Redefining identity: corporate evolution in the AI era

Corporate identity (CI) is strongly linked to strategy, and as a concept, it assumes relevance as it ensures that all units of the enterprise move towards a single goal (Balmer, 2012). In the face of contemporary changes, artificial intelligence (AI) poses many challenges and is modifying and shaping a new world, a mixed world: potentially stronger but equally potentially weaker. AI make firms more interconnected and dependent on technology, networking and interaction, changing management practices (Troisi et al., 2024; Dissanayake et al., 2024). According to the current optimistic and partly biased forecasts of one of the leading companies, by 2040, we could have 10 billion humanoids (Kelly, 2025). Robots capable of performing dangerous tasks are beginning to be reality (Gonsalves et al., 2024) digital twins, digital replicas of analogue entities and digital agents are attracting more and more attention, e.g. Kim and Lee (2025) propose a Digital Agencity theory, thus it is possible that we will soon be faced with clones of companies or even thinking companies personified in robots, for example, Topalian (2003) propose the example of a robot of Richard Branson that could ask and speak about Virgin group. It looks like science fiction, but the humanoid robots themselves are nothing more than such a representation and may soon feature a section dedicated to the “founder” and its enterprise, they could propose what values and principles the founder or the firm embodies, as in fact recently underlined by Anujan et al. (2024) brand avatar foster strong connection between consumer and enterprises.

The intersection between CI and AI is stronger than we might think, AI constitutes a new type of agency that is still unclear how to integrate into businesses and different sectors (Holmström, 2022). The present study aims to understand how AI is shaping and changing a company's identity. Technological innovations, depending on their transformative or destructive potential, can revolutionize entire sectors and businesses as ecosystems redefining their boundaries and dynamics (Secundo et al., 2024). In the pre-Fordist period, technology consisted of isolated machines with no connections, and production was primarily manufacturing. The industrial revolution, driven by electricity and new mechanical systems, replaced many manual tasks with interconnected machines, leading to product and process standardization (Dezi, 2010). The digital revolution, globalization and agile business management approaches have resulted in sectoral convergence, diversified organizational structures and innovative business models such as networked enterprises, holonic-virtual organizations, digital platforms and online marketplaces like eBay and Google. This digital transformation has disrupted businesses by redefining boundaries, professionalism and operations, causing the decline of traditional sectors and industry models (Utesheva et al., 2016). It has paved the way for the fourth industrial revolution (Osei and Cheng, 2023), with AI driving a fifth wave that is redesigning industry and society (Troisi et al., 2024). Similarly, the financial sector has been profoundly changed by the rise of fintech, with companies such as PayPal, Revolut and Stripe redefining the way payments, transactions and financial services are handled (Secinaro et al., 2025). The healthcare sector has also undergone a transformation with the introduction of telemedicine and healthtech (Secinaro et al., 2021), making the sector more accessible and digitized enhancing and creating new value creation opportunities and processes (Leone et al., 2021; Cannavale et al., 2022).

Today, AI is generating an equally significant transformation (Secinaro et al., 2025). We could state that the focus is no longer on physical space, but a new “phygital” ecosystem in which the physical and digital worlds are integrated (Ammirato et al., 2019). This new revolution is based on interconnected machines, operating mainly on virtual data with the aim of increasingly pushing customization, automation, optimization and adaptability of processes. In the face of these changes, it becomes crucial to study how AI is redefining the identity of the enterprise. Understanding this evolution is essential to anticipate the challenges and opportunities that AI brings and help its appropriation at all levels of the enterprise (Corvello, 2025). It is about starting to give evidence to the metamorphosis of businesses that the AI era is opening up and understanding that related to adoption, there is the adaptation and, as noted, implementation. We focus on the financial sector because that as noted by Chlouverakis Ernst and Young - EY (2024) “AI's impact on banking extends beyond technological upgrade, reshaping the sector's future.” The sector is one of those that will be most affected by the artificial evolution, in particular if revenues in terms of 2 billion of dollars can be expected (Kelly, 2025) there will also be a loss of 200.000 jobs in the next 3–5 years (Shaw, 2025), the identity of financial firm will be transformed due to automation and the introduction of AI solutions not only for customer management, but also for risk management, regulatory compliance and credit scoring, possibilities of AI in creating trends rather than tracking them (U.S. Securities and Exchange Commission, 2025).

AI is revolutionizing the financial industry (Fares et al., 2023); different are the AI impacts on the sector by automating routine tasks, improving decision-making through data-centric insights, creating new human–AI interactions, albeit achieving interoperability, building trust and producing mutual knowledge gains (Naeem et al., 2025) but also enhancing customer service with personalized experiences, e.g. through robo-advisor (Fares et al., 2023). Key benefits include increased operational efficiency through AI-powered automation, which streamlines back-office processes, reducing human errors and operational costs. AI algorithms analyze vast datasets in real time, offering financial institutions deeper insight into market trends, risk assessments and crisis prevention (Fasano et al., 2024; Rodgers et al., 2023). Chatbots and AI-driven advisory services provide personalized financial solutions and 24/7 customer support (Savastano et al., 2024). Moreover, AI fosters innovation, enabling firms to develop new products and revenue streams, such as algorithmic trading and robo-advisory services (Zhu et al., 2024) but also content tailored on the firm's identity and values (Garbuio and Lin, 2021), but not only lead to a total rethinking of the consumer (Schmitt, 2025). Despite these advantages, the adoption of AI in finance presents criticalities, including data privacy concerns, algorithmic biases, regulatory compliance and the need for workforce upskilling. AI open challenges for the traditional identity of banks and financial firms, thus we want to answer to the present research question: “How AI transform the Corporate Identity of financial firms?” Addressing these issues is crucial for the sustainable implementation of AI technologies and for the financial sector to develop new strategies, manage its dual nature as opportunity as weakness and for academics to understand the role of AI in the field of CI becoming not only a disruptive innovation but a new voice to take into account. To capture these possible transformations, a qualitative analysis through secondary data and interviews and their content analysis was carried out on a representative sample of Italian companies listed on the FTSE MIB, which is Italy's primary stock market benchmark index. It tracks 40 major Italian companies with high liquidity across various Industry Classification Benchmark sectors. Four financial companies listed on the mentioned index were analyzed. Each company contributes a different type of impact and application of AI, allowing us to explore the evolution due to AI transformation on media and infrastructure, product, content and client as stakeholders' relationship and core nature of the interviewed companies and capture possible tensions and criticalities and how manage them. Given the current context, the paper is structured as follows. The next section presents the literature on CI, analyzing its dimensions over time, the main drivers of change, and how, in particular, the introduction of AI is leading firms to reshape their structure and nature. The third section outlines the methodology adopted, which follows a qualitative approach: an initial selection of 14 financial companies listed on the FTSE was conducted, followed by interviews with 7 key elite managers responsible for AI across 6 companies, complemented by the analysis of corporate reports.

The fourth and fifth sections present the findings and the discussion, respectively. In the section dedicated to implications, we introduce the theoretical framework developed in this study, adding a new dimension to the study of CI: AI signature and artificial identity, as well as the concept of Promtumer or Promptuser. Furthermore, with the aim of providing more specific and actionable guidance for managers, consultants, students and other organizations, as already done in Kerzel (2020), we refine and propose the CI Canvas, an operational tool designed to map identity characteristics, define strategic actions and identify potential conflicts that may emerge among the different identity dimensions examined.

Looking ahead, firms will increasingly be recognized not just for what they offer, but for how their systems think, decide and interact, making AI not only a new vector of identity, but also a driver of corporate behavior.

The identity of a company defines what the company is and determines the basis for the company's strategies, providing the unity that characterizes the company as a whole and enabling it to carry out its intentions in the long term (Abratt and Mingione, 2017). The first impression of CI is often in its mirroring of the brand (Balmer 2012). CI represents a complex and multifaceted construct that remains challenging to define precisely (Signori and Flint, 2020), with theoretical development progressing through distinct phases across management and marketing literature. The conceptual foundation emerged from organization theory (Albert and Whetten, 1985), subsequently expanding to incorporate social identity dimensions and complexity theory perspectives (Devereux et al., 2020). This theoretical evolution reflects the recognition that CI operates across multiple organizational levels, with different stakeholders often holding divergent perceptions of the same organizational identity. Corley (2004) demonstrated this multiplicity by showing how managers typically view CI through a strategic lens, while employees perceive it as more stable and deeply connected to corporate culture and values. The theoretical landscape has been shaped by several influential frameworks that attempt to operationalize CI across different dimensions. The ACID model and its variants (AC2ID, AC3ID, AC4ID) developed by Balmer and Soenen (1999) and refined by Balmer (2012) distinguishes between Actual Identity, Communicated Identity, Ideal Identity and Desired Identity, with its core principle emphasizing comprehensive diagnosis across these four identity dimensions to ensure organizational coherence. Building upon this foundation, He (2012) proposed a seven-element framework encompassing ownership, vision and mission, values and beliefs, business operations, personality attributes, external image and strategic performance, while Tourky et al. (2020) offered a complementary three-level perspective distinguishing external, internal and holistic identity dimensions. Instead, Flint et al. (2018) identified five primary categories: product, marketing processes, operations, supply chain and organizational characteristics. The Audit Model by Illia et al. (2004) provides a diagnostic and preventive approach, though it remains predominantly focused on external elements and challenges to CI. All the frameworks converge on a critical insight: CI dimensions are inherently interconnected and require careful management to prevent internal conflicts and external misalignment. The literature consistently emphasizes that CI is not a static concept (Albert and Whetten, 1985), with organizations navigating the tension between necessary change and innovation while maintaining identity congruence (Flint et al., 2018). This dynamic nature creates ongoing challenges for identity management, particularly as organizations face technological disruptions that may fundamentally alter their operational, strategic and cultural foundations. The alignment challenge becomes particularly acute when considering the interconnected nature of CI dimensions, as misalignment between organizational identity and constituent expectations can generate conflicts that threaten enterprise continuity (Balmer, 2012; Abratt and Mingione, 2017; Flint et al., 2018).

While existing frameworks provide comprehensive approaches to understanding and managing CI, the literature lacks empirical investigation of how AI adoption affects CI dynamics and as Rudko et al. (2025) suggest, there is a need to study AI as both the result of institutional forces and as an institutional force itself. This represents a significant theoretical gap, as AI technologies can simultaneously transform multiple organizational dimensions, from operational processes and strategic capabilities to cultural values and external positioning, potentially creating unprecedented challenges for identity alignment and coherence that existing research has not yet addressed.

As human identity changes, so does the identity of the enterprise change; it adapts to the environment in an “adaptive instability” (Gioia et al., 2000) and builds itself through confrontation with other stakeholders (Balmer et al., 2009) or by adapting to the wider industry in which the enterprise operates (Signori and Flint, 2020). A CI changes through processes of consonance and dissonance that result in negotiations, reinterpretations and reformulations of their identity (Hoholm and Stronen, 2011; Dezi, 2010). The internal factors that determine the change and continuous renegotiation of CI are, as mentioned, at the level of ownership structure and leadership as the employee level (Tourky et al., 2020). Externally, forces can come from the need to adapt to changes in the sector and industry in which one operates (Signori and Flint, 2020). Currently, sustainability is driving change and rethinking CIs. Companies are changing their identity to meet the demands of consumers who are more aware of the sustainability of products and ready to boycott them or initiate lawsuits against a company's unethical actions. Again, sustainability brings about a change in the CI to truly act responsibly towards different stakeholders to avoid reputational damage, comply with new regulatory impositions and reduce their impact on the environment and act ethically (Battaglia et al., 2015; Abratt and Mingione, 2017).

Identity transformation processes are inherently complex, with potential conflicts arising between communicated and perceived identity dimensions (Saran and Shokouhyar, 2023), as CI moderates the relationship between corporate image, social performance and innovation outcomes (Fosu et al., 2024). Simultaneously, CI functions as a strategic enabler of organizational change, as demonstrated by Tourky et al. (2020a, b), who highlighted its facilitative role in implementing corporate social responsibility activities. Among the most significant external drivers of identity transformation are technological shifts (Schumpeter and Clemence, 1989), particularly digital transformation processes that have disrupted entire sectors. Aksjonenko and Rutitis (2024) identified how digital transformation fundamentally affects CI management by creating imperatives for companies to align identity with digital capabilities, adopt digitally oriented mindsets and develop capacities to manage associated risks.

This technological impact on identity is exemplified by Utesheva et al. (2016), whose case study of the newspaper industry revealed profound identity shifts imposed by digital disruption, highlighting the substantial divergence between traditional and emerging organizational identities in digitally transformed contexts.

The evolution of news production and consumption occurred through an interconnected process that transformed the identities of all key participants, including the news organization, its content, its creators and its audience. In addition to identifying the main metamorphoses of the enterprise, the authors identify some possible strategic actions to reduce the effects of change. When an organization faces an identity crisis, the most effective approach is to support a collaborative identity re-imagining among all stakeholders. This approach should help everyone adapt to new circumstances and challenges, rather than trying to rigidly maintain the organization's existing identity framework. The goal is to align identities flexibly with emerging opportunities and limitations presented by the source of disruption, allowing for a more organic and responsive transformation. Similar to the digital revolution, AI is undoubtedly the most disruptive innovation that is affecting change in businesses (Holmström, 2022). In an unstoppable exchange, moreover, the real identity of the company influences the introduction of new technologies (Knorr and Hein-Pensel, 2024).

AI is metamorphose the way individuals construct and perceive their identity, influencing personal, social and cultural aspects. How could the effects of AI transformation at the CI level still remain underinvestigated despite its transformative power being perceived also at the ecosystem level (Secundo et al., 2024).

AI has evolved from the simple reproduction of human behavior to the possibility of automating it, thus enabling companies to replicate complex working systems, hire robots capable of performing risky or highly reiterative tasks, as well as performing multiple tasks and integrating multiple technologies (Cammarano et al., 2023). In enterprises, AI systems can be used in three main ways (Davenport and Ronanki, 2018): Automation of cognitive processes, Automating back-office administrative and financial activities, reducing operational time and costs; enable cognitive insights, analyzing large amounts of data to detect patterns and provide meaningful interpretations through machine learning algorithms, improving the quality of strategic decisions, as Cognitive interaction: engage employees or customers via chatbots, intelligent agents and natural language processing technologies, improving user experience and support efficiency. As noted by Silayach et al. (2025), e.g. AI companions change the way individuals build relationships in the digital world and that satisfaction with these systems depends on both functional elements and emotional boundaries, and as Kumar et al. (2025) add, this balancing of warmth and competence are associated with the authenticity of the tool. Schmitt (2025) provides evidence on how engagement, but the essential nature of the consumer and consumer researcher, could be treated.

Thanks to advances in AI, firms can entrust more and more complex tasks to machines, increasing creativity, analysis and decision-making capabilities (Duan et al., 2019). AI is thus capable of transmute businesses and relationships, and as identified in Su et al. (2024), these transformations depend on the different stages of implementation of scalable AI solutions. Businesses have implemented AI in their daily activities and especially to increase the quality of products and services offered so much that AI is become a firms strategic asset (Solaimani et al., 2024), in fact AI technologies allow to increase quality and efficiency while reducing costs and time, in essence enabling outcomes at the financial, technological and organizational level changing business competition in all markets also the higher regulated such as healthcare (Ali Mohammad et al., 2023). Generative AI tools, as stated in Yee et al. (2024), are not just tools but independent Agent. Recent studies have emphasized how AI is not only a technological enabler but also a strategic asset capable of reshaping organizational identity and business models (Ghosh, 2025). For instance, as Gallego-Gomez and De-Pablos-Heredero (2020) evidence, the implementation of AI in the banking sector has shown how firms can reconfigure their traditional operations and customer relationships through dynamic capabilities such as detection, absorption, integration and innovation. The authors analysed the experiences of MasterCard, Royal Bank of Scotland and Caixa Bank and demonstrated how AI enables firms to develop new forms of customer engagement, personalize services and enhance responsiveness across the value chain. Thus, these transformations are not merely operational but also symbolic, as they contribute to redefining the firm's identity in the eyes of stakeholders, aligning with the broader shift toward AI-enhanced corporate environments. Recent empirical research has further highlighted the strategic role of AI in enhancing organizational performance through its interaction with marketing strategies and internal capabilities. Nuseir and El Refae (2022) demonstrate that in the service sector, especially tourism, AI and marketing strategies are positively associated with organizational performance and that this relationship is mediated by organizational capabilities and moderated by organizational behavior. This suggests that AI is not only a technological tool but also a catalyst for organizational transformation, influencing how firms structure their internal processes and interact with external markets. These findings reinforce the view of AI as a strategic asset that reshapes both operational efficiency and CI.

Hence, AI in its various forms and upcoming evolutions will have a disruptive impact on our society by launching it toward a Society 5.0 fueled by a new Industry 5.0 where companies will have developed technology-enhanced models and environments where deep interconnections between technologies and people will drive it (Troisi et al., 2024). But the AI transition is not easy for firms both from the point of view of resistance to adoption that depends on characteristics of individuals (employees and leaders) as well as on the type of AI, where robots still find the greatest resistance and from the enterprise itself, in fact the costs in terms of purchase, migration, maintenance as well as upgrading and innovation of these new technologies are high and posit a resistance to adopt (Osei and Cheng, 2023; Agostini et al., 2020). In addition to technological issues, as posed by Holmström (2022), an AI readiness is required, and it should be monitored at the level of technologies, activities, boundaries and goals, conducting a similar assessment allows for capturing potential weaknesses and capturing what capabilities are required for the AI transition. Indeed, the nascent field with the presence of AI biases and vulnerabilities experienced by people across industries requires a responsible use of AI and guidelines, as the recent EU AI Act (van Kolfschooten and van Oirschot, 2024; Varsha, 2023) and several reflections and issues on the ethical applications of AI in business (Tani et al., 2025).

The adoption of a new technology requires technological and organizational transformations of the enterprise coupled with the potential to amplify the dynamic capabilities that AI has (Hossain et al., 2022) as well as the new capabilities that AI enables, such as at the predictive and analytical, automation, ecosystem and relational levels (Holmström, 2022) and a determinant of green absorptive capacity (Cheng et al., 2024) or to manage environmental issues (Cimino et al., 2025). Thus, how AI is remodeling companies and CI appears a relevant investigation, present general inquiries remain always confined to specific firms' dimensions or to organizational and human resource's themes (Bankins et al., 2024).

To achieve the research objective, it was decided to use a qualitative method that allows for in-depth investigations, thus better capturing possible changes in identity (Gioia et al., 2000; Bankins et al., 2024). For the selection of cases, a sample of Italian companies was used, selecting them from the FTSE MIB (Financial Times Stock Exchange Milano Index) is the main stock index of the Italian Stock Exchange, representing the 40 Italian and foreign companies with the highest capitalization and liquidity listed on the Italian regulated market. The index measures the performance of these 40 stocks, aiming to reflect the broader Italian equity market's sector weightings. The index is float-adjusted market capitalization weighted to ensure accurate sector representation. The FTSE MIB is a thermometer of the Italian economy and of investor confidence in the country's stock market. Selected companies should be comparable in terms of sector, strategic challenges or innovative approach in order to extract useful and generalizable lessons, for this reason, we decide to focus on the financial sector that, as previously mentioned, will be one of the most influenced by the Artificial Revolution with at least an AI adoption rate of 83% globally (CNEL, 2024). The amount of investment planned in Italy for the next four years in primary IA technology projects is estimated at 60 million euros. Italy represents a compelling case for studying AI adoption in financial services due to its unique economic, regulatory and technological landscape, as well as being the nation in which banks and financial firms originated, showing great evolutionary capacity over time. Italy, as the third-largest economy in the Eurozone, plays a crucial role in European markets by balancing traditional banking models with the pressure to innovate.

Unlike the more digitally mature financial hubs of the USA and the United Kingdom, Italy's financial institutions are adopting AI incrementally and adaptively. This makes Italy a valuable case study for understanding the challenges and opportunities in AI-driven transformation. Moreover, Italy's financial sector is expected to be the most impacted by AI adoption at the national level, with projected productivity gains of 26.7%, exceeding those of other sectors such as energy (23.3%) and agriculture (16%) (Microsoft, 2025). The integration of AI is shifting the sector towards data-driven decision-making, raising important questions about balancing automation with personalized financial services. Large international financial groups are rapidly adopting AI and making strategic acquisitions to streamline operations. In contrast, smaller financial entities face slower AI adoption rates due to limited skills in implementing AI infrastructure and management (CNEL, 2024).

The Italian banking system is characterized by a high proportion of small and medium-sized institutions, legacy IT infrastructures and a relatively cautious approach to digital innovation. These features are not unique to Italy but are shared by many financial systems across Europe and in emerging markets or smaller economies (Fares et al., 2023). Moreover, the challenges faced by Italian firms in adopting AI, such as regulatory compliance, workforce reskilling and integration with existing systems, are widely documented in the international literature as common barriers to AI-driven transformation. As such, Italy serves as a representative case for studying how AI technologies are integrated into complex, regulated environments, offering insights that are transferable to other contexts undergoing similar digital transitions. Furthermore, recent research on digital transformation emphasizes that the mechanisms driving AI adoption, such as institutional pressures, technological readiness and strategic alignment, are not exclusive to large or highly digitized economies but are also observable in less mature digital environments (Kraus et al., 2021). Therefore, Italy serves as a representative case for studying AI-driven transformation in complex, regulated and moderately digitized financial systems, offering insights that are transferable to a wide range of international contexts. Although more digitally mature markets may exhibit higher levels of AI integration, the rapid pace of AI evolution means that the transformation of CI remains a continuous and adaptive process across all contexts.

The companies analysed in this study are themselves at different stages of AI development and specialization across strategic areas – for instance, some are more advanced in customer-facing applications and risk management. Moreover, firms in more mature markets may face distinct challenges, such as regulatory constraints introduced by frameworks like the EU AI Act, which can shape or even limit the scope of AI implementation. These dynamics reinforce the relevance and flexibility of the proposed model beyond the Italian context. As of 19 November 2024, 14 financial institutions are listed in the FTSE MIB, all of them are integrating AI to improve their competitiveness, but they are different in terms of specific financial products or services, in fact we have nine banks and financial intermediary (Finecobank, Banca Mediolanum, Banco Bpm, Bper Banca, Intesa Sanpaolo, Banca Monte Paschi Siena, Mediobanca, Unicredit and Banca Pop Sondrio) two insurance companies (Unipol and Generali), one asset management company (Azimut), one payment services company (Nexi) and one other financial services business (Poste Italiane). While these companies have in common the use of AI to optimize operations, reduce risk and improve customer service, they differ in terms of specific applications and strategic approaches, reflecting the diversity of the financial sector and providing a distinctive “laboratory setting” to observe early-stage identity shifts prompted by AI adoption. This unique context not only allows us to capture emerging dynamics in real time but also offers insights that can later be extended to other markets. The analysis of secondary data, such as strategic reports, industry analyses, social media communications and advertising campaigns, was enriched with interviews conducted with key managers in the field of AI. These secondary sources were systematically analysed to provide contextual and longitudinal insights into each company's strategic positioning and narrative on AI adoption. Together with the interviews, they constituted an integrated and complementary corpus of data, allowing for triangulation and enhancing the depth and credibility of the findings. Interviews were conducted via Zoom and Google Meet platforms, and each session was audio-recorded (with participant consent) to ensure accurate transcription of verbatim data. On average, each interview lasted 48 min. The resulting transcripts were then coded and integrated with the secondary data sources to strengthen the analysis. However, following the logic of elite interviews (Cucari et al., 2023; Hänle et al., 2022; Vigfússon et al., 2025), we focused on interviewing senior managers or executives directly involved in AI strategic implementation, as they possess privileged and in-depth knowledge regarding CI transformations. Although this resulted in a relatively small sample, it represents a deliberate methodological strength: these informants offer strategic oversight, specialized competences and privileged knowledge of CI transformations. In this sense, the study privileges analytical depth and richness of insights over numerical representativeness. Furthermore, the interviews and the corresponding companies provided comprehensive and sufficiently detailed contributions that, when integrated with the secondary data sources, met the criteria for data and theoretical saturation. Data and theoretical saturation are considered to be achieved when further interviews or data collection no longer yield novel themes or insights, suggesting that the current coding framework adequately reflects the diversity and depth of the dataset (Tight, 2024). This allowed for the identification of converging themes and patterns, reinforcing the robustness and validity of the emerging theoretical framework. Table 1 shows the descriptive characteristics of the sample of interviewees; however, for reasons of privacy and respect for anonymity, no reference will be made to specific companies' names.

The data coding analysis was conducted with manual coding and applying the Gioia method. This approach was not only specifically developed for studying identities (Gioia et al., 2000) but is also valuable for facilitating the development and enrichment of theory (Gioia et al., 2013). It is particularly well-suited for examining emerging and novel phenomena, allowing for structured progression from raw data to well-developed theoretical frameworks. We employed open, axial and theoretical coding techniques to systematically organize the data into a coherent framework (Corbin and Strauss, 1990; Trabert et al., 2023). Each member of the research team independently coded the raw data from interviews and documents. To enhance the credibility and accuracy of our findings, the researchers engaged in frequent discussions to refine coding practices, resolve potential conflicts and address any uncertainties in the coding process. The initial coding phase allowed us to identify key themes and recurring expressions. Through cross-code analysis, specifically axial coding, we recognized patterns of similarities and differences among the codes and assigned appropriate category labels. A subsequent analysis was carried out to ensure concordance between the codes identified by the team and to minimize the influence of prior experiences and personal biases. By comparing second-order codes, the team was able to determine the selective codes that led to the inductive identification of key patterns, ultimately classified as concepts and categories (Magnani and Gioia, 2023). Several essential themes – representing the final theoretical codes – emerged from this analytical process. These themes will be explored in detail in the following section, where we present the most significant participant quotations. The coding procedure led to the emergence of 86 first-order concepts, 34 second-order themes (i.e. practices) after this analysis the researchers decide to aggregate in 7 aggregated dimensions following the structure of Utesheva et al. (2016) and define how: media and infrastructure, product, content, client, role and type of relationship with stakeholders, enterprise core nature and adding the new tension and criticalities related to AI. To further validate our findings, we employed data triangulation (Trabert et al., 2023), utilizing multiple data sources, available on request: strategic plan and reports, investor relations materials, promotional content and various announcements on company websites and LinkedIn profiles of the selected firms were examined.

AI is driving a significant reconfiguration of infrastructure within enterprises, enabling a new digital intelligence ecosystem. While traditional AI has been widely adopted, generative AI remains in the experimental phase, utilizing both internally developed tools for managing knowledge bases and ensuring security, as well as external open-source tools for basic tasks to manage costs effectively. Respondents and reports indicate that AI is pushing toward a multichannel banking approach, supporting both employees in their daily tasks and clients in managing their financial assets remotely. As one respondent noted, “We develop the systems, we develop them for others within the group so that they can use them to their best advantage and we use them to do fraud identifications, to make processes more efficient, or for internal chatbots, so there are many different uses not only for customers” I4. Another report highlighted, “With its multichannel approach, [brand name] combines traditional and digital channels, ensuring clients can count on the personal attention of their Banker, while monitoring their financial assets remotely” report enterprise 2.

These practices reflect the emergence of what we define as an AI Signature, a distinctive configuration of technological infrastructure but also a cognitive dimension that shapes how AI is embedded and moves each enterprise. Similar dimensions and the attention to infrastructure become a strategic imperative: “The Bank's complete modernisation will enable the flawless execution of these three strategic pillars through the following enablers: Technology, Security and Artificial Intelligence” I5 Financial Statement.

Increasingly sophisticated tools are being developed to enhance predictive analytics and asset management solutions, turn simple platforms into intelligent dashboards and optimise communication with customers: I3 has implemented an extensive Smart Automation project, combining Robot Process Automation, Natural Language Processing and process mining to automate claims, anti-fraud and settlement processes, algorithmic underwriting and pricing. These orchestrated efforts reveal a distinctive AI Signature, albeit AI become a recognizable imprint of how each organization integrates intelligence into its operational fabric. I6, as stated in its sustainability report, uses “intelligent sorting systems for emails and customer enquiries to direct them to the appropriate operators, reducing response times” and as reported from our I7 “I supervised also all the RAG systems- Retrieval Augmented Generation programmes.” Such implementations are not isolated; they form part of a broader pattern where AI subtly scripts and signs the operational choreography of the enterprise. This transformation is expected to lead to the development of customized AI agents, as one respondent mentioned, “I believe and feel in the marketplace that we are moving toward AI systems that do things, these agents will have to be continually controlled and monitored to make sure that no operational risks are generated, but they could also operate for the client, there is a lot to experiment with yet” I2. Despite these advancements, preserving the relationship with the customer remains paramount, as emphasized: “it is critical to use multiple channels to optimize the channel of contact and the moment of contact of customers” I3.

This ongoing transformation marks a clear shift – from static systems and linear channels, once centered on physical infrastructure and traditional media, to dynamic digital ecosystems. These are defined by multichannel tools, intelligent dashboards and AI agents capable of automating processes and fueling continuous innovation and adaptation. In this new paradigm, CI is no longer fixed, but shaped by how systems behave, decide and interact – blurring the boundaries between operational efficiency and strategic self-definition.

Financial firms note how AI has not had a disruptive impact in terms of strategic alliances, as will emerge even at that level it has “augmented” such relationships: “On the issue of alliances instead strategic to other players I don't see any radical changes” I4 and has however resulted in the development of new partnerships and relationships, mostly with large technology giants “We have alliances, we work a lot with the Big Tech, it is almost inevitable, we also use external consulting firms that support us in carrying out our projects” I2 But also initiated new relationships with universities and research centers often created together: “we want to generate new opportunities through collaboration between researchers and industrial players. Today we are even more convinced that pooling complementary skills will allow the birth of a center of excellence destined to become an important reference point on issues of great relevance for the future” Report Enterprise 1 and creating a joint venture intended to invest in high-potential digital startups in the Fintech, Insurtech and Proptech sectors, with the aim of innovating the banking group's offerings. From what emerges, the transformation is not so much with the different strategic partners, who remain pretty much the same, but in terms of impact on stakeholders: “The other point of focus will be that they use it not only for business efficiency, but also to increase their own work, increase in the sense of increasing creativity, the creation of new value for customers and for the whole community of stakeholders. So not only customers in the strict sense, but the whole community of people towards whom the company has impact” I1. AI in fact changes exchanges, relationships and the way of working by determining new professional figures and new ways of exchanging value: “Certainly there is a desire to get closer and closer to the changing, evolving society, and society in this case changing and evolving, the getting closer also goes through the involvement of professional figures that until some time ago did not even exist, and maybe in involving these professional figures and having them tell in what they do for the company: “create a more efficient and effective work environment, where people participate in the design of new solutions and processes and are trained in the use of new technologies, thus contributing to the evolution of the way of working in this new era” Report 1 I1. We are thus witnessing a shift from hierarchical and linear relationships: defined, time-scope and mediated through fixed channels; to synergistic, multidimensional collaborations. These involve co-creation with tech giants, universities, research centers and even emerging start-ups often becoming financial firms by becoming financiers themselves, for example, Firm 1 launch a joint venture and Firm 3 in its strategic plan includes more than €1.1 billion in investments in digitization and innovation tech, with €250 million allocated to its Venture fund dedicated to fintech and insurtech. Every interaction becomes a space for shared innovation, where value is continuously redefined and the boundaries between internal and external actors grow increasingly porous.

At the product level, however, interviewees agree that the offerings will be enriched and diversified both in terms of service and product enrichment: “it will allow us to offer significant added value by offering new things even non-core business to give that extra thing that improves a person's life and remain a market leader” I4 as well as achieve an increasingly granular level of personalization: “tailored advice to support the network by improving loyalty also through personalization driven by the individual and the data that tells his story, of products and services” I4, “AI allow to offer quality, customized reporting and tailor-made investment advisory” Report I1, until leading to “tomorrow in a logic of more and more personalization of messages, generative artificial intelligence could help us to decline the same message differently according to sub targets or microtargets or micro clusters of customers” I2 and the CEO of I1 recently stated that they want to “build a bank for the future where AI is a key tool for efficiency and personalisation” the firm, in collaboration with academia, have sponsored a workshop on Algo-trading and DEFI, Methods and Technologies, indicating an active interest in the area of algorithmic trading and decentralized finance.

At the product level it emerges how the traditional Key Performance Indicators- KPIs assessing for example loyalty, churn rate and targeting-are largely still the same and will remain those, obviously also more predictive and detailed: “I don't think it's necessary to introduce new metrics compared to the ones we already have, in the sense that then the satisfaction of our customers, the quality of credit, the performance of the initiatives that we do, we will continue to measure all these dimensions that are the ones that we have identified consistently with the values of the bank and therefore also the so-called caring” I2. However alongside them the new products and services require new KPIs: “we thought of introducing specific metrics on those tools with respect to the quality of the output they will provide, as well as in light of the levels of adoption that our colleagues will have on the different tools to be sure about the usability and security of these tools” and always informing the customer of the AI solutions used and as an additional source of value creation “it will allow us to better capture suggestions for other business ideas using customer feedback” I1. The introduction of AI stamp also in the product system thus brings about a transformation for the enterprise from offering standardised financial services and products with relatively static processes oriented toward traditional risk and transaction management to offering diversified and customised financial products, where content and processes are continuously refined through AI integration and in real time, with predictive insights and analysis (robo-advisory and new AI agents, algorithmic underwriting and algorithmic trading).

At the Customer level, it emerges that AI is mostly used at the predictive level and for service optimization. However, AI does not oust the human, its role, on the contrary, remains even more central “there will always be the so-called human in the loop” I2. According to respondents, the customer expects more and more digitization: “Customers will change, young people, their ways of interacting with the bank, in my opinion, will be the more or less standard ones in a few years” I3 and the ability to remotely and increasingly instantaneously manage the services of different financial players will be a basic requirement. It is pointed out that firms in the sector will still continue to offer both services for those who most prefer human contact and through other groups as well, all-digital services: “In these cases, we have direct insurance companies in the group rely heavily on AI for service, with algorithmic mechanisms determining online policy quotations without human intervention” I4. As it turns out, the relationship with the customer is increasingly mediated, even unseen, by AI especially more traditional and developed entirely: “interaction with the center and everything is gradually becoming more and more an interaction with intelligent systems that guide the user without making them wait or minutes on hold” I2, and for example, firm 1 partnered with two tech realities in joint venture to launch an online trading platform that uses AI to deliver financial analysis, news, research and educational content, supporting private investors to manage their assets independently toward increasing client autonomy working directly with AI by communicating via prompt. As recent studies show and empirical case of startups coimproved (e.g. promptvestor.com), integrating advanced language models into decision-making flows requires multilevel prompts, bidirectional dialogues and systematic source verifications-demonstrating that it is not enough to question AI, but to know how to guide it effectively through competent prompting, In addition, as Oehler and Horn (2024) investigated tools such as ChatGPT provides better financial advice for one-time investments than robo-advisors, suggesting that retail investors can obtain portfolios more closely matched to their profiles through effective prompts. These testimonies support the idea that a Prompt-investor does not just use AI tools, but acts in a deliberative way: he or she defines the context, guides the analysis with precise prompts and receives highly personalized and predictive responses. In this framework, each prompt acts as a micro-strategic input that algorithmically structures the interaction, imprinting the organization's unique AI Signature and dynamically guiding investment processes in a participatory and adaptive way.

However, the human contact is not lost but it becomes much more specialized “on the more specific issues there is always the human who solves so it is also an evolution here” I2 - “So as it happens even now, one wants to take a 5-year loan, you go to the site, push the little button and do it, but if, on the other hand, there is a need for something slightly more complex, less standard, I think the interaction between bank colleagues and customers will remain” I1 supported by the new AI solutions for example firms I5 created a platform that includes a system called Robo4Advisor, which supports advisors in providing optimized investment advice, improving the effectiveness and personalization of the service on the similar line is working also firm I6, but always trying to guarantee simplification of procedures, but also high security “we use it to track and strengthen risk management or for example fraud and mitigation management”I4. In the field of asset management, statements emerge that profile investors as prompt actors in cognitive and decision-making. It is emphasized that AI now enables the “creation of intelligent trading systems that can generate consistent profits” and optimize advanced analytics in risk analysis and investment decisions, as stated by firm I3 in its strategic plan for 2025–2027. At the same time, the adoption of generative AI tools and semantic analytics as active support for portfolio decisions, capable of processing predictive and highly personalized insights, is recognized. Indeed, the customer experience remains the driving element: “delivering a smoother, more efficient and personalized banking experience, always putting the customer at the center will always remain in the way we do things” I1.

Traditionally, investors were externally controlled, segmented and largely passive, relying on standardized advice and predefined tools. Today, this model is giving way to a new profile: a digital, active and aware individual who interacts directly with advanced AI systems. This new actor is called a Prompt-user or, in the financial context, a Prompt-vestor. The Prompt-vestor is a digitally empowered investor who engages with AI through strategic, iterative and personalized prompts. Unlike traditional investors, they do not simply receive recommendations; they co-create financial value by actively participating in the dynamic configuration of AI services. Their preferences, requests, behaviors and feedback become generative inputs that fuel the ongoing evolution of investment strategies, risk assessments and personalized financial paths. Interaction with AI is neither passive nor merely transactional: it is generative and constitutive. Each prompt represents a micro-contribution that shapes the service itself, making the financial experience not only more tailored to individual needs but also capable of transforming, in real time, the relational identity between investor and company.

Technological evolution impacts content, with AI as a key asset rather than an end, serving as a “means to better achieve objectives” I2. AI's signature becomes a strategic pillar linked to significant investments in “Skills and Human Capital Management” through communication and knowledge management: “we have an established experience coming from more than six years of projects” I1 – “internal communication has changed and we are working on the corporate culture through these tools that allow us to spread the culture and do culture sharing in an efficient way” I2.

AI enhances and complements the human element: “I think generative AI will be a complement to the work of our employee” I2 – “let's say AI was never the goal but, here it is, the enabler of so many things we already do” I4. Creating a digital intelligent ecosystem requires significant training investment: “the colleague will have to be accompanied on this journey to get the maximum benefit” I3. Companies develop training programs at various levels, including communities, internal influencers and performance management: “we have a whole system of performance management that leads us to set annual goals with our resources, with our managers” I4. Continuing education is essential: “continuing education of the person working in the company to be effective in the company, updated to market demands” I2.

AI implementation leads to new professional roles and process rethinking, legitimizing new infrastructure and professionalism. Interviewees agree on AI as an accelerator: “it is an accelerator, it is a factor that will allow us to do them better and be more effective, at the moment it helps us to pursue the strategy more effectively and not to change it” I3. The acceleration strategy is fundamental: “yes it's okay to run but be very careful where you run” I3. AI supports precise assessments, decision-making and ethical approaches. Institutions develop “principles of Responsible AI,” focusing on non-discrimination and transparency and implementing rigorous data governance and security: “we also had to implement rigorous data governance policies and invest in advanced security technologies precisely to avoid problems and to ensure ourselves by giving guidelines” I4. In the financial sector, AI is a synergic evolution: “it is in the nature of generative AI to make things simpler” I4, always keeping the human at the center of innovation. Ultimately, the adoption of AI marks a shift in the way companies construct and communicate their identity content. There is a shift from linear communications and static content, grounded in an unambiguous brand truth, to fluid, polyphonic and dynamic content, generated and continuously reworked by the interaction between data, people and intelligent systems. CI is thus reconfigured as a moving canvas, woven by continuous input, capable of telling multiple stories in real time and adapting to increasingly active audiences, such as prompt-users or prompt-vestor. However, this evolution requires careful governance to avoid drifts such as over-personalization, algorithmic bias or echo-chamber effects. AI not only enables, but forces a radical rethinking of the content, coherence and ethics of corporate communication, making identity a co-created, sensitive and accountable process.

Finally, we arrive at the heart of the transformative process due to AI indelible sign. We witness changes in “values” and “organizational structure” guiding the new “data-driven enterprise” to balance “resilience and adaptability of strategies” and set “business-driven strategies” and “digital evolution strategies” determined by an increasing “extension of enterprise boundaries” and attention to “360° sustainability.” Interviewees highlight the evolution toward becoming increasingly “data-driven,” requiring precise “data management”: “AI is only as good as the data fed into it; data strategy is the number one process” I2 – “My company has been on a path of transformation and adoption for years now, becoming what is commonly referred to as a Data Driven Company.“I4 – In its Industry plan (2022–2024), I3 highlights its ambition to become a “customer- and data-focused innovator”.

Being data-centric supports decision-making: “Decision-making is facilitated because, based on data, it is more transparent, accountable, and precise” I3 and achieving objectives, enhancing the mission and core business: “The mission is essentially the same; the way these objectives are achieved has changed because, with more advanced and efficient technological tools, we can have more efficient processes” I1 – “The core business itself has not changed because the black box remains, but there are some additional services that I can provide either faster or even save your life, which I could not have done without analyzing your data. The insurance sector is constantly evolving, as are the needs and aspirations of our clients, but one thing has remained steadfast: helping people build a safer and more sustainable future, taking care of their lives and dreams. This has always been the beating heart of my company, a strong vocation AI or not AI” I4.

AI sign business models: “This has changed our business model. In some way, it is changing to be a more digital bank than before, but we are still a bank that provides banking services, not other types of services currently, but we do it with more sophisticated and efficient tools” I1. However, these transformations are driven more by markets than by AI itself: “I do not see particular threats to the sector from the use of AI, so it is not an AI issue; it is a matter of what will happen at the geopolitical level in general, not specifically related to AI, but market dynamics. In our market, there are three major phenomena: increasing digital payments, increasingly important e-commerce, and the increasing digital retail and merchant world. This will guide the transformation” I2 – “We aim for a business-driven strategy” I3.

To face this transformation, there is a rethinking of the organizational structure, both in terms of personnel: “Through 9,000 exits without social impacts, of which 7,000 in Italy and 2,000 in international subsidiaries, by 2027, with a resilient business model in the scenario of digitalization and artificial intelligence” report 2 I2 on similar line the firm I5 announced plans to reduce its workforce by about 2,000 by 2027 as productivity gains are achieved through the adoption of AI and generative AI. However, the bank also plans to hire about 1,100 new employees in digital and IT-related roles, highlighting a balance between automation and internal skills development; as in terms of governance, which must adopt specific “frameworks”, strategic plan enterprise 1. Enterprises facing AI must strengthen their capacity for “Resilience and Adaptability” through agile strategies: “It is a fail fast” I4. Enterprise values move following this balance, becoming relevant values such as speed and ownership: “Ownership means taking charge and being responsible for one's activities, being independent and carrying out one's activities feeling responsible for the finished product” I2, while fundamental values such as security, trust, attention to the individual, resilience, tradition and innovation remain: “The combination of these values helps the bank to always be at the frontier” I2. There is a stronger focus on sustainability and ethics and in build anthropocentric AI solutions: I1 are building “ethical and anthropocentric AI, where technology is at the service of people and not vice versa. The group is in the world's top 5 banks for contributions to the development of a Responsible Artificial Intelligence, and first in Europe” I1 Corporate site. “Ethics and sustainability drive progress in a conscious way” report I1, which AI allows managing comprehensively: “Having developed them internally, they have passed all security and usability criteria, including bias, i.e., ethical terms” and environmentally: “We use AI to make energy consumption more efficient and manage the movements of our vehicles, but we more clearly monitor the carbon footprint, understand which solutions to put in the cloud, which servers have less impact” I1, combining the potential of data and AI solutions to ensure sustainability: “Connections not obvious until a few years ago and that today are taken for granted, such as those between climate change and credit risk” I1.

Summarizing, as report of I2 recalls: “Our commitment is to develop artificial intelligence solutions in a transparent and safe manner, following a responsible approach based on four principles: Explainability, to design models that are explainable to users. Fairness, to ensure that the code of ethics is respected in AI models. Quality of the data, to guarantee quality and reliability of the response outputs. Human in the loop, to ensure that it is always a human being who supervises or controls the decisions made with AI models”.

The transition from traditional, centralised organizations characterised by hierarchical and slow decision-making processes to analytics-driven, agile and adaptive enterprises represents a substantial change in CI. Under the sign of AI, the enterprise enhances decision-making capabilities, promoting transparency, accountability and efficiency, without replacing the human role but integrating it into an intelligent digital ecosystem. The identity of the enterprise thus evolves toward a “Dynamic Core”: a stable ethical and strategic centre, capable of balancing resilience and adaptability through agile strategies and an organizational culture based on ownership and sustainability. Data management becomes crucial, guiding not only operational choices but also founding values, in which ethics, security, inclusion and sustainability emerge as essential pillars. In this scenario, the company not only reacts to market changes but also anticipates the future, maintaining a dynamic and participative identity coherence, fundamental to building trust and creating shared value in the long term.

The holistic AI implementation reveals challenges and tension for the firm's identity, from the findings emerge three critical dimensions: criticalities, resistance and management of tensions.

The integration of AI within contemporary enterprises unveils a complex landscape of technological promise and organizational challenges, where the trajectory of innovation intersects deep-seated institutional resistances and transformative potential. The adoption of AI is not merely a technological upgrade but a profound reconfiguration of organizational identity and operational paradigms. The B2B ecosystem, characterized by its inherent conservatism, presents a particularly rigid framework for innovation “B2B is a bit of a more rigid world so a bit more blocked to innovation” I3. Companies find themselves navigating a delicate balance between technological advancement and institutional inertia, where the fear of being left behind must be carefully calibrated against the risks of premature or ill-considered technological investments “following all the newer tools can also lead to problematic and critical failures” I2. AI Cost emerges as a critical constraint, particularly for smaller organizations. AI implementation is not a uniform solution but a strategic investment requiring meticulous business case evaluation: “Artificial intelligence is very often an extremely effective, powerful tool, but it is also expensive, so if a non-large enterprise wants to adopt AI it has to start from the business needs and make a business case on the cases in question, because otherwise you risk having standalone solutions that cost a lot and then remain isolated and also the operational costs can be high” I2. The potential for standalone solutions that are financially burdensome yet operationally isolated represents a significant organizational risk. This economic dimension intertwines with technological capability, demanding a nuanced approach that prioritizes strategic use cases and incremental learning. Psychological resistances play a paramount role in AI adoption. Employees oscillate between feelings of technological threat and professional presumptuousness, often believing they can perform tasks more effectively without technological intervention: “They said: I don't need this help because I already know how to do-which is a bit of a mix between feeling threatened and also presumptuousness that I know better” I2. This sentiment manifests as a complex interplay between technological skepticism and professional ego, creating internal friction that can impede innovative trajectories. Transparency and ethical considerations further complicate the AI integration landscape. The potential for algorithmic hallucinations and the inherent limitations of machine learning models generate profound uncertainties. To manage the present criticalities, emerge particular solutions the interviewee admits having adopted to frontiers them (manage tensions).

Strategic responses to these tensions involve creating internal ecosystems of technological engagement. Innovative approaches include voluntary internal AI communities, cross-functional collaboration between technology teams and business lines and a commitment to incremental, specific, supervised and sized use-case driven learning. These strategies aim to demystify AI, build organizational credibility and create a culture of collaborative technological exploration. The fundamental lesson emerges clearly: AI implementation is less about technological replacement and more about creating synergistic human-machine environments. Success lies not in technology itself, but in the nuanced, strategic and culturally sensitive approach to its integration. As emphasized by Interviewee 5a and Interviewee 2, companies aiming to successfully navigate this transformation should focus on three key dimensions. First, the organizational structure must be clearly defined, for example, “a matrix structure supported by dedicated service infrastructures” (I5a). Second, it is crucial to adopt specific business cases guided by a “business-driven strategy” (I3).

Third, the technological architecture should be robust and integrated: “an important solid technological architectural framework that integrates data architecture, IT architecture, and AI architecture; otherwise, we risk being dominated only by tools” (I5a). As Interviewee 1 also underlined, these tools are costly and may destroy value if not carefully evaluated.

Finally, a strong governance model is essential: “a third level involves a governance model where you first engage the board of directors, share the roadmap, introduce a risk governance framework, and every time a new AI tool is developed, the internal risk management structure is involved to assess whether to proceed and to continuously monitor” (I5a). The indelible signature of AI and related transformations has only just begun. The mark it will leave, both in positive and negative terms, is still largely unknown. However, it is impossible to approach this reflection without recognising that, through technological challenges, organizational changes and social transformations, AI will profoundly alter companies and the way they interact with the entire ecosystem and posing new strategic priorities.

In agreement with Knorr and Hein-Pensel (2024), the identity of a company takes time to form and transform. The empirical evidence reveals a nuanced perspective on AI's role in corporate metamorphosis, transcending traditional technological implementation paradigms. Fundamentally, AI emerges not as a disruptive force, but as a strategic pillar and indelible sign that redesigns organizational identity through multidimensional interventions. The main evolutions of CI could be seen in Table 2. Although the contemporary context requires continuous, fast and forced investments (Osei and Cheng, 2023; Agostini et al., 2020) and CI changes, it emerges that in the face of AI imprint, companies are starting a more cautious transformation compared to previous transformations that have occurred in the case of digital transformation and broader digitalization of some businesses. Moreover, AI transformation is writing the identity and dynamics of enterprise much earlier than is now being discussed, for some companies we are talking about 6–10 years ago, already digitalization and data collection had made it possible to automate and create intelligent systems, especially for the management and valorization of customers from the point of view of revenue and support. The contemporary context of even earlier implementation of AI sees companies engaged in the transition dictated by AI signs, where the main impact will be from the increasing implementation of genAI systems. The firm I2 is undergoing a more profound transformation as it transitions towards becoming a digital-first bank. At the level of personnel and functions, for example, it is reorganized into three global platforms: Technology, Data and Business. These platforms will drive group's global technological development, while individual countries will focus on the last mile, creating products tailored to their specific needs. It is therefore highlighted that compared to the previous identity, the media and infrastructures are changing, increasingly moving towards financial companies that operate on broader digital business ecosystems with different stakeholders and making use of multi-channel tools and dashboards and AI agents capable of automating processes and activities, serving the customer in a discreet, timely and constant manner. The resources put in place to initiate the transition are seeing companies changing their value chain (Oosthuizen et al., 2021), and to do so, they are not only introducing new professionals and new functional areas (Bankins et al., 2024) but also renovating their infrastructure, initiating new relationships with different stakeholders and even rethinking. Stakeholder relations compared to the past do not have a radical change, remain the same and with their confrontation and transformative potential (Balmer et al., 2009; Signori and Flint, 2020). Despite the fact that the stakeholders' relationship changes in terms of impact and manners, in fact, financial firms' strength and start stronger partnerships with technology and communications companies and the role of universities is re-evaluated. To remain competitive and manage the disruptive dimension that has introduced AI systems, specific laboratories and institutes have been created that largely see the convergence of TELCO realities, as well as research centers and national universities and venturing opportunities. Co-creative manners are the most developed and activated, and the impact of these collaborations covers more broader themes. If not in a change of their core business, their business model is firmly rewritten also in a more sustainable way, and they are able to offer more diversified products. The customer, on the other hand, has increasingly digitally evolved, demanding services even remotely and increasingly personalized. Despite that, there remain a usual customer base that seeks human confrontation and support to make important decisions such as managing wealth and ensuring its security throughout multiple life experiences. Financial sectors, therefore, seek to develop personalized solutions and become customer life agents that can optimally manage the individual's wealth needs to achieve his or her goals but always preserving quality (Daugherty and Wilson, 2024). The integration of AI is not only aimed at improving operational efficiency but also at enhancing customer experience. New leadership paradigms are emerging, where AI is leveraged not just as a tool, but as a strategic orchestrator of transformation – a force that leaves a distinctive AI Signature across both front-end and back-end operations.

This signature manifests in the way AI personalizes services, optimizes customer journeys and enables omnichannel engagement, all while maintaining a human touch.

Financial firms envision AI driving operational efficiency, improving the customer experience across wealth management and allowing new life security and safety solutions, offering tailored services at scale. To do this, if the products do not change substantially except in a more diversified and personalized way, it is the content of the products, services and processes of these businesses that change. In terms of content, the integration of AI is fundamentally reshaping enterprises, positioning AI as a strategic driver rather than an end goal. CI’s AI Evolutions necessitate substantial investments in skills and human capital management, emphasizing the importance of effective communication and knowledge dissemination. AI enhances decision-making by providing transparency, accountability and precision, thereby supporting the achievement of business objectives without altering the core mission but enhancing quality. AI acts as an accelerator of existing strategies, although caution is advised to avoid the pitfalls of rapid technological adoption. Ethical considerations are paramount, with institutions developing principles of Responsible AI to ensure non-discrimination and transparency. AI's role is to complement human capabilities, creating a digitally intelligent ecosystem that requires significant training investments. Continuous education is crucial for adapting to market demands and maintaining competitiveness. The integration of AI has led to the emergence of new professional roles and the rethinking of processes, legitimizing new infrastructure and professional standards. At the core nature of the financial companies, the transformative process driven by AI involves changes in values and organizational structures, guiding data-centric enterprises to balance resilience and adaptability. This evolution requires precise data management, as AI's effectiveness depends on the quality of data. Being digital intelligence organizations enhances decision-making, rendering it more transparent, accountable and precise, without fundamentally altering the mission or core business but enhancing them with advanced technological tools. Central to this transformation is the development of the organization's AI signature, which reflects how AI integration shapes the firm's unique cognitive processes and identity across all levels, internal and external. Organizational structures are rethought to support this transformation, emphasizing resilience and adaptability through agile strategies. Values such as speed, ownership, security, trust and innovation become crucial, alongside a strong focus on sustainability and ethics. AI enables comprehensive management of economic, social and environmental sustainability, finding new patterns and connections between phenomena. The commitment to developing AI solutions responsibly is based on principles of explainability, fairness, data quality and human oversight. In the face of possible conflicts and critical ethical issues and use of AI, according to Cheng et al. (2024), this evolving AI signature requires ongoing attention to firm's organization, IT infrastructure as ethical governance and stakeholders' relationships. All the selected companies have initiated activities to broaden data and AI literacy, both internally in a still highly voluntary and nudge way and externally to their mostly retail customers. AI will not only foster operational efficiency and personalization of the offer but also become an engine and indelible sign for continuous improvement and responsible innovation, promoting a balance between technological progress and respect for the environment. Confirming Solaimani et al. (2024), AI becomes a strategic asset in terms of competition.

In Figure 1, it is proposed the conceptual framework of the new AI CI. This conceptual framework offers a structured lens through which to understand the evolving identity of firms in the age of AI, evidencing how in the first six dimensions there is a need to consider the role of AI signature on the enterprise. At its apex lies the firm itself, defined by its core governance structures and content, which together shape its strategic intent and internal coherence. Central to the model is the value proposition, encompassing products and services that reflect the firm's unique positioning and core operational focus. This core is influenced by two critical and interdependent forces: the AI Signature and the Stakeholder Ecosystem.

The AI Signature represents the technological imprint of the firm, encompassing a spectrum of capabilities such as data management, automation agents, R&D systems, provenance detection and other AI-driven solutions. These elements not only reflect the firm's technological maturity but also contribute to the distinctiveness and recognizability of its identity. The AI Signature becomes the traceable pattern of how intelligence is embedded into the enterprise: in its workflows, its communications, its products and its values.

On the opposite side, the stakeholder dimension captures both internal and external actors – ranging from employees and clients to academic institutions and technology partners – whose expectations, collaborations and competitive dynamics shape the firm's strategic responses and identity evolution.

At the base of the framework, media and communication channels serve as the interface through which the firm's identity is projected, negotiated and perceived. These channels mediate the relationship between internal innovation and external perception, reinforcing or challenging the coherence of the firm's AI-driven transformation.

This framework implies that a firm's identity is not static but dynamically co-constructed through the interplay of governance, technological signature, stakeholder engagement and communicative practices. Future research could operationalize this model by mapping firms across different stages of AI-identity integration, examining how the density and distinctiveness of AI solutions align with brand values and stakeholder expectations. Investigating the metrics for evaluating AI-infused CI and the theoretical responses from organizational studies and strategic management would further enrich this line of inquiry.

At a theoretical level, this study positions itself as a pioneering investigation into how AI is reconfiguring CI. Our central thesis is that AI is not merely a technological tool but a non-human agent that catalyses an evolution of CI, challenging traditional models and becoming a new firm's asset on how build a sustainable competitive advantage. While prior literature has described CI evolution through processes of consonance and resonance (Hoholm and Stronen, 2011), our findings demonstrate that AI introduces a radically accelerated temporality and a pervasive dynamic. The adaptive instability (Gioia, 2000) inherent to every industry is exponentially amplified by AI's predictive capabilities, triggering cascade effects where micro-algorithmic adjustments can generate macroscopic systemic transformations. Fundamentally, AI emerges not as a disruptive force, but as a key asset that recalibrates organizational identity through multidimensional interventions and leaving its specific signature. This is an evolution that has just begun where the generative AI that will be developed in the next few years will be the turning point. Thus, AI acts as a catalyst that forces a fundamental reconceptualization of classic CI models. The models of Balmer (ACID Test), Flint et al. (2018), and Signori and Flint (2020) are invaluable, but they were conceived in an analogic or early digital era. We argue that AI does not simply add a new layer to these models; it alters their core operating logic. The cornerstone of Balmer's AC4ID model is the diagnosis and management of gaps between the Actual, Communicated, Ideal and Desired identities. Traditionally, closing these gaps is a slow, resource-intensive strategic effort. Our research indicates that AI functions as a dynamic bridging agent, collapsing the temporal and conceptual distance between these identity states in real-time, and there is a need to add a new dimension the Artificial identity represent by the new AI signature on the firms behaviors and organizational models (Gallego-Gomez and De-Pablos-Heredero, 2020; Ghosh, 2025; Cimino et al., 2025). The strategic challenge will be in shifting from managing identity gaps to governing a state of continuous, algorithmic congruence. The risk of conflict, as highlighted by Balmer (2012) and Abbratt and Mingione (2017), does not disappear but changes in nature. It is no longer about static misalignment, but about the potential for algorithmically induced dissonance, bias or the loss of an authentic core in a sea of hyper-personalization. This leads to a second major theoretical contribution. Flint et al. (2018) and Signori and Flint (2020) correctly state that congruence must persist even amidst change and that a CI can hold paradoxical meanings (e.g. tradition and modernity). Building on Utesheva et al.’s (2016) six-dimensional framework, we propose a new conceptual framework with the addition of a seventh, meta-dimensional construct: the AI signature. Rather than operating across all identity dimensions, this construct is conceptualized as a distinct yet integrative block within the firm's identity architecture. It functions both as a cognitive element, capturing the organization's evolving sense-making processes and as an infrastructural component, embedding AI systems that shape how identity is enacted and perceived.

The AI signature reflects the firm's unique AI configuration, its data practices, automation capabilities, provenance systems and competitive positioning, which together form a recognizable and adaptive identity imprint. Just as a person's handwritten signature is a unique, recognizable mark that authenticates their identity, intentions and presence in the world, the AI Signature of an organization represents its distinctive imprint in the age of intelligent systems. It is not merely a technical configuration or a set of tools – it is the expression of how an enterprise thinks, acts and evolves through AI. A human signature is shaped by personality, experience and context. It is consistent enough to be identifiable, yet flexible enough to evolve over time. Similarly, an AI Signature is formed by the strategic choices, ethical frameworks, data cultures and technological architectures that a company adopts as it integrates AI into its operations. It reflects how AI is used to make decisions, interact with customers, manage knowledge and shape the future.

It does not sustain identity coherence across all dimensions directly but rather interacts with and informs the value proposition, stakeholder relationships and communication channels. In doing so, it enables the firm to navigate the tension between stability and responsiveness, supporting what we define as adaptive instability: the ability to remain ontologically grounded while being epistemologically fluid.

This reframing positions CI not as a static essence, but as a dynamic, AI-mediated ecosystem capable of learning, evolving and expressing itself in contextually relevant ways, opening the way to what and how new firms' capabilities emerge and how they enable firms, for example, to adapt and be ambidextrous to environmental changes as stated in Cimino et al. (2025). Considering that AI is emerging as a new independent agent within organizations, it may be valuable to explore how, according to institutional theory, forces such as institutional legitimacy, environmental pressures and isomorphism not only influence AI adoption but may also be shaped by it (Rudko et al., 2025). In a world where AI is no longer optional but foundational, the AI Signature becomes the new signature of the firm – a living, evolving mark of its identity, accountability and vision. In light of the evidence, we argue that the notion of an AI Signature must be conceived as an emergent identity layer: not merely a technological enabler, nor solely a catalyst of organizational change, but a constitutive dimension of CI in its own right. Much like sustainability in the past decades, AI operates simultaneously as an accelerant of transformation, a lens through which existing values and practices are reframed, and a dynamic window of change that continually reconfigures the boundaries of the firm. Its significance lies in the way it mediates between data, decision-making and stakeholder expectations, anchoring the evolving identity of the enterprise in a socio-technical equilibrium that is at once ethical, strategic and adaptive. Future research should examine how mobile block interacts with more stable identity dimensions and under which conditions it stabilizes as a permanent layer or remains a contingent driver of evolution.

Finally, the most profound implication emerges when we consider how AI is reshaping personal identity. As individuals increasingly use AI to curate their lives, preferences and even their self-perception, they develop what can be described as an “algorithmic self.” This transformed individual, the Promptuser, projects new expectations of hyper-personalization, immediacy and dynamic identity onto the corporations they engage with.

To lend greater substance to this perspective, we introduce the neologisms Promptuser or Promptumer and Promptvestor, specific for investors. As stated in Schmitt (2025) the new consumer and consumer researchers are changing, thus and as starting point, we propose such terms to describe a new class of stakeholders (users, consumer, investors) who no longer interact passively with companies but actively co-create them through continuous, iterative inputs into their AI systems and who engagement, journey and consumer related dimension require new framed on AI dimension investigations (Gallego-Gomez and De-Pablos-Heredero, 2020). Each “prompt” submitted by these stakeholders becomes a micro-contribution that shapes corporate narratives, products and services, and ultimately the very essence of CI. For example, in financial services, these prompts might involve personalized financial advice or the real-time customization of investment strategies, enabled by increasingly sophisticated AI assistance.

This emerging feedback loop, where the evolution of individual identity drives the evolution of CI, and vice versa, creates a real-time, dialogic dynamic. It requires a significant extension of traditional CI models based on co-creation and stakeholder engagement to explicitly include the symbiotic relationship between human and AI and new practices to avoid biases and create never-ending eco chambers. Future CI research cannot overlook this co-evolution; it must investigate how algorithmically mediated individuals and algorithmically mediated corporations mutually shape each other in an ongoing, iterative process.

Our research contributes to a more dynamic understanding of CI by proposing a shift from static to generative identity models. CI emerges as a self-generating system through continuous AI-mediated interactions rather than a predetermined organizational construct. This evolution toward distributed CI challenges traditional centralized identity models, positioning CI as co-constructed across stakeholder networks. The transition from passive to active stakeholder engagement introduces shared responsibility theory for CI formation. When stakeholders actively co-create organizational identity through AI interactions, traditional boundaries of organizational responsibility become blurred, requiring new theoretical frameworks for understanding accountability in AI-mediated identity processes.

The emergence of algorithmic innovation management represents a paradigm shift where micro-algorithmic adjustments generate macroscopic organizational changes, necessitating frameworks that integrate algorithmic unpredictability into traditional innovation processes. The concepts of Promptumer and Promptvestor introduce human-AI co-innovation as a continuous, symbiotic process rather than linear stage-gate models. This real-time innovation dynamic requires temporal innovation management theories that account for AI's exponential acceleration of change cycles. Practically, organizations must develop fluid identity management capabilities that treat CI as a dynamic, continuously evolving construct rather than a static organizational attribute. The predictive capabilities of AI create proactive feedback loops that fundamentally alter traditional cause–effect relationships in CI evolution, requiring new theoretical frameworks for managing identity transformation.

Our findings reveal that AI-driven CI transformations require fundamentally new innovation management practices.

We recommend several managerial actions to manage the transformative impact of AI. First, develop a clear AI strategy by defining objectives, governance and creating a detailed roadmap outlining the steps for AI adoption, including timelines and milestones. Invest in talent and training by implementing comprehensive programs to upskill employees in AI technologies, hiring specialized talent with AI and data science expertise and fostering a culture of continuous learning and development. Enhance data management by ensuring high-quality data collection and management practices, implementing robust data governance policies to maintain data integrity and security and focusing on specific use cases to learn critically from them. Implement ethical AI practices by developing and enforcing principles of Responsible AI, focusing on fairness, transparency and accountability and establishing ethics committees to oversee AI initiatives and ensure compliance with ethical standards. Foster human-AI collaboration by designing AI systems to complement human capabilities, enhancing productivity and decision-making and ensuring human oversight in AI-driven processes to maintain accountability. Adapt organizational structures to create agile frameworks that can quickly adapt to technological changes and form cross-functional teams to drive AI initiatives and foster collaboration. Monitor and evaluate AI impact by developing metrics to assess AI's effect on business performance and establish feedback loops to continuously improve AI systems and processes. Enhance internal communication to keep employees informed and engaged in AI initiatives and engage stakeholders to build support and understanding of AI-driven changes. Focus on sustainability by integrating AI with sustainable business practices to ensure long-term viability and use AI to optimize resource usage and reduce environmental impact. Finally, prepare for future trends by continuously analyzing emerging trends in AI and technology to stay ahead of the curve and establish innovation labs to experiment with new AI technologies and applications. By implementing these actions, managers can effectively navigate the transformative impact of AI, ensuring that their organizations remain competitive, resilient, and ethically sound in the evolving digital landscape.

We proposed in the next table, Table 3, a strategic roadmap around short, medium and long-term priorities for companies navigating AI-driven identity transformations.

This is an evolution that has just begun, where the generative AI that will be developed in the next few years will be the turning point. It will be the signature and first evidence of a firm.

The transition from a top-down model of identity management to a more agile, data-informed process of discovery implies a redistribution of agency within the organization. Employees, customers and even algorithms become active participants in shaping CI.

This shift requires organizations to develop ambidextrous capabilities: on one hand, leveraging AI to reinforce core identity elements – such as trust, consistency and operational efficiency – and on the other, creating protected spaces for experimentation, such as AI identity sandboxes, where emergent identity facets can be explored without compromising the brand's integrity. This dual structure reflects a broader societal need to balance stability and innovation in the face of rapid technological change.

As part of the managerial implications, and with the aim of helping firms effectively manage their emerging AI-enabled CI, we introduce the CI Canvas as a strategic tool designed to support managers, scholars and organizational designers in navigating the complex interplay between CI transformation, in this case AI, as we can see on the dedicated block and CI changes. This Canvas, illustrated in Figure 2, enables firms to systematically assess how AI technologies influence the key CI dimensions theorized in our conceptual framework, while maintaining coherence with the firm's core values and historical DNA and incorporating strategic opportunities and risks. The blocks above represent the internal dimensions of the company, from its core nature, such as values, mission, vision, purpose, governance and leadership, as well as general culture and strategy. In the next block, we find the dimensions related to products and services, from the company's broader business portfolio and value proposition to innovation, quality management and business-related strategies. Then follows the block of content to be conveyed and the final block that already leads to the external dimension of channels and technical infrastructure. In the line below are the dimensions pointing outwards from the enterprise: customers, the ecosystem of stakeholders and finally the AI signature, which is both structural and cognitive, i.e. through generative AI tools it is both an expression of the enterprise and its way of “thinking” and an expression of the interest and demands of customers, thus permeable membrane to both. The last block of synthesis concerns the opportunities and risks that can be generated between the different blocks and that can be monitored through swot analysis, gap analysis, conflict analysis, defined in terms of a strategic map, but also through scenario analysis for future developments and monitoring of CI. While existing frameworks such as the Enterprise AI Canvas (Kerzel, 2020) offer valuable guidance for aligning AI use cases with business value and technical feasibility, they primarily focus on operational integration and decision optimization. In contrast, the CI Canvas proposed in this study provides a more strategic and symbolic lens, emphasizing how AI reshapes the foundational elements of CI – such as values, culture, stakeholder relationships and communication infrastructures.

Thus, the proposed Canvas serves as both a diagnostic and a design instrument: it facilitates reflection on CI, helping leaders identify areas where AI initiatives reinforce or potentially contradict the firm's core traits. Managers can employ it to map key identity attributes, align strategic actions (e.g. mission, channels and AI solutions), use it as an identity audit tool and anticipate tensions that may emerge across different identity dimensions. This enables more coherent decision-making and organizational alignment. It offers practitioners a structured yet flexible framework to visualize, assess and manage the multiple dimensions of CI in the context of AI-driven transformation. Furthermore, startup founders and smaller firms could use this to map out existing identity components, such as values, culture, stakeholder relationships, communication channels and organizational structures – and understand how AI integration interacts with and reshapes each element. The Canvas is also highly effective as a collaborative tool in workshops and cross-departmental strategy sessions. Bringing together leaders from IT, marketing, HR and innovation teams, for example, the Canvas facilitates dialogue on how AI reshapes organizational values, communication and stakeholder engagement. By visually representing both stable identity elements and dynamic AI-driven changes, teams can discuss trade-offs, co-create solutions and ensure coherence between strategic objectives and technological adoption.

Moreover, the Canvas introduces the concept of “AI Signature”, allowing organizations to assess how AI contributes to or challenges their identity narrative. It also incorporates dimensions such as regulatory pressures, ethical positioning and identity resilience, which are particularly relevant in highly regulated sectors or during periods of rapid technological change.

The framework is designed to be actionable across multiple time horizons: it supports short-term alignment, medium-term transformation and long-term strategic positioning. Consultants may use it to guide diagnostic processes and design targeted interventions that enhance identity consistency. Students and scholars can adopt it as an analytical device to explore the evolving nature of CI in AI-driven contexts.

Ultimately, by offering a structured approach to visualize and manage identity-related elements, the CI Canvas connects theoretical insights with empirical realities, empowering organizations to address immediate challenges while preparing for future opportunities in a cohesive and reflective manner. The CI Canvas can be used by managers much like the Business Model Canvas: as a framework to visualize how AI reshapes identity dimensions and to support strategic reflection on a firm's identity configuration. It can structure workshops where departments discuss how identity influences values, processes and communication, helping both startups and established firms align AI-enabled practices with long-term positioning. For scholars, the Canvas is a versatile research and teaching tool. It supports the analysis of secondary data, the design and interpretation of interviews and classroom exercises where students examine or simulate identity transformations. In this dual role – managerial and academic, it operates as a structured yet flexible tool to investigate how CI evolves in the AI era or in contested contexts, while also enabling retrospective analyses. In teaching, the Canvas can be introduced to highlight the multidimensional nature of CI and to involve students in applying the framework to real or simulated firms. Students may map identity dimensions using reports, case materials or interviews and reconstruct how firms communicate their identity across the Canvas or use it to design the first elements of a new business venture. In research, it offers a systematic way to collect, organize and compare data. It provides structure for coding interviews, analyzing secondary sources and conducting cross-industry studies. As both an analytical grid and a generative framework, it helps capture the complexity of identity while enabling comparability across cases.

By operationalizing the concept of AI Signature, this tool empowers organizations to move beyond reactive AI adoption toward a more intentional and identity-conscious transformation. It offers a practical framework for managing adaptive instability, enabling firms to remain authentic while dynamically responding to technological and societal shifts.

The emergence of AI signature as a corporate asset has far-reaching social and political implications. It redefines the relationship between organizations, individuals and institutions, introducing new forms of agency, power and risk in AI-mediated environments.

At the social level, the rise of the Promptuser or Promtumer – a firm's user or consumer who co-constructs CI through continuous interaction with AI systems – marks a shift toward participatory capitalism. However, this participation is not without consequences. As AI systems adapt to Promptumer input, they risk creating algorithmic echo chambers, where identity narratives are hyper-personalized and self-reinforcing. This undermines pluralism, reduces exposure to alternative perspectives and fosters fragmented, insular experiences of corporate reality. The illusion of agency may conceal deeper forms of algorithmic conditioning, where users unknowingly reinforce the very systems that shape their perceptions and choices.

Politically, these dynamics intersect with emerging regulatory frameworks such as the European Union's AI Act, which aims to ensure that AI systems are safe, transparent and aligned with fundamental rights. The AI Act introduces a risk-based classification of AI systems and imposes strict obligations on high-risk applications, including those that influence decision-making in employment, finance and public services. However, the fluid and adaptive nature of AI signature challenges the assumptions of the AI Act, which is largely built around the idea of stable, classifiable systems. When CI itself becomes dynamic and context-sensitive, regulatory enforcement becomes more complex, raising the risk of regulatory arbitrage through identity fluidity.

Moreover, the ability of firms to present different identities to different stakeholder groups – while maintaining internal coherence – raises concerns about transparency, accountability and democratic oversight. Traditional governance models assume that corporate actors are stable entities with identifiable characteristics. AI signature disrupts this assumption, enabling organizations to modulate their identity in real time, potentially evading regulatory scrutiny or manipulating stakeholder perceptions, future research could go more in deep on how AI enables what we term “paradoxical congruence” – the capacity of organizations to maintain a coherent identity while dynamically adapting to contextual demands.

The institutional lag between the speed of AI-driven identity transformation and the slower pace of legal and democratic adaptation creates a critical vulnerability. As AI systems evolve rapidly, social institutions may struggle to keep up, leading to gaps in protection, oversight and public trust. This calls for a rethinking of regulatory paradigms – moving from static compliance models to adaptive, real-time governance frameworks capable of engaging with the fluidity of algorithmic identity.

The study suffers from several limitations. It is investigated only with a qualitative approach and data only from Italian cases, although highly representative and offering different evaluations, future research will have to extend the survey sample to cross-country or sector firms and integrate the data with further qualitative or quantitative research or longitudinal studies. Therefore, the insights from this study can serve as a comparative baseline for future cross-national research exploring how AI sign express CI across different levels of digital maturity. The theoretical and managerial implications are, however, many and open several proposals for future research, in particular regarding the impacts of firm's evolution and transformation on the structure and performance of CI as well as the possible generation of conflicts and the innovative practices implemented to address AI transition. Future research could also investigate the strategic dimension and the relationships between stakeholders, B2B business and how AI, especially the generative AI modifies them in a process of continuous innovation. Future research could explore the various stages of AI-Identity transformation undertaken by firms. This could involve mapping companies along dimensions such as the pervasiveness and density of AI solutions, the distinctiveness of AI in relation to the brand, and the alignment of AI with the firm's core values and mission. Such a framework would help identify firms at different maturity levels – ranging from nascent adopters, to emerging and integrated firms, up to those where AI has become a defining element of their organizational DNA.

Additional research could investigate the specific metrics required to assess CI in the age of AI, as well as how management practices and organizational theories are adapting to these transformations. Furthermore, it would be valuable to examine how shifting consumer needs and behaviors are influencing firms' identity construction and strategic positioning. Finally, a further research point could be to investigate possible integration of AI for sustainability purposes, because many of these structural and hard solutions have also been implemented to follow more transversal and soft transformations to the company, such as sustainability and ethics in its various facets. In the near future, companies may no longer be recognized solely for the products or services they offer, but for the ways their systems think, decide and interact, making algorithmic behavior a key marker of organizational identity. This vision urges us to imagine firms not only as economic agents, but as evolving intelligences, whose strategic essence is co-defined by their AI systems.