From Idea to Impact: The Role of Artificial Intelligence in the Transformation of Business Models

AI has become a basic component of the digital corporate landscape. Gentsch (2019) characterises AI as a collection of technologies that maintain human cognitive functions, including perception, learning, and decision-making. Early AI systems had limitations to certain tasks, whereas modern AI functions inside adaptive, data-driven frameworks capable of processing extensive data in real time and delivering insights that surpass human abilities. From a business perspective, AI facilitates the automation of regular tasks, increases sophisticated decision-making frameworks, and investigates predictive modelling for prospective results.

AI has evolved from a supplementary instrument to a disruptive entity capable of transforming entire industries, as contended by Sjödin et al. (2021). In this setting, AI influences both operational procedures and strategic direction as well as organisational identity. Enterprises employing AI technologies are not just enhancing current models but also devising novel frameworks for value generation. This transition is particularly apparent in the emergence of platform-based and digitally native companies, where AI serves as a driver for innovation and competitiveness. Advanced machine learning technologies are progressively being utilised to improve the predictive skills of companies. Vasenska (2024) asserts that these technologies enhance the precision of time series forecasts and assist in the strategic planning of businesses in dynamic and seasonal industries. This demonstrates the capacity of machine learning to provide a competitive advantage to creative businesses, especially those applying AI in data-centric sectors, by enhancing decision-making and adapting business models to variable market requirements. The incorporation of AI, digital twins, and the Internet of Robotic Things (IoRT) in the context of Industry 4.0 enables new forms of predictive maintenance and intelligent automation. These technologies enable manufacturing systems to autonomously optimise operations, predict equipment failures before they occur, and continually analyse real-time data, as explained by Nagy et al. (2025). Such capabilities provide a foundation for scalable, sustainable, and future-proof business models, particularly for innovative companies that are entering the smart manufacturing or industrial AI markets. Additionally, they provide efficiency benefits. Startups can enhance their resilience to technological disruptions and market fluctuations by integrating AI into their core production and monitoring processes, thereby facilitating more rapid innovation cycles.

Startups represent a unique organisational form with their own agility, adaptability, and innovation potential. Sestino and De Mauro (2021) claim that startups leveraging AI can optimise internal processes, deliver customised services, and markedly decrease time to market. Hartmann et al. (2016) observe that the distinction among AI businesses is not isolated in their technology focus but also in their strategic ambition. Several companies are structured using artificial intelligence as the foundation of their business model, indicating that their value proposition, client interaction, and commercialisation methods are predicated on data-driven concepts. AI-driven startups, as noted by Tang et al. (2025), can quickly adjust to fluctuating market situations, leverage global networks via digital platforms, and explore innovative value configurations that would be excessively risky or intricate for larger corporations. Sjödin et al. (2021) promote this perspective by asserting that startups facilitate both technological spread and social innovation. Startups are progressively addressing difficulties in education, healthcare, and environmental sustainability through the application of AI. These initiatives highlight the dual function of startups as businesses and catalysts for social transformation.

In the field of innovative businesses using AI, it is imperative to evaluate not just technological innovation and profitability but also the ethical and sustainability dimensions of corporate operations. Metzker (2024) highlights that small and medium-sized organisations frequently lack the organised ability for corporate social responsibility in comparison to bigger corporations. Nonetheless, their reaction to environmental, economic, and ethical concerns is frequently influenced by the demographic background and moral orientation of their leaders. For AI-driven companies, this necessitates the incorporation of ethical governance and stakeholder-centric innovation from the first phases of company creation.

The economic performance of SMEs, particularly in the manufacturing sector, is significantly influenced by the regional development context. As Svobodova et al. (2024) indicate, public investment in research and innovation is a critical determinant of regional disparities and an enabler of company-level performance. This discovery underscores the significance of integrating into regional networks and innovation-orientated ecosystems for companies that are founded on artificial intelligence. The value-creation potential of these businesses may be substantially increased by access to regions' innovation policy instruments, R&D funding, and knowledge infrastructure, as discussed by Wang et al. (2024). Consequently, the spatial and policy environment in which AI-driven businesses operate must be regarded as a critical component of their strategic development and long-term sustainability.

Recent market data reveals a growing disparity between the perceived value of AI-driven and non-AI-driven startups. As described by Mohammadi and Shafiee (2022) and Fu et al. (2022), startup financing, valuation stages are typically categorised as seed (early development and market entry), Series A (initial scaling and team expansion), and Series B (broader market penetration and operational growth). According to CB Insights (2024), the median valuation of AI startups (Figure 1) significantly exceeded that of non-AI startups across all funding stages in 2023. At the seed stage, AI startups reached a median valuation of $18.6 million, compared to $15.4 million for non-AI startups. This valuation gap widened notably in later stages, with Series A AI startups valued at $69.7 million, compared to $50.2 million for non-AI startups. Most significantly, the median valuation of Series B AI startups increased to $188.8 million, compared to $118.6 million for non-AI startups. This upward trend suggests strong investor confidence in the scalability, adaptability and innovation potential of AI-based business models.

Figure 1.

Median valuations of AI and non-AI startups worldwide by funding stage in 2023

Source: own processing based on CB Insights (2024)

The consistently higher valuations of AI startups signal a structural market preference driven by the transformative capabilities of artificial intelligence. Investors are increasingly acknowledging AI as a strategic asset that is capable of providing real-time insights, automating complex processes, and supporting dynamic adaptation to market changes, attributes that are particularly important for high-growth companies. Moreover, the dynamic growth in valuations between the Series A and Series B phases reflects the expectation of considerable returns on investment as AI technologies mature and scale. These findings are also consistent with broader theoretical perspectives that position AI as a general-purpose technology with profound implications for business innovation, competitive differentiation, and long-term resilience. The current valuation momentum thus serves not only as a financial indicator but also as empirical support for the central role of AI in shaping the future of business ecosystems.

The integration of AI across many sectors has resulted in the development of numerous businesses. The use of AI in healthcare is utilised for diagnostic assistance, patient data administration, and predictive analytics. Jan et al. (2022) assert that clinical decision support systems (CDSS) employing AI algorithms can markedly enhance diagnostic accuracy and refine treatment protocols. The prospective influence of AI in this industry is substantial; nevertheless, it also engenders regulatory and ethical dilemmas, particularly with data privacy and the transparency of algorithmic outcomes.

In the banking sector, AI enhances fraud detection, customer segmentation, automated underwriting, and chatbot-driven customer services. According to Piotrowski and Orzeszko (2023), these technologies enable startups to compete with established financial institutions by providing expedited, precise, and personalised financial solutions. AI is a highly transformative technology, and its implementation in corporate financial management could fundamentally alter conventional paradigms. As noted by Balcerzak and Valaskova (2024) and by Dabija and Vătămănescu (2023), the implementation of AI to enhance the financial efficiency of enterprises is not merely a matter of technological advancement but also of the strategic adaptation of organisations to the emerging challenges of the global economy. The present advancements in this domain, as discussed by Zafar (2024), underscore numerous applications of AI, encompassing financial forecasting, administrative task automation, decision-making optimisation, and risk management. Simultaneously, the implementation of AI engenders novel inquiries concerning technological intricacy, data integrity, ethics, and regulation.

Education, industry, and agriculture are increasingly becoming promising sectors for AI-driven enterprises. AI applications in industry facilitate predictive maintenance, quality control, and resource optimisation, whilst in agriculture, they promote precision farming, crop health monitoring, and supply chain management (Soussi et al., 2024). These examples illustrate that AI may markedly enhance efficiency and sustainability across various sectors while generating chances for novel business models.

AI falls under the broader category of digital technologies, including blockchain and the Internet of Things (Balcerzak et al., 2022). It includes comparable characteristics that relate to the fundamentals of technology; however, it also exhibits unique features that elucidate the sustained interest the technology has received. AI's fundamental contribution to business is based on its capacity to transform business paradigms. Traditionally, business models define how organisations generate, provide, and secure value. The emergence of AI is redefining these aspects. AI is transforming customer relationships by facilitating personalised experiences derived from behavioural forecasts, as shown by Libai et al. (2020). It is revolutionising operations via automation and strategic resource allocation. Nowadays, AI is a driving force behind innovation and a shift in corporate strategy rather than a far-off possibility. According to Dabija and Vătămănescu (2023), AI technologies are now important tools for improving decision-making, boosting organisational flexibility, and rearranging value generation procedures. This implies that using AI early on may provide creative companies a competitive edge, build resilience, and create new opportunities for business model innovation. Furthermore, in highly volatile markets, the proactive and strategic application of AI promotes long-term sustainability and distinction, as demonstrated by Carayannis et al. (2025).

The study employs a qualitative comparative design to investigate how artificial intelligence affects the structural configuration of business models in startups, compared to traditional companies. The primary objective is to examine and contrast the components of the Business Model Canvas across two types of companies in three strategically relevant industries: banking, automotive, and retail. These industries were selected based on their digital maturity, innovation dynamics, and the presence of both AI-orientated and traditional business strategies. In each industry, two companies were selected to form a case pair. The sample includes one AI-driven startup and one traditional company per industry: Revolut and Barclays in banking, Tesla and Ford in automotive, and Zalando and H&M in retail. These companies were selected according to the accessibility of relevant data, as well as a deliberate effort to achieve geographic balance. Both banks operate in the UK, the automotive companies are based in the US, and the retailers reflect the European business environment with headquarters in Germany and Sweden. This sampling strategy enables meaningful comparisons while maintaining industry-specific contextual coherence across the case pairs.

The primary analytical framework used in this research is the Business Model Canvas, developed by Osterwalder and Pigneur (Stork et al., 2023). The BMC is a strategic management tool that outlines nine interdependent components through which a business creates, delivers, and captures value. The framework provides a comprehensive and visual structure that is widely used in both academia and practice for analysing business model logic and configuration (Macha-Huamán et al., 2023). This research employs BMC to delineate and contrast the business models of the selected companies. It serves not only as a descriptive tool but also as a comparative instrument for assessing the impact of artificial intelligence on strategic priorities and operational structures. Data for constructing the BMCs were sourced from publicly accessible documents, including annual reports, official websites, blog content, investor materials, and relevant case studies. For each company, the collected data were thematically categorised according to the nine BMC blocks, with a particular focus on identifying where and how artificial intelligence played a role in shaping business design.

In order to clarify the framework used for analysis, the following outlines the nine key components of the Business Model Canvas that structure our comparison: - Customer segments:

Identifies the specific segments of individuals or organisations that the company aims to serve. Effective segmentation allows the company to customise value propositions and marketing strategies to meet the distinct requirements of each target demographic (Jin et al., 2021).

The core analytical approach of this study is a qualitative comparative analysis, conducted within industry-specific case pairs. Each pair, including one AI-driven startup and one traditional company, was examined independently using the BMC framework. Subsequently, the two models were analysed to discern differences in business model design, specifically the role of artificial intelligence in fostering innovation, value creation, or strategic differentiation. The comparative method preserves industry-specific characteristics while enabling the investigation to uncover cross-sectoral patterns in the application of artificial intelligence. Qualitative analysis assists in contextual insights and acknowledges the distinct trajectories of each company, particularly in their engagement with technological development. Rather than aiming for generalisations across the broader business population, this design enables a focused and in-depth understanding of the evolving relationship between artificial intelligence and business model design.

Barclays' business model shown in Table 1 represents a symbiosis of traditional banking and adaptation to digital transformation. Customer segments are stratified in order to address the full range of financial requirements across individual clients, small and medium-sized enterprises, large corporations, and government institutions. A diversified client portfolio of this kind requires a comprehensive and flexible value proposition that combines a focus on trust and investment services with digital solutions and global reach. The strategic objective is to cater to conservative clientele through the provision of stable banking services, whilst simultaneously attracting more dynamic segments through the utilisation of ESG investing and contemporary digital products. The value proposition is thus not predicated on one-sided specialisation; rather, it is founded on the capacity to address the multifaceted expectations and life circumstances of clients. This enhances the model's resilience to market fluctuations.

Distribution channels, which include both physical and digital presence, are designed as a multi-layered communication bridge between the customer and the bank. Traditional branches and ATMs provide support for clients who value personal contact, while digital platforms ensure real-time service availability (Table 1).

Telephone and video banking complement this combination with channels that combine a personal approach with flexibility. This system builds on a strategy of building long-term relationships with customers, combining personal care with digital efficiency. Customer relationships are built not only as a business necessity but also as a tool to strengthen brand reputation through transparency, financial education, and a focus on consistency in interactions.

Barclays' revenue streams affirm its focus on stability and innovation. In addition to interest and fees from accounts, which form the core income, the bank is developing revenues from premium services, asset management and fees for services provided to institutions. This model deliberately builds on revenue diversification, which reduces dependence on individual segments and enhances overall financial sustainability. Key resources such as capital reserves, human capital and technological infrastructure provide the operational foundation for delivering high value-added services. Relationships with regulatory authorities, technology partners and investors also support the smooth functioning and adaptability of the bank in an increasingly complex environment. It is evident that these strategic elements are also reflected in the cost structure. Indeed, in line with standard expenses, investments in digital transformation, compliance and human capital development dominate, ensuring the bank's long-term competitiveness.

Revolut's business model displayed in Table 2 is a dynamic response to the changing needs of retail customers in the digital age. Their services are aimed at a wide range of clients, from everyday users to digital nomads, freelancers, entrepreneurs, and global companies. Revolut builds its value proposition on intuitive services and the removal of traditional banking barriers, appealing primarily to technology-savvy and younger users. Personalised spending reports and analytical tools using artificial intelligence, along with the ability to hold multiple currencies, trade cryptocurrencies, and purchase flexible insurance, create an ecosystem focused on customer autonomy and financial confidence. Another key element is gamification within the RevPoints loyalty system, which strengthens loyalty and increases interaction with the platform. That strategy is not happening in isolation but is based on deep knowledge of customer behaviour, which is rooted in algorithmic evaluation of their needs.

The distribution channels have been designed to function as a fully digital environment, with web and mobile platforms playing a primary role. These channels are reinforced by referral systems, AI push notifications, and integration with fintech entities. Another significant component pertains to the capacity to establish API connections for corporate clients and e-commerce partnerships. This capability enables Revolut not only to augment its operational scope but also to assimilate into the ecosystem of other services. This technological foundation serves as the cornerstone of customer relationships, which are established through automated and personalised real-time communication. The utilisation of chatbots, peer-to-peer support, and the continuous testing of novel channels serve as evidence of the perpetual evolution of UX (User Experience) within this model. It is noteworthy that UX is not merely an ancillary component in this paradigm; rather, it is a pivotal element in ensuring customer satisfaction. Furthermore, the integration of gamified elements has been demonstrated to effectively transition passive customers into active participants within the ecosystem, thereby unveiling novel prospects for cross-selling and brand fortification.

The company derives its revenue through a diversified portfolio, with interest income and fees from standard and premium accounts constituting the majority of this revenue. In addition, the company generates revenue from cryptocurrency trading, currency conversion, insurance, and premium services. The absence of traditional branches in this model is compensated for by reliance on technological infrastructure and agile development teams, which enable scaling without being tied to physical resources (Table 2).

Key activities such as the development of digital banking products, ensuring regulatory compliance, and building fintech partnerships are complemented by the intensive integration of new technologies and the localisation of services on a global scale. These activities are supported by a strong brand among the younger generation and flexibility in licensing, which reduce expansion costs. Operating expenses are focused on IT development, marketing, security solutions, and digital infrastructure, demonstrating a strategic focus on growth potential and innovation. The overall architecture of the business model thus reflects an effort to redefine banking towards a decentralised, customer-centric, and technology-driven approach.

A comparison of the business models employed by Barclays and Revolut reveals fundamental differences in their approaches to value creation, service distribution, and customer relationship building. Barclays, as a representative of traditional banking, has a diversified client base, covering a wide range of segments from individual clients to large corporations and government institutions. The company's services are founded on principles of trust, stability, and a personalised approach while concurrently acknowledging the advent of the digital age by extending its services to encompass digital platforms and ESG products. Conversely, Revolut leverages the digital-native nature of its user base, catering primarily to tech-savvy retail customers, digital nomads, and small businesses. The value proposition of Revolut is predicated on automation, speed and personalisation, thereby enabling dynamic and adaptable user experiences without reliance on physical infrastructure.

The distribution logic of these two models highlights the contrast between Barclays' traditional multi-channel presence and Revolut's fully digital architecture. While Barclays operates through branches, ATMs, digital interfaces, and partner networks, Revolut concentrates its customer contact exclusively in the digital space while investing in interactive features such as push notifications, chatbots, and API integrations. This focus on technology solutions is also reflected in relationship building. Barclays continues to emphasise relationship managers and personal contact as a tool for building trust, while Revolut relies on personalised UX interfaces, gamification, and peer-to-peer support. In this way, Revolut creates a community around the brand, while Barclays focuses on maintaining trust as the foundation for long-term relationships.

In terms of revenue and cost architecture, Barclays clearly benefits from a diversified revenue portfolio, including interest, fees, and complex services for institutions, while Revolut relies on innovations such as cryptocurrency services, premium accounts, and FX conversion revenues. The fundamental difference lies in the cost structure. Barclays manages a cost-intensive physical infrastructure and workforce, while Revolut minimises fixed expenses thanks to its digital environment, increasing its flexibility for expansion. The differences are also evident in key activities. A traditional bank emphasises compliance, risk management, and relationship management, while Revolut focuses on rapid scaling, UX solution development, and service automation. For Revolut, AI and algorithms are not just operational tools but a fundamental part of a strategic architecture that is redefining the concept of banking as a dynamic platform. From the perspective of sector transformation, Revolut represents the advent of so-called post-physical banking, where value chains are concentrated in digital interfaces and customers become active co-creators of services.

Ford's business model illustrated in Table 3 is characterised by a multifaceted strategy that is both comprehensive and layered. This approach is founded upon historical continuity, global reach, and diversification of product and customer lines. Ford caters to a diverse range of segments, from individual customers to corporate fleets and government agencies, thereby establishing a network of interconnected needs and market expectations. The value proposition is tailored to this spectrum and is based on brand trust, a robust portfolio of powertrains, including combustion, hybrid and electric vehicles, and the provision of integrated mobility solutions. When considered in conjunction with Ford Credit financial services, which serve to reduce barriers to market entry and promote sales, this establishes a model that prioritises affordability, functionality and the cultivation of long-term customer relationships. Flexibility in powertrain choice and focus on safety and performance are also means of serving price- and-value-differentiated segments.

Ford's distribution system is deeply rooted in a traditional sales network consisting of a globally ramified network of authorised dealers, independent dealers and specialised distributors for specific segments. This network is complemented by digital configurators, online reservations and tools such as FordPass, which serve to integrate the customer experience into the digital environment without disrupting the traditional sales framework. Customer relationships are built on the basis of constant service availability, digital support and loyalty programmes, without neglecting personal contact through service advisors and dealers. Feedback and long-term customer relationships create a stable foundation for improving the offering and promoting loyalty.

Ford's revenue structure is diversified across the sale of different vehicle classes, after-sales services, spare parts, financial products, and digital services. An important component is the focus on international markets and specific segments such as SUVs and commercial vehicles. Profitability is supported by a combination of licensing and franchising agreements and contracts with government and institutional partners. Key resources are based on manufacturing infrastructure, long-standing brand reputation, human capital, and research and development capabilities. Moreover, the Ford Credit finance division offers an internal instrument for sales management and cash flow support, enhancing the company's autonomy and responsiveness.

Ford's cost structure reflects the traditional nature of the automotive industry, where production, logistics, marketing and service expenses dominate. Given the strong dependence on a complex supply chain, it is necessary to manage extensive networks of partners, which affects both costs and flexibility. Research and development spending is focused on improving the efficiency of combustion engines and developing electrified vehicles. The increasing costs of compliance and ESG reporting are indicative of endeavours to align the conventional manufacturing paradigm with prevailing regulatory and social responsibility mandates. Nevertheless, this transition has not yet resulted in the disruption of the conservative logic of the business model, which continues to rely on physical presence, personal relationships, and institutional partnerships.

Tesla's business model (Table 4) signifies a pioneering approach to the automotive industry, fundamentally disrupting traditional manufacturing and distribution paradigms. The core feature of the company is its focus on customers who are technologically proficient and who expect innovation, environmental sustainability, and a user experience that is tailored to their specific needs. The value proposition is predicated on the concept of electromobility as a platform in which the vehicle is not merely a product but also a carrier of software and service solutions. This philosophy is embodied in products with sophisticated design, high performance, and the possibility of continuous software improvement. Tesla does not seek to cover the entire market but targets segments with a high degree of technological affinity, building a community around the brand that perceives the vehicle as an identity and lifestyle.

The distribution model is based on the complete elimination of intermediaries and maximising direct control over the customer experience. Tesla is replacing the traditional dealer network with its own sales centres and online sales, which allows it to reduce costs, standardise processes, and intensively collect data on customer preferences. A mobile service fleet and digital communication via an app ensure seamless after-sales service. This direct contact enables constant feedback and active UX shaping. The customer relationship is built on trust in a system that independently monitors the technical condition of the vehicle, alerts to potential problems, and updates software. Tesla is thus redefining the concept of after-sales services towards predictive and autonomous care.

Tesla's financial architecture is characterised by diversification and several innovative pillars. In addition to conventional vehicle sales, the company derives a substantial proportion of its revenue from software features, autonomous driving subscriptions and the Supercharger network infrastructure. The sale of energy solutions such as Powerwall and Megapack also generates added value, thus positioning Tesla as an integrator of mobility and energy.

The sale of emission credits and the subscription system are transforming the business model from one-off transactions to recurring revenues. The company's key resources are based on vertically integrated manufacturing capabilities, in-house software and AI system development, and a brand that is strongly positioned in the minds of the younger generation. The strong community around the brand also acts as an informal marketing channel, minimising Tesla's advertising expenses.

Tesla's operating logic is based on the synergy of technology, scalability, and independence. Battery development, its own operating system, and the ability to perform vehicle updates without physical presence are elements that enable the company to grow without the traditional supply chain burden. The cost structure reflects investments in research and development, the construction of Gigafactories, and infrastructure development. Partnerships with technology suppliers and universities strengthen the company's innovation potential, while strategic alliances in the fields of energy and smart cities open up new market opportunities. Tesla is not only competing within the automotive sector but is transforming it by redefining the product, distribution and customer experience as part of a single digital ecosystem.

A comparative analysis of the business models of Ford and Tesla reveals fundamental discrepancies in their value orientations, distribution structures, and technological ambitions. As a representative of the traditional automotive industry, Ford relies on a diversified portfolio of powertrains, an extensive global dealer network, and an emphasis on reliability and affordability. The model is founded on stability and gradual adaptation, with the transition to electrification occurring concurrently with the maintenance of traditional combustion platforms. In contrast, Tesla operates from a completely different strategic starting point, where electric cars are not just an alternative to vehicles but part of a digital ecosystem connected to home energy storage, software, and cloud infrastructure. Tesla's value proposition is anchored in a concept of continuous innovation, personalised UX, and autonomy, with every customer touchpoint taking place in the digital space without intermediaries.

The distribution models of both companies reflect their historical development and technological strategy. Ford continues to rely on a globally distributed network of dealers and service centres, maintaining a high level of personal contact and flexibility for a wide range of customers. Tesla replaces this approach with direct sales and mobile service, which reduces transaction costs and enables immediate feedback via an app. Customer relationships are thus based on completely different logics: Ford uses a combination of classic care and digital services, while Tesla relies on fully automated diagnostics, OTA updates, and customer participation in the development of autonomous features. This marks a shift from service as a response to failure to service as a prediction of potential failure based on algorithmic detection.

The economic architecture of these companies is indicative of their divergent operating philosophies. The primary revenue sources of Ford are vehicle sales, spare parts, and financing through Ford Credit. The cost structure of the company is significantly influenced by the conventional supply chain. In order to diversify its revenues, Tesla offers digital subscriptions, energy solutions and software features for sale, thereby transforming the vehicle into a service carrier. Concurrently, the elimination of intermediaries and the internal manufacturing of batteries serve to reduce fixed costs. A fundamental distinction emerges with regard to the position of AI at the core of the innovation process: while Ford currently employs AI in complementary functions, for Tesla it constitutes a fundamental building block of strategic differentiation. Consequently, Tesla's operational paradigm deviates from that of a conventional vehicle manufacturer, adopting a technology platform that is redefining mobility in the era of digital transformation.

H&M's business model in Table 5 represents a global retailer's strategy that combines affordability with trendy designs and gradually integrated sustainability. H&M's customer segments are broadly stratified and include all age and income groups, which is supported by the diversity of its brand portfolio and product range. The main pillar of the value proposition is the combination of affordable fashion with a quick response to changing trends, while the brand strives to combine aesthetic inspiration with a responsible approach (e.g., through the Conscious collection), recycling initiatives and collaboration with designers. At the same time, H&M is developing new lines such as H&M Home and H&M Move, expanding its reach into other lifestyle segments.

H&M's distribution strategy is based on a traditional retail network with a global presence, complemented by a robust online channel. This multi-layered approach allows the brand to reach customers who prefer personal contact as well as those who expect digital convenience. While flagship stores serve as experience centres for brand building, mobile apps and digital fitting rooms in stores enhance customer interaction and simplify the decision-making process. Customer relationships are built through personalisation via the H&M Member programme, feedback, and social media presence. Another important element is the ability to combine physical and digital contact, which enables a smooth customer experience and increases loyalty.

The generation of revenue is attributable to sales of clothing, accessories, home furnishings, and limited collections. The brand is increasingly allocating investment to digital platforms and higher-margin segments. Emerging commercial paradigms, such as cross-marketing, incentive schemes, and partnerships with designers, also assume a significant function. H&M has achieved success by leveraging its extensive brand portfolio, its international supply network, and its expertise in the domain of fast fashion.

The principal activities encompass the creation of proprietary collections, proficient supply chain administration, and the promotion of the brands. The cost framework reflects the conventional model, characterised by elevated production, retail operations, and logistics expenditures, which are progressively being augmented by investments in technology, digitalisation, and ecological initiatives. This hybrid strategy enables H&M to preserve its global standing while adapting to evolving consumer expectations in responsible and digital commerce.

Zalando's business model exhibited in Table 6 represents a digitally anchored approach to fashion retail, with a technology platform enabling broad personalisation, scalability, and inclusivity as its core pillars. Zalando's customers are typically digitally savvy consumers who prefer the convenience of online shopping, a high degree of personalisation, and fast logistics. H&M primarily targets a broad demographic group, with an emphasis on affordability.

Conversely, Zalando serves customers looking for a data-driven shopping experience, from algorithmic recommendations to community-orientated content and interaction with influencers. The Zircle platform and B2B (Business to Business) partnerships within the Connected Retail network have been utilised to expand customer segmentation to include environmentally conscious customers.

Zalando's value proposition is predicated upon a meticulously curated assortment of in excess of 5.000 fashion and lifestyle brands, augmented by its proprietary collections and sustainable alternatives. The principal emphasis resides in the creation of a seamless digital experience through the AI Fashion Assistant, sophisticated logistics, and supply chain automation via its proprietary ZEOS platform. The amalgamation of user experience design, personalised marketing, and extensive data capabilities is revolutionising fashion retail from a product-centric paradigm to a platform ecosystem wherein the customer emerges as an active participant in digital commerce. It is this capacity to intertwine technology with the emotive dimension of fashion that constitutes one of the most profound distinctions from traditional retailers.

Zalando's distribution and communication model is based on an omnichannel architecture dominated by the digital channel, where the mobile app integrates AI recommendations, stylistic combinations, and social elements. Physical pick-up and outlet locations complement this, but the main strategic shift is the integration of the platform with partner brands' brick-and-mortar stores through Connected Retail. Interactivity assumes a pivotal function in client relations. Attributes such as desideratum lists, polling, and the Zalando Plus initiative provide not merely exclusive advantages but also significant information for the continued enhancement of the offerings. The comprehensive ecosystem is bolstered by a robust logistical framework that guarantees swift delivery and effective returns processing, as it constitutes a fundamental aspect of online fashion.

In terms of revenue generation, Zalando employs a hybrid model encompassing both B2B and B2C (Business to Consumer) strategies. In addition to the direct sale of fashion products, the company derives income from marketing services (Zalando Partner Marketing Services), logistics capacities for partner brands (Zalando Fulfilment Solutions), and subscription-based services such as Zalando Plus. Furthermore, the company's technological framework is complemented by its possession of algorithms, customer data, and UX teams that facilitate continuous innovation. The cost structure reflects the high degree of digitalisation: IT infrastructure operation, AI development, marketing based on personalised content, and environmental costs associated with the carbon footprint of logistics. In this way, Zalando is redefining not only fashion retail but also the very way the fashion industry operates in the era of digital platformisation.

H&M and Zalando represent contrasting approaches to fashion retail, each responding to market needs in different ways. H&M is based on a traditional model with an emphasis on in-house production, affordability, and broad appeal to various demographic groups. Its business strategy is based on fast fashion and its own brands that reflect the latest trends. In contrast, Zalando is a digital platform with an emphasis on scalability, personalisation, and customer engagement in the purchasing process through data analytics and artificial intelligence. While H&M reaches a broad audience through brick-and-mortar stores and complementary online sales, Zalando targets digitally savvy customers who prefer an online environment, a personalised experience, and conveniently integrated logistics services.

The differences are also significant in terms of value proposition. H&M builds on a combination of style, affordability, and a responsible approach, expanding its offering with collections made from sustainable materials and collaborations with designers. New categories such as home accessories and sports collections are also an important part of the strategy. Zalando, on the other hand, does not produce its own fashion to the same extent but creates an ecosystem that connects thousands of brands with end customers through technology. The main goal is a seamless digital experience that includes personalised recommendations, fast logistics, secondary sales through the Zircle platform, and interaction with stylistic content.

The distribution and communication strategies employed by the two brands are also divergent. H&M relies on a global network of physical stores that function as brand experience centres while developing digital channels to support this. Zalando operates as a purely digital entity, supported by a robust IT infrastructure that facilitates mobile applications, algorithmic recommendations, and logistics management. The company employs its own order and returns processing system. At H&M, customer communication is based on physical contact, supplemented by membership in a customer programme. By contrast, Zalando emphasises constant interactivity, feedback, and digital engagement.

In terms of revenue streams, H&M focuses on direct sales through its brands, including special collections, and is expanding its portfolio with new segments. Zalando's business model integrates conventional B2C sales with B2B services, including marketing solutions and fulfilment for external brands. This approach has resulted in the creation of a more diversified revenue stream. With regard to cost structure, H&M is encumbered by elevated fixed costs associated with physical operations and manufacturing. By contrast, Zalando's primary investment is in the development of digital solutions, predictive analytics, and personalised content. In essence, these two approaches to retail can be considered to represent two discrete and divergent visions. One seeks to modernise the traditional model through sustainability and digitisation, while the other redefines fashion retail as a technology platform focused on customer data and experience.