

cross verified using different data sources or methods to ensure resilience (Yin, 2018). 

During the research process, the interviewees did not always express their views 

explicitly, which required careful interpretation to uncover answers addressing the 

research question. The necessity of alternating between English and Finnish posed 

challenges, potentially compromising data efficiency. To overcome this, the researcher 

attended numerous conferences, online workshops, and other events to better 

understand AI utilization in the healthcare sector and its adoption among Finnish SMEs. 



 
 

 
 

40 

A structured approach to interviews and data collection, as advocated by Patton (2002), 

enhanced the clarity, and focus of the research. However, maintaining some flexibility 

allowed the researcher to explore unexpected insights that emerged during the process. 

This balance ensured that the research remained both systematic and adaptable. Also, 

the structured nature of the interviews proved to be the most effective approach for 

tackling the research question. This method provided a clear, predefined path, which 

enabled the researcher to navigate back and forth to confirm findings as needed. 

Additionally, a substantial number of articles and references were important in validating 

the collected data. The data was further verified by an interview with a scholar who had 

conducted extensive research and published papers on AI utilization within the 

healthcare industry. She held an important role in a big corporation specializing in 

medical imaging. She highlighted several challenges in the healthcare industry. She 

pointed out that AI is a big leap forward potentially capable of solving these problems. 

Her insights were invaluable as they helped in confirming the results of the interviews 

conducted. Surprisingly enough, in her current role as a researcher and lecturer, a paper 

investigating the employment of AI technology in healthcare was published to showcase 

the effectiveness of such technology in the industry. Which helped great deal clarify few 

sticking points and inspired in exploring other areas concerning specifically Finnish SMEs 

within the medical field. 

Evidently, ethical considerations were paramount in the data collection process. This 

included obtaining informed consent from participants, ensuring confidentiality, and 

being transparent about the research objectives. That is why ethical research practices 

helped in building trust with participants and in gathering more candid and reliable data 

(Creswell & Creswell, 2018). 

This comprehensive approach ensured that the research findings were both reliable and 

insightful. Eventually, contributing significantly to the understanding of AI adoption in 

Finnish SMEs within the healthcare sector. Overall, the data collected from the selected 

SMEs is highly pertinent and purposeful.  



 
 

 
 

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The reason being that they showed different levels of AI adoption. Some of them were 

strongly dependent on AI in their offering and others were in the discovery phase of AI 

potential. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



 
 

 
 

42 

 

   4  Findings 

It is important to note that not one of the interviewed companies has reached a high 

maturity level in AI adoption. SMEs often struggle with limited resources, making it 

difficult to implement AI effectively. Financial barriers also posed a problem, as 

substantial upfront investments were required, and the return on investment was often 

delayed. However, several strategies were hypothetically developed for healthcare SMEs 

to become digitally servitized by using AI. For instance, the use of AI-powered IoT devices 

enabled real-time monitoring of patients' health. This allowed for predictive 

maintenance that anticipated and addressed potential health issues before they become 

critical. This not only enhanced service offerings but also significantly improved patient 

care as advocated by the findings of this paper and Debnath et al. (2023). The targeted 

SMEs showed efficient use of machine learning models and IoT devices in the 

development of their product offerings. Additionally, there were no examples of AI 

applications in the automation of administrative tasks such as scheduling and billing 

among the selected SMEs. Despite the obvious fact that this can significantly reduce the 

administrative burden on healthcare providers. As found in this paper and seconded by 

Smith & Jones (2021), such AI application would allow them to dedicate more time and 

resources to direct patient care, thus improving efficiency and service quality. Ultimately 

become fully servitized.  

 

 

 

Figure 6: AI adoption level for a Finnish SME to digital servitization. 



 
 

 
 

43 

As illustrated in the Figure above, the recommended AI adoption level for a Finnish SME 

operating in the healthcare sector to achieve a level of digital servitization is 4 (see Figure 

6). This estimation arises from the conducted research and indicates that this is the 

phase where the company is “adopting” AI to the fullest.  It also stems from the insight 

that the interviewed companies were ranked: one for company 3 and three for both 

companies 2 and 3. Company 3 demonstrated limited strength in the talent and cultural 

dimensions, which were found to be essential for AI adoption. However, it could be 

positioned at a foundational level in other areas, even reaching an "aspirational" status 

due to its awareness of AI’s transformative potential. Companies 1 and 2 exhibited 

distinct strengths and weaknesses across different aspects, as each company 

emphasized unique AI capabilities and technologies. While neither displayed a robust AI-

oriented talent base or culture, both companies showed a mature approach in the 

dimensions of strategy, data management, and technology. Consequently, this is a 

description of each company and how dynamic capabilities were manifested in their 

operations: 

Company 3 provides specialized in-home healthcare services that matches the needs of 

various clients, particularly the elderly. It also emphasizes internal innovation but lacks 

the important factor of proactive customer engagement that is necessary to effectively 

tap into its dynamic capabilities. Their services include mobile health clinics and 

specialized medical support that is brought directly to the client’s location. Their mobile 

health clinic allows the access to a nurse and remote doctor consultations, which 

promotes convenience and immediate care. Additionally, the company emphasizes 

client-centered care, which reduces wait times, and ensures familiarity with each 

patient’s health history to improve quality of service and comfort. Company 3 senses 

technological shifts through an innovation readiness approach but focuses primarily on 

internal productivity gains rather than customer-driven advancements. To seize 

opportunities, they aim to apply for government funding. However, the lack of a concrete 

strategy for implementing technologies like cloud computing limits their market 

responsiveness. Their reconfiguring capabilities are underdeveloped compared to 



 
 

 
 

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competitors, as their internal improvements lack a customer-centric or flexible 

approach, that is important for the adaptation to evolving market needs. 

Company 2 specializes in AI-driven solutions for monitoring incidents in healthcare and 

elderly care settings. Its core solution is based on video analytics and AI that detects 

potentially dangerous situations and alerts nursing staff through a nurse call system. This 

technology is widely used in hospitals, care facilities, and even home care settings to 

enhance patient safety and support staff efficiency. Since its establishment, company 2’s 

solution has reduced healthcare incidents significantly across multiple healthcare 

facilities. Company 2 stands out with a strong dynamic capability framework that 

includes effective sensing, seizing, and reconfiguring, driven by a customer-centric 

approach. It has a highly developed sensing capability that keeps it closely aligned with 

customer needs and market trends. It utilizes AI to gather and analyze data for early 

identification of emerging demands. This capability supports its exceptional seizing 

approach. The AI-driven insights help it to rapidly lead to tailored solutions that meet 

market needs. Reconfiguring is a dynamic process for the company that is enabled by a 

flexible structure that allows easy adjustments to technologies and services.  

Lastly, company 1  specializes in advanced automation solutions for medical device and 

diagnostics manufacturing. They provide a range of automation systems tailored to high-

precision manufacturing needs, including platforms for lateral flow devices, 

microfluidics, and other medical devices. Company 1 exhibits a reactive capacity 

primarily focused on compliance and data privacy. This potentially limits its dynamic 

capabilities. The company demonstrated a cautious sensing capability, focusing on 

monitoring technological advancements that might impact current operations but 

lacking a proactive drive to leverage these for strategic advantage. While the firm’s 

seizing capabilities are apparent in its ability to respond to changes with updates to 

systems, this response is often delayed. Their strong emphasis on data privacy reinforces 

their seizing approach by ensuring compliance and customer trust in new 

implementations. However, reconfiguring is limited, as the company prioritizes 



 
 

 
 

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compliance and data security over internal restructuring, thus often missing the 

potential to fully exploit emerging technologies. 

 

4.1 Digital servitization journey limited by resources and skills gap yet high alignment, 
adaptability and market responsiveness for future integration 

 

Company 3 commencement on a digital servitization journey was expressed by a strong 

interest in adopting new technologies to enhance productivity. However, its progress was 

slowed down by challenges, primarily stemming from a lack of available talent and 

financial resources. The company is aware that integrating AI is crucial for improving 

efficiency and transforming its business model from a purely product-centric approach 

to one that is service-enhanced through digital solutions. There is a clear focus on using 

AI to drive productivity improvements. This indicated an aspiration to develop tech-

enabled services that will elevate their offerings. For instance, the company envisioned 

utilizing AI for automated diagnostics and remote monitoring, which could empower 

them to provide predictive insights and more efficient digital services to customers. In a 

strategic move to enhance its capabilities, the company actively pursued government 

funding to develop a mobile application, reflecting its commitment to advancing digital 

solutions. Yet, the ongoing skills gap in AI and dependency on external funding continue 

to slow their journey toward digital servitization. Recognizing the importance of 

cultivating in-house AI talent, the company’s efforts highlighted their understanding of 

the need for sustained digital capabilities.  

By overcoming these skills barriers, the company would be better positioned to shift 

towards a service-oriented model that fully leverages AI in customer service. This shift 

to digital servitization would allow it to provide continuous digital services in addition to 

its products. As the company built its internal AI capacity, it stood to transform from 

merely selling products into a provider of ongoing digital services that enrich the product 

lifecycle.  

 



 
 

 
 

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This evolution positioned it as a digitally enabled service provider. The journey of the 

company highlighted the interplay between technology adoption and the need for skill 

development and funding, laying the foundation for a more dynamic and service-

oriented business model. 

Company 2 was on a remarkable digital servitization journey. This was reflected through 

a high alignment with customer needs and a strong adaptability to market trends. It 

operated with a customer-centric strategy, leveraging AI as the core driver of its 

products. This responsiveness allowed the company to continuously innovate and 

introduce new, value-added services tailored to individual customer requirements, 

which solidified its position as a digital service provider rather than merely a traditional 

product seller. Although the company primarily focused its AI-driven solutions on 

customer-facing products, its lean SME structure enabled rapid adaptation to customer 

demands. This flexibility reinforced a service-oriented, digital-first model that prioritized 

innovation in response to evolving customer needs. The company's ability to monitor 

market trends closely ensured that it could adjust its offerings to include the latest AI-

enabled solutions. Such agility was vital in a digital servitization context, as it enabled 

the company to consistently deliver high-value digital services that set it apart from 

competitors. Furthermore, this adaptability strengthened customer loyalty, as clients 

benefited from services that are designed to evolve with them. As a result, the 

company’s strategy positioned it well to fully leverage digital servitization and thrive in a 

competitive landscape. They have the potential to offer subscription-based, AI-enhanced 

services or other digital products that adapt continually to customer requirements. This 

shift transformed their business model from being solely a product provider to one that 

emphasizes ongoing service and support.  

Company 1 was on a digital servitization journey, even though there were limitations 

that slowed its full potential. The company relied on cloud computing to support its 

servitization efforts, which laid a foundation for future integration of digital solutions. 

However, current offerings were not heavily driven by AI, and their focus remained 

largely on compliance and data security rather than leveraging data for advanced 



 
 

 
 

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services. This resulted in a reliance on manual processes that reflected a traditional 

operational approach, preventing them from fully embracing digital servitization. Instead 

of utilizing advanced AI, the company employed basic automation, restricting its capacity 

to deliver AI-driven services that could greatly enhance customer experiences. Their 

cautious, reactive approach to technological advancements indicated a careful 

servitization strategy. While this strategy could help mitigate risks, it also limits their 

ability to gain a competitive edge in a rapidly evolving market. Despite these challenges, 

the company’s commitment to cloud computing reflected an underlying structure that 

could support future servitization efforts. The leveraging of their cloud capabilities and 

beginning to integrate AI for customer analytics and predictive services permitted them 

to transition towards more value-added digital offerings. However, without significant 

strategic adjustments, they risk missing out on opportunities for rapid growth through 

digital servitization. 

 

 

4.2 AI integration in operations 
 

Companies are increasingly recognizing the importance of integrating AI to enhance 

productivity and efficiency in their processes. The journey of AI within the interviewed 

companies revealed a combination of innovation, challenges, and adaptation. Together, 

they formed an example of how AI shapes industries, from ambitious aspirations to 

grounded realities, as they all tried to leverage technology for a competitive advantage: 

Company 3 demonstrated a strong commitment to AI adoption to improve its 

operations. Despite its eagerness, the company faced significant challenges due to talent 

shortages and funding constraints, which slowed its ability to fully implement AI 

solutions. The company’s ambition for productivity improvements remained steadfast, 

as it continues to explore various AI applications that could help streamline operations. 

 



 
 

 
 

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In contrast, Company 2 relied heavily on AI as the cornerstone of its product offerings. 

This company used AI to enhance customer experiences but did not integrate it into its 

internal operations. Due to its status as a SME, the complexity of administrative tasks did 

not warrant advanced AI tools. Instead, the company prioritized maintaining efficiency 

through conventional methods, allowing them to direct their resources toward 

innovative product development. 

On the other hand, Company 1 integrated AI into its product lines but limited its 

application within its internal processes. It focused more on basic automation, which 

sufficed for the simplicity of its operational needs. By doing so, the company aimed to 

improve efficiency without getting into the complexities of advanced AI systems.  

 
 

4.3 Customer centricity vs market approach 

The distinction between customer-centric and market-responsive approaches has 

become increasingly significant in determining how companies thrive. The contrast 

among these three companies underscored a crucial point: a truly customer-centric 

approach was not just about being reactive or focusing solely on innovation. Instead, it 

required a delicate balance of understanding customer needs while remaining 

responsive to market movements. Company 2 showed this ideal, but Companies 1 and 

3 showcased the challenges that could emerge when businesses prioritize one aspect 

over the other. 

Company 3 exemplified a focus on innovation readiness and operational productivity, it 

placed a strong emphasis on improving efficiency and exploring new technologies. 

However, this ambition came at a cost; the company did not cultivate an explicit 

customer-centric or market-responsive approach. While it aimed to innovate, it tended 

to prioritize internal metrics for productivity over direct engagement with customer 

needs.  

 



 
 

 
 

49 

In contrast, Company 2 clearly stood out as a customer-centered firm. Its business model 

hinged on aligning its AI applications directly with customer requests and shifting market 

trends. By continuously adapting its offerings based on real-time feedback and insights, 

the company reflected a flexible, market-responsive strategy that resonated deeply with 

the evolving demands of its customers. This proactive stance enabled the company not 

only to enhance its service offerings but also to foster loyalty among its clientele. This 

ensured that they remained engaged and satisfied. 

On the other hand, Company 1 navigated a middle ground. While it could be described 

as customer-driven, its strategy was predominantly reactive. The company focused on 

keeping an eye on technological advancements and responding as issues arise, rather 

than proactively innovating solutions to anticipate customer needs. This approach, while 

practical, could lead to missed opportunities and delays in meeting customer 

expectations. Furthermore, the company placed a strong emphasis on data privacy, 

recognizing that maintaining customer trust was paramount in achieving a loyal 

customer base. The prioritization of transparency and ethical data handling practices 

helped it build a foundation of trust. 

 

 

4.4 Use of supporting technologies 

In today’s competitive business environment, the use of supporting technologies such as 

cloud computing and data privacy measures is pivotal for companies aiming to enhance 

their operations and service delivery.  

The contrasting approaches among the interviewed companies highlighted the essential 

role of supporting technologies in today’s business environment. Company 1’s success in 

using cloud services alongside robust data privacy measures demonstrated a proactive 

stance that other companies might consider adopting. The way each company integrated 

these supporting technologies into its strategy was crucial to shape its future growth and 

market competitiveness. Companies that embraced a comprehensive technology 



 
 

 
 

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strategy, including cloud computing and data privacy, are better positioned to adapt to 

changing market demands while enhancing customer satisfaction and trust. 

Company 3 seemed to be at a critical time in its development, as it planned to leverage 

new technologies to improve its services. It sought governmental funding to support this 

initiative while also focusing on building internal talent. However, it remained unclear 

whether the company actively utilized specific technologies, such as cloud computing, 

which could facilitate a smoother transition to digital solutions. This uncertainty left a 

gap in their technological strategy, as the lack of concrete tools could hinder their 

progress. 

On the other hand, Company 2 was effectively using AI as the core driver of its product 

offerings. This company’s focus on AI allowed it to innovate and adapt to market 

demands efficiently. However, notably absent from their strategy was any mention of 

supplementary technologies, such as cloud computing or other privacy measures that 

could enhance its operational framework. This lack of detail suggested that while the 

company was on the right path with AI, it might not be fully exploiting potential 

technological advancements that could further strengthen its position. 

In contrast, Company 1 stood out for its heavy reliance on cloud computing to support 

its service delivery. The utilization of cloud technology enabled this company to improve 

flexibility and optimize cost efficiency. Additionally, the company placed a strong 

emphasis on data privacy through a strict policy designed to protect customer data. This 

commitment to data security not only fostered trust among customers but also helped 

the company comply with regulatory requirements regarding data protection. 

 

 

 

 

 



 
 

 
 

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  4.5 Recommendations based on expert interview 

An important, friendly interview took place with an expert on the mathematical 

approach of AI. She had published a few papers on the application of AI. She worked in 

a company specializing in medical imaging and constantly reviews new technological 

advancements in several fields. In her most recent publication, she emphasized areas 

where AI can be leveraged to drive digital servitization in a Finnish SME. 

According to her: 

Data privacy in healthcare is a multifaceted issue, especially under stringent regulations 

like the General Data Protection Regulation (GDPR) in Europe. The GDPR ensures that all 

personal data is handled with the highest level of security and privacy, which is 

particularly crucial in healthcare due to the sensitivity of medical records. These 

regulations apply to various settings, such as preventive care, emergency services, and 

home care. That creates a comprehensive protective umbrella over patients' data.  

However, the complexity of healthcare data, which includes both patients with existing 

conditions and healthy individuals participating in preventive measures, presents unique 

challenges. Each data point, regardless of its origin, must be precisely safeguarded, 

necessitating advanced data management systems capable of adhering to GDPR 

standards while ensuring seamless healthcare delivery. 

One significant obstacle in the healthcare industry is the reluctance of hospitals and 

healthcare institutions to share data. This hesitancy stems from concerns about patient 

privacy, data security, and potential misuse of information. As a result, collaborative 

efforts that could enhance patient care and drive medical innovation are often 

interrupted. The introduction of collaborative learning systems, designed to operate 

independently and safeguard against external breaches, offers a potential solution. 

These systems can manage and analyze medication data and other critical health 

information without exposing it to outside entities. Nevertheless, maintaining the 

balance between data utility and privacy remains a delicate act. That is why ensuring 

large-scale data anonymization is critical to protect patient identities.    



 
 

 
 

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Thus, permitting the possession of comprehensive health studies that can lead to 

breakthroughs in medical research and treatment strategies. 

The integration of AI into healthcare further underscores the importance of effective 

data management and communication between specialties. Diseases like diabetes, 

which can cause a multitude of complications affecting various parts of the body, 

highlight the need for interdisciplinary collaboration. However, current practices often 

see a lack of communication between different healthcare providers, that alone leads to 

fragmented care. AI can bridge these gaps by synthesizing data from diverse medical 

fields, providing a holistic view of the patient's health. This technology can assist doctors 

in understanding the interplay between different conditions. Hence, the