digital transformation research

Digital transformation: a review, synthesis and opportunities for future research

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• Published: 18 April 2020
• Volume 71 , pages 233–341, ( 2021 )

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• Swen Nadkarni 1 &
• Reinhard Prügl 1  

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In the last years, scholarly attention was on a steady rise leading to a significant increase in the number of papers addressing different technological and organizational aspects of digital transformation. In this paper, we consolidate existing findings which mainly stem from the literature of information systems, map the territory by sharing important macro- and micro-level observations, and propose future research opportunities for this pervasive field. The paper systematically reviews 58 peer-reviewed studies published between 2001 and 2019, dealing with different aspects of digital transformation. Emerging from our review, we develop inductive thematic maps which identify technology and actor as the two aggregate dimensions of digital transformation. For each dimension, we derive further units of analysis (nine core themes in total) which help to disentangle the particularities of digital transformation processes and thereby emphasize the most influential and unique antecedents and consequences. In a second step, in order to assist in breaking down disciplinary silos and strengthen the management perspective, we supplement the resulting state-of-the-art of digital transformation by integrating cross-disciplinary contributions from reviewing 28 papers on technological disruption and 32 papers on corporate entrepreneurship. The review reveals that certain aspects, such as the pace of transformation, the culture and work environment, or the middle management perspective are significantly underdeveloped.

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1 Introduction

Digital transformation, defined as transformation ‘concerned with the changes digital technologies can bring about in a company’s business model, … products or organizational structures’ (Hess et al. 2016 , p. 124), is perhaps the most pervasive managerial challenge for incumbent firms of the last and coming decades. However, digital possibilities need to come together with skilled employees and executives in order to reveal its transformative power. Thus, digital transformation needs both technology and people. In the last years, scholarly attention, particularly in the information systems (IS) literature, was on a steady rise leading to a significant increase in the number of papers addressing different technological and organizational aspects of digital transformation. In the light of this development, we are convinced it is the right time to map the territory and reflect on the current state of knowledge. Therefore, in this paper we aim at providing a descriptive, thematic analysis of the field by critically assessing where, how and by whom research on digital transformation is conducted. Based on this analysis, we identify future research opportunities.

We approach this objective in two steps. First, we adopt an inductive approach and conduct a systematic literature review (following Tranfield et al. 2003 ; Webster and Watson 2002 ) of 58 peer-reviewed papers dealing with digital transformation. By applying elements of grounded theory and content analysis (Corley and Gioia 2004 ; Gioia et al. 1994 ) we identify important core themes in the literature that are particularly pronounced and/or unique in transformations enabled by digital technologies. In a second step, in order to assist in breaking down disciplinary silos (Jones and Gatrell 2014 ) and avoiding the building of an ivory tower (Bartunek et al. 2006 ; Fuetsch and Suess-Reyes 2017 ), we supplement the pre-dominantly IS-based digital transformation literature with a broader management perspective. Accordingly, we integrate cross-disciplinary contributions from reviewing 28 papers on technological disruption and 32 papers on corporate entrepreneurship.

We find these research fields particularly suitable for informing digital transformation research for two reasons. First, by reviewing the literature on technological disruption we hope to derive implications regarding technology adoption and integration. Burdened with the legacy of old technology, bureaucratic structures and core rigidities (Leonard-Barton 1992 ), incumbents may face major challenges in this respect during their digital transformation journey. Second, we expect corporate entrepreneurship to add a more holistic perspective on firm-internal aspects during the process of transformation, such as management influence or the impact of knowledge and organizational learning.

Our findings and related contributions are threefold: First, based on a systematic and structured analysis we develop digital transformation maps which inductively categorize and describe the existing body of research. These thematic maps identify technology and actor as the two aggregate dimensions of digital transformation. Within these dimensions, we reveal nine core themes which help to disentangle the particularities of digital transformation processes and thereby emphasize the most influential and unique antecedents and consequences of this specific type of transformation. Thus, it becomes possible to identify the predominant contextual factors for which research would create the strongest leverage for a better understanding of the challenges inherent in digital transformation. Second, we contribute to the advancement of this field by elaborating opportunities for future research on digital transformation which integrate the three perspectives mentioned above. In particular, informed by corporate entrepreneurship, we find that the important middle management perspective on digital transformation has thus far been largely neglected by researchers. Also, emerging from our review we call for more studies on the various options for integrating digital transformation within organizational architectures and existing processes. Third, in reviewing the adjacent literature on technological disruption and corporate entrepreneurship, we strengthen the valuable management perspective within the primarily IS-based discussion on digital transformation. This way we avoid the reinvention of the wheel while at the same time enable the identification of cross-disciplinary research opportunities. We hope to stimulate discussion between these different but strongly related disciplines and enable mutual learning and a fruitful exchange of ideas.

2 Conceptual foundations

Technology as a major determinant of organizational form and structure has been well acknowledged by academics for a long time (Thompson and Bates 1957 ; Woodward 1965 ; Scott 1992 ). Following a significant decline of interest in this relationship until the mid-1990s (Zammuto et al. 2007 ), innovations in information technologies (IT) and the rise of pre-internet technologies have revitalized its relevance in the context of organizational transformation. Thus, the literature on IT-enabled organizational transformation, a concept which originates from the field of information systems (IS) that has caught considerable academic attention starting back in the early 1990s (Ranganathan et al. 2004 ; Besson and Rowe 2012 ), may be seen as one of the scholarly roots of digital transformation research. In his seminal book, Morton ( 1991 ) argued that companies must experience fundamental transformations for effective IT implementation. In the course of the years a shift of attention occurred from technological to managerial and organizational issues (Markus and Benjamin 1997 ; Doherty and King 2005 ). Non-technological aspects such as leadership, culture, and employee training were found to be equally important for successful IT-enabled transformation (Markus 2004 ). This is supported by Orlikowski ( 1996 ) who found empirical evidence from a 2-year case study that organizational transformation was in fact enabled by technology, but not caused by it.

Today, information technologies have become ‘one of the threads from which the fabric of organization is now woven’ (Zammuto et al. 2007 , p. 750). Digital technologies are considered a major asset for leveraging organizational transformation, given their disruptive nature and cross-organizational and systemic effects (Besson and Rowe 2012 ). In order to achieve successful digital transformation, changes must occur at various levels within the organization, including an adaptation of the core business (Karimi and Walter 2015 ), the exchange of resources and capabilities (Cha et al. 2015 ; Yeow et al. 2018 ), the reconfiguration of processes and structures (Resca et al. 2013 ), adjustments in leadership (Hansen and Sia 2015 ; Singh and Hess 2017 ), and the implementation of a vivid digital culture (Llopis et al. 2004 ). Therefore, the scope of our review revolves around digital transformation at the organizational level only (in contrast to implications at the individual level).

In this study, we conceptualize digital transformation at the intercept of the adoption of disruptive digital technologies on the one side and actor-guided organizational transformation of capabilities, structures, processes and business model components on the other side. In other words, and in line with Hess et al. ( 2016 ), we define digital transformation as organizational change triggered by digital technologies. Hence, we argue that two perspectives of digital transformation within organizations must be captured: a technology-centric and an actor-centric perspective. To exploit the technology-centric perspective we include the literature on technological disruption (e.g. Tushman and Anderson 1986 ; Anderson and Tushman 1990 ) and merge it with research on digital transformation. For the actor-centric perspective, we derive essential implications from the field of corporate entrepreneurship (Guth and Ginsberg 1990 ), which we believe may add valuable insights regarding actor-driven innovation and renewal processes within firms. In the following, we offer a brief introduction to both concepts and their relationship with digital transformation.

Rice et al. ( 1998 ) define disruptive innovations as ‘game changers’ which have the potential ‘(1) for a 5–10 times improvement in performance compared to existing products; (2) to create the basis for a 30–50% reduction in costs; or (3) to have new-to-the world performance features’ (p. 52). Similarly, Utterback ( 1994 ) emphasizes this disruptiveness at the firm and industry level and provides a similar ‘game changer’ definition in terms of ‘change that sweeps away much of a firm’s existing investment in technical skills and knowledge, designs, production technique, plant and equipment’ (p. 200). Tushman and Anderson ( 1986 ) distinguish between product and process disruptiveness. Product disruptiveness encompasses new product classes, product substitutions, or fundamental product improvements. Process disruptiveness may take the form of process substitutions or process innovations which radically improve industry-specific dimensions of merit. Christensen and Raynor ( 2003 ) introduce a further form of disruptive innovations, namely disruptive business model innovations, which represent the implementation of fundamentally different business models in an existing business.

We argue that digital technologies may reflect in all of these definitions of disruptive innovation. They may represent new-to-the-world product innovations, dislocate existing processes, and open up entirely new business models. As resumed in a recent study by Li et al. ( 2017 ), e-commerce for instance is defined as a disruptive technology (Johnson 2010 ) which involves significant changes to an organization’s culture, business processes, capabilities, and markets (Zeng et al. 2008 ; Cui and Pan 2015 ).

Corporate entrepreneurship (CE) on the other side is a multi-dimensional concept at the intersection of entrepreneurship and strategic management in existing organizations (Zahra 1996 ; Hitt et al. 2001 ; Dess et al. 2003 ). We adopt the conceptualization proposed by Guth and Ginsberg ( 1990 , p. 5), who argue that corporate entrepreneurship deals with two phenomena ‘(1) the birth of new businesses within existing organizations, i.e. internal innovation or venturing, and (2) the transformation of organizations through renewal of the key ideas on which they are built, i.e. strategic renewal.’ Particularly the aspect of strategic renewal in corporate entrepreneurship, also labelled as strategic change, revival, transformation (Schendel 1990 ), reorganization, redefinition (Zahra 1993 ), or organizational renewal (Stopford and Baden-Fuller 1994 ), provides a promising interface to digital transformation. As stated by Covin and Miles ( 1999 , p. 50), corporate entrepreneurship ‘revitalizes, reinvigorates and reinvents’—processes also required for digital transformation. Various authors have stated that corporate entrepreneurship is a vehicle to improve competitive positioning and transform corporations (Schollhammer 1982 ; Miller 1983 ; Khandwalla 1987 ; Guth and Ginsberg 1990 ; Naman and Slevin 1993 ; Lumpkin and Dess 1996 ). Considering the disruptive nature of many current digital technologies, we believe that organizations need to fundamentally renew and redefine the key ideas of their business in order to fully exploit the potential of digitization and eventually achieve successful transformation. The literature places particular attention on the role of middle managers as the locus of corporate entrepreneurship (Burgelman 1983 , Floyd and Wooldridge 1999 ). Concluding, we will review the research on corporate entrepreneurship and identify those contributions which we believe may offer valuable knowledge regarding actor-driven internal renewal and change processes in the light of digital transformation.

Our review of the literature on digital transformation, technological disruption and corporate entrepreneurship is conducted in a two-step approach. First, we review, analyze and synthesize existing articles on digital transformation. Then, in a second step we supplement these findings be simultaneously reviewing the literature stream on technological disruption and corporate entrepreneurship. We believe a separate analysis and contrasting of the research streams is appropriate for two reasons: first, it provides the reader with more clarity on the status quo of digital transformation knowledge and prevents the confusion of concepts emerging from different literature fields. Second, white spots and opportunities for future research regarding digital transformation become much more visible in such a structured approach.

3 Research methodology

A systematic review is a type of literature review that applies an explicit algorithm and a multi-stage review strategy in order to collect and critically appraise a body of research studies (Mulrow 1994 ; Pittaway et al. 2004 ; Crossan and Apaydin 2010 ). This transparent and reproducible process is ideally suited for analyzing and structuring the vast and heterogeneous literature on digital transformation. In conducting our review, we followed the guidelines of Tranfield et al. ( 2003 ) and the recommendations of Denyer and Neely ( 2004 , p. 133) Footnote 1 as well as Fisch and Block ( 2018 ) in order to ensure a high quality of the review.

The nature of our review is both scoping and descriptive (Rowe 2014 ; Paré et al. 2015 ) as we aim to provide an initial indication of the potential size and nature of the available literature as well as to summarize and map existing findings from digital transformation research. By developing opportunities for future research, our review further contributes to the advancement of this field and stimulates theory development.

For the purpose of data collection, we exclusively limit our focus on peer-reviewed academic journals as recommended by McWilliams et al. ( 2005 ). Thus, we opted to exclude work in progress, conference papers, dissertations, or books. First, based on discussion among the authors and the reading of a few highly-cited papers, we designed our search criteria using combinations of keywords containing ‘ digital* AND transform*’ , ‘ digital* AND disrupt*’ , ‘ digitalization’ , and ‘ digitization ’. Then, we manually searched each issue of each volume of the leading journals in the management Footnote 2 and IS field (AIS Basket of eight). Footnote 3 In addition, we run our search query against five different electronic databases: Business Source Premier (EBSCO) , Scopus , Science Direct , Social Sciences Citation Index (SSCI) , and Google Scholar . We used all years available and only included articles referring to business, management, or economics in order to exclude irrelevant publications. We abstained from including digital innovation in our search (the only exception in our sample is a recent literature review by Kohli and Melville ( 2019 ), in order to capture consolidated insights). Although we realize that it is a hot topic in IS research at the moment (e.g. Fichman et al. 2014 ; Nambisan et al. 2017 ; Yoo et al. 2010 , 2012 ), we aim to concentrate our focus on papers dealing with digital transformation on a broader level (firm and industry), rather than with transitions within innovation management.

Our first search query was conducted mid 2017 and yielded an initial sample of 1722 publications. This very large sample was mainly due to the broad ambiguity of the terms ‘digital’ and ‘disrupt’. Given these broad search parameters, we anticipated that only a small fraction of this very large sample would prove to be of substantive relevance to us. To select these relevant articles for our final sample, we performed a predefined and structured multi-step selection process (similar to the approach of Siebels and Knyphausen-Aufseß 2012 ; Vom Brocke et al. 2015 ) and defined specific criteria for inclusion (Templier and Paré 2015 ). The filters during our selection process included (1) scanning the titles, (2) reading abstracts, (3) removing duplicates, (4) full reading and in-depth analysis of the remaining papers, and finally (5) cross-referencing and backward searching by looking through the bibliographies of the most important articles to find additional relevant work. The initial pool was split in half between two panelists who separately performed the scanning of titles, analysis of abstracts and removal of duplicates. After these early steps, the sample could be narrowed down to 155 articles. As we arrived at step 4 “full reading and in-depth analysis of the remaining papers”, both panelists read and independently classified each of the remaining 155 studies. During this process, papers qualified for the final sample if they satisfied three requirements: (1) articles were required to have their primary focus and contribution within digital transformation research or digitally-induced organizational transformation (e.g. a vast number of papers inadequately captured the topic of digital transformation as they primarily focused on business model innovation), (2) articles needed to be based on a sound theoretical foundation and therefore not primarily practitioner oriented (such as articles that offer popular recommendations to business leaders on how to survive digital transformation), (3) papers that were not addressing digital transformation at an organizational level (e.g. the rise of home-based online businesses by entrepreneurs) were dismissed. Whenever disagreements emerged regarding the inclusion or classification of an article, we engaged in discussion and tried to resolve the issue together to make our selection rules more reliable. We updated the review in the autumn of 2018 for any articles that had appeared between then. Following this approach, 58 studies passed all five selection steps and were included in our final sample.

Within this sample, conceptual articles (27) and case studies (20) are dominant. Roughly 60% of the articles stem from the IS literature, while 40% cover a broader management perspective of digital transformation. While the reviewed papers span a time frame from 2001 to 2018, approximately eighty-percent of articles were published within the past 5 years, indicating the relative novelty of digital transformation as a research discipline. The distribution of our sample according to journals is provided in Table  4 of “ Appendix ”.

Upon the recommendation of Webster and Watson ( 2002 ), our categorization and analysis of the literature was concept-centric. First, to facilitate analysis and build a basis for our initial coding, each selected paper was reviewed to determine the following database information.

(1) Article title, (2) outlet, (3) research methodology, (4) sample, (5) region, and (6) key findings (see full database in Table  5 of “ Appendix ”). Next, we started coding our sample, adopting elements of the approach introduced by Corley and Gioia ( 2004 ). We began by identifying initial concepts in the data and grouping them into provisional categories and first order concepts (open coding). Then, we engaged in axial coding (Locke 2001 ) and searched for relationships and common patterns between and among these provisional categories, which allowed us to assemble them into second order themes. Finally, we assigned these second order themes to aggregate dimensions, representing the highest level of abstraction in our coding. In sum, reviewing and analyzing the extant literature, 194 coded insights were generated within the field of digital transformation: 61 first order concepts, nine second order themes, and two aggregate dimensions. The nine second order themes represent core themes across the papers, which finally constitute two aggregate dimensions: technology and actor. In conclusion, we define digital transformation as actor-driven organizational transformation triggered by the adoption of technology-driven digital disruptions. The result of the coding process is a high-level inductive map of the core themes in digital transformation research (Fig.  1 ).

Digital transformation high-level thematic map emerging from the analysis of the literature

The reviewed studies from our sample provide a rich body of knowledge regarding the specific contextual factors of digital transformation. This may be beneficial to both researchers and practitioners enabling a more comprehensive understanding of the peculiarities of digital transformation (in comparison to previous technology-driven transformations).

4.1 Macro-level findings

On a macro level, the central observation emerging from our review is that both technology- and actor-centric aspects take center stage within this debate. This is also reflected in various definitions of digital transformation provided in the sample. For example, Lanzolla and Anderson ( 2008 ) represent the technology-centric side and emphasize the diffusion of digital technologies as an enabler for transformation. Such digital technologies may include big data, mobile, cloud computing or search-based applications (White 2012 ). Similarly, Hess et al. ( 2016 ) note that digital transformation is ‘concerned with the changes digital technologies can bring about in a company’s business model, which result in changed products or organizational structures or in the automation of processes’ (p. 124). However, Hess et al. ( 2016 ) also highlight the role of actors (e.g. managers) in promoting transformation processes, while facing the challenge of simultaneously balancing the exploration and exploitation of resources. Leaders must have trust in the value and benefits of new IT technologies and support their implementation (Chatterjee et al. 2002 ).

In total, we find an almost even distribution of papers studying the two dimensions of technology and actor: 33% are technology-centric, 34% are actor-centric, and 33% of papers cover both technology and actor. However, within these two dimensions we observe a rather uneven distribution of articles by second order themes. On the technology-centric side, we find that understanding the implications of digital technologies on the consumer interface and market environment are highly active research streams. In comparison, understanding the pace of change in times of digital transformation and its direct impact on incumbents is so far comparably understudied. On the actor-centric side, our review reveals a very dominant focus on leadership and capabilities in a digital context, while in contrast company culture and work environment thus far received less recognition. We also find that the status-quo of digital transformation literature is rather diverse, in a sense that papers discuss topics across various categories of our thematic map and are therefore not restricted nor focused to a specific unit of analysis. The vast majority of articles is related to adjacent topics of digital transformation underpinning its nature as a diverse and broad field of research while again indicating its emerging nature.

In addition, we observe some degree of diversity in the theoretical foundations drawn upon. Different theories are applied by several authors to capture the context of digital transformation, e.g. alignment view, configuration theory, resource-based view, dynamic capabilities, organizational learning theory, network view or business process reengineering. It would be interesting to use other theoretical angles, for example from the literature on corporate entrepreneurship and technological disruption, in order to increase theoretical diversity. Such an exchange with different fields of research would broaden the scope of the field and help bridging an ivory divide . Finally, from a methodological perspective, we observe that actor-centric papers primarily use case studies while technology-centric studies at this point are pre-eminently conceptual. In general, the literature is scarce regarding quantitative empirical evidence. We see this as a strong indicator for the early stage of digital transformation research.

4.2 Micro-level findings: the technology-centric side of the equation

In the following, we present and discuss the most important findings of the second order themes within the technology-centric dimension. In Fig.  2 we provide a thematic map for this dimension and in Table  1 a brief summary including illustrative quotes.

Thematic map for technology-driven themes in digital transformation literature

4.2.1 Pace of change and time to market

In times of digital transformation, the speed of technological change is disproportionally accelerating with new digital capabilities being rolled out every year. The technological capability of applications such as the Internet of Things (IoT), big data, cloud computing, and mobile technologies significantly increases the overall pace of change. For example, entire industries, like the newspaper business, have been transformed and digitized within a very short period of time (Karimi and Walter 2015 ). Further, the cloud and online platforms have revolutionized the process and pace of turning an innovative idea into a business (Vey et al. 2017 ). Today, innovative ideas can be realized within days and companies set-up literally ‘overnight’. In this sense, in the digital world striving for a ‘first-mover advantage’ due to a ‘winner takes it all’ environment has become more important for incumbent firms (Grover and Kohli 2013 ) as they have much less time to respond to such threats and should not give away first-mover advantages too easily.

Moreover, pure digital companies like Facebook, Google or Amazon have substantially raised the overall time to market and speed of product launches (Bharadwaj et al. 2013 ). With continuous improvements in hardware, software and connectivity, these companies set the pace for a tightly timed series of product launches. Thus, firms in the hybrid world (digital and physical) are being put under enormous pressure to also accelerate their product introductions. In a digitally transformed market, the control of speed of product development and launches is increasingly transferred to an ‘ecosystem of innovation’ in the sense of a network of actors with complementary products and services (Bharadwaj et al. 2013 ).

4.2.2 Technology capability and integration

The technological capability and power of digital transformation applications, such as for example the Internet of Things (IoT), big data, cloud computing, and mobile technologies, is in terms of computing power, data storage and information distribution in many cases significantly higher than in previous technology-driven transformations. Earlier business transformations were mostly concerned about introducing internal management information systems such as enterprise resource planning (ERP) or customer relationship management (CRM). These transformations were usually limited to improvements to business processes within firm boundaries (see Ash and Burn 2003 ; Kauffman and Walden 2001 in: Li et al. 2017 ). But today, cross-boundary digital technologies such as IoT devices (Ng and Wakenshaw 2017 ), 3D printing (Rayna and Striukova 2016 ), and big data analytics (Dremel et al. 2017 ), drive transformations that go far beyond internal process optimizations as they potentially induce drastic changes to business models (Rayna and Striukova 2016 ), organizational strategy (Bharadwaj et al. 2013 ), corporate culture (El Sawy et al. 2016 ; Dremel et al. 2017 ; Sia et al. 2016 ), and entire industry structures (Kohli and Johnson 2011 ).

Further, the review confirms that the role and significance of data itself is changing profoundly and that personal data has become one of the most powerful assets in the digital era (Ng and Wakenshaw 2017 ). In fact, we believe the impact of the massive increase in quantity and quality of data generated every day (Bharadwaj et al. 2013 ) and the game changing power of big data analytics (Günther et al. 2017 ) are yet to be fully experienced and understood by society, economy and academics.

With regards to the process of dematerialization of tangible products and objects (e.g. CDs, books, machinery etc.), triggered by the transformative capabilities of digital technologies, the most notable insight is that intriguingly, in many cases the digital substitutes, for example e-books, offer superior performance and higher customer benefits than their physical counterparts (Loebbecke and Picot 2015 ). This, for example, is in contrast to the assumptions provided by Christensen ( 1997 ) more than 20 years ago, arguing that new disruptive technologies usually provide different values from mainstream technologies and are often initially inferior to mainstream technologies, therefore only serving niche markets in the beginning.

Finally, regarding technology integration, the current state of research emphasizes the importance of flexible IT (Cha et al. 2015 ), new enterprise platforms (El Sawy et al. 2016 ), and a strong and scalable operational backbone (Sebastian et al. 2017 ) as part of an agile digital infrastructure. The old paradigms of technology integration are not effective any more. However, in a second step we need to reach a more comprehensive understanding of ‘how’ and ‘where’ the integration of technology and transformation activities should be embedded within the organizational architectures of incumbent firms.

4.2.3 Consumer and other stakeholder interface

With regards to the customer interface, which is currently receiving the highest levels of attention by scholars, we conclude that there is some solid research particularly on changes in consumer behavior (Berman 2012 ; El Sawy et al. 2016 ; Ives et al. 2016 ; Lanzolla and Anderson 2008 ), consumer preferences (Vey et al. 2017 ) and consumer knowledge (Berman 2012 ; Granados and Gupta 2013 ). Firstly, our review confirms that in the new digital marketplace, consumers behave differently than before, and traditional marketing techniques may not apply anymore. Today there are myriad choices to easily gather information about products and services far before the actual purchase. For instance, customer buying decisions are increasingly influenced by online customer-to-customer interaction via platforms and social media, where users share products feedbacks, upload home video clips, or publish blog entries (Berman 2012 ). In this sense, digital technologies are also transforming firms’ customer-side operations (Setia et al. 2013 ) and customer engagement strategies (Sebastian et al. 2017 ). For example, reaching out to customers in a digital environment requires digital omnichannel marketing, including e.g. social media, mobile apps, and augmented reality (El Sawy et al. 2016 ). Secondly, we may note that digital technologies increasingly reduce the information asymmetries between sellers and buyers (Granados and Gupta 2013 ). In this sense, information ubiquity (Vey et al. 2017 ) and instant access to data via mobile technologies (Berman 2012 ) profoundly change the long-established seller–customer relationship. And thirdly, the current literature raises awareness for the emergence of multi-sided business models. While in the ‘old’ world, intermediaries were matching sellers and buyers, in the digital market place, intermediation increasingly takes place through the establishment of multi-sided digital platforms and networks (Bharadwaj et al. 2013 ; Evens 2010 ; Pagani 2013 ).

4.2.4 Distributed value creation and value capture

The review of the literature reveals that the value chain has become far more distributed in times of digital transformation—particularly value creation and value capture. Two major changes can be observed here: (1) digital technologies offer opportunities to customers to co-create products with the manufacturer, e.g. via digital platforms (El Sawy et al. 2016 ; Ng and Wakenshaw 2017 ), and (2) on an inter-firm level value is increasingly co-created and captured in a series of partnerships in a value network (Evens 2010 ). As Bharadwaj et al. ( 2013 ) argue, network effects are the key differentiator and driver of value creation and capture in a digital world. The focus of value creation is therefore shifting from value chain to value networks. For this purpose, companies like Google are experimenting with multi-sided business models. In such a multilayered business model, a company gives away certain products or services in one layer to capture value at a different layer (Bharadwaj et al. 2013 ). Google is giving away its Android operating system for free and captures value via the ability to control advertising on every phone that uses Android.

In more general terms, we may conclude that control of value in the digital world is less and less determined by R&D capabilities, competitors, or industry boundaries. Instead the buyer, not the seller, determines the dimensions of value that matter (Keen and Williams 2013 ). Therefore, businesses need to engage with their customers at every point in the process of value creation (Berman 2012 ). Also, the strong impact of digital technologies on incumbent’s value chains imply some degree of deviation from the classical and often analog core business. For example, new product-related competencies, platform capabilities or value architectures will be required. And, incumbents must prepare for new forms of monetization in the digitized marketplace.

4.2.5 Market environment and rules of competition

This is a rather broad and diverse categorization in our review, as it comprises technology-driven changes in the market environment. After consumer-centric aspects this research stream received the most attention by scholars in the review (on the technology-centric side). In sum, the current state of literature recognizes three major developments. First, digital transformation redefines, blurs and even dissolves existing industry boundaries which may lead to cross-industry competition (Sia et al. 2016 ; Weill and Woerner 2015 ). Dominant industry logics (Sabatier et al. 2012 ) apparently do not work anymore in times of digital transformation. The ‘new kid on the block can come out of the blue’ (Vey et al. 2017 , p. 23) and even individuals can become competitors as 3D Printing is expected to lead to a sharp increase in competition from SMEs and individual entrepreneurs (Rayna and Striukova 2016 ). And with the emergence of multi-sided business models also incumbents are starting to disrupt new markets (Weill and Woerner 2015 ). For instance, Google is disrupting the mobility sector with its self-driving car subsidiary Waymo, while Amazon has introduced AmazonFresh as a grocery delivery service which is seen as a potentially tough competitor to supermarkets. Second, with the emergence of digital platforms, networks, and ecosystems the market infrastructure becomes increasingly interconnected (Grover and Kohli 2013 ; Majchrzak et al. 2016 ; Markus and Loebbecke 2013 ). In a broader sense, we see a shift from controlling or participating in a linear value chain to operating in an ecosystem or network (Weill and Woerner 2015 ). As different types of innovation networks with different cognitive and social translations regarding knowledge emerge, novel properties of digital infrastructure in support of each network are required. Digital technologies therefore increase innovation network knowledge heterogeneity (Lyytinen et al. 2016 ). Third, the free flow of digital goods precipitates an erosion of property rights and higher risks of imitation (Loebbecke and Picot 2015 ).

4.3 Micro-level findings: the actor-centric side of the equation

In the following, we present and discuss the most important findings of the second order themes within the actor-centric dimension. In Fig.  3 we provide a thematic map for this dimension and in Table  2 a brief summary including illustrative quotes.

Thematic map for actor-driven themes in digital transformation literature

4.3.1 Transformative leadership

Understanding the impact of digital transformation on leadership and management behavior is a very active and prioritized research focus. In total, 23 papers in our review explore this aspect. First and foremost, research calls for a shift in the traditional view of IT strategy as being subordinate to business strategy (El Sawy et al. 2016 ). In the course of the past two decades information technologies have surpassed their subordinate role as administrative ‘back office’ assets and evolved into an essential element of corporate strategy building. Thus, incumbents should align IT and business strategies on equal terms and fuse them into ‘digital business strategy’ (Bharadwaj et al. 2013 ).

Also, emphasis is placed on the changing nature of leadership itself, caused by digital transformation. Such changes may include rapid optimization of top management decision-making processes enabled by instant access to information and expansive data sets (Mazzei and Noble 2017 ), new communication principles (Bennis 2013 ; Granados and Gupta 2013 ), or changes in leadership education (Sia et al. 2016 ). Further, there is consensus that senior management requires a new digital mindset in order to captain their company’s digital transformation journey. Therefore, incumbents should also rethink their leadership education practices. In the past, leadership programs have been primarily about leadership and communication skills. But in times of digital transformation, executives must become ‘tech visionaries’ and develop their transformative powers. For example, Sia et al. ( 2016 ) have conducted a case study on an Asian bank that uses hackathons to educate their senior managers. Media transparency and exposure are further key challenges of digitization where top managers may require some additional education. Given the ubiquity of information and the speed of online data dissemination (via mobile phones, viral effects of social media etc.), leaders today are significantly more exposed publicly than their analog predecessors. Therefore, according to Bennis ( 2013 ) leadership in the digital era needs to be learned through embracing transparency and adaptive capacity (specifically resilience as the ability to rebound from problems and crisis).

Finally, the vast extent and complexity of digital transformation leads to the emergence of an additional position at the top management level—the Chief Digital Officer (Dremel et al. 2017 ; Tumbas et al. 2017 ). Given the immense challenges of digital transformation and the claim for a new mindset and different skills, CEOs or even CIOs are conceivably not the best match (Singh and Hess 2017 ). Particularly not if they are expected to drive digital transformation in addition to their original tasks.

4.3.2 Managerial and organizational capabilities

Our analysis suggests that in order to effectively drive digital transformation additional and refined capabilities are required—both managerial and organizational (Li et al. 2017 )—in comparison to the analogue world.

At the managerial level, for one thing, a much faster strategy and implementation cycle is needed to cope with the pace of digital transformation (Daniel and Wilson 2003 ). The turbulent and ever-changing digital environment is forcing managers to make decisions and implement strategies significantly faster than they had been previously required to. In order to study managerial capabilities in the context of digital transformation, some studies have adopted the theory of dynamic capabilities (Daniel and Wilson 2003 ; Li et al. 2017 ; Yeow et al. 2018 ) as introduced by Teece et al. ( 1997 ), Teece ( 2007 , 2014 ). In particular, results indicate that dynamic capabilities may support the refinement of digital strategy and are therefore not separate from alignment, but on the contrary have the potential to enact and guide the process of aligning.

At the organizational level, one of the most intriguing challenges for incumbents will be to manage the ambidexterity of capabilities in terms of analog and digital capabilities. Firms need to incorporate ‘old’ and ‘new’ capabilities into their organizational structure in a complementary and not impeding way. In addition, capabilities in two further areas are of particular importance to many firms. First, capabilities to implement and operate in networks (Bharadwaj et al. 2013 ), platforms (Li et al. 2017 ; Sebastian et al. 2017 ), and ecosystems (El Sawy et al. 2016 ; Weill and Woerner 2015 ). Depending on contextual factors like for example their industry or business model, companies must learn to take advantage of network effects in terms of complementary capabilities while also learn how to become more of an ecosystem rather than continue managing value chains. Second, in the digital era it is essential to develop sensing capabilities, such as entrepreneurial alertness and environmental scanning (Kohli and Melville 2019 ), in order to identify new ideas and critically evaluate, design, modify and eventually deliver new business models (Berman 2012 ; Daniel and Wilson 2003 ).

4.3.3 Company culture

Digital transformation is not exclusively a technology-driven challenge but requires deep cultural change. Everyone within the organization must be prepared with an adaptive skill set and digital know-how. Two major insights can be identified within the existing literature. First, digital transformation demands a data-sharing and data-driven corporate culture (Dremel et al. 2017 ). Data as such must be recognized much more as a valuable resource and an enabler to become a digital enterprise. This will require higher operational transparency in daily-business and work-routines and a data-sharing mindset among employees. In this sense, incumbents need to develop their informatic culture to an informational culture (Llopis et al. 2004 ). In comparison to an informatic culture, an informational culture values IT as a core element of strategic and tactical decisions and clearly understands the financial and transformative potential of digital technologies. Second, digital transformation may trigger cultural conflict between younger and comparably inexperienced digital employees and older but more experienced pre-digitization employees (Kohli and Johnson 2011 ). Management is well advised to prevent that two different cultures arise within the same organization—a group of employees who understand digital technologies and those who have a long-standing track record in the traditional business but are technologically lagging behind. Facilitating a learning friendly culture (Kohli and Melville 2019 ) and publicly affirming support and trust by the executive level may effectively mitigate such a potential cultural divide.

4.3.4 Work environment

Our review reveals that digital transformation is changing the daily work environment in incumbent firms in terms of work structures (Hansen and Sia 2015 ; Loebbecke and Picot 2015 ), job roles, and workplace requirements (White 2012 ). For example, digital interconnectivity enables the emergence of flexible and networked cross-location teams across the entire geographical company map. In this context, traditional hierarchical work structures dissolve and new opportunities emerge beyond company boundaries, such as the integration of external freelancers (Loebbecke and Picot 2015 ). Also, the implementation of a digital workplace becomes inevitable. Particularly for ‘born digital’ younger employees a digitally well-equipped workplace may represent a major criterion for their choice of employer (El Sawy et al. 2016 ). According to White ( 2012 ), a digital workplace must be adaptive, compliant, imaginative, predictive, and location-independent.

However, the most notable insight in this perspective is that—in addition to a potential cultural divide—digitization may effectively lead to a growing skills gap between pre-digitization workers and recently hired digitally savvy employees (Kohli and Johnson 2011 ). In fact, while digital technologies significantly help to optimize and accelerate many work processes and thereby increase productivity, incumbents must be aware that many employees might not keep pace with this digital high-speed train and feel left behind. It is unclear how such a tradeoff is considered and how firms could handle related conflicts.

5 Avoiding an ivory tower: drawing on existing knowledge from adjacent research fields

We assume that pre-existing knowledge on corporate transformation processes in general is partly already available and may provide implications for digital transformation. Therefore, at this point in our review, we aim to stimulate a theoretical discussion by identifying potential white spots abstracted from adjacent research fields. For this purpose, we additionally reviewed 28 studies from the literature on technological disruption (to gain technology-centric input) and 32 papers from corporate entrepreneurship (to expand the actor-centric view). By this, we supplement the pre-dominantly IS-based digital transformation literature with a broader management perspective. First, by reviewing the literature on disruptive innovations we hope to derive implications regarding technology adoption and integration. Burdened with the legacy of old technology, bureaucratic structures and core rigidities (Leonard-Barton 1992 ), incumbents may face major challenges in this respect during their digital transformation journey. Second, we expect corporate entrepreneurship to add a more holistic perspective on firm-internal aspects during the process of transformation, such as management contribution or the impact of knowledge and learning.

We rigorously conducted the same review and analysis process as for our digital transformation sample. A database and concept matrix (Webster and Watson 2002 ) for the sample on technological disruption and corporate entrepreneurship are provided in Tables  6 and 7 of “ Appendix ”. The data structures, which summarize the second order themes for both the actor-centric and technology-centric dimension of these additional research fields are illustrated in Figs.  5 and 6 of “ Appendix ”. Within the main body of this article, we only draw attention toward three key implications (Fig.  4 ). In the following, we provide a brief synthesis of these implications and their grounding in the respective literature. In a second step, we transfer and apply these implications to the context of digital transformation and integrate them into an agenda for future research opportunities.

Expanding the digital transformation high-level thematic map with insights from technological disruption and corporate entrepreneurship

5.1 Insights from technological disruption

Existing knowledge from the adoption of disruptive technologies suggests that in order to successfully integrate, commercialize or develop disruptive technologies incumbents need to create organizations that are independent from but interconnected in one way or another with the mainstream business (Bower and Christensen 1995 ). The reasons for this are manifold. For example, managers are encouraged to protect disruptive technologies from the processes and incentives that are targeted to serve established customers. Rather, disruptive innovations should be placed in separate new organizations that work with future customers for this technology (Bower and Christensen 1995 ; Gans 2016 ). Further, separation potentially helps to unravel the discord between viewing disruptive innovations as a threat or an opportunity. Exempted from obligations to a parent company, separate ventures are more likely to perceive a novel technology as an opportunity (Gilbert and Bower 2002 ). And lastly, a freestanding business also enables local adaptation and increased sensitivity to changes in the environment (Hill and Rothaermel 2003 ).

5.2 Insights from corporate entrepreneurship

Our review of the corporate entrepreneurship literature identifies two major implications that have not been (adequately) considered in digital transformation research yet.

First, the literature indicates that middle management plays a crucial role in redefining a firm’s strategic context and by this driving organizational transformation. A middle management perspective has thus far been completely neglected in digital transformation research. We see this as a major gap, since the middle layers of management are ‘where the action is’ (Floyd and Wooldridge 1999 , p. 124). Top management should control the level and the rate of change and ensure that entrepreneurial activities correspond to their strategic vision (Burgelman 1983 ), but middle managers at the implementation level are the driving force and key determinant behind organizational transformation. However, on the downside, middle managers may also represent a major barrier to organizational change (Thornberry 2001 ). Typically, managers have the task to minimize risks, make sure everything is compliant to the rules and perform their functional roles. Thus, middle managers usually have the most to lose from radical changes and are therefore often the least likely to be entrepreneurial or to support transformations (Thornberry 2001 ). In order to solve middle and operational manager’s risk-awareness and unleash their entrepreneurial spirit, research suggests encouraging autonomous behavior (Shimizu 2012 ). In sum, reviewing the literature on corporate entrepreneurship raises our awareness for the impact of hierarchy and management levels on organizational transformation (Hornsby et al. 2009 ).

Second, a closer cooperation and regular exchange between incumbents and start-ups in order to accelerate entrepreneurial transformation is proposed (Engel 2011 ; Kohler 2016 ). Incumbents should recognize start-up companies as a source of external innovation and develop suitable models for collaboration (e.g. corporate accelerators). In particular, incumbents are advised to implement three common best practices from successful start-ups in order to facilitate transformation: (1) working in small omni-functional teams, (2) goal-driven rapid development instead of bureaucratic processes, and (3) field-level exploration of market potential instead of complex and tedious quantitative models (Engel 2011 ). In addition, corporate entrepreneurship underlines the importance of organizational learning as a vehicle to drive and shape cultural transformation (Dess et al. 2003 ; Floyd and Wooldridge 1999 ; Zahra 2015 ). We come to understand that learning, and in fact also knowledge management, are intimately tied to the concept of organizational transformation. A culture of learning and knowledge drives experimentation, encourages the development of an adaptive skill set, reshapes competitive positioning, and opens the minds of employees to new realities (Zahra et al. 1999 ).

6 Opportunities for future research

Based on the cross-disciplinary perspectives from reviewing the literature on digital transformation, technological disruption and corporate entrepreneurship, we propose opportunities for future research on digital transformation. Using our thematic map as a lens to view future research opportunities, we focus on the two dimensions of technology and actor. For the technology-centric dimension we expand on the structural and operational integration of digital technologies and organizational transformation initiatives as well as gaining a deeper understanding of the pace of technological transformation. For the actor-centric dimension we address three topics: we start at the leadership level by emphasizing the relevance of middle management in digital transformation, after that we refer to the potential skills gap and threat of an employee divide in incumbent organizations induced by digital technologies, and finally we move beyond organizational boundaries to turn toward the potential benefits and drawbacks of cooperating with start-ups and pure digital companies to boost transformation. For each area, we propose a set of research questions. Altogether, the agenda is organized around five guiding topics (Table  3 ).

6.1 Integration of digital transformation within organizational structures and activities in incumbent firms

Our review of the literature on digital transformation reveals a knowledge gap regarding this topic. However, we do gain some interesting cross-disciplinary insights from technological disruption at this point. In fact, as already discussed, studies on technological disruption indicate that in order to successfully integrate, commercialize or develop disruptive technologies incumbents need to create organizations that are completely independent from but interconnected in one way or another with the mainstream business (Bower and Christensen 1995 ; Gans 2016 ; Gilbert and Bower 2002 ; Hill and Rothaermel 2003 ).

Thus, the question arises as to how incumbents should incorporate their digital transformation activities. Several options and interesting questions arise in this matter that future research may investigate on:

Which forms of organizational architecture are most suitable for digital transformation? Seamless integration of digital technologies requires building an agile and scalable digital infrastructure that enables continuous scalability of new initiatives (Sia et al. 2016 ). For example, Resca et al. ( 2013 ) suggest a platform-based organization. In addition, digital transformation demands a new kind of enterprise platform integration (El Sawy et al. 2016 ). Given the high intensity of interactive digital connectivity between the outside and inside of a company, traditional enterprise platforms (like ERP) and the ‘old’ supply chain management integration paradigm are in many cases not the most suitable solution anymore. Therefore, flexible IT is a key transformation resource in the digital world (Cha et al. 2015 ). Pursuing an open innovation approach might be another alternative for incumbents.

When and why is it an advantage/disadvantage to start digital transformation in a new organization which is completely independent from traditional business, as suggested by technological disruption research? Under what circumstances and why do spill - over - effects to the parent organization happen/not happen? ? For example, Ravensburger AG , a German toy and jigsaw puzzle company, founded Ravensburger Digital GmbH as a subsidiary in 2009. The purpose of the subsidiary was to become the firm’s digital competence center. In 2017, the digital subsidiary was reincorporated in the parent organization as a digital unit with the goal to apply their digital knowledge to transform the traditional business segments. We call for more qualitative case study research devoted to this question to develop our understanding in this topic.

How, when, and why do incumbents benefit from adopting a ‘let a hundred flowers bloom’ philosophy versus taking a ‘launch, learn, pivot’ approach? In the first scenario, a company would start its digital initiatives across all divisions simultaneously and locally to encourage broad experimentation. Such an approach was adopted by AmerisourceBergen Corp. , an American drug wholesale company. The company is convinced that digital transformation is a matter of culture that needs to be established across the entire organization. For this purpose, it implemented agile project teams throughout the entire enterprise, of which each focused on different aspects. On the downside, companies following such a broad approach may risk losing focus and at some point, the various initiatives may start competing against each other. Hence, we believe it is crucial to have a big picture in mind and accordingly allocate resources and attention very thoughtfully. Alternatively, incumbents may start with a pilot transformation project in a smaller market or subsidiary. Arguably, a major advantage is the opportunity to assure that customers are happy with the transformation results and everything is working out well before starting the large roll out in other markets. And it provides incumbents time to fine-tune their initiatives. For example, American medical company Alcon premiered their initial transformation efforts in Brazil before ramping up their rollout in 27 further countries.

6.2 Pace of digital transformation

The rapid pace of technological change is perhaps the most defining characteristic of digital transformation in distinction to previous IT-enabled transformations. Yet, as this topic is only addressed by four papers in our sample it is still to be studied in more depth. For example, there is consensus among the studies that the pace of change has accelerated significantly, however the parameters that define the pace of change remain yet to be defined. Further, we are informed that some industries like the newspaper business have been digitally transformed within a very short period of time (Karimi and Walter 2015 ), while other branches are still under transformation or are yet to be converted. We posit two exemplary research questions regarding the pace of digital transformation:

What are the parameters that define the pace of change? Our review reveals that the speed of product launches (Bharadwaj et al. 2013 ) and the time it takes to turn an idea into a business (Vey et al. 2017 ) are two potential indicators, but we certainly need to obtain a more comprehensive conceptualization at this point.

Why do industries adopt to digital transformation at a different speed? For example, consider front-runner industries like the media or publishing versus late-comers such as oil and gas. In this specific case, the easiness to dematerialize and digitize the product portfolio is certainly a main reason. However, other industries are less obvious, and we would like to invite future research to investigate upon these conditions. What are the parameters that define whether an industry is more or less transformative?

6.3 The role of middle management in digital transformation

We have learned from our review of the corporate entrepreneurship literature that middle managers are the locus of organizational transformation in incumbent firms (Floyd and Wooldridge 1999 ; Hornsby et al. 2002 , 2009 ; Shimizu 2012 ). While top management controls the level and rate of change, middle managers are in charge of execution (Burgelman 1983 ). Hence, one may conclude that middle managers are the kingpin of digital transformation. Yet, there is not a single paper in our sample that covers a middle management perspective in digital transformation. We believe that this subject has been highly neglected in research to this point and deserves far more attention in future. Several topics are particularly interesting:

How and why is digital transformation affecting the role, tasks and identity of middle managers? How and why do middle managers react to these changes? Based on our review, we expect a deep change in the nature of middle management’s role and influence in a ‘digitally transformed’ company ranging from administration to leadership aspects. Middle managers require a new attitude as they move from directing and controlling stable processes and people at the middle of hierarchy to managing resources and connecting people in the middle of networks. In addition, middle managers in the digital era must step up to their role of supporting, enabling, and coaching people to use the available digital tools. They are expected to facilitate the organization.

What kind of new responsibilities and functions in middle management hierarchy are required to accelerate digital transformation? The odds are that change fatigue might grow on employees and digital transformation may start faltering. For this purpose, horizontal functions such as business-process management layers or central administration platforms may be implemented (McKinsey & Company 2017 ). They could be shared across multiple initiatives within the organization and help to accelerate transformation.

Which mindset and digital literacy do middle managers need to be the driving force behind digital transformation? How, when, and why are middle managers motivated/not motivated to drive transformation? Research on corporate entrepreneurship emphasizes that middle managers are often the least likely to support change as they are inherently risk-averse, hardly entrepreneurial and very attached to their functional routines (Thornberry 2001 ). In addition, middle managers may easily get stressed about their ‘sandwich’ position in-between senior management and the operational level. So how can we expect middle managers to be the speedboat of digital transformation? Also, incumbents need to carefully evaluate the existing digital skills and literacy of their middle managers. How comfortable do they feel with digital tools, social media, the cloud and similar trends? They may not fulfill their coaching and leadership role if they heavily struggle with technology in the first place.

How and why is digital transformation affecting the interface of the top management team (TMT) and middle managers? The relationship between the TMT and middle managers is a very special and important relationship which significantly affects both strategy formulation and the quality of implementation. Middle managers are the organizational ‘linking pins’ between top and operational level and thus heavily rely on a good exchange with their superiors. To what extent and in which ways does digital transformation affect this special leader–follower relationship? How are digital technologies changing the speed and quality of information exchange? What is the impact on the inter-personal level?

What is the impact of digital transformation on the overall importance of the middle management layer? Since the 1950s, research indicates the decline of middle managers in terms of both numbers and influence (Dopson and Stewart 1993 ; Leavitt and Whisler 1958 ; Pinsonneault and Kraemer 1997 ). The shift in emphasis from planning and controlling to speed and flexibility is severely affecting the assumedly ‘slow’ middle. Are middle managers afraid that digital technologies will replace most of their traditional tasks and functions, e.g. communicating and monitoring strategy? Will digitalization naturally empower lower level operational managers at the bottom and consequently eliminate the middle layer?

6.4 A growing skills gap and threat of an employee divide

Given the complexity and explosive pace of digital technologies, there is a threat of a growing skills gap between pre-digitization workers and recently hired digitally savvy employees (Kohli and Johnsons 2011 ). A couple of topics are particularly interesting for future research:

How, when and why are incumbents able/unable to mitigate a growing skills gap and employee divide in the face of digital transformation? Given the increased complexity of digital technologies, traditional IT trainings may not be effective anymore. In a similar vein, how could different levels of knowledge and experience residing within different employees be integrated in the context of digital transformation? Future research might examine the mechanisms required for facilitating or hindering such an integration.

How and when are incumbents able/unable to incorporate ‘old’ and ‘new’ capabilities within their organization? On the one hand firms need to develop new capabilities to continuously transform their business, while on the other hand they must leverage their existing knowledge and skills in order to maintain their existing operations. Thus, for the time of transformation incumbents need to develop multiple, often inconsistent competencies simultaneously. In this context, how do firms ensure not to lose focus while mastering the challenge of ambidexterity in times of digital transformation?

Who in the company is managing the development and transformation of skills (e.g. HR, senior leadership, IT division, functional teams, employees etc .), and how and why does that impact outcomes of digital transformation ? This question is not addressed by current research at all. However, according to a survey (Capgemini Consulting 2013) this lack of alignment with digital strategy is rather worrisome. Responsibilities for skills transformation and development in times of digitization need to be clearly defined and allocated. Empirical academic research in this direction might be helpful to understand the status-quo in incumbent firms regarding this issue.

6.5 Cooperation with startups and pure tech companies to accelerate digital transformation

Corporate entrepreneurship proposes a closer cooperation and regular exchange between incumbents and start-ups in order to accelerate entrepreneurial transformation (Engel 2011 ; Kohler 2016 ). In fact, start-ups are often perceived as the forerunners of digital transformation. They are praised for faster innovation capabilities, higher levels of agility, a culture of risk-taking, and supremely digitized processes and workflows. In contrast, incumbents have more experience, access to capital, established brand trust and a huge customer base. Hence, a cooperation between start-ups and incumbents may be beneficial for both parties. In addition, non-tech incumbents may also consider cooperating with pure digital players which are beyond their start-up phase but are important knowledge carriers in digital matters. Two topics are particularly interesting:

Assuming that successful start - ups have a good digital culture — what are the constituent pillars of such a digital culture? And how could incumbents incorporate these “best practices” and “lessons learned”?

What are the benefits of employee exchange programs with technology companies or start - ups to scale - up digital skills? For example, in early 2008 consumer goods giant Procter and Gamble and Google have been swapping two dozen employees in an effort to foster creativity, exchange thoughts on online advertisement and strengthen their mutual relationship. This program worked very well for both sides.

7 Limitations and conclusion

Our review is not without limitations. First, the specific objectives and nature of our filtering process applied during the review naturally come with a certain selection bias. For example, data collection, analysis and interpretation remain influenced by the subjective assessments of the researchers. Also, despite being the common rule within systematic literature reviews, searching exclusively in peer-reviewed academic journals might have omitted some relevant research contained in books or dissertations. However, by means of a rigorous and transparent search process, an as complete as possible review sample was collected and analyzed subsequently. Second, using a high-level thematic map for such a complex multi-dimensional phenomenon like digital transformation highlights particular connections while it potentially fails to capture others. Specifically, critics may point to the lack of analytical depth within each second order theme. However, we believe that within the limited scope of a review our broad thematic description nevertheless adds value to the advancement of this field and should rather be seen as a holistic starting point for future research to dive deeper into the characteristics of sub-themes of digital transformation. Finally, we are aware that our focus on the organizational level of digital transformation within the private sector does not fully capture the implications of digital transformation for our society, as it also occurs at various other levels, such as the individual level or public sector. As such, future researchers may apply alternative approaches to review and synthesize the existing literature on digital transformation. For example, in contrast to our inductive method to code and analyze our sample, it may also be interesting to apply a more deductive and pre-structured method, in particular when focusing on a deeper understanding of the sub-themes emerging from our analysis. Accordingly, future research could benefit from adopting a phenomenon-based research strategy as proposed by von Krogh et al. ( 2012 ).

Concluding, our paper contributes to the extant discussion by consolidating, mapping and analyze the existing research on digital transformation, sharing important macro- and microlevel observations in the literature and proposing corresponding future research directions. Emerging from our review of 58 studies, we develop a thematic map which identifies technology and actor as the two aggregate dimensions of digital transformation and that elaborates on the predominant contextual concepts (second order themes) within these dimensions. From a macrolevel perspective, we observe that the status-quo of digital transformation literature is rather diverse, in a sense that papers discuss topics across various clusters and concepts. Further, we find some degree of diversity in the theoretical foundations drawn upon as well as confirm that the existing literature in general is scarce regarding quantitative empirical evidence. Another important contribution of our paper is bringing different lenses together by integrating knowledge from related disciplinary areas outside IS management, such as technological disruption and corporate entrepreneurship. With our review, we hope to provide a comprehensive and solid foundation for the on-going discussions on digital transformation and to stimulate future research on this exciting topic.

The development of clear and precise aims and objectives; pre-planned methods; a comprehensive search of all potentially relevant articles; the use of explicit, reproducible criteria in the selection of articles; an appraisal of the quality of the research and the strength of the findings; a synthesis of individual studies using an explicit analytic framework; and a balanced, impartial and comprehensible presentation of the results.

The search included Academy of Management Journal , Administrative Science Quarterly , Entrepreneurship Theory and Practice , Journal of Management Studies , Strategic Management Journal .

The search included European Journal of Information Systems , Information Systems Journal , Information Systems Research , Journal of the Association for Information Systems , Journal of Information Technology , Journal of Management Information Systems , Journal of Strategic Information Systems , MIS Quarterly , MISQ Executive .

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Data structure for the technology-centric dimension of technological disruption

Data structure for the technology-centric dimension of corporate entrepreneurship

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Nadkarni, S., Prügl, R. Digital transformation: a review, synthesis and opportunities for future research. Manag Rev Q 71 , 233–341 (2021). https://doi.org/10.1007/s11301-020-00185-7

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The Nine Elements of Digital Transformation

In-depth research with executives at a wide range of companies shows how managers can use technology to redefine their businesses..

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Digital transformation — the use of technology to radically improve performance or reach of enterprises — is a hot topic for companies across the globe. Executives in all industries are using digital advances such as analytics, mobility, social media and smart embedded devices as well as improving their use of traditional technologies such as ERP to change customer relationships, internal processes and value propositions. Other executives, seeing how fast digital technology disrupted media industries in the past decade, know they need to pay attention to changes in their industries now.

Where can you look for digital transformation opportunities? We interviewed 157 executives in 50 companies to find out. These companies are large — typically $1 billion or more in annual sales — and spanned 15 countries. To provide balanced perspectives, approximately half of the interviewees were business leaders such as CEOs, line of business managers, marketing heads or COOs, while the other half were IT and technology leaders.

The companies we interviewed are moving forward with digital transformation at varying paces and experiencing varying levels of success. Some are transforming many parts of their organizations while others are still doing only the basics. Others are encountering organizational issues or other challenges that prevent them from transforming successfully.

But one thing we found was clear. The best companies — those we call Digirati — combine digital activity with strong leadership to turn technology into transformation. This is what we call Digital Maturity. Companies vary in their digital maturity , and those that are more mature outperform those that are not.

Leading digital change requires managers to have a vision of how to transform their company for a digital world. So, where can you look? What digital activities represent good opportunities for your business?

Analysis of the interviews shows clear patterns. Executives are digitally transforming three key areas of their enterprises: customer experience, operational processes and business models. And each of these three pillars has three different elements that are changing. These nine elements form a set of building blocks for digital transformation.

Currently, no company in our sample has fully transformed all nine elements.

About the Authors

George Westerman is a research scientist at the MIT Center for Digital Business. Didier Bonnet is global practice leader and executive sponsor of Digital Transformation at Capgemini Consulting. Andrew McAfee is assistant director at the MIT Center for Digital Business. This essay was excerpted and adapted from their report Digital Transformation: A Roadmap for Billion-Dollar Organizations , which was named one of the top five thought-leadership pieces of the decade by Whitespace/Source.com.

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Digital transformation: A meta-review and guidelines for future research

João reis.

a Industrial Engineering and Management, Faculty of Engineering, Lusofona University and EIGeS, Campo Grande, 1749-024, Lisbon, Portugal

Nuno Melão

b CISeD–Research Center in Digital Services, Polytechnic Institute of Viseu, Campus Politécnico, 3504-510, Viseu, Portugal

The emergence of digital transformation has changed the business landscape for the foreseeable future. As scholars advance their understanding and digital transformation begins to gain maturity, it becomes necessary to develop a synthesis to create solid foundations. To do so, significant steps need to be taken to critically, rigorously, and transparently examine the existing literature. Therefore, this article uses a meta-review with the support of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Protocol. As a result, we identified six dimensions and seventeen categories related to digital transformation. The organizational, technological, and social dimensions are still pivotal in digital transformation, while two new dimensions (sustainability and smart cities) still need to be explored in the existing literature. The need to deepen knowledge in digital transformation and refine the dimensions found is of paramount importance, as it involves some complexity due to organizational dynamics and the development of new technologies. It was also possible to identify opportunities, challenges, and future directions.

1. Introduction

In recent years, academics have provided in-depth knowledge regarding Digital Transformation (DT). These contributions were carried out in the production industry [ 1 ], service industry [ 2 ], healthcare [ 3 ], and education [ 4 ], just to name a few areas. However, these studies are dispersed across several academic fields. As the academic community realized this limitation, researchers became interested in gaining a broader view of DT through systematic literature reviews (SLR) within each field [ 5 – 7 ] and some of them about the DT phenomenon itself [ 8 ]. Although the aforementioned works have contributed to significant advances in knowledge, there are no records of articles providing a detailed holistic view of DT. To fill this gap in the literature, we followed the suggestions of notable scholars [ 9 , 10 ] and set out to undertake a meta-review. Along with this, we also identified reports of other phenomena about DT, such as the paradox of digital technologies [ 11 ]. If, on the one hand, there is a belief in the benefits of adopting DT, on the other hand, there has been some frustration with DT and its impacts on organizations. Conceptually, DT benefits organizations with better operational efficiency [ 6 , 12 ], greater innovation [ 13 ], and cost reduction [ 14 ] in the medium-long term. However, the implementation of DT is complex as it entails initial costs, requires changes, and creates resistance from workers [ 15 ]. Therefore, DT adoption may be risky without models and tools that assist its implementation across organizations. Viewed in isolation, this meta-review may be considered ambitious; however, it can become a relevant work if viewed from a holistic perspective, along with other systematic reviews. We opted for a meta-review because it can ensure reproducibility and transparency of the entire review process. To this end, we explained the methodological process in detail and included the content analysis process (see Appendix A) to make the entire process visible to readers. With DT changing rapidly, the need to identify opportunities, challenges, and future directions is critical. In this regard, we developed the following research question: What are the drivers of DT promoting scientific growth? The answer to the previous question can be achieved by addressing the following objectives: (1) identifying the most relevant thematic areas; (2) categorize the literature on DT; and (3) propose future research based on recent studies. We consider this study original and innovative because it fills an important gap in the literature. In November 22nd, 2022, after performing a search on Elsevier Scopus with the search terms “digital transforming” and “meta-review” in the title of the document, no result was found; in title-abstract-keyword only four documents were found, but they were not directly related to the theme. These results obtained in one of the most important international databases are surprising, especially considering the exponential growth of research on DT in recent years.

The next section provides a conceptualization of DT and associated terms. We then explain the PRISMA process and how the data was collected and analyzed. The results section presents a holistic theoretical-conceptual model of DT and a research agenda. Finally, the conclusions section focuses on managerial, theoretical, and original contributions.

2. Conceptual overview

In the existing literature, concepts referring to DT are still inconsistent or treated simplistically [ 16 , 17 ]. Although there is still some difficulty in accepting a consensual definition of DT, this section describes the relationship between digitation, digitalization, and DT. If it was common to find conceptual miscellanea in the past between digitization, digitalization, and DT, this issue now seems to be overcome. In that regard, Kohli & Johnson [ 18 ] stress that digitization is commonly associated with transforming traditional processes into digital ones. Loske & Klumpp [ 19 ] also consider that digitization is a “process of converting analog data into digital data sets.” Furthermore, recent research argues that digitization encodes or shifts analog tasks and information into a digital format so that computers can store, process, or transmit information without altering value-creating activities [ 20 ]. An excellent example of digitization is e-books or downloadable music, i.e., converting tangible products into products delivered digitally [ 18 ].

Digitalization, in turn, is described as digital technologies that can be used to alter existing business processes. In that regard, companies are investing in products and process innovation through new digital solutions, allowing them to deal with more data and information [ 21 ]. One example is the creation of online or mobile communication channels allowing customers to connect with companies more conveniently than through traditional interactions [ 22 ]. Thus, within the scope of digitalization, companies must apply digital technologies that allow the optimization of existing business processes, i.e., better coordination between processes and creating value for the customer. In short, the difference between digitation and digitalization lies in creating value and improving the customer experience.

Although the concept of DT has gained significant notoriety only recently, it dates back to the 90's [ 23 ]. DT goes beyond digitalization as it involves changing organizational processes and tasks, which typically lead to developing new business models [ 17 ]. Thus, DT consists of integrating information technologies in companies' operations, whether internal or external [ 24 ]. It can also be considered as a change that occurs with the implementation of technologies in a system within a company [ 19 ]. This transformation is supported by the adoption of new technologies from which new performance, new processes, and new business models emerge [ 25 , 26 ]. In addition, DT is not only linked to technology, but also to an improvement in the business model, collaboration, and culture [ 27 ]. This transformation arises with the use of digital tools in the daily activities and processes of the company, being subsequently achieved through its promotion inside and outside it [ 28 ]. For instance, DT can be employed in several domains, such as the healthcare sector; in this regard, the wide and deep use of information technologies changes how health services are delivered and processed [ 29 ]. A company that opts for DT seeks to offer a product and/or service through new digital formats, thus achieving a link between physical processes and virtual processes [ 23 ]. Some authors identify several possible contributions of DT in a company, such as: (1) optimization of physical and digital resources; (2) obtaining greater competitive advantage; (3) greater creation of value for the customer; and (4) cost reduction [ 30 , 31 ].

However, not all industries have been able to keep up with this technological pace and adopt digital technologies, either due to investment difficulties or lack of adaptation of their business model [32, p. 141]. In a digital company, success involves accepting market uncertainty and volatility, identifying opportunities and having the ambition to realize them, as well as making quick decisions taking into account innovation, customers and competitors [ 33 ]. DT has played a disruptive role in various sectors of activity. However, the retail sector was considered one of the sectors most prone to DT [ 30 , 32 ]. This is due to the emergence of new consumers called “digital natives”, who have driven the use of digital platforms and, consequently, the need for innovation in current business models [ 7 ]. The next section discusses the data collection process, the content analysis, and the research limitations.

3. Materials and methods

This article uses a meta-review, as it aims to synthesize the existing body of completed and recorded work produced by researchers [ 34 ]. Meta-reviews are methods known to be able to gather the literature and which can have a significant influence on research, practice, and policy [ 35 ]. A Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) also supports the meta-review to discover new ideas, concepts, and debates in a critical, rigorous, and transparent way. PRISMA included a checklist of 27 items and four-phase flowchart ( Fig. 1 ), enabling data extraction from two of the largest abstract and citation databases of peer-reviewed literature.

PRISMA flowchart.

The search was conducted in Elsevier's Scopus and Web of Science Core Collection (WoS) on December 8, 2021 ( Fig. 1 ). This search combined the terms “digital transformation” and “systematic literature review” in the Title-Abstract-Keywords (TITLE-ABS-KEY) to identify the manuscripts within the area of research (identification phase). Then, we applied pre-selected filters (i.e., language, source, and document type) to identify the most relevant manuscripts (screening phase). The next phase included accessibility criteria (eligibility phase), which encompassed removing duplicated articles and those that were not strictly related to the topic. Finally, articles not identified in the Scopus and WoS databases were included (inclusion phase). Incorporating additional articles allowed to justify and/or reinforce the arguments used in the results section. That is, highly cited conference papers in DT [ 17 ] can also be relevant and should not be left out. We were careful with the issue of transparency, and, for that reason, we included the flowchart ( Fig. 1 ) and their respective explanation. As mentioned earlier, data collection in the Scopus and WoS databases was carried out until the end of 2021. Both databases were selected because they are considered the largest international and multidisciplinary research databases of peer-reviewed manuscripts. This argument is also used by researchers who have published articles on DT in top-tier Journals, such as Benavides et al. [ 5 ] and Lombardi and Secundo [ 36 ], or in some cases, just one of the selected databases, such as the WoS, by Zhu [ 8 ], and Scopus by de Bem Machado et al. [ 37 ]. A more objective argument that justifies using Scopus and WoS is related to the coverage of journals in the area of Natural Sciences and Engineering [ 38 ], areas typically associated with DT. Moreover, we could have much broader data coverage [ 39 ] and free access if we selected Google Scholar. However, despite being a powerful search engine, it does not guarantee that the documents included have been peer-reviewed.

After performing the search using the terms “digital transformation” and “systematic literature review” in the TITLE-ABS-KEY, we identified 262 manuscripts. Following this, we applied the filter by full-text journal articles to obtain high-quality research articles. For readability and interpretation reasons, we selected only articles in English; otherwise, difficulties in interpretation could lead to biased results. This phase resulted in the selection of 79 scientific journal articles. The eligibility phase allowed the elimination of 17 duplicate articles and 33 articles that did not correspond to the research objectives, resulting in 29 articles. The last phase included 5 more articles, so in the end, we were left with 34 articles to analyze. The PRISMA protocol we followed uses the same process of identification, screening, eligibility, and inclusion of other relevant scientific articles published in Q1 journals, whose databases were Scopus and WoS [ 40 ].

Data were encoded twice. First, the articles were manually encoded. That is, the articles were read in full, and repeated words and text excerpts were identified ( Appendix A). Data analysis was performed using low-tech material (e.g., Excel). However, as a significant number of articles were being examined, text analysis using a computer-assisted data analysis package is recommended. Therefore, the second step included using NVivo12 [ 41 ], a qualitative data analysis software for researchers. Data were analyzed using the content analysis technique [ 42 ]. This technique allowed coding the most important phrases and words [ 43 ], making it possible to identify patterns in emerging codes and ideas. Specifically, the process was carried out in four stages: first, we read the entire texts to identify the most relevant phrases and ideas, followed by a coding process; second, we associated excerpts/codes from the selected articles with the categories and added new ones as necessary; third, we identified emerging patterns and ideas (dimensions); lastly, we revised the previous categories, making adjustments, until redundancies and contradictions were clarified and the results were easily interpreted. In short, this technique enabled to code and analyze a large volume of data. After the content analysis, we also followed a verification process: first, we compared the two analyzes, the aforementioned manual cross-analysis with NVivo12; secondly, a verification that included the analysis of the articles’ keywords. The latter step included cross-checking the categories and sub-categories (i.e., our manual categorization) with the 34 articles’ keyword statistics (i.e., authors' choice) and which can be retrieved directly from Scopus. This process allowed to identify discrepancies in the data analysis. As we found similarities, we consolidated the coding process.

Despite the advantages of meta-review, this methodology also has limitations. Applying filters may have excluded relevant documents from other databases (PubMed, etc.), search engines (e.g., Google scholar), or other forms of publication (e.g., books, chapters). However, the PRISMA technique has an advantage over traditional systematic reviews because, unlike the latter, PRISMA (last phase) allows the inclusion of relevant articles overcoming the aforementioned limitation. Lastly, this article presents a “snapshot” of the reality, as both databases are permanently being updated.

4. Results and discussion

4.1. digital transformation overview – influential topics and subject areas.

This section aims to respond to the first research objective. To transparently identify the most relevant thematic areas, we use the graphs provided directly by the Scopus database, which is the leading database for this article (similarly used by Lombardi and Secundo [ 36 ]). Compared to WoS, Scopus was selected for covering a wider range of journals, both in keyword search and citation analysis [ 16 ]. Additionally, most papers indexed in WoS are included in Scopus [ 44 ]. Indeed, when we exclude repeated articles (i.e., screening phase, Fig. 1 ), most of the selected articles come from Scopus. Therefore, for this section, the first initial terms “digital transformation” and “systematic literature review” were used in the Scopus TITLE-ABS-KEY (resulting in 157 articles), which allowed us to identify the most relevant thematic areas. This graphical analysis aims to provide the most holistic view possible in order to provide readers with an overview of the results. For example, from this analysis, the reader can easily infer that the topic is growing exponentially ( Fig. 2 ) and that only 30% of Scopus documents have been analyzed ( Fig. 3 ). For quality reasons, the content analysis had to focus only on journal articles, being therefore more restricted.

Documents by year (retrieved from Elsevier Scopus).

Documents by type (retrieved from Elsevier Scopus).

Fig. 2 shows the upward scientific interest in DT, especially from 2018 onwards. This phenomenon is probably explained by the maturity of the topic, making it possible to analyze the existing literature with some relevance. In particular, we can see that published studies have mainly focused on business model strategies [ 45 – 49 ], digital business [ 48 , 50 , 51 ], the use of disruptive technologies [ 47 , [52] , [53] , [54] ], sustainability [ 55 , 56 ], human resources [ 57 – 59 ], and smart cities [ 45 ]. In turn, Fig. 3 shows the types of documents focused on DT. The publication in conference proceedings is an indicator that DT is arousing the interest of researchers in the scope of the discussion of ideas and the search for solid knowledge on the subject. In terms of article publishing, we have seen that the appetite of top-tier indexed Journals is high, as 45% of the articles are from Q1 Journals and 31% from Q2 Journals.

Regarding the distribution of papers by country, we can see that Germany, the United Kingdom, and Brazil are the ones that stood out the most ( Fig. 4 ). Germany stands out from the other economies, as German industry is one of the main drivers of Industry 4.0 (I4.0). To do so, Germany has made a significant investment in research, which is essential for initiatives aimed at digitizing the manufacturing industry [ 56 ]. For instance, Siemens has formed a research alliance in industrial automation and digitization with the state-funded Technical University of Munich, the Ludwig-Maximilians University, the German Research Center for Artificial Intelligence, and the Fraunhofer Institute for Applied and Integrated Security Applications [ 60 ].

Documents by country or territory (retrieved from Elsevier Scopus).

Considering that one of the drivers of the German economy has been I4.0, it is not surprising that the areas with the greatest scientific research are computer science (26.6%) and engineering (15.1%) in the context of the development of cyber-physical systems, cybersecurity, cloud computing, advanced robotics, just to name a few. Fig. 5 , with no surprise, also includes the subject area of business, management, and accounting (17.7%), given the impact of its coverage in different countries, industries, companies, and people. In that regard, Kraus et al. [ 61 ] argue that DT has led to considerable changes in many organizations, no longer seen as just a technological opportunity but as a way to introduce new processes that can improve the main structures of how companies do business.

Documents by subject area (retrieved from Elsevier Scopus).

4.2. Digital transformation overview – dimensions and categories

This section presents a general view of the existing literature regarding DT, thus responding to the second research objective. We focused exclusively on the analysis of the 34 articles that were selected from Scopus and WoS ( Fig. 1 ). Table 1 shows the dimensions and categories identified during data analysis. Appendix A presents a series of tables with more detailed information (including codes/phrases). Although it is not common to see tables with the complete content analysis available in scientific articles, we decided to make all the information available to the reader for transparency and reproducibility reasons.

Dimensions and categories.

4.2.1. Business models

The first dimension addresses (but not limited to) topics, such as ( Table A1 ): (1) business process innovation, which is improving the competitive position of organizations [ 45 , 54 ] and bringing disruptive DT to the global industrial value chain [ 53 , 60 , 62 ]; (2) digital business strategy that enhances productivity [ 46 , 63 , 64 ] and creates new value for customers [ 65 ].

With regard to innovation, the trend is for organizations in DT environments to implement value-added innovation by integrating social and economic dimensions from different types of innovation, such as product-service and process innovation, as well as innovation in business and organizational models [ 54 , 60 ]. Developing a digital business strategy is critical for organizations as DT involves business and technology issues, transcending organizational boundaries [ 46 ]. Furthermore, selecting technologies (i.e., tech-oriented) is vital to the business strategy and can significantly add value to the business [ 63 ]. Initially, Information Technology (IT) strategy was seen as a functional- and secondary-level strategy component; however, nowadays, DT is the central pillar of the strategy, driving the emergence of the “digital strategy” concept [ 48 ]. Thus, in the context of the digital age, the organizational environment is also more volatile, uncertain, complex, and ambiguous (VUCA), so the rapid changes in competition, demand, technology, and regulations are more challenging than ever. In that regard, the pressure on companies to align their business strategy with the changing technological environment has increased significantly with the emergence and growing importance of new disruptive digital technologies [ 60 , 64 ]. Therefore, a digital business strategy demands strong leadership, an agile and scalable core, and a clear focus on customer engagement or a digitized solutions strategy [ 65 ]. The “tech-oriented” view fails to capture the more fundamentally important role of the “procedural” character of DT, demanding a deeper and more complete “transformational” effort on vision, strategy, culture, human skills, resources and infrastructures, business model, and company's competitiveness [ 48 , 61 ].

In short, with regard to business models, we found that process innovation is changing the business landscape, increasing competitiveness through the development of new digital services and products. In that regard, the business strategy focuses on disruptive technologies. The VUCA environment pushes for a more comprehensive and transformational strategy where people and resources adapt to organizational needs.

4.2.2. Digital business

The second dimension addresses (but not limited to) topics, such as ( Table A2 ): (1) digital culture, literacy, and digital skills that are enhancing DT efforts [ 52 , 58 , 64 ]; (2) digital economy and the challenge of measuring the potential generated by digital technologies [ 65 , 66 ]; (3) innovation and socio-technological shared values, being seen as an opportunity to balance the responsibilities assigned to humans and machines [ 54 , 65 ].

When it comes to digital business, organizations wanting to benefit from their technology investments need to strengthen the digital skills of their workforce [ 58 ]. Therefore, the workforce is one of the key actors in transforming the organization, as digitally capable human resources will be managing and using technology [ 48 , 66 ]. Furthermore, employees working in digitally mature organizations describe their culture as more collaborative and innovative than traditional ones [ 64 ].

The success of the digital economy is expected to be ensured by strengthening the position of companies through the quality of corporate governance and financial structure, aligned with the latest technologies. The digital economy is seen as an economy that accelerates the DT of existing economic sectors, promotes new ecosystems enabled by digital technologies, and develops a digital industry [ 66 ]. Thus, the digital economy includes a combination of digital infrastructure, socio-technical processes, and information and communication technologies [ 56 ]. The risk of the digital economy is associated with the large-scale acceleration of the development of new technologies, which seems almost unstoppable due to the intensive innovation trend. Moreover, recent studies have also stressed that the greatest challenge many organizations face when investing in DT is finding a way for equating, reimagining and redefining the employees experience and bringing their digital literacy up to date. At this level, artificial intelligence (AI) is demanding greater skill in terms of problem solving, as it begins to outperform human performance in executing analytically complex cognitive tasks. Thus, the challenges appear to be twofold, both from the point of view of technological acceleration and the digital literacy of the workforce.

4.2.3. Technologies

The third dimension addresses (but not limited to) topics, such as ( Table A3 ): (1) technology and innovation management, which has been one of the main drivers of DT [ 48 , 52 , 61 , 64 , 65 , 67 ]; (2) AI and big data, which have been propelling significant developments in carrying out analytical-cognitive activities both in organizations and in the industry [ 55 , 56 , 58 , 62 , 64 , 68 ]; and the (3) Internet of Things (IoT) and I4.0, which involves the interconnection of computing power and intelligent data flow, enabling process control in the service and production industry [ 48 , 62 ].

Technology is one of the main drivers of DT, giving a significant boost to organizations that integrate this key factor into their strategy [ 62 ]. As mentioned earlier, technology is an enabler of DT that is causing a change in value creation, as it supports the development of new business models and a focus on acquiring new skills and competencies [ 67 ]. One of the largest consultancies, McKinsey & Company, proposed a model based on six building blocks that allows implementing a successful end-to-end transformation for industrial companies. These six blocks naturally go beyond the simple technology upgrade and are: (1) Create a business-led technology roadmap; (2) Talent development and qualification; (3) Adopt an agile delivery methodology; (4) Moving to a modern technology environment; (5) Focus on enriching data management; (6) Conduct the adaptation and scaling of digital initiatives [ 52 ]. With regard to technology, DT has aroused interest in specific digital technologies, such as AI and big data [ 65 ]. Due to VUCA pressure, companies are aligning their business strategy with digital technological change (e.g., AI, Big Data) [ 64 ]. In that regard, AI is defined as the transformation of service-product processes into automated processes, dependent on intelligent computer systems or robots that do not require human intervention to perform tasks associated with intelligence [ 6 , 47 ]. Despite the well-known advantages of AI and robotics, current discussion often covers the risks of automation. Debates have focused more on the adaptability of jobs in DT than on replacing human labor [ 69 ]. Most studies suggest that complex socioemotional tasks continue to be performed by human beings, while cognitive-analytic tasks will be increasingly migrated to machines [ 70 ]. DT has therefore led to the formation of the digital organization, whose most volatile asset is AI and computational capital, evidenced in the continuous growth of automated information and the creation of digital products [ 56 ]. Digital technologies such as AI, big data analytics, and social platforms generate positive improvements for society (smart cities) and industry (I4.0) [ 55 ]. Thus, DT has been described as the change in an organization's structure, processes, functions and business models due to the adoption of digital technologies such as IoT, AI, machine learning, augmented reality, just to mention a few [ 17 , 58 ]. Therefore, DT does not focus only on organizations, but on almost all domains of knowledge, as it radically changes the concepts traditionally defined in organizational and management science [ 68 ].

4.2.4. Sustainability

The fourth dimension addresses (but not limited to) topics, such as ( Table A4 ): (1) sustainable businesses that focus on the integration of new and disruptive technologies [ 53 , 55 , 56 ]; (2) sustainable competitive advantage by integrating these technologies into the companies’ business processes [ 47 ]; (3) sustainable development with an emphasis on the United Nations Sustainable Development Goals (SDGs) [ 56 ]; and (4) sustainable innovation with an emphasis on open innovation theory [ 53 ].

Transformation to I4.0 has involved occupational adaptations to ensure quality and sustainable business models [ 56 ], leading to carbon emissions reductions [ 55 ] and an augmented degree of social responsibility [ 53 ]. Within the scope of DT, industry-specific IT resources are valued because they reduce costs, supporting sustainable competitive advantages as a result [ 62 ]. Therefore, the objective of companies is to establish sustainable performance and competitive advantage by integrating technology in the decision-making process with corporate strategy [ 47 ]. Additionally, the open innovation paradigm suggests that a holistic and cognitive approach to corporate governance, based on a regime of cooperation between internal and external resources for value creation, opens the possibility of redefining business models in which knowledge develops horizontally. This is achieved by involving all actors in the corporate ecosystem to gain a long-term sustainable competitive advantage [ 53 ]. The interest is in understanding and presenting the impact of digitization initiatives on economic growth and the achievement of the United Nations SDG [ 56 ].

4.2.5. Human resources (HR)

The fifth dimension addresses (but not limited to) topics, such as ( Table A5 ) employee experience, career dynamics, and type of human-machine relationships [ 57 , 58 ].

Within DT, HR concerns have been about the ability of employees to establish Human-Robot Interaction and Collaboration (HRI-C) relationships. At this level, the discussion is broad and involves a change in culture, mindset, and skills required from employees [ 58 ]. However, dealing with DT and the establishment of HRI-C dynamics can be challenging, particularly if employees are not ready for them. Therefore, the pressure to create HRI-Cs can create information overload and employee anxiety [ 58 ]. On top of that, while the benefits of a diverse workforce are well known, the career dynamics of individuals with technical differences over the rest are not well understood [ 57 ]. These different levels of expertise conflict with the balance between the professional and personal lives of the workforce. Therefore, companies must find strategies to balance professional and personal life for individuals who move to more specialized fields.

Furthermore, the literature also highlights that “a change management strategy to gradually change the mindset of the workforce and senior management, and instill the idea that there is no end to change” [52, p. 15]. It is recommended that organizations should develop change management models in DT environments, similar to traditional models (e.g., Lewin's or Kotter's change management models). In that regard, Attaran and Attaran [ 63 ] go further, stating that organizations fail to change because leaders do not pay enough attention to change management, which negatively affects the companies’ HR, making the next change more challenging to implement.

4.2.6. Smart cities

The sixth dimension addresses (but not limited to) ( Table A6 ) smart manufacturing [ 45 , 55 , 60 ], in particular the use of disruptive technologies to produce high-value products and services. Smart cities are not exactly smart manufacturing; however, smart manufacturing contributes to a larger scenario, acting as an enabler of smart cities. This aspect emerges from our analysis and is in line with the arguments of Suvarna et al. [ 71 ]. According to these authors, smart manufacturing contributes to smart cities not only from a technological point of view but also because it satisfies sustainability issues, which are important indices that make up a smart city. Other authors, such as Lom et al. [ 72 ], followed the same argument when they stated that process-based I4.0 with smart city transportation systems could create very effective, demand-driven, and highly productive manufacturing companies, while contributing to the sustainable development of society.

DT has attracted increasing interest from academics and practitioners regarding sustainability and intelligence/automation, such as smart cities, smart homes, smart governments, and smart production [ 45 ]. In particular, the alliance between sustainability and intelligence is at the center of academic discussion, highlighting themes such as sustainable smart manufacturing being enabled by digital technologies, such as IoT, cloud computing, big data, cyber-physical systems, AI, etc. [ 55 ]. These disruptive technologies have been offering unprecedented opportunities to create and develop value-added products and services [ 73 ]. In that regard, we identified that smart cities work as an extensive smart ecosystem, including different value activities and specific business functions and technologies [ 60 ]. To stimulate research on smart cities, there have been numerous special issues published by top-tier journals [ 73 , 74 ]. Thus, according to our analysis, smart cities are in increasing development, being a promising research area.

4.3. Proposed research agenda

The meta-review sets the stage for a research agenda. This review documents what is already known and, using critical knowledge gap analysis, helps to refine research questions, concepts, and theories to point the way for future research [ 75 ]. The articulation between the research question and the DT dimensions allowed the definition of the research agenda. Thus, the proposed research agenda defines the research areas and priorities that guide scholars.

Early in this article, we presented four figures that allowed us to identify the publication of documents by year ( Fig. 2 ), type ( Fig. 3 ), country ( Fig. 4 ), and subject area ( Fig. 5 ). The areas of research identified with the most remarkable growth are open innovation ( Table A4 . Sustainability) and I4.0 ( Table A1 . Business Model and Table A3 . Technologies), within the scope of (1) Computer Science; (2) Business, Management, and Accounting; (3) Engineering ( vide Fig. 5 ). An example that illustrates the scientific development of the areas above (i.e., open innovation and I4.0); is given by Savastano et al. [ 60 ], referring to the case of the alliance between Siemens with the state-funded Technical University of Munich, the German Research Center for Artificial Intelligence, and the Fraunhofer Institute for Applied and Integrated Security Applications.

Some topics described above were also identified in the content analysis stage (i.e., six dimensions and respective categories), allowing us to pinpoint the research priorities for DT. Below, the reader can find the main contributions of the article that frame the research agenda:

• • According to the literature, VUCA environments are pushing for comprehensive and transformational digital strategies, changing the business landscape by increasing competitiveness in developing new services and products. To streamline research on the development of smart services and products, several special issues have been published by leading journals [ 76 ]. Therefore, disruptive technologies (AI, Big data, etc.) and innovation have been one of the main drivers of DT in building new digital services and products, and this trend is likely to continue.
• • Compared with an early DT literature review, published in 2018 by Reis et al., new dimensions have been highlighted in this article. The three dimensions identified by Reis et al. [ 17 ] are still widely explored, namely organizational ( Table A1 and A2 ), technological ( Table A3 ), and social ( Table A5 ). However, the new dimensions, namely sustainability ( Table A4 ) and smart cities ( Table A6 ) are still underdeveloped. What is new in this article is that while sustainability and smart cities are widely explored in other research domains (e.g., social sciences, engineering, etc.), within the scope of DT (i.e., business and management), it still falls far short of expectations. This argument may be also supported by a quick search in Elsevier Scopus (dated May 15th, 2022) with the keyword “sustainability” in TITLE-ABS-KEY, which indicates that the top 3 subject areas are Environmental Sciences (18.2%), Social Sciences (15.2%), and Engineering (11.3%); Business, Management, and Accounting represents only 7.5% of worldwide research. With regard to “smart cities”, a similar search shows that the top 3 subject areas are Computer Sciences (31.7%), Engineering (19.6%), and Social Sciences (11.2%); Business, Management, and Accounting represents only 2.6% of the worldwide research. This is a significant gap, considering that, in the scope of DT, the subject area Business, Management, and Accounting is in the top two with 17.7% ( Fig. 5 ).
• • From our analysis, future research may focus on the latter two dimensions (i.e., sustainability and smart cities). In that regard, researchers point out that empirical studies linking DT and sustainability are still scarce [ 77 ]. At the same time, recent growth in digital technologies is enabling cities to streamline smart services and offering new products [ 78 ]. This argument is also pointed out by some recent studies that have investigated the literature on DT in the context of meta-reviews Reis et al. [ 73 ] or meta-synthesis [ 79 ] in smart cities. Therefore, we argue that additional efforts are needed to reduce the knowledge gap between these two concepts (sustainability and smart cities) and DT.
• • During data analysis, we tried to use the MECE rule (mutually exclusive and collectively exhaustive). MECE is a framework that allows solving complex problems by dividing them into sub-problems that are mutually exclusive (they do not overlap) and comprehensively exhaustive (cover all possibilities). The application of MECE rule was impossible in this context because of the difficulty of developing mutually exclusive sub-dimensions; nevertheless, the attempt presented interesting results. We delved deeper into this issue and realized that MECE is particularly important for creating taxonomies, as vague definitions cause overlaps between dimension characteristics [ 80 ]. An example is represented by the difficulty in the past in distinguishing between digitization, digitization, and DT. Since then, DT has been extensively investigated, with a clear conceptual distinction. But DT is so comprehensive that the concept crosses several research domains and dimensions (such as those identified in this article). For instance, the HR dimension is transversal to all other dimensions, such as technology (i.e., redefinition of HR skills) or digital business (sociotechnical values). In real terms, the dimensions identified are closely related to each other, covering all possibilities (i.e., comprehensively exhaustive). The MECE rule may still be used in the future, for mixed studies that incorporate literature review and empirical research for each of the dimensions identified in this article.
• • Lastly, the research agenda includes the suggestion to analyze the impact of incorporating various technologies and how they can influence companies at different levels – individual, departmental, and organizational. In this regard, Kozanoglu and Abedin [ 58 ] argue that future studies could investigate one or several technologies to determine how their number and/or qualities can influence employees at an individual and company level. More specifically, they give the example of the article by Du et al. [ 81 ] that analyzes the use of blockchain in the business processes of a financial company.

In short, when answering the research question, we found six dimensions of DT, along with seventeen categories and sixty-six codes. Four dimensions, out of six, have already been explored in early reviews of DT literature [ 17 ]. Thus, this article is original insofar as we evidenced that “sustainability” dimension has been driven by open innovation in the context of improving new business models; and the “smart city” dimension has been driven by disruptive technologies in the context of the development of smart systems.

5. Conclusion

5.1. theoretical contributions.

To the best of our knowledge, this is the first time a meta-review on DT has been carried out. For that reason alone, this article is already original, bringing a timely contribution. From what we could extract from the analysis, there was a significant growth in literature reviews on the subject. Therefore, the academic interest in meta-reviews per se justifies publication. The article contributes to the theory as it provides clear guidance on research paths. The main contribution is, therefore, the definition of a research agenda focused on six dimensions, namely: 1) business models; 2) digital business; 3) technologies; 4) sustainability; 5) human resources; 6) smart cities. In that regard, we also provided the categories that emerged from the analysis, giving a clearer perspective of each dimension.

In general terms, it was possible to identify two new dimensions compared to previous studies – sustainability and smart cities. The existing literature points out that empirical studies link DT and sustainable business. While the most skeptical readers of this article might claim that sustainability is a widely explored dimension, it seems to fall short of expectations in the context of DT. In this context, sustainability has been driven by open innovation in terms of improving new business models. With regard to smart cities, the development of disruptive technologies has been the key driver of progress. It seems pertinent, thus, to reduce the knowledge gap on sustainability and smart cities in the context of DT.

5.2. Managerial contributions

With regard to managerial contributions, the results of this article are somewhat limited. First, because this article follows a literature review strategy; second, because the article's objective was to define a scientific agenda. Nevertheless, we were able to identify some contributions. In particular, it was possible to verify that due to the link between DT and technology, the significant areas of development are connected to computer sciences and engineering. Thus, for companies that intend to invest in DT, from the point of view of recruiting and training of HR, it may be helpful to consider investments in the areas of industrial engineering, computer engineering, and management. At the organizational level and in the context of the digital age, managers who intend to pursue a DT strategy should pay special attention to the open innovation ecosystem (e.g., n-Helix), rather than investing in company-centric innovation. From a business point of view, there are opportunities within the scope of smart cities that should be explored, namely in developing new technologies and sustainable development.

5.3. Original contributions

According to the results of the meta-review, we found that the most relevant concern is the need to reduce the gap regarding sustainability and smart cities in the context of DT. Crossing that gap in the literature and what is new and original in this article, we would like to highlight some frustration with the DT implementation, specifically with sustainable HR, a neglected dimension both empirically and theoretically. In that regard, the literature stresses that a change management strategy is essential to develop sustainable HR by instilling the idea that there is no end to change. Thus, organizations must develop management models for change in DT environments, similar to those traditional models that already exist, such as the ADKAR model or Kotter's change management model. The suggestion of developing new DT HR models is particularly relevant in digital business. Technological acceleration is forcing organizations to strengthen the digital skills of their workforce. The debates around adapting the workforce to DT contexts are not new. However, we advocate the development of HR sustainability models to adapt the workforce to Digital VUCA environments, where technological acceleration persists. Moreover, the existing literature refers the need to develop comprehensive transformational organizational efforts, particularly from a socio-technical perspective [ 48 ]. From our analysis, the smart cities dimension is very focused on smart production/manufacturing. Thus, in our view, the socio-technical approach is underdeveloped in this context. The same is not valid regarding the business model and digital model dimensions. We may have found our mutually exclusive sub-dimension in the sociotechnical issue. In other words, the socio-technical issue is a subset that still is not transversal to the different DT dimensions. However, as far as we know, there are already several articles outside the context of this research that analyze the socio-technical issue in smart cities [ 82 , 83 ] (although not focused on DT), which leads us to believe that a greater degree of scientific deepening is needed.

Appendix A. 

Business Models dimension

Digital Business Ecosystems dimension

Technological dimension

Sustainability dimension

Human Resources dimension

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A framework to help leaders decide where to invest.

Digital transformation can mean a lot of different things. For leaders, it can be hard to know where you should be focusing investment — and what kind of digital transformation you’re really after. The authors outline four pillars of digital transformation: IT uplift, digitizing operations, digital marketing, and new ventures. Which pillar is the right starting point for your company depends on your context, needs, but also your digital maturity.

Despite years of discussion, understanding what digital transformation means for established companies remains a daunting challenge. Leaders put in charge of a digital transformation feel pulled in many different directions, with competing demands from IT, marketing, sales, and operations. Without a clear understanding, the wrong people are often put in charge, with the wrong resources, and the wrong KPIs, setting the digital transformation project up for failure.

• Nathan Furr is a Professor of Strategy at INSEAD and a coauthor of five best-selling books, including The Upside of Uncertainty, The Innovator’s Method, Leading Transformation, Innovation Cap i tal , and Nail It then Scale It .
• Andrew Shipilov is a John H. Loudon Chaired Professor of International Management at INSEAD. He is a coauthor of Network Advantage: How to Unlock Value From Your Alliances and Partnerships .
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In his research, M.S. Krishnan, Accenture Professor of Computer Information Systems and professor of technology and operations, explores how digital technology and artificial intelligence are shaping modern business practices. Building off his work on digital transformation and technological innovation, his recent case studies on Tesla and Gooru explore how the development and implementation of technology affect companies and disrupt industries.

Krishnan began developing a framework for connecting technology and business in the late 1990s. At the dawn of the “dot com” boom, his research primarily focused on how software design could improve business functions and make business technology integration more efficient.

“At the time, there was a big disconnect in terms of how companies could implement technology. Many companies spent a lot of money on technology but couldn’t actually implement it to create business value,” said Krishnan.   

READ THE FULL INTERVIEW ON MICHIGAN ROSS NEWS

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• Published: 20 June 2024

Digitalizing health trials by the Clinical Trials Transformation Initiative

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The Clinical Trials Transformation Initiative (CTTI) provides recommendations to unlock the full potential of digital health trials, including tools to develop digital biomarkers or endpoints, apply remote technology and interact with health authorities.

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Karas, M. et al. Predicting Subjective Recovery from Lower Limb Surgery Using Consumer Wearables. Digit Biomark. 4 (Suppl. 1), 73–86 (2020).

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Brasier, N. et al. Next-generation digital biomarkers: continuous molecular health monitoring using wearable devices. Trends Biotechnol. 42 , 255–257 (2024).

Lustenberger, C. et al. Auditory deep sleep stimulation in older adults at home: a randomized crossover trial. Commun. Med. 2 , 30 (2022).

Aguilar, M. Bellerophon Therapeutics trial fails despite innovative digital endpoint. Stat News https://go.nature.com/3XmnZxD (5 June 2023).

Tenaerts, P., Madre, L., Archdeacon, P. & Califf, R. M. The Clinical Trials Transformation Initiative: innovation through collaboration. Nat. Rev. Drug Discov. 13 , 797–798 (2014).

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Acknowledgements

We thank all participants for sharing their perspectives and experiences with the CTTI to inform the CTTI recommendations noted in this Comment as well as the project teams for their contributions to CTTI’s digital health trial projects. We thank the Wellcome Trust for the unrestricted grant to support the workshop leading to this publication. The research to inform the development of the CTTI recommendations referred to in this paper was supported by the FDA of the US Department of Health and Human Services (HHS) as part of an award totalling US$3,778,241.33 with 15% financed with non-governmental sources. The contents are those of the author(s) and do not necessarily represent the official views of, nor an endorsement, by FDA, HHS or the US Government. For more information, please visit FDA.gov. Partial funding was also provided by pooled membership fees from the CTTI member organizations ( https://ctti-clinicaltrials.org/who_we_are/funding/ ).

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All authors drafted, critically reviewed and edited the manuscript. L.K. provided project management of CTTI’s digital health trial work, J.G. and N.B. organized the symposium, which served as the base for this publication.

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How COVID-19 has pushed companies over the technology tipping point—and transformed business forever

In just a few months’ time, the COVID-19 crisis has brought about years of change in the way companies in all sectors and regions do business. According to a new McKinsey Global Survey of executives, 1 The online survey was in the field from July 7 to July 31, 2020, and garnered responses from 899 C-level executives and senior managers representing the full range of regions, industries, company sizes, and functional specialties. their companies have accelerated the digitization of their customer and supply-chain interactions and of their internal operations by three to four years. And the share of digital or digitally enabled products in their portfolios has accelerated by a shocking seven years. 2 We looked at the past results for the degree of digital adoption reported in each of these areas of business operations. Based on the average percentage of adoption in each survey, we calculated a trendline to represent the average rate of adoption in 2017, 2018, and just before the crisis, which respondents were asked about in the 2020 survey. The acceleration time frame was calculated from the amount of time it would have taken to reach the current level of digital adoption respondents report if the precrisis pace of change had continued. Nearly all respondents say that their companies have stood up at least temporary solutions to meet many of the new demands on them, and much more quickly than they had thought possible before the crisis. What’s more, respondents expect most of these changes to be long lasting and are already making the kinds of investments that all but ensure they will stick. In fact, when we asked executives about the impact of the crisis on a range of measures, they say that funding for digital initiatives has increased more than anything else—more than increases in costs, the number of people in technology roles, and the number of customers.

To stay competitive in this new business and economic environment  requires new strategies and practices. Our findings suggest that executives are taking note: most respondents recognize technology’s strategic importance as a critical component of the business, not just a source of cost efficiencies. Respondents from the companies that have executed successful responses to the crisis report a range of technology capabilities that others don’t—most notably, filling gaps for technology talent during the crisis, the use of more advanced technologies, and speed in experimenting and innovating. 3 We define a successful organization as one that, according to respondents, has very effectively implemented their initial responses to COVID-19-related changes.

Digital adoption has taken a quantum leap at both the organizational and industry levels

During the pandemic, consumers have moved dramatically toward online channels , and companies and industries have responded in turn. The survey results confirm the rapid shift toward interacting with customers through digital channels. They also show that rates of adoption are years ahead of where they were when previous surveys were conducted—and even more in developed Asia than in other regions (Exhibit 1). Respondents are three times likelier now than before the crisis to say that at least 80 percent of their customer interactions are digital in nature.

Chart: The COVID-19 crisis has accelerated the digitization of customer interactions by several years

Chart summary.

2020 adoption acceleration 1

• Global: 3 years
• Asia-Pacific: 4 years
• Europe: 3 years
• North America: 3 years

1 Years ahead of the average rate of adoption from 2017 to 2019.

McKinsey & Company

Perhaps more surprising is the speedup in creating digital or digitally enhanced offerings. Across regions, the results suggest a seven-year increase, on average, in the rate at which companies are developing these products and services. Once again, the leap is even greater—ten years—in developed Asia (Exhibit 2). Respondents also report a similar mix of types of digital products in their portfolios before and during the pandemic. This finding suggests that during the crisis, companies have probably refocused their offerings rather than made huge leaps in product development in the span of a few months.

Across sectors, the results suggest that rates for developing digital products during the pandemic differ. Given the time frames for making manufacturing changes, the differences, not surprisingly, are more apparent between sectors with and without physical products than between B2B and B2C companies. Respondents in consumer packaged goods (CPG) and automotive and assembly, for example, report relatively low levels of change in their digital-product portfolios. By contrast, the reported increases are much more significant in healthcare and pharma, financial services, and professional services, where executives report a jump nearly twice as large as those reported in CPG companies.

The customer-facing elements of organizational operating models are not the only ones that have been affected. Respondents report similar accelerations in the digitization of their core internal operations (such as back-office, production, and R&D processes) and of interactions in their supply chains. Unlike customer-facing changes, the rate of adoption is consistent across regions.

Yet the speed with which respondents say their companies have responded to a range of COVID-19-related changes is, remarkably, even greater than their digitization across the business (Exhibit 3). We asked about 12 potential changes in respondents’ organizations and industries. For those that respondents have seen, we asked how long it took to execute them and how long that would have taken before the crisis. For many of these changes, respondents say, their companies acted 20 to 25 times faster than expected. In the case of remote working, respondents actually say their companies moved 40 times more quickly than they thought possible before the pandemic. Before then, respondents say it would have taken more than a year to implement the level of remote working that took place during the crisis. In actuality, it took an average of 11 days to implement a workable solution, and nearly all of the companies have stood up workable solutions within a few months.

Chart: Executives say their companies responded to a range of COVID-19–related changes much more quickly than they thought possible before the crisis.

1 Respondents who answered "Entry of new competitors in company's market/value chain" or "exit of major competitors from company's market/value chain" are not shown; compared with the other 10 changes, respondents are much more likely to say their companies have not been able to respond.

2 For instance, increased focus on health/hygiene.

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The largest changes are also the most likely to stick in the long term

Of the 12 changes the survey asked about, respondents across sectors and geographies are most likely to report a significant increase in remote working, changing customer needs (a switch to offerings that reflect new health and hygiene sensitivities), and customer preferences for remote interactions (Exhibit 4). Respondents reporting significant changes in these areas and increasing migration to the cloud are more than twice as likely to believe that these shifts will remain after the crisis than to expect a return to precrisis norms.

Respondents report that the crisis spurred shifts in their supply chains as well. The nature of these shifts varies significantly by sector, and they have taken place less quickly than other changes because of contracts that were already in place before the pandemic. Respondents in consumer-facing industries, such as CPG and retailing, often cite disruptions to last-mile delivery (that is, who interfaces directly with customers). Other shifts, such as building redundancy in the supply chain, are reported more often in sectors that create physical products.

The results also suggest that companies are making these crisis-related changes with the long term in mind. For most, the need to work and interact with customers remotely required investments in data security and an accelerated migration to the cloud. Now that the investments have been made, these companies have permanently removed some of the precrisis bottlenecks to virtual interactions. Majorities of respondents expect that such technology-related changes, along with remote work and customer interactions, will continue in the future. Nearly one-quarter of respondents also report a decrease in their physical footprints. This signifies a longer-term shift than would likely occur among the 21 percent reporting a drop in their number of full-time equivalents—at some companies, that could represent a temporary move in the earlier days of the crisis. What’s more, when we asked about the effects of the crisis on a range of company measures (including head counts), respondents say that funding of digital initiatives has increased more than anything else—more than costs, the number of people in digital or other technology roles, and the number of customers. 4 The other measures tested in the survey were revenues, the total number of full-time equivalents, physical footprints, the number of channel partners, earnings before interest and taxes (EBIT), enterprise-wide capital budgets for 2020, and digital M&A budgets for the next 12 months.

We also looked at the underlying reasons some changes would or would not stick: their cost-effectiveness, ability to meet customers’ needs, and advantages for the business. In addition, we examined the relationship between the length of the crisis and the permanence of the changes as “new” becomes “normal” over time.

Of the 12 changes, remote working and cloud migration are the two that respondents say have been more cost effective than precrisis norms and practices. Remote working is much less likely to meet customer expectations better than it did before the crisis; the changes that have done so best are, unsurprisingly, responses to the increasing demand for online interactions and to changing customer needs. Investments in data security and artificial intelligence are the changes respondents most often identify as helping to position organizations better than they were before the crisis. Across these changes, remote working is the likeliest to remain the longer the crisis lasts, according to 70 percent of the respondents.

Technology-driven strategy for the win

We’ve written before about the need for digital strategies to be true corporate strategies that take digital into account. And from earlier research, we know that at leading companies, digital and corporate strategies are one and the same . The COVID-19 crisis has made this imperative more urgent than ever. While the alignment on overall strategy and strong leadership have long been markers of success during disruptions or transformations , the extent of technology’s differentiating role in this crisis is stark (Exhibit 5). At the organizations that experimented with new digital technologies during the crisis, and among those that invested more capital expenditures in digital technology than their peers did, executives are twice as likely to report outsize revenue growth than executives at other companies.

The results also indicate that along with the multiyear acceleration of digital, the crisis has brought about a sea change in executive mindsets on the role of technology in business. In our 2017 survey , nearly half of executives ranked cost savings as one of the most important priorities for their digital strategies. Now, only 10 percent view technology in the same way; in fact, more than half say they are investing in technology for competitive advantage or refocusing their entire business around digital technologies (Exhibit 6).

This mindset shift is most common among executives whose organizations were losing revenue before the crisis began (Exhibit 7). Those reporting the biggest revenue hits in recent years acknowledge that they were behind their peers in their use of digital technologies—40 percent say so, compared with 24 percent at companies with the biggest revenue increases—and also say that, during the crisis, they have made much more significant changes to their strategies than other executives report.

What’s more, respondents say that technology capabilities stand out as key factors of success during the crisis. Among the biggest differences between the successful companies and all others is talent, the use of cutting-edge technologies, and a range of other capabilities (Exhibit 8). A related imperative for success is having a culture that encourages experimentation and acting early. Nearly half of respondents at successful companies say they were first to market with innovations during the crisis and that they were the first companies in their industries to experiment with new digital technologies. They are also more likely than others to report speeding up the time it takes for leaders to receive critical business information and reallocating resources to fund new initiatives. Both are key aspects of a culture of experimentation.

The notion of a tipping point for technology adoption or digital disruption isn’t new, but the survey data suggest that the COVID-19 crisis is a tipping point of historic proportions—and that more changes will be required as the economic and human situation evolves. The results also show that some significant lessons can be drawn from the steps organizations have already taken. One is the importance of learning, both tactically, in the process of making specific changes to businesses (which technologies to execute, and how), and organizationally (how to manage change at a pace that far exceeds that of prior experiences). Both types of learning will be critical going forward, since the pace of change is not likely to slow down.

The contributors to the development and analysis of this survey include Laura LaBerge, a director of capabilities for digital strategy in McKinsey’s Stamford office; Clayton O’Toole , a partner in the Minneapolis office; Jeremy Schneider , a senior partner in the New York office; and Kate Smaje , a senior partner in the London office.

This article was edited by Daniella Seiler, an editor in the New York office.

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• Merging machinery with IT to bring digital factories to life

The “digital transformation” of factories is all the rage among manufacturers, but extracting useful data from the nexus of machinery and IT systems is often easier said than done. Integrating legacy infrastructure with software can be technically challenging, and seasoned machinists and IT experts often do not see eye-to-eye on how to innovate.

In this roundtable, Senior Director of Optimize to Grow at Hitachi Rail, Gianfranco Messina; Director of Marketing at Hitachi Digital Services, Shamik Mehta, and VP, Manager of the IoT Edge Lab at Hitachi America R&D, Sudhanshu Gaur address these challenges and more. Whether they are referring to manufacturing processes, the “digital maturity” of customers, or generative AI, their position is clear: the status quo is untenable: market, societal and even geopolitical forces are driving machinery — or "operational technology” (OT) in Hitachi’s parlance — and IT systems closer together. Their proposal — focus on outcomes, manageable projects, and close communication among stakeholders to build digitally enabled factories that change the way people work, as well as drive business growth.

(Published 28 June 2024)

Sudhanshu: Thanks, everyone, for coming together for this conversation. I’m looking forward to diving into our discussion on digital transformation, or DX, and smart factories. First off, could we define what we mean by “factory of the future,” “smart factory,” or “digital factory”?

Gianfranco: At Hitachi Rail, we envision smart factories to include four dimensions: process, technology, people, and sustainability. In our view, a smart factory must address all these areas. Our latest facility in Hagerstown, Maryland in the U.S. addresses all these requirements.

On the digital front, we are working with Hitachi America R&D and other Hitachi group companies such as GlobalLogic and Hitachi Digital Services to coordinate our entire technology stack and innovation efforts across an array of processes, from inventory and supply chain management to quality inspection and health and safety management, among others. The intelligence that we extract from these processes will, we believe, enable us to fundamentally transform the nature of factory work along with delivering bottom-line benefits.

In terms of people, the current generation is connected like never before. A factory that doesn’t enable digital workflows is a strange work environment for the new generation of factory workers. Usage of smartphones and tablets on the factory floor is a given. We need to implement solutions that make factories people-centric, as well as technologically innovative.

Lastly, there’s sustainability. Factories have a big impact on CO 2 levels, so we must design with efficiency in mind. Smart factories need to be more than just digital factories. They need to be green factories. This is Hitachi’s vision. We believe it’s our social responsibility and we want to set an example for other companies.

Hitachi Rail’s digital factory under construction in Hagerstown, Maryland, USA

Sudhanshu: My take is that the definition of “smart factory” has evolved over time. In the early 1900s, we had mass production. Now we are turning to more personalization and hyper-customization at scale. This shift is made possible by a highly elastic cloud, AI, robotics, 5G and other advanced technologies.

Gianfranco, your point about resilient supply chains is spot on. COVID disrupted supply chains globally. Currently, we are plagued by geopolitical instability. In addition, we face a shortage of skilled labor. How can you operate a factory in such uncertain times? Factories that aren’t equipped with the right tools and software to protect production from potential disruptions are just not viable.

Shamik: I see the emergence of smart factories as a cultural phenomenon involving the way companies and people think about manufacturing and work.

The U.S.A. manufactures 30 percent more products today than it did three decades ago, but with a fraction of the people. Expectations for productivity and profitability have changed dramatically. And, to your point Sudhanshu, expectations on customization and personalization have changed. But we’re working with one-fiftieth of the people we used to. This is happening in the U.S. as well as globally.

In short, human productivity is at an all-time high, enabled by more automation, more software, and more AI. We are doing more with less, of course, but we are also doing things differently.

So, investments in digital technologies will continue unabated. According to Gartner, 80 percent of CEOs in the manufacturing industry are increasing investments in digital technologies, led by AI, IoT, and data and analytics. 1 Innovations based on these technologies will manifest themselves on the factory floor like never before.

*1 Sources of Greenhouse Gas Emissions, EPA, United States Environmental Protection Agency, https://www.gartner.com/en/industries/manufacturing-digital-transformation

Where art thou, customer?

Sudhanshu: I think we can all agree that the digital transformation of factories is essential, if not inevitable. But where do our customers stand? If you look at reports from industry analysts on the success rate of so-called DX initiatives involving smart manufacturing, the numbers, at under 10 percent, are extremely low.

When we talk about manufacturing, we’re working with predominantly legacy infrastructure. That makes it difficult to scale any digital solution from one production line to another, or any one factory to another. This has been a big challenge. How do we enable customers to integrate new tools into their factories?

Shamik: At Hitachi Digital Services, we find that our customers occupy four different areas in the “digital maturity model.”

Some customers are extremely sophisticated, using sensors to capture and digitize information on digital “gemba,” or factory floor, boards. This information is collected and shared by production leaders within the company, and in some cases around the world, to gain visibility into production runs.

Some customers have signed up with vendors, including Hitachi, who can help them create digital twins for their assets.

On the other end of the digital maturity model, people are writing down their observations on sticky notes placed on whiteboards inside the management room above the factory floor, things like what knobs were set to where, or how many times did the machine break down, rattle too much, or get hot?

Some customers are just now beginning to investigate basic data integration tools. This gets complicated because their existing assets are old－they were built before the Internet, and they’re not connected.

The OT x IT challenge

Sudhanshu: I’m with you, Shamik. Drawing from my experience working with auto parts manufacturer Hitachi Astemo, which has over 130 manufacturing sites spread across more than 25 countries, there’s a great deal of variation in digital maturity. They are at various stages of the technology adoption lifecycle.

Also, I think we need to make a more nuanced distinction as to whom we mean by “customer.” With manufacturers, there's the IT division, which is more eager to bring digital technologies into the organization to help the production team. But the production team, which has been using the same set of tools and technologies for the last 40 years, would rather avoid any friction created by new tools being introduced to the shop floor.

When we talk about smart manufacturing, it’s at the cusp of these two different roles, the IT part and the machinery or production part, what we at Hitachi call “operational technology,” or OT.

Sometimes the production teams doubt that they will benefit from the latest IT tools. There can be a complete lack of awareness of the benefits of IT, although this is changing with the advent of technologies such as the metaverse that allow manufacturers to model and visualize the benefits of their investments.

At auto parts manufacturer Astemo, they were among the few global automotive players who used the cloud to orchestrate and manage production data. The cloud offers scalability and ease of integration. You don’t have to worry about managing or maintaining the infrastructure and it’s pay-as-you-go, which is crucial for early trials where ROI may not be immediately verifiable. We are only now beginning to see the benefits. Astemo is well-prepared now, with readily accessible data and cloud infrastructure which connects their factories globally. That makes it possible to bring in any emerging digital solution or technology without investing too heavily in additional capital up front.

Shamik: We’re seeing many customers struggle with merging OT and IT data. If you want to make something like a ChatGPT model work, it can’t work with just IT data. It must understand operational data. You need to figure out trends, apply a model, and correlate it with higher-level processes, like a supply order, customer demand, pricing information, or requests for customization, which are of course information technology data sets.

Operational data is where you have an asset performance model, a digital twin, or a model that helps you measure the quality or performance throughputs of equipment. Operational technology and the data sets, formats, locations, places and tools that store and manage it are very different from informational enterprise data sets such as customer relationship tools, sales outlook, production demands, and other business planning software.

For a company to truly benefit from OT and IT integration, they cannot just blend the data. Rather, they need to understand what data is useful, drop what’s not, and figure out a way to contextualize and normalize the data so it’s usable by multiple personas, based on their needs. They need to start with the business process. That way, you can draw insights out of holistic, merged IT and OT contextualized data.

Empowering the factory floor: DX at Hitachi Rail

Gianfranco: I’ve spent the last thirty years working on transformation projects. “Smart factory” essentially means innovation in manufacturing, and we have two types of innovation: step-by-step, or continuous improvement, on one hand and radical innovation on the other.

IT and OT enable continuous improvement by integrating business processes with new tools and features. Where we see the most advancement on the factory floor is in the support function, which includes improving quality checks, logistics, and testing.

At our factory in Maryland, we look to improve efficiency and quality, and we are experimenting with new technologies to improve training and collaboration. We see the cloud enabling us to scale quickly, though we are of course cautious about cybersecurity.

We recently tested a truly transformative solution with Hitachi America R&D that combines Hitachi’s AI models and a robotic dog, running on industrial 5G. The solution enables quality control inspectors to examine factory operations and products to ensure they meet essential quality and safety standards. It integrates seamlessly with other factory systems including manufacturing execution systems (MES), providing an integrated experience of automated quality inspection triggered by events on the factory floor. We believe the productivity gains from this solution will be remarkable. The worker experience will be transformed as well.

Sudhanshu and his team are also centralizing management of the data that we gather from such solutions, along with data from our supply chain and health and safety initiatives, in a unified data layer. The longer-term plan is to make this platform available to customers.

Sudhanshu: As mentioned, between the labor shortage, trade wars, and uncertainty in the supply chain, companies want to have their suppliers closer to home. And the Maryland factory is a great showcase for that.

Gianfranco: Indeed, it is. We can also invite Hitachi Digital Services’ customers to demonstrate what we’re able to do, promoting onshore manufacturing to other manufacturers.

Before and after generative AI

Sudhanshu: I’m certain that solutions like quality inspection combining AI and robotics will become mainstream. Additionally, generative AI now enables workers to interact with technology using natural language, creating a seamless interface between technology and the workforce. Furthermore, since these solutions are multilingual, they offer significant benefits to companies operating factories worldwide.

But of course, the impact of generative AI extends way beyond language recognition. Generative AI is triggering a major shift in how people think about technology and work. There’s a before and after scenario at play, with generative AI playing a huge role in areas such as maintenance, quality inspection, and customer service, among others. The mindset of manufacturers is changing in terms of how they may be able to benefit from advanced digital tools and technologies.

Also, as mentioned, with generative AI, combined with augmented reality (AR) and the metaverse, we can now unlock new levels of flexibility and interactivity in modeling and visualizing solutions. The technology is highly accessible to end users, enabling them to actively engage with, imagine and visualize their future operations. This is an enormous benefit.

Natural language processing can bridge the gap between your application and the unstructured data that lies behind it. You can finally glean insights from the data without building dedicated software solutions. Generative AI can foster greater innovation and adaptability, whether it’s for predictive maintenance, production efficiency, or addressing the labor gap in the market.

Visualize a vast ocean, but start with a bottle of water: Bringing smart factories to life

Sudhanshu: In closing, what advice would you give customers who are looking to modernize their factories leveraging digital solutions and advanced technologies?

Shamik: My advice is simple: ask why. Why are you pursuing digital transformation? What is your business case? What outcomes do you want to realize? Are you focused on the top line or bottom line, or both?

You need to make your intentions very clear from the start and have all stakeholders agree on them. Your OT and IT teams need to be working together. If they’re not, it’s most likely because the business impact of making a joint vision has never been agreed upon, let alone discussed.

To your point, Sudhanshu, production teams often resist the adoption of new technologies. But without their buy-in, a DX initiative will go nowhere. I recently met with a customer that abandoned their scheduling optimization project after four months because they realized they didn’t need it.

You can capture tons of data from “man, machine, materials and methods,” but what is it for? Why invest time on and pay for extracting and storing all this data if you can’t visualize its business impact?

Again, from an implementation standpoint, you need to start with the process that you are looking to digitize－whether it’s scheduling optimization, quality inspection, or inventory management－and work backwards. This way you can identify the OT and IT data that you need and be more efficient as well as impactful.

Sudhanshu: I think the way to navigate the complex labyrinth of OT and IT integration is, for one, to involve important stakeholders from both the OT and IT teams in the solution design from the outset. Early alignment is essential.

Second, once you've got the integration to work, to replicate the solution across multiple deployments without too much customization, it's beneficial to conduct a thorough analysis of the variability across manufacturing lines, including users, and to design an architecture accordingly, while also aligning with the company's IT policies for a smoother rollout.

Finally, considering the financial impact on the customer, particularly around operational expenses, and the total cost of ownership, is of course mandatory as well. Careful planning of capital investments and a sharp focus on ROI estimation are indispensable, especially when the technical aspects of the digital solution have been well-addressed.

Gianfranco: I want to give you a metaphor about digital transformation. A customer should think of a beautiful ocean, say, the Caribbean. But their thoughts should start from a small bottle of water. That bottle of water could become a bucket, a small lake, then a big one, and then the ocean.

In other words: Think big and think better but act small. And escalate fast.

Find a partner who can help you advance quickly because time is of the essence. If you take two years to understand the return on investment for a project, that’s too long. How could you explain the return on investment for the Caribbean Sea? But you can easily explain the return on investment of a bottle of water.

My last piece of advice is about people. Choosing the right people to engage on a project is key. Digital advancement means co-creation. You can’t only act internally. That would only lead to incremental progress. You need someone with domain expertise, who’s passionate about technology, to break all the paradigms, and reshape and redesign the process from the outside in. You need the right mix of passion and resilience.

Sudhanshu: That’s a great note to end on. Thanks, everyone, for participating today and sharing your valuable insights with us.

The digital transformation of factories is not just about integrating hardware and software. It’s about merging different mindsets and work styles. Working with machinery is very different from operating software and managing data. Bridging these functions requires strategic thinking and a commitment to outcomes. Knowledge of the latest technologies, from generative AI and the metaverse to robotics and 5G, is of course mandatory. But strong communication skills, if not diplomatic talent, are also essential. Enlisting partners with deep manufacturing and IT expertise can also bring clarity and focus to a factory’s DX journey. At the end of the day, digital transformation is about welcoming change and collaboration.

(This discussion has been edited for clarity.)

(As at the time of publication)

Gianfranco MESSINA

Senior Director, Optimize to Grow Hitachi Rail Ltd.

Gianfranco Messina has more than 20 years of experience in business transformation projects that integrate operational technology and IT to achieve maximum operational efficiency and cost reduction. He has led complex, global, multi-site projects in the aerospace, information technology, oil and gas, and railway industries.

Currently, Gianfranco is leading smart factory lighthouse projects for Hitachi Rail in Italy and in the United States. For Hitachi Rail’s current project in Hagerstown, Maryland in the U.S., Gianfranco’s team is aiming to build one of the most advanced digital factories in the world whose innovations can be shared with Hitachi’s manufacturing customers worldwide.

Gianfranco has a master’s degree in business process reengineering.

Shamik MEHTA

Director, Marketing Hitachi Digital Services, LLC

Shamik Mehta is the director of marketing for cloud and application modernization services at Hitachi Digital Services. He has 25 years of experience in product and strategic marketing and applications in data management and data analytics software, industrial IoT, semiconductors, renewable energy, and e-mobility solutions. He has held roles in product marketing, product management, operations and technical sales for complex technology products, including software services, solutions and data platforms for banking, financial services, and industrial applications.

Shamik has experience managing global product marketing, GTM activities, thought leadership content creation and sales enablement activities for technology and software applications for Banking and Financial Services, Manufacturing, Energy, and Transportation verticals. Shamik is a Silicon Valley native, having lived, studied and worked there since the early 90’s.

Sudhanshu GAUR, PhD

Vice President and Manager of the IoT Edge Laboratory Research & Development Division Hitachi America, Ltd.

Sudhanshu Gaur leads the edge computing R&D program, nurturing innovative solutions across diverse technologies including 5G, robotics, computer vision, and generative AI applications. His role extends beyond the R&D organization, helping to shape the Industry 4.0 vision for digital transformation across key players like auto parts manufacturer Hitachi Astemo and Hitachi Rail. Currently, Sudhanshu is leading the entire IT x OT orchestration, including partner ecosystem development, to enable the lighthouse vision for Hitachi Rail’s new factory in Hagerstown, Maryland, USA. Previously, Sudhanshu served as Chief Architect of Smart Manufacturing at Astemo, where he was responsible for driving the Industry 4.0 initiative across more than 140 manufacturing sites globally.

Sudhanshu was named among the top 25 leaders transforming manufacturing by the Society of Manufacturing Engineers (SME) in 2020. Before joining Hitachi, he received his Ph.D. degree in Electrical & Computer Engineering from the Georgia Institute of Technology, and Master’s and Bachelor of Science degrees from Virginia Tech and the Indian Institute of Technology, Kharagpur, respectively.

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Is India Building Enough to Power Its Digital Transformation?

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It is estimated that India will be adding 464 MW of new Colo capacity each year until 2028. Despite this, India still needs to catch up with other comparable nations. We believe that accelerating this pace could unlock significant opportunities for India’s data centre and technology sectors.

The Indian Data Centre Landscape

An average Indian cell phone user consumes over 19 GB of data per month i.e., the highest in the world. India is experiencing an exponential rise in adoption of internet services, smartphones, social media, and OTT channels. Consequently, the demand for data centres  is of high interest to transform India’s digital infrastructure.

In response to this, a steep rise in construction of data centres has been witnessed in India. Both colocation data centres and cloud firm-owned data centres are being built at growing speeds over the last few years. India’s Colo data centre capacity stood at 977 MW (IT Load) across the top 7 cities (H2 2023). About 258 MW of Colo capacity came in 2023. This is a formidable number and surpassed the capacity addition in 2022 which stood at 126 MW, indicating a 105% YoY growth. This exponential growth is driven by several factors, including increased data consumption due to widespread digital adoption and the use of data-intensive technologies.

How many data centres does India need?

Despite a projected exponential rise in capacity addition of colocation data centres, we believe that India has potential to absorb more. While adding an average of 464 MW of Colo capacity each year until 2028 may seem like good delivery speed, India shall keep building more to capitalize on its digital transformation story. Two key reasons supporting this hypothesis are:

• Over the next 5 years, India is likely to see fastest growth in penetration of smart phones, internet, OTT subscriptions, and social media usage.
• With the launch of 5G services in 2022, future applications that use generative AI and IoT will require much higher DC infrastructure support than that required today.

INDIA’S DIGITAL ADOPTION TO WITNESS A MULTI-FOLD RISE

Read our report as we explore two key questions regarding the future of DCs in India:

Why India needs to ramp up its Colo DC project delivery speed?

• Is the ongoing capacity addition enough to mitigate the current problem of under-penetration?

* We believe this report is the first step towards uncovering the demand mystery. Our team has conducted a detailed demand assessment, and if you wish to know more, we would be happy to connect you to our relevant experts .

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Report showcases digital transformation underway at UC

Improvements include new state-of-the-art 911 system.

Digital Technology Solutions released its annual report for 2023-24 highlighting the many ways the University of Cincinnati is meeting the needs and protecting the security of students, faculty and staff. 

The 2023-24 report looks back at key milestones that support the university’s digital transformation goals to increase IT operational excellence, bolster cybersecurity and resiliency, and modernize IT infrastructure and systems.

The report also showcases examples of digital transformation already happening at the university.

Highlights include:

• 911 Transformation:  UC rolled out a new, state-of-the-art 911 system in 2024 that will help improve emergency response times on campus and can help support the safety of the surrounding local community.
• AI Symposium:  A day-long event at Tangeman University Center explored topics around AI in education, learning technologies, research, innovation, ethics, policy, and social impact. Presentations included keynotes from UC’s Dr. Kelly Cohen and former Synchrony Financial Chief Technology Officer Greg Simpson as well as 16 breakout sessions led by UC students, faculty, and staff.  
• Bearcats Health App:  The app uses optical character recognition and artificial intelligence features to securely scan, digitize, and validate required student immunization and screening records.

“Digital transformation happens when people, processes, and technology come together on behalf of our purpose—student success,” said UC Vice President & Chief Digital Officer Bharath Prabhakaran. “DTS’s partnerships with UC students, faculty, and staff will continue to help accelerate Next Lives Here at UC.”

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TDWI Insight Accelerator | The Cloud Journey Continues: Unlocking AI and Digital Transformation Through Migration and Modernization

June 28, 2024.

Modern cloud data platforms help organizations support projects that may be too difficult, expensive, or slow to develop with existing systems.

As organizations become more competitive, they realize that data and advanced analytics are necessary for success. To support more complex use cases, organizations are modernizing their data estate, which includes the move to cloud platforms.

Cloud risk is now perceived to be low, even for mission-critical applications, so more organizations are modernizing in the cloud. Modern cloud data services such as AI services give organizations the opportunity to explore new data, take advantage of advances in database technologies, and more easily utilize new technologies in AI.

This TDWI Insight Accelerator discusses key challenges and recommendations for moving to the cloud as part of a modernization effort—enabling agility, availability, and scalability.

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