The article substantiates the need to develop a flexible and context-sensitive methodology for assessing the digital maturity of business process transformation using information and communication technologies (ICT). A critical analysis of current digital maturity models (such as DCF, DMI, CMMI) has revealed their limited adaptability to different types of enterprises. Based on conceptual modelling of digital technologies and expert validation, a multi-stage methodology is proposed and tested on three types of enterprises: a large industrial enterprise, a medium-sized IT company, and a small serviceoriented logistics business. The assessment was carried out using the Digital Maturity Model (DMM), which encompasses five key domains: analytics, artificial intelligence, cybersecurity, process automation, and the strategic integration of digital initiatives. The results demonstrated significant differences among the enterprises, confirming both the sensitivity and versatility of the model. The article concludes with recommendations for further development of the model and the creation of digital tools for systematic monitoring of digital transformation.
Digital transformation is viewed as a process of fundamentally reshaping traditional business
models
and
operations through
digital technologies, aimed
at increasing
the
efficiency,
competitiveness, and adaptability of enterprises to changing market conditions [
It is important to emphasize that digital transformation is not merely the implementation of
modern ICT but a dynamic process of profound organizational change that encompasses all
business processes of an enterprise [
Researchers note that the adoption of automation, cloud services, big data analytics, artificial intelligence, and the Internet of Things (IoT) impacts key aspects of operations – productivity, innovation, flexibility, responsiveness, decision-making efficiency, and competitive advantages. Thus, ICT acts as a catalyst for improving business processes: digital tools make it possible to reengineer processes, eliminate inefficiencies, make data-driven decisions, and quickly adapt to customer needs.
One of the key aspects of digital transformation is data collection and analysis–digital technologies enable the processing of vast volumes of information about consumers, markets, and operations, which deepens analytics and enhances the quality of management decisions. As a result, enterprises with a high level of digital maturity establish new digital processes, interaction models, and products that ensure resilience and success in the market.
Digital maturity reflects the extent to which an enterprise has adopted and integrated digital technologies into all aspects of its activities and essentially serves as an indicator of the «state» of digital transformation within the organization.
Digital transformation of business processes is one of the key trends in the modern development of enterprises, enabling not only increased productivity and improved management quality but also long-term competitiveness. ICT serves as the foundation for transformational change, integrating into all functional subsystems of the enterprise – from production to strategic management.
However, the effectiveness of implementing digital solutions directly depends on an enterprise's ability to assess its digital development: determining its current level of digital maturity, identifying critical gaps in transformation, and forming a well-founded strategy for change. Technologies such as cloud computing, artificial intelligence, IoT, mobile applications, and automation tools can significantly enhance the flexibility, scalability, and productivity of business processes.
Despite the substantial body of research on digital transformation and information technologies, there remain several important unresolved issues in academic discourse that determine the relevance of this study (Table 1).
Thus, there is a clear need within the academic space to develop an applied, flexible, and context-sensitive methodology for assessing the digital transformation of enterprises based on ICT. This methodology should consider the dynamics of change, security risks, institutional constraints, and the specific characteristics of the Ukrainian business environment. The proposed study aims to address these challenges by creating a scientifically grounded foundation for monitoring and managing digital changes within enterprises.
In academic literature, digital maturity is defined as the level of completeness and readiness of an
organization to achieve a desired future state in the context of digitalization [
indicators that integrate both “hard” and “soft” factors
approaches is required for a comprehensive assessment
Most digital maturity concepts envision the step-by-step development of an enterprise from an
initial (low) level to a high one. A classic example is the CMMI (Capability Maturity Model
Integration), which outlines five sequential levels:
̶ initial (characterized by ad hoc, unstructured processes);
̶
̶
̶
̶
managed (reactive process management).
defined (proactive and standardized processes);
quantitatively managed (data-driven management using metrics);
optimizing (continuous process improvement) [
This logic is also evident in other digital maturity models: from basic digital capability levels to
the highest level, where digital technologies are fully integrated into the enterprise’s strategy,
structure, and culture, enabling continuous innovation. Modern economic trends underscore the
need for the algorithmization of business processes to enable further improvement through the
application of artificial intelligence [
The need to assess digital maturity stems from the importance for enterprises to understand
their starting point and measure their progress in digital transformation. As researchers note,
assessment tools «provide the necessary framework for systematic analysis» of a business’s digital
state and the development of an effective action plan [
The use of formalized methodologies enables measurement across multiple indicators (key
digital development areas) and helps identify gaps between the current level of the enterprise and
industry best practices [
development of the technological infrastructure; ̶ level of process automation and optimization; use of data and analytics for decision-making; digital skills of personnel and an innovation-driven culture; ̶ focus on customer digital experience, etc.
A comprehensive assessment across these criteria provides the enterprise with a clear reflection
of its digital development, highlighting strengths and weaknesses in the digital domain and
enabling the formulation of a transformation roadmap. Therefore, regular digital maturity
assessments are essential for tracking transformation progress, adjusting strategies in a timely
manner, and ensuring sustainable development in the digital era [
In recent years, numerous models and frameworks have been developed and adapted for
assessing digital maturity, both globally and within Ukraine. These models act as normative
frameworks (reference models) designed to comprehensively assess an enterprise’s current digital
development state across various dimensions and levels. They make it possible to measure and
analyze a company’s existing capabilities in areas such as technology, processes, structure and
culture, workforce competencies, and management practices [
These models serve as digital development roadmaps: in addition to diagnostics, they suggest the next steps needed to achieve higher maturity levels (i.e., what specifically needs to be improved). There are both universal models applicable to most industries and sector-specific or specialized approaches. For instance, major consulting and IT firms offer their own frameworks (MIT CISR, Gartner, Deloitte, McKinsey, KPMG, etc.), while in Europe, maturity assessment methods have been integrated with digital innovation hub initiatives.
Below, we will examine several of the most well-known enterprise digital maturity assessment models and their key characteristics (Table 2).
Thus, assessing the level of digital maturity using modern models is a critically important tool
for managing digital transformation within an enterprise [
First, it allows organizations to identify the readiness of their business processes for digitalization and to pinpoint weak areas that require attention.
Second, the use of maturity models establishes a shared «language» between management and IT professionals when discussing digital strategy, ensuring alignment in the vision for development.
Third, the assessment results serve as a starting point for developing a digital transformation
roadmap [
Brief Description TCroannnsefoctrimonattioonDGigoitaalsl Assesses digital strategy, Helps identify technological gaps infrastructure, analytics, and shape strategic directions for and culture change 9Q0ucarnitteitraitai,v6e mraantkuirnitgyalcervoeslss Supseptodrteismvbepelornopcvmhemeenaftfrikpcirineiognrcaiytniedshteolps Focuses on business process optimization and change management
company’s digital state and helps build a roadmap for development
Ultimately, improving a company’s digital maturity is a continuous process, and regular reassessment using a chosen model allows for monitoring the dynamics of change and fostering a culture of continuous improvement in the enterprise’s digital development.
The aim of this study is to justify the need for and to develop a context-sensitive methodology for assessing the digital transformation of enterprise business processes. The proposed methodology is designed to account for the specific characteristics of the business environment, including economic instability, limited resources, and information security risks, while also integrating both «hard» (technological) and «soft» (managerial and organizational) aspects of digital maturity.
The objectives of the study are as follows: ̶ to systematize existing approaches to assessing digital transformation.
̶ to identify key gaps in current models, particularly regarding their adaptability to Ukrainian realities.
̶ to develop a practical assessment toolkit that enables enterprises to track the dynamics of digital changes over time.
̶ to create ICT-based analytical solutions to support informed decision-making in the area of digital strategy.
The relevance of this research is driven by the growing need for Ukrainian enterprises to understand their level of digital maturity, optimize transformation strategies, and ensure long-term competitiveness through the effective use of digital technologies.
The methodology for developing a model to assess the digital maturity (Fig.1) of enterprises
consisted of several stages, combining literature analysis, conceptual modeling, expert
involvement, and empirical validation (Table 3). This approach aligns with the commonly accepted
phases of maturity model development (scope definition, design, population, testing,
implementation) [
Key areas – such as Artificial Intelligence, Cloud Computing, Analytics, Safety, Edge Computing, and Monitoring – are highlighted as prominent nodes, while the lines represent informational or functional interrelationships between the technologies. Source: Authors’ development.
To assess the digital maturity of a large industrial enterprise, a medium-sized IT company, and a
small logistics business, the Digital Maturity Model (DMM) – developed by the TM Forum
consortium in collaboration with Deloitte – was used [
The assessment results for the selected enterprises are presented in Table 4.
The model evaluates digital maturity across key domains, including data analytics, the use of artificial intelligence (AI), cybersecurity, process automation, and the integration of digital strategy into management. Each enterprise was analyzed based on these criteria using expert evaluations and case analysis, which helped identify the strengths and weaknesses of their digital transformation efforts. 1. Analysis of existing models
2. Conceptual Modeling
weighting factors were defined; a questionnaire, assessment
profiles, and methodological guidelines were created.
As shown in Fig. 2, the IT company has the most balanced and highest digital maturity profile, the industrial enterprise displays a medium-level profile with strengths in security and automation, while the small business lags behind in nearly all indicators.
The model evaluates digital maturity across key domains, including data analytics, the use of artificial intelligence (AI), cybersecurity, process automation, and the integration of digital strategy into management. Each enterprise was analyzed based on these criteria using expert evaluations and case analysis, which helped identify the strengths and weaknesses of their digital transformation efforts.
The graphical profile confirms the results described above: the curve for the large industrial enterprise (blue area) extends moderately along the «Security» and «Automation» axes but is less pronounced on the «Analytics» and «AI» axes. In contrast, the medium-sized IT company (green area) demonstrates nearly maximum values in «Analytics» and «AI», along with high values in «Strategy»; its profile is the broadest and most balanced, corresponding to a high level of digital maturity. The profile of the small service enterprise (red area) is compact and does not extend far from the center of the chart: its highest values are around 2 (out of 5) in most domains, reflecting a low maturity level.
This visual analysis clearly differentiates the three enterprises: the IT company outperforms in analytics and AI; the industrial company holds a uniquely strong position in security and automation (likely due to the need to protect production systems and use of robotics); and the small business lags in all areas, especially in AI adoption and strategic digital transformation.
The findings are consistent with expectations based on industry trends: technology firms are
leaders in digital maturity [
An important aspect is the universality of the digital maturity assessment model when applied to different industries and business sizes. The results show that the model’s core domains (analytics, AI, security, automation, strategy, etc.) are relevant to all the enterprises studied, though the degree of development varies. In other words, the model demonstrated the ability to capture key areas of digital transformation in both manufacturing and service/IT sectors. This indicates the model’s broad applicability: its criteria are general enough to be used in diverse contexts.
For example, domains like data analytics and cybersecurity are essential for any modern business, from a factory to a small logistics company – the only difference lies in the scale and complexity of implementation.
At the same time, the pilot test also revealed industry-specific characteristics that the model should account for. For instance, in an industrial enterprise, the automation domain largely refers to industrial technologies (e.g., robotics, IoT in production), whereas for a service business, automation relates more to digitizing office workflows. The model is clustered into functional blocks flexibly enough to accommodate such distinctions, but the interpretation of results requires an understanding of the industry context.
Another example is AI: in manufacturing, its use may be limited to specific applications (e.g., defect detection on a conveyor), while in an IT company, AI may be the core of the product. The model evaluates the overall level of AI adoption, but an analyst must understand that a low AI score in manufacturing doesn't necessarily indicate underperformance if the technology is still emerging in that sector.
Thus, the model is largely universal but sensitive to industry-specific nuances. When comparing
different enterprises, one must consider the characteristics of their respective industries. Literature
notes that some digital maturity models are tailored to specific sectors to better reflect their unique
needs [
Our model, however, produced valid results across three different sectors without the need for significant modification, which indicates its wide applicability. Nevertheless, there is potential for future enhancement or adaptation of the model – for example, adding specific subdomains such as «Industry 4.0» for manufacturing or «Customer Experience» for service enterprises could improve assessment accuracy for certain sectors.
Overall, the results of the pilot study confirm that the developed model can serve as a crossindustry tool for assessing digital maturity, provided that results are interpreted with attention to the specific context of each business sector.
Based on the conducted study, it can be concluded that information and communication technologies for assessing digital transformation maturity should be viewed through the lens of the following components: analytics, artificial intelligence, cybersecurity, automation, and strategy. The experiment carried out is promising and may be used as a foundation for further research.
However, the evaluation of the three enterprises is illustrative in nature and does not capture the full diversity of real-world business scenarios. Each enterprise was assessed using expert judgment, which introduces an element of subjectivity. Nevertheless, for the purpose of this study, it was necessary to validate the developed conceptual approach. The model focuses on selected digital technology domains identified through literature and case analysis. However, there is potential for expanding the assessment components that are not fully addressed in the current model. For example, organizational culture and employee readiness for change are indirectly reflected within the strategic domain but are not separately distinguished. In some cases, «soft» factors – such as innovation culture and leadership – can be the decisive elements for the success of digital transformation. The absence of an explicit parameter for these aspects could be a limitation of the model.
Furthermore, the digital environment is dynamic: digital maturity is not a static characteristic. Enterprises evolve rapidly, and the model can be applied at various stages of transition. In the future, automated change tracking could be introduced as a valuable indicator for monitoring maturity levels and enabling proactive interventions. Ongoing refinement of the model may create the foundation for adapting it to evolving enterprise conditions.
The discussion is grounded in conceptual frameworks and experimental findings; the next step should be empirical validation – conducting digital maturity assessments across dozens of enterprises in various sectors and analyzing statistical patterns. This would allow for evaluating whether the identified domains remain equally relevant for different types of enterprises or whether sector-specific variations emerge. Such insights could support the introduction of weighted parameters by industry or the addition of new criteria.
The current limitations of existing maturity models have also been noted by other researchers –
no single model provides universal answers, and some may be overly abstract [
The developed digital maturity model serves as a useful diagnostic tool for evaluating the state of digital transformation in various types of enterprises. The observed differences between the experimental cases confirmed the model’s informativeness. The discussion of results highlighted the model’s flexibility and its ability to accommodate the specific characteristics of different enterprises. Furthermore, it outlined pathways for further development to meet practical business needs and to contribute to a scientific foundation for evaluating digital maturity in the era of digital transformation.
Declaration on Generative AI
The authors have not employed any Generative AI tools.