Digital Culture

It is often quoted that culture eats strategy for breakfast, and, to the extent, this observation holds good, it is important to understand digital culture as a different construct, because a strategy that leverages  digital may also benefit from a culture that is digital.

Culture – along with strategy and leadership – is among the most commonly written about topic in management.  A discussion on digital culture therefore raises questions, such as: What is digital culture?  Is it different, or simply a top-up – an overarching layer, or an internal part of an existing culture? What are various elements of digital culture?  What would happen if a digital organization continues to have, say, a non-digital culture? Will it impact performance? Given that digital technologies are constantly evolving and new disruptive technologies  become mainstream, does it mean that digital culture, if it is a stand-alone construct, need to be re-calibrated every time? If not, would that still the case, if disruptive technologies lead to a new business model, as a second order effect? Does it mean that culture will likely need small or substantial alignment every time when first-order effects (changes in technologies) or second order effects (changes in business models) take place?

In one study^[1], three categories of firms were identified based on their digital advancement, namely, early-digital firms (32% of the sample), developing digital firms (42% of sample), and digitally maturing firms (26% of sample) (Note: the term “maturing” is used, because there is no end or finish line to digitalization, as new technologies keep coming).  This study noted that “Simply implementing and using new technologies can easily fall flat. Although specific digital strategies will be unique to a company’s industry and opportunities, our research demonstrates that effective digital culture is the common denominator that propels digital efforts across industries and stages of digital maturity. Once that culture is in place, committed and engaged employees will help make strategies take flight.” (pp. 17).

The study also found that though digital strategies differ by industry and firm’s unique situation, digital cultures in digitally maturing firms, irrespective of industry, share common features.  Interestingly, firms in the same category shared similar cultural characteristics irrespective of their industry.   This is an important finding. This suggests that digital cultures change mostly on the basis of level of digital sophistication and maturity, and not industry or digital strategy itself. An important implication being firms can start planting the seeds of digital cultures (which take time to bear fruits in any case) and the culture is likely to be future-proof irrespective of technologies a firm may decide to use in future, and industries it may expand or diversify into at a later stage.

1)         Why Digital Culture?

There is a saying that organizations are always perfectly designed for the outcomes they produce. This can be generalized also for culture – organizations always have perfect culture for the outcomes they produce.  Organization culture is not optional. All firms, whether digital or not, have cultures. It could be the culture that leaders did not want (though it is not always so simple) or at least did not want few elements in it. A deeper point is, unless leaders are mindful and intentional about the culture they want, firm could risk having a different (and possibly less desirable) culture.

Do digital organizations have enough unique features for their culture to be categorised as a separate, digital culture. A case can be made for digital culture for the following reasons:

• Culture is defined as the way things get done in an organization. Using this perspective, being digital “is inherently cultural: Digital technologies are intended to comprehensively change the way organizations do things around here.” (pp. 17) [2].   One study noted “Although technology is an important part of the story, it certainly isn’t everything. It may not even play a starring role. Many companies understand technology yet struggle to derive business advantage from it through needed changes to talent, culture, and organization structure.”[3]  It is also noted that the gap between digitally ready firms and firms that are still developing digitally is growing wide. Study cited above also noted that this gap “threatens to become dangerously wide, leaving many companies decidedly unprepared to compete in rapidly changing digital environments.” (pp. 16) [4]
• Strategy and culture are frequently cited among the top five reasons, why digital transformations are not successful. Successful cases of digital transformation, such as in DBS, Klöckner & Co, and MasterCard suggest that a successful digital transformation involves extensive culture change. Additionally, many firms (such as GE and Klöckner & Co)  undertook digital transformation through a spin-off or new unit separate from the parent firm suggesting that both in culture and business terms, digital is different from traditional businesses.
• Surveys suggest that digital environments are different. In one study[5], when respondents were asked on the biggest difference between working in a digital environment vs a tradition one, pace of business (speed and rate of change), culture and mindset (creativity, learning, risk-taking and collaboration) and flexible, distributed work-place (collaboration, decision-making and transparency) were identified as top three main differences. Another survey showed “that digital business environment is fundamentally different from the traditional one.  Digitally maturing companies recognise the difference and are evolving how they learn and lead in order to adapt and succeed in a rapidly changing market” (pp. 3)[6]. Digital firms, therefore, risk culture becoming their weakest link in the digital transformation process, if it is not adapted to the requirements of the “digital resources” including technologies.
• The work-practices of digitally native firms, such as Google, Netflix and Amazon have received attention and covered in business writings. These digitally- native firms are seen as having elements in their culture that enable digital resources to be used to their near full-potential. To the extent, culture in these firms is different from “traditional” cultures, and contributes to their successes (and failures), it can be argued that there are distinct cultural elements in a digital context.
• The fast pace of technology growth and the learning experiences generated from it suggest that in non-routine, atypical, and high-value (innovation) cases, human (and cultural factors) play an important – often pivotal – role. Daugherty and Wilson[7]  identified three stages in the evolution of intelligent technologies (AI). In the first stage, AI was used to automate routine tasks. In the second stage, AI was used to augment human capabilities (instead of replacing them), and in the third (and current stage), leading firms taking a decisive turn towards human-centered technology, where humans work with technology to produce innovation and hard-to-solve problems.  Because of this, they argue that human and humans are in ascendence. The full potential of technology, therefore, depends on how humans-and technology interact with each other – an area that gets influenced by leadership and culture.

2)         Framework for Digital Culture

For the purpose of this paper, a working definition of culture is proposed below:

Digital culture is defined as a set of collective values, norms, expectations, relationships, processes, and other considerations – whether or not derived from overall corporate culture – that influence the purposes, ethics, practices and outcomes related to the use of “digital resources”, digital technologies, and digital eco-systems – whether internal or external – that are subject to the influence of digital firm.

The discussion below sets out additional context to this definition:

• The definition proposed above is built on the generally accepted framework and intuitions underlying current understanding of organizational culture. They relate to concepts, such as “patterns of assumptions”, levels (assumptions, espoused values, and artefacts), and symbols and beliefs. These ideas continue to be at the core of, and inform our understanding of digital culture.
  It is assumed that whilst, in general, corporate culture serves as an overall umbrella within which digital culture exists, the relationship between corporate and digital culture could vary. Digital culture could be a sub-culture, if new digital business is launched in a separate location, or digital culture could be a part of overall cultural DNA, as is likely in digitally-native firms.  In practice, digital culture will either exist as a sub-culture (say in a separate business unit possibly located away from the main unit) or as main organizational culture itself.

• A distinction is drawn between a digital organization and digital culture. A firm could be a digital organization, because of its’ use of digital resources, but have a non-digital (traditional) culture. Building a digital culture, particularly for firms that are not digitally-native firms, is an intentional effort, often a key success factor in their digital transformation process. It may not always be necessary for a digital firm to practice elements of digital culture (such as experimentation and risk-taking). However, digital firms may consider having a culture aligned to digital context, as this “organizational-environmental” fir may improve performance, particularly if the organization has a high performance appetite, bar or survival pressures.

• Ethics is included in the definition, because new technologies, by definition, deal with new applications, use-cases and innovative ideas, where ethical standards are contextual and not clearly established. Data privacy and surveillance capitalism provide two such examples.  Practices on ethics and governance in an industry, which is relatively new and where its’ practices are contextual in nature, is to a large extent discretionary, and therefore a digital firm’s stance towards ethical issues is an important part of a digital culture.
• The form and influence of “digital culture” could also vary depending on the level of digitalization, history, type of firm (digitally native or traditional) and other factors. It could be largely limited to a business unit that implements digital plans, or could be broader, if firm chooses to implement, say, specific firm-wide digital culture initiatives. One such example is provided in the digital transformation of ATOS[8], where the firm launched a plan to prioritise building of digital skills.
• The definition extends the scope of digital culture to include digital eco-systems external to the firm to account for the spill-over, connectivity and amplification effects of digital resources (such as data, technologies, connectivity). The most common digital business models (such as platforms and eco-systems) include external participants, and therefore likely to effected by the digital culture of the main firm.
• The proposed definition is more “operational” as opposed to (rightfully) abstract definitions of corporate culture, because it is specifically linked to the features of the “digital resources” and how it influences them and gets influenced by them. These are developed in the next sub-section as markers of a digital culture.

3)         Features of a Digital Culture

Digital cultures present different challenges to firms depending on the business, extent of digitalization, existing culture and talent available.  Firms that are digitally-native may have different cultural recipe than a firm, which has both physical and digital businesses (as is becoming common now) to firms.

In theory, the role of culture in a digital context is crucial, because digital firms deal with more “known unknowns” and “unknown unknowns”, the windows of opportunities open and close fast, and threats and new technologies emerge without advance warnings. In such a context, it is impossible to set out rules and processes, because most situations are “unknown”. Culture provides a way to manage these, and, in a way, serves uncertainty and risk management purpose.

What are some of the key features of a digital culture that can be inferred out of available writings and anecdotes?  Whilst the list is long – and to a good extent overlaps with features already identified for traditional firms that are in volatile environments – the manner and role of these elements in a digital context may be different.

A digital culture is likely to be salient on the following elements, which are discussed below in terms of their role and relevance in the context of digital technologies. These elements overlap, inter-connect and influence each other, and also influence and influenced by strategy and leadership practices.

The list is not collectively exhaustive, as many elements, already known through previous research on dynamic environments, are not discussed mainly to avoid repetition.

• Digital savviness
• Digitally aligned organizational processes and interventions
• Data centricity in business management
• Ethics and governance framework
• Digitally aligned HR processes

3.1)          Digital Savviness

Digital savviness is high on the list both as a leadership requirement as well as a cultural trait. In one survey[9],  82% of respondents called out digital savviness as a non-negotiable leadership skill for the future.  Terms similar to digital savviness are also frequently used. These include digital competency, digital mind-set, digital acumen, and digital literacy. It is likely that there are differences among these terms, but the basic idea to understand the nature and function of digital technologies in the service of business is at the core of these and other similar terms.

The idea of digital savviness overlaps with, but different from the technical digital skills.  In a survey[10], only 18% of the respondents listed technological skills as the most important to succeed in the digital economy.   In the same survey, other leadership attributes, such as transformative vision (22%), to be a forward thinker (20%) and change-oriented mind-set (18%) were listed as more or similarly important. “Leading a digital company does not require technologists at the helm …And digital leaders aren’t necessarily high-tech wunderkinds.” (pp 4)[11].

The idea of digital savviness is also related to digital mind-set (discussed in detail in the section on digital leadership). Leonardi and Neeley[12] defined a digital mind-set as the set of approaches we use to make sense of, and make use of, data and technology” (pp. 10).  In their view, digital mind-set requires redefining fundamental ways of doing three main processes, namely, Collaboration, Computation, and Change.  Leonardi and Neeley also note that digital mind-set requires only 30% fluency in handful of technical topics, an observation they call “The 20 Percent Rule”. (pp. 11).  In the context of discussion here, these three – and various elements within them – provide a framework to develop digital savviness to the extent familiarity with the technical areas is concerned. It is expected that the nature and detail of technical familiarity may vary from firm, business, digital context, digital plans and other contextual factors.

The main cultural imperative in the digital context is that employees, particularly leaders, understand how digital technologies work at a general level, main technological trends, the potential and risks digital technologies entail, role and potential of digital technologies for a given business /eco-system, building business (along with project management and investment funding) case for the adoption of new digital technologies, limitations of technologies, and, important, how technologies and business intersect, and what they do for the strategy, innovation and customer value-creation.

As noted elsewhere in the paper, research suggest that 7 out of 10 firms show minimal or no impact from the AI, and that successful firms are able to, among other things, align AI production with AI consumption.  AI consumption means having executives, who buy-in, and use AI – and also contribute to its’ further development through feedback – are involved in an active manner in developing digital user cases and “solutions”. Cultivating business savviness is essential to have a culture, where AI consumption happens with AI production, and  AI (or any other digital technology) leads to value-creation. Leading AI practitioners provide boot-camp or equivalent training environment to executives, so that they are able to engage in problem-solving through the AI lens[13].

Approaches to developing digital savviness vary depending on the context.  Practices include Silicon valley safaris and bootcamps to experience first-hand the digital environment (including fail-fast approach, design thinking, customer value-creation and innovation processes). This is, however, just a start and a follow-through through digital strategies, digital skill-gap analysis, and partnerships with digital firms[14] is needed for the digital savviness to take roots.   Executives are also encouraged to learn the basics through the online courses or online tools, or organizationally provided resources.

−         Digital Technologies / Tools as “team members”

The advancement in digital technologies have reached a point where digital technologies (such as AI) are now considered a part of team, i.e., a team member in its own right.  Though AI earlier functioned as a virtual “employee”, when it was used as smart chatbot and did narrow range of tasks (such as scheduling meetings), now, “with advances in machine learning, NLP, and AI, technologies are becoming more like “real” team members themselves that can interact with you, make suggestions, and act on decisions.” (pp. 43)[15].

Though not usually recognized as such, “the keys to working successfully with machines are not the same as …needed to work successfully with humans … and the rules of interaction are not the same” as when machines work with an individual as opposed to working for an individual. (pp. 27)[16].  In such cases, depending on the use application, the skills needed to ask for clarification, express disagreement, ask for additional alternatives, understand the logic and reasoning behind recommendations (given that in most cases, AI shares only “outcomes” and reasoning is not available), “complain”, and finally overrule such “machine team members” are different.

By default, “human” employees, when working with machines based on digital technologies, deal and interact with them (machines, technology) as if they are “humans”. Treating machines as machines, instead of as if they are humans, is a key part of skill-set that HR policies could help with, and one key are is developing digital savviness and competencies.

3.2)          A Bias towards Data and Analytics.

Most digital technologies enable data collection at scale and at a low cost, which can be processed, analysed and used for operational excellence as well as strategic purposes (such as to develop innovative products and services).  Data and data-analytics, in this context, also include technologies, processes, expectations, and training needed to use them in strategic ways.

The presence and potential of technology, however, do not automatically translate into consumer value and firms’ competitive advantage.  In a survey of nearly 65 Fortune 1000 or industry-leading firms, it was “discovered that 72% of large, sophisticated companies have not achieved data-driven cultures”[17]  Further, 69% have not created a data-driven organization, 53% stated that they do not treat data as a business asset, and 52% reported that they are not competing on data and analytics[18].  Building a data-driven culture (in practice, data-driven organization) is a high bar and a long haul.

A data-driven culture, at its’ basic level, means that decision-making at all levels is based on data (at least data and analytics from it are referenced) instead of intuitions and gut feelings. However, at a deeper level, it “means culturally treating data as a strategic asset and then building capabilities to put that asset to use not just for big decisions but also for everyday action on the front line.”[19]   Using data, whether for operational excellence or strategy and innovation, requires understanding full role of data in strategic, technical, structural, cultural, governance, and ethical terms across the organization.

a)          Role and nature of data and analytics

The processes underpinning data and data-analytics could vary from basic to sophisticated depending on the stage a firm is in.  Data exists in many types (quantitative, qualitative, subjective), formats (voice, text, images), features (legacy and real time-time), source technologies (mobile, web-site, value-chain, public information), use types (operational efficiency, predictive modelling), and sensitivity (governance and data-security requirements, and ethical and privacy considerations). Strategically, analytics exist in various forms, such as descriptive analytics (standard reports, ad-hoc reports, query/drill-down, alerts, etc), predictive analytics (statistical analysis, forecasting, predictive modelling, anomaly detection, classification), prescriptive analytics (experimental design, optimization) and autonomous analytics (machine learning)[20].  It is argued that the potential competitive advantage increases with more sophisticated analysis[21].

Digital technologies enable not only efficient collection of static and legacy data, episodic data, but also interactive data,[22]  as well as private data[23] (obtained in an ethical and responsible way) that could be in form of qualitative, quantitative or proprietary models (that analyse and predict outcomes in bespoke situations). It is argued that the availability and use of interactive data -not possible at scale with digital technologies – enables firms to build products and services (including medical alerts) not previously possible. These in tun can form the basis of new business models, such as product as a service, charge by the usage, or predictive maintenance (as opposed to routine maintenance, which could be unnecessary and uneconomical).

One of the well-known cases of analytics is provided by Michael Lewis’s book[24] on Moneyball, which used a new statistical system to move away from established matrices (such as stolen bases and slugging percentages, and commonly held heuristics) to less common matrices (such as on-base performance and slugging percentage). This new system accounted for players’ performance in relation to team’s performance as a whole, and identified undervalued players. This new analytical approach allowed Oakland Athletics baseball team to compete successfully at a very modest budget. More importantly, and often overlooked, are changes that occurred off-the field. Organization charts had new roles and new job function, and individuals were hired that could tell machines what to predict and use that to acquire players.[25]   Though players played the same way, the internal culture changed, as new roles got added and inputs on decision-making changed.

Yet another firm with a reputation of data-driven culture is Google. It used analytics to make decisions on the optimal size and shape of cafetaria tables and length of lunch lines[26], limit the number of interviews required (note: analysis found that more than four interviewers did not lead to higher quality hiring), on-boarding agenda for employees’ first four days of work that improved productivity by up to 15%, and produced an algorithm to review rejected applications that in turn led to hiring some good individuals that its’ normal process would have missed[27].

Analytics can provide strategic options that may be otherwise be unknown or inaccessible, if relied upon through intuition alone. For example, a New York based analytics firm provides analytics to help restaurants to choose delivery areas based on existing traffic norms. As a research report noted, the implications go beyond efficiency and convenience. Further, “if restaurants can optimize delivery logistics, then they can locate where real estate is less expensive. They can even have restaurants that are just kitchens with no onsite dining options.”[28] (pp. 7).

b)          Stages in organizational development in terms of data culture

Given the immense scope and complexity of technical and organizational challenges in building data-capabilities, it is not surprising that firms go through various stages of evolution.

Davenport and Harris[29] identified five such stages:   Stage 1 (Challenged by poor quality, multiple definition, and siloed systems), Stage 2 (collection of transaction data, independent and soloed initiatives),  Stage 3 (availability of various tools, data repositories though non-transaction data is still unavailable or cannot be used, as it is unintegrated and inaccessible), Stage 4 (enterprise-wide, integrated approach and governance process, but not leveraging fully on big, unstructured data), and finally Stage 5 (advanced analytics including use of autonomous and cognitive technologies, integration between internal and external data, enterprise-wide approach and governance processes).

Not all data are same. Researchers have identified four sets of characteristics of big data, commonly referred to as 4Vs (volume, velocity, variety and veracity.  Each adds a different value, but developing capability and expertise in each also requires a different organizational approach.

c)           Challenges in implementing the data-culture

Implementation of data-culture is not easy. It requires strategic, leadership, technological and structural support along-side employee training and also understanding the limitations of data and data-analytics. Data, analytics and prediction models, as various accounts suggest, could source of governance, prediction and reputational risks.

The lack of data availability, and, to the extent it is available, its fragmentation across organizational functions and siloes is one of the key bottlenecks. Though this problem is being increasingly recognized, it is not easy to technically, structurally and behaviourally to overcome the inertia to integrate data across various functions and siloes, and lead to a full “data-lake”.

Another challenge is developing data-aptitude among employees, as all employees are not data-savvy to the same level. This also includes knowledge of privacy, regulatory, ethical and governance considerations that apply to the data they use or could potentially be using. An organizational-wide data culture along with aligned HR policies (such as selection, training and performance management) are needed till data-culture comes of age.

Finally, as important as data and analytics are, it is also important to be aware what they are not.  They do not explain, or provide cause and effect relationships.  They are also subject to human cognitive biases as well as data quality issues on which algorithms and predictive models were “trained” on.  A key, and a human part, of data analytics is also to frame the question or problem that is being addressed in a way that allows maximum “data” insights. For data-analytics to deliver value, the problem and questions need to be framed in strategic ways. Finally, if the approach and faith in data-analytics is blind, it could create pose risks in unanticipated and unintended ways, as incomplete or misleading insights could be acted upon

3.3)          Digitally Aligned Organizational Processes and Interventions

As noted in the previous sections, traditional firms operate in environments that are relatively stable and predictable in nature.  Their main focus is towards efficiency maximisation and risk-elimination. The organizational process implications of this traditional context are somewhat straight-forward. Strategic plans and budgets set the directions for the next year by incorporating all possible constraints and contingencies, and once they are in place, they are broken down for each department and role.  The design that emerges is a hierarchal structure, top-down strategy and communication, rule-based decision making, structured roles with little empowerment, and elimination of redundancies in processes. This organization design is based on (to varying degrees) principles of scientific management, which were developed in a different context and era.

Subsequent management research provided for fluid and flexible structures, when the environments are dynamic, unstructured, complex and ambiguous. This flexibility and fluidity in decision making, communication, problem-solving and empowerment was achieved, however, largely through structures and processes (such as integration or cross-function teams, periodic review meetings, etc) to respond to changes.

Digital environments pose challenges to this “flexible” design in different ways.  In a digital context, environmental changes require coordination, integration and agility both reactively and proactively in different areas on a more frequent basis and at various levels (if not always across levels).  Since the content, variety, frequency of change in digital context is relatively higher, and both internally and externally driven, it is important that organizational design reflects these imperatives of digital environments.  This also reinforces the point previously made that organization design needs to be optimized not for efficiency alone, but also for adaptability, agility, innovation, risk-taking, and cross-collaboration.  Two specific aspects of organizational culture and design, namely, become salient in these contexts:

• Risk-taking, experimentation and innovation, and
• Collaboration, cross-functional teams and agile working are discussed below).

a)          Risk-taking, Experimentation and Innovation

One of the key challenges faced in a fast-changing digital environment is that “it requires more explorations and experimentations than most companies are prepared to manage. Experimentation is at the heart of digital maturity”[30].  This is even more the case with AI. As one report noted, “…experimentation and learning with AI can take much longer than other digital initiatives, with a higher variability of success and failure.”[31]

A survey finding reported that that digitally maturing firms “have a strong propensity to encourage risk taking, foster innovation and develop collaborative work environments…digitally maturing firms are considerably less risk averse”[32]   The same survey found that more than half of respondents from less digitally mature firms identified fear of risk as a major shortcoming compared to 36% from digitally mature firms.  Another survey noted that “Overcoming aversion to risk is perhaps the most important characteristic of digitally mature cultures.”[33]   Risk-aversion is mainly derived from a focus on efficiency maximisation in traditional product context – and covered separately from a strategic perspective in digital mind-set section under digital leadership section– and moving away from it requires organization interventions that support experimentation, empowerment, creativity, agility and cross-collaboration.

Digitally mature firms are able to take more risks, because, in general, they are able to manage risks through their cultural framework, structure, and processes. In particular, such firms have a culture of experimentation, learning through failures, and innovation.

An important factor is the support of the top management in creating processes that signal that risk-taking, experimentation and innovation is not only acceptable, but wanted, and also a recognition that it means there will be failures and that is part of the process.  As an example, “The U.S. Department of Agriculture’s Risk Management Agency monitors the cost growth of digital initiatives as an indicator of experimentation … if the cost growth is too low, then the organization’s exposure to risk may be increasing because adequate experimentation and learning are’t taking place.”[34] (pp. 15).

During the digital transformation of Mastercard, the CEO Ajay Banga created an innovation and experimentation culture through various structural and process interventions that included creation of Mastercard Labs (with sufficient autonomy),  the Ideabox funnel (with two tracks that cover an innovation challenge and a continuous general track to tap other innovative ideas) and a general experiment and innovation framework (including funding, coaching and an opportunity to focus full time if the idea is selected for the final round and an opportunity to go back to old job when project is completed).   Mastercard also considered experiments as forming a portfolio, and financed them on that basis with guidelines. In the portfolio approach, adopted commonly by VCs, it is known at the time of investing that most ideas will not be successful, but it is accepted under the assumption that even if a small percentage are, the gains will be more than costs for the entire portfolio.  Mastercard acknowledged that most projects will fail, but “failing fast is a natural part of the process.” (pp. 5).  During its much-acclaimed digital transformation process, DBS[35] created “Dare to Fail Award” to institute innovation culture and also send a message and recognize that it was okay to fail (pp. 13).

How risk-taking, experimentation and innovation processes in digital firms are different?

Research suggests that firms successful in their digital journey not only do more experiments or accept (or even celebrate as in DBS case) failure, but also do them differently.  They recognize that there are different types of innovations ideas. As an example, such firms pay attention to both innovation/experiment with small “I” (innovations with experimental efforts) and big “I” (innovations with enterprise-wide efforts).  They are two and half times more likely to do both small, iterative experiments and undertake enterprise-wide initiatives than the firms, which are at the low-end of their digital journeys[36].  They are also better in their ability to scale-up experiments, when they seem promising.  As one research notes, “While much is made of the mantra to “fail fast”, deciding what to do when experiments succeed can be a much bigger challenge.”[37] (pp. 9). The relationship between small “i” innovation (or experiments) and big “I” innovation (or experiments) is an important one.  Leaders in firms, which are advanced in their digital journeys, consider their business problems, translate them into big “I” equivalents, and then further translate and conduct experiments that could result in small “I” innovations.  At digitally advanced firms, small “I” experiments lead to bigger “I” innovations than at other firms.

Another important difference is where and how experimentation and innovation happen in successful digital firms.   Firms, which are digitally maturing, do not limit experimentation (and innovation) to a function (or a lab) set-up, or limited to technical staff.  The experimentation (and resulting innovation) is attempted in all areas and processes, often cross-functionally. Agile and cross-functional teams are engaged depending on experiment (or problem), organizational culture and design.

Digitally maturing firms also leverage on external alliances including with eco-system partners.  In a survey, 80% of respondents from digitally maturing firms said that   their firm is building partnerships externally to innovate compared to 33% of respondents from the least digitally mature businesses[38].   Yet another notable difference is that the external partnerships are more relationship based as opposed to formal, tight and detailed contracts. “This …allows novel solutions to arise more often and more quickly than in tightly controlled systems.”[39] (pp. 11)

Digitally maturing firms not only do experimentation (and their iterations) and innovation, they also, through design and processes, seek to maximise the return on experimentations and learnings from them.  Experiments and innovations are linked to strategy, have an enterprise-wide approach (as opposed to stand-alone experiment happening once in a while as a side activity), strive to learn and build capabilities, and, when successful, scale-up over the enterprise well. In short, digitally maturing firms “execute” on experiments and innovation better. In doing so, digitally mature firms provide both structural and process mechanisms to share new ideas and test them across the organization, share feedback on such ideas and outcomes of experiments, and enable learnings from the failed experiments.  Digitally mature firms also more likely to use the learnings from the experiments to bring about change at a larger, firm-wide level, when potentially possible.

b)          Collaboration, Cross-functional teams and Agile working

Research seems to suggest that collaboration has a beneficial effect in digital world.  In a survey[40], more than 80% of the respondents from digitally maturing firms agreed or strongly agreed that their workplace environments are collaborative compared to 34% of respondents from the early-stage firms. In the same survey, 44% of respondents from digital maturing firms reported as more likely to use cross-functional teams compared to 16% from early-stage digital firms.   In another survey report[41], more than 70% of digitally maturing businesses were found using cross-functional teams towards digital business priorities compared to 30% from less digitally maturing firms. Compared to 55% of early digital firms, 83% of the digitally maturing firms use cross-functional teams towards innovation initiatives.   The beneficial effects of cross-functional teams in furthering innovations are reflected in another survey[42] report.

The benefits of collaboration, cross-functional teams, and agile working are various, and include faster speed, better problem solving, effective execution, benefits of diverse ideas and perspectives, and better relationships. From an employee perspective, they include better morale, enhanced work experience, and broader perspective. These benefits accrue whether a firm is digital or non-digital, though the extent and need could differ depending on various contextual factors including opportunities to do so, because it is needed to meet external challenges as opposed to it being organisationally a nice and optional thing to do.

In theory, the need for collaboration, cross-functional teams and agile working becomes more pressing, as the need for faster response, adaptation and possibility of digital disruption increases.  As Shamin Mohammad[43], Chief Information and Technology Officer of used car US retailer CarMax noted, “If you think about how fast technology is changing and how fast customer expectations are changing, to deliver what the customers are looking for, you have to organize as cross-functional teams …No single-function team can really deliver at the speed the customer is expecting.” (pp. 10).

In addition, both business and technical areas in digital technologies are becoming increasingly super-specialist, and therefore digital products, services and features require closer collaboration among many super-specialist teams. As an example, “Apple has hundreds of specialist teams … dozens of which many be needed for even one key component of a new product…the dual lens camera with portrait mode required the collaboration of no fewer than 40 specialist teams…Because no function is responsible for a product or a service on its’ own, cross-functional collaboration is crucial”[44].

Another reason that collaboration and cross-function approach could be performance differentiator in a digital context is the need to leverage on data whilst developing innovative products and services.  If this data is available in an uneven and fragmented way across functional silos, it will limit the abilities of digital technologies to generate value.  Iansiti and Lakhani[45] note, “Silos …are the enemy of AI-powered growth …When each silo in a firm has its own data and code, internal development is fragmented, and it’s nearly impossible to build connections across silos or with external business networks or eco-systems. It’s also nearly impossible to develop a 360-degree understanding of the customer.”

Another often-overlooked reason for collaboration and cross-function team is that being digital changes the nature of organization.  This is exemplified through two specific applications:

• As products become smart (or in the language of this paper, as businesses start becoming more software and digital technology based), they start losing their solo-functional nature. The smart product, by its very nature, is not the domain of product engineering alone, but also of other functions, such as software design, system integration, data analytics, cloud connectivity, and other elements.  Further, as Porter and Heppelmenn[46] noted, “Smart, connected products require a rethinking of design. At the most basic level, product development shifts from largely mechanical engineering to true interdisciplinary engineering ..” (pp. 6).  An additional implication of overlay of software to product and services is that the “…clock speed of software development is much faster than that of traditional manufacturing…” (pp. 14). To generalise, if every business is relying more on software based, which requires inputs from through-out the firm and serves as a glue, then collaboration and cross-function way of working is almost a necessity.
• As discussed elsewhere in this paper, research on firm’s ability to reap the benefits of AI shows that out of surveyed firms, 7 out of 10 had minimal or no impact from AI (so far) and less than 2 out of 5 (i.e., less than 40%) suggest obtaining any business gains from it in the last three years[47]. The research pointed out that firms that are successful in obtaining value from their AI strategies do many things, including aligning “the production of AI with the consumption of AI, through thoughtful alignment of business owners, process owners, and AI expertise…” (pp. 2). The report further notes that “While the development or “production” of AI algorithms tends to get all the glory, for AI solutions to materially affect business, it’s critical to have willing and capable consumers of AI — people within the business with the desire and ability to exploit AI solutions to make a difference.” (pp. 13). In short, collaboration and cross-functional working is necessary to generate value from AI.
• Emerging Practices on Collaboration, Cross-functional teams and Agile working

Research suggests that it is not only the high frequency of use of cross-functional teams, it is also how teams’ function differently in digitally maturing firms. In particular, teams in the digitally maturing firms have more autonomy on now to accomplish goals (69% versus 38% from early digital firms), evaluated as a unit (as opposed to individual performance, 54% versus 20%) and have higher support from the senior management (73% versus 29% from early digital firms)[48].  One approach to build the autonomy is to share the business problem with the cross-functional team that needs to be solved, build KPIs to work with, and let the team work through on how to solve the problem[49].

Another way to change the mind-set towards cross-function teams is through the funding process. One such example (MasterCard) was provided in the previous section. Another approach is used in CarMax, where annual project-based funding is moved to product-based mind-set, where number of product teams and business goals influence the funding, and product-teams results are tweaked as it progresses[50].

Finally, many firms use versions of sprints, bootcamps and hackathons to address specific problems, business goals, or a general objective of building digital savviness and culture and in the process tapping the entrepreneurial energies of their employees.

3.4)          Ethics and Governance

 The need and role of good ethics and governance in the functioning of an organization and producing good outcomes is self-evident.  In spite of this, these topics in the context of digital firms are now a matter of mainstream discussion to the extent that there is a good possibility that digital firms will be subject to new and higher regulations in future.  The disappointments and controversies leading to this are multiple: privacy violations, data-selling, amplification of fake news, the “black-box” nature of AI, and a rush to use algorithms that are not fully “mature”, well-tested or audited to save costs – or be the first in the market to benefit from “winner-takes-all” advantages. The complexities, risks and leadership challenges posed by poor ethical and governance standards in digital firms are significant enough to include them as a separate feature of digital culture.

The practical dilemma in developing a right ethical and governance framework in digital firms is that some of their celebrated cultural elements (such as high empowerment and autonomy, need for agility and speed, fail fast and break things) when juxtaposed with imperatives of digital competition (such as disruption, winner-takes all mentality, innovation, new business models, external eco-system partnerships) also give rise to governance and ethical risks.  In principle, these dilemmas can be managed. For example, more empowerment and autonomy under right cultural norms could lead to better ethical outcomes. This, however, requires that ethical and governance considerations are embedded in the culture in the right way at all levels. This also requires that leadership establishes proper “ethical and governance guardrails” in the beginning rather than leaving them to chance, convenience and after the fact events.

a)          Why governance and ethical challenges in digital firms are more complex?

Whilst the theoretical case for higher standards of governance and ethics can be easily made, the desired ethical and governance framework is not so easy to build. Ethics and governance in digital contexts present additional complexities for the following reasons:

• Digital firms rely on digital resources – data, information, algorithms – to provide them competitive advantage, and these resources differ from traditional resources, such as capital, plant and machinery, land and human resources. Traditional resources have decades, if not centuries, of accumulated norms, expectations and crystallized practices with regards to right governance and ethics (often codified in terms of industry-wide and regulatory requirements). In most cases, traditional resources are tangible, impersonal, objective, finite and not subject to duplication, which, in some ways, makes it easier to apply governance and ethical considerations. In addition, tangible resources or assets have physical constraints, which makes it slower to distribute them giving more time for ethical and other risks to emerge and be managed.  In contrast, main digital resources – information and data – are intangible, could be subjective and personal, infinite and can be amplified and shared with minimal constraints of time, cost and physical location.  These features have already led to concerns and call for more governance of social-media firms, perhaps the most visible form of digital firms.
• Digital technologies are often celebrated for their potential to give rise to business models, innovations, new services, and other forms of new value additions, often to address previously unexplored or new needs. However, any new business model, innovation, or a form of new value-proposition would also, by implication, have governance and ethical angles that are new, untested or do not have established norms to apply in a new context. An example: Though a general norm now, when Netflix was considering use of credit card information to charge its’ subscribers after the first month free membership is over, it felt that it is not a clear-cut choice (from ethical and customer’s point of view)[51] and debated about it internally.
• Digital firms also work in a competitive landscape that is prone (and expected) to winner-takes-all competitive mind-set. This provides incentives to digital firms to be aggressive (at least to start with), as their business matrices (and fundings) are tied to competitive land-grab. For example, Uber got its bad reputation in part because of the aggressive style and choices of its former CEO Kalanick. However, as suggested by one of Kalanick’s former associates, “If [Kalanick] were less brash, I don’t think he would get as far as he[has]”[52]. (pp. 15). While this does not serve to negate the enormity of issues that resulted from the style, it highlights a broader problem as well – that winner-takes-all nature of competition in digital context makes governance and ethical issues a low priority, at least in the short run.
• Digital firms are subject to both network effects and unintended consequences, and this accentuates the ethical and governance issues, as the example of Facebook (now Meta) shows. As Nohria and Taneja note[53], “Mark Zuckerberg, for example, didn’t start Facebook intending for third-party abuse and political interference to run rampant on the platform. Yet, fueled by the mantra of “move fast and break things,” a platform intended to “give people the power to share and make the world more open and connected” ended up having devastating unintended consequences…”.  In a different way, this example also highlights the risks and challenges in business models that are based on platforms and eco-systems. These business models, by their nature, derive strength from the large participation (network effects) from its’ participants. However, digital firms may not have full, real-time awareness of the actions of members of platforms and eco-systems, and may not have anticipated problem issues to provide for proactive governance, as some of it is new. The issue is not limited to social-media firms, but also to new (digitally powered) industries, such as autonomous vehicles, virtual and augmented reality, gene-testing, robotics, and use of drones in various situations.

b)          Regulation, Digital culture, and governance and ethical challenges in digital firms

There are two main ways to ensure that firms are subject to right governance and ethical behaviours. There is externally mandated governance (mainly through regulations or through industry standards set by professional bodies) and internal (discretionary) governance (internal governance policies, values, and culture).  For most manufacturing and product firms, the externally mandated governance provides a time-tested, acceptable and level playing field for all firms to follow, as they main use and compete through traditional resources.

In case of digital firms (competing mostly on the basis of digital resources), the externally mandated norms usually lag behind, because the digital context is new, key problems or issues have not been envisioned, critical mass of experience slow to build, and field itself is fast changing. The governance and ethical issues are also contextual in nature (for example, privacy and data usage norms in Europe differ from, say, many Asian countries). In the near absence of external regulations or equivalent norms, digital firms are subject to their own discretionary governance in matters that relate to use of data, information and other digital resources. This, however, has two main risks:  Firstly, being discretionary, it could be based on subjective business and individual assessment, and not proportionate to the extent needed. Secondly, discretionary governance, also has the side-effect of creating an uneven playing field with regards to ethical standards, and digital firms following high standards lose the competitive space as high ethical standards becomes a competitive dis-advantage at least in the short-run.

c)           Experience from the Financial Sector

Financial sector is one of the leading users of leading technology, and constantly innovates through new business models (such as FinTechs) and new technological innovations (such as Crypto-currency and blockchain). Financial sector also operates in an inter-connected context, and subject to network effects and unintended consequences, as shown through the Global Financial Crises 2008.  To avoid a repetition of the crises, financial sector was subject to new (and onerous) regulations in most leading economies.

The experience from last many years of regulations shows that though they are effective, (because no major crises occurred since, so far), the modern economy and firms that participate in the sector are complex, and it is not possible to envision all kinds of risks and provide a one-size fits all regulatory interventions.  Regulations are usually reactive in nature, and deal relatively effectively with “known knowns” and “known unknowns”, but not with “unknown unknowns” – an arena, which is becoming more common with new technologies, innovation, new business models, and economic externalities.  Beyond a point, regulations are not so effective in improving conduct and build trust in the firm, its’ consumers and indeed the sector itself. This can be done effectively and more efficiently by culture.  For these and similar reasons, the financial regulators (particularly the Financial Conducts Authority in the UK[54]), started emphasising culture as key to ensuring better safer, resilient and trust-worthy financial sector.  A right culture can ensure self-regulation and fill-in the gaps that regulations, by their very nature, can-not.

d)          A case for Governance and Ethics as a Cultural Feature

In the sub-section (ii) above, it was argued that self-imposing high ethical and governance standards, when there is no externally mandated need, may lead to competitive dis-advantage, when other firms are not following same standards. That said, digital firms and technologies have been around for some-time now for users, and indeed general public, to have moved up on the initial learning curve (aided by some bad experiences) with regards to the risks and dangers that digital firms can pose.  This is reflected in the growing expectations for a stronger regulation, and mainstream media and scholarly material on the ethical and poor governance risks posed by digital firms. The debates within the technology industry itself and emergence of privacy driven internet products (browsers, emails, etc) suggests that at least some parts of industry are slowly changing and recognize that self-imposed governance and ethical standards could be a competitive advantage, and, to the extent, it is established early enough, it could serve as an entry barrier for others.  While their numbers are small – and most likely will not make an impact in the short run – a trend is reversed, an established narrative to maximise data value is softened, an industry convention to be light-touch on ethics is overcome, and privacy and ethics themselves have become a source of new competitive strategy and driver for business models.

It is also in the interest of industry (and its main firms) to set a high bar on governance and ethics, as otherwise it risks building bad name for the industry and losing trust of users, which translates into poor growth for the industry itself.  The need to self-regulate, even if external mandates to do so do not exist, could build trust in the users and public, and lead to greater industry growth.

An additional consideration is hiring and motivation of new generation of employees, who have the required digital skills and also active members of digital economy. As a survey report noted, “Digital workers want their values, not just their value, explicitly acknowledged – if not publicly embraced – by top management”[55] (pp 1).

A digital culture that intentionally cultivates ethical and governance considerations also provides a downside protection.  In terms of anecdotal observations, digital firms that have had governance and ethical challenges have had a difficult time to re-gain their earlier strategic position and goodwill. Both Meta (earlier Facebook) and Uber, for example, are still, nearly four years later still trying to get back to their old positions.  For both leadership and long-term investors, this is a high level of risk to bear.

e)        Digital Culture and Governance and Ethics  

Given that digital context is different and relies mostly on digital resources, how does one ensure that ethics and good governance are part of the digital culture, particularly when the culture requires speed, innovation, trying out new services (in beta mode), fail-fast culture and external partnerships? How can leadership ensure that poor ethical choice and inadequate governance are not consciously or unconsciously influencing the decision-making processes at all levels?

The old gold-standard and enduring recipe (organizational values and purpose, mission statements, performance management that focusses on how results are achieved, etc) still provide a good start point. In a survey, it was found that 76% of digitally maturing firms have policies that support ethical standards with regards to initiatives compared to 43% of early-stage firms[56].   But the survey also cautions that a common mistake made on digital ethics is to assume that legacy policies are adequate, or the existence of guidelines will deliver right ethical outcomes.

A challenging part of building ethics and governance is to change the narrative from being a bureaucratic and compliance requirement to a business value-add requirement.   Just as quality improvement programs (mostly from Japan) in the 1970s and 80s showed that they actually reduce wastage, customer returns, costly repairs, or to say in short save money, digital products and services can also benefit from the same logic.

A survey[57] looked at the firms practicing responsible AI, 72% reported that they actually increased the financial benefits of AI, and 62% reported that they decreased the operational risk. (Note: Responsible AI was defined as “improving fairness in algorithms and reducing biases in decision-making; promoting inclusivity and a diversity of perspectives; providing model interpretability and explainability to Al end users; ensuring data privacy and security in Al; complying with legal requirements; and monitoring the social impact and ethics of Al” (pp 11[58])). At an intuitive level, it is similar to the logic behind the quality movements, i.e., by investing more time and effort during the (upstream) production process, firm reduces the faults and problems that would have gone (downstream) consumption phase and resulted in costly fixes and reputation losses.

It is increasingly being suggested, notably by Sir Tim Berners-Lee, the inventor of world wide web, that ethical considerations be made fundamental to product design[59].  In addition, there is no real reason that they could not be made a part of product proposals and review process including making AI less mysterious blackbox like.

To summarise, ethical considerations and good governance needs to be a part of digital culture, because “unknowns” outnumber “knowns” and therefore a comprehensive rule book is not possible. A bigger reason is, there is much at stake in terms of trust and growth of the industry, and a bigger risk exposure to firm itself in case of ethical lapses. A key part of building ethics and good governance into digital culture is to change the narrative around ethics and governance from it being slowing work and adding bureaucracy to its being building trust in the firms’ products and saving money and reputation. At the same time, an awareness is needed those ethical guard-rails, values and principles, and review processes will not deliver, if employees find that the leadership commitment to them is weak, or worse a gamed-one.

3.5)          Digitally-aligned Human Resources (HR) processes

 The relationship between digitally aligned Human Resources policies/ practices and culture is a complex one. A given culture could be because of, or in spite of HR processes and practices. In practice, HR policies play a significant role, and whilst not sufficient by themselves to institute a right culture, they are necessary in building one.

To understand ways in which HR policies and practices could support digital culture and strategy, it is useful to analyse if the context of human resources in a digital organization is different, and if so, in what ways. This analysis depends on the extent to which a firm is a digital organization already, or aspires to be.  Though digitally-native organizations face full breadth of HR challenges specific to digital context, the challenges for non-digitally native firms are also complex given the need to navigate the two worlds of HR – traditional as well as digital, and integrate them effectively.

a)          HR challenges related to digital talent

Digital firms, like most industries, have their own HR challenges; however, since digital is becoming universal – all firms are digital to a lesser or greater extent – HR challenges vary depending on the extent of digitalization, business strategy, and HR goals.

There are few familiar challenges: A great – and increasing – demand-and supply gap in the talent market, particularly in the fields of data science and AI.  From a digital firm’s perspective, it is very difficult to forecast the skills that will be needed in, say, three years (because of changes in business and technologies available) and develop HR strategies around that.  Another implication of this demand-supply gap is digital talent is very expensive. This in turn increases pressure on the managers and leaders that the hired talent is utilised most productively.

Given the high levels of pay, hiring, engagement and retention are important HR challenges, as there are significant variations in the productivity levels (and by implication value-add) among the digital (specialist) talent. An analysis by McKinsey[60] showed “that top engineers can be 10 times more productive than their more junior peers.” Another analysis[61] showed that “exceptional engineers and researchers could perform 300 times better than average people.” (pp. 8).   In one well-known firm, it was noted that there are “situations where two people doing the same work can have a hundred times difference in their impact, and in their rewards. For example, there have been situations where one person received a stock award of $10,000, and another working in the same area received $1,000,000. This isn’t the norm, but the range of rewards at almost any level can easily vary by 300 to 500 percent, and even then there is plenty of room for outliers…”(pp. 241)[62].

On one study[63], IT professionals were categorised into five different categories (namely, Expert, Proficient, Competent, Advanced Beginner, and Novice) and based on research, three talent models were developed, namely, Pyramid model, Talent-heavy model, and Diamond model.  The diamond model was found to have 50% of the headcount of pyramid model, but offer same productivity output (as pyramid model), whilst talent-heavy model needs only 30% of headcount of pyramid model to achieve the same productivity output.

Though these numbers may be context- and research-specific (i.e., depend on the definitions and analysis used), they do suggest significant differences in the productivity levels among individuals, and this is a game-changer for HR.  This implies that HR policies and practices from the recruitment, training, career development, performance management and compensation, and exits recognize the significant productivity differences among professionals, and design and position HR policies and practices accordingly.

i)            Training and Development

Digital talent, due to their training and nature of work, is generally engineering-minded, and may not always appreciate and value managerial responsibilities.  Speaking in anecdotal terms, digital professionals are not always comfortable with the “soft” aspects of their management roles.  In fact, the question “whether managers matter formed the basis of a broad research project” at Google, and was called Project Oxygen. The research found evidence that there are significant differences between high-scoring and low-scoring managers in terms of their impact on job satisfaction, retention, and performance[64]. As an anecdote recalls, “Engineers hate being micro-managed on the technical side, but they love being closely managed on the career side.” (pp. 9)[65].

Given the fast pace of change including improvements in existing technologies and emergence of new technologies, opportunities to keep pace with new technologies are an important consideration for digital employees, and almost an organizational imperative for engagement and retention purposes.  In a survey[66], more than 90% of respondents said that they need to update their skills at least yearly to work effectively in a digital world with 44% saying that they need to update them continually.

In another survey[67], it was found that companies that are able to provide their senior talent, the resources and opportunities to develop are more likely to retain talent, as opposed to 30% of senior talent who, lacking such opportunities, plan to find new jobs and leave in less than a year.  The survey also found that 75% of digitally maturing firms provide such opportunities (as opposed to 14% of early-stage firms) and 71% of them reported their success in getting new talent (as opposed to 10% of early-stage firms) based on their use of digital. In short, there is a correlation between firms’ advancement into digitalization spectrum, and provision of opportunities and resources to employees.

A well-embedded talent development helps to address the talent uncertainty and provide confidence to the firm and its employees about the digital future. In the above survey, 71% of employees in digital advanced firms report that employees in their firms have knowledge and ability to execute their digital strategy. This percentage drops to 22% for firms at the low end of digital maturity.

Digital talent, like any normal talent, is motivated by empowerment.  Many digital firms provide opportunity to them to set some time for their own “projects”. Google is probably one of the well-known examples, as employees can spend “20% of their time on projects of their own choosing.” (pp. 11)[68]   In a survey[69], 86% of employees from digitally maturing firms said that 10% or more of their time is spent on experiments or innovation, as opposed to 40% of employees in early digital firms reported spending less than 10% (or no time at all) on experimentation or innovation. Given that digital firms need to have new ideas, this opportunity to spend time on favourite projects, besides improving engagement and skill-build, also generates new ideas for the firm and helps in building digital capabilities. At Google, both Gmail and traffic information in the Google Maps were ideated upon in the 20% time[70].

b)          HR challenges in a Digital Context

The challenges with regards to attraction, development, engagement, compensation, and retention of digital employees are a sub-set of the larger set of HR challenges faced by digital firm, which includes non-digital employees as well.  A good part of the “new” HR challenges in the digital space comes into being when a firm has to cover two boats at the same time (Old (traditional business) and its’ futuristic (digital) world) and integrate the two. A related and overlapping challenge is integration of business requirements with HR to implement the next stage of digital transformation.

i)            New Roles

The rise of new technologies and their application into various business contexts has also led to proliferation of new roles, roles that did not exist, say, five years back, and therefore by implication, there is limited talent availability, market compensation data, and experience to integrate the role with other roles and business strategy itself for best results.  These roles (about three years back) included Commercial (Digital business developer, Digital product manager,  E-business manager), Technology (Chief Data Officer, Traffic manager, Data scientist, Data protection officer), Web (Web project manager, web-integrator, webmaster), Marketing (SEM/PPC Manager, User experience designer, Media acquisition manager), Facilitator (Service design thinker, Crowd innovation facilitator, Chief listening Officer), Human Resources (Design learning manager, Digital work experience expert, employer brand director, social media strategist) (pp. 28) ^[71]. Such roles present their own challenges in ensuring internal and external equity.. Also, the rate of growth of job content of these roles is largely a function of digital environment, as opposed to the growth of role due to higher work-load (arising out of business growth), and this has different HR implications than when traditional roles grow (mostly due to business growth).

ii)          Working with Digital Technologies

Though some of the HR challenges of working with (digital) technologies, such as concerns about job losses, acquiring new skills, and getting work done when technologists work with non-technologists, are familiar (and not covered here), there are a few dimensions that are of recent origin and becoming salient as technologies become more advanced.

−         Learning with and Training of Digital Technologies

Historically, technologies were relatively passive in nature (such as ATM machine, information data-bases, and digital kiosks).  These technologies were tested to work reliably for a narrow range of tasks (say, cash withdrawal) and training was provided tousers (or employees) in their use. Recent digital technologies, such as AI and machine learning, are not static, and require an on-going relationship, so that “AI” is trained on an on-going basis to perform well covering a range of possible situations, particularly those that the historical data, to the extent it was available and accurate, did not reflect fully.  This is also referred to an alignment between “production” and “consumption” of AI, an area where pioneering AI firms do well.

As previously noted in the sub-section on risk-taking, experimentation and innovation, one of the key challenges faced in a fast-changing digital environment is that “it requires more explorations and experimentations than most companies are prepared to manage. Experimentation is at the heart of digital maturity”[72].  This is even more the case with AI. As one report noted, “…experimentation and learning with AI can take much longer than other digital initiatives, with a higher variability of success and failure.”[73]

Since new situations keep becoming known, particularly when the initial training was on a limited data, the “training” period could be long and on-going in nature. In addition, the nature and range of tasks AI is called upon to do are becoming increasingly more sophisticated and high-stake (for example, medical diagnosis and fraud detection) and require a good “training” from the users.  Though the concept of organizational learning is not new, it has acquitted a different – and possibly more complex – dimension with digital technologies, particularly AI.  As one research report noted,

“Only 10% of companies obtain significant financial benefits with artificial technologies … research shows that these companies intentionally change processes, broadly and deeply, to facilitate organizational learning with AI …their strategic focus is organizational learning, not just machine learning…Organizational learning with AI is demanding. It requires human and machines to not only work together but also learn from each other. Mutual learning between human and machine is essential to success with AI. But its’ difficult to achieve at scale … with organizational learning, the odds of an organization reporting significant financial success increase to 73%.” (pp. 1-2)[74].

The report[75] looked at AI and activities / processes involved in its’ implementation, and calculated the probability of level of significant financial benefits coming through each such activity.

• The first main activity called “Discovering AI” involved implementing AI in targeted applications and gives 2% probability of a significant financial benefit. Additional activities, such as infrastructure (investing in data, technology, etc), talent (developing AI skills) and Strategy (integration with business strategy) increase the probability of significant final benefits by 2%, 3% and 14% respectively.
• Next Stage (Scaling AI) includes two activities (aligned production and consumption of AI) and beyond automation (applying AI across various user cases) and increase the probability of significant financial benefit by 6% and 12% respectively.
• However, it is only when organizational learning with AI is implemented that the probability of significant financial benefit increases to an appreciable level. This includes knowledge (sharing knowledge between AI and humans) and roles (structuring AI and human interactions) which increase probability by 22% and 12% respectively, and when all stages previous activities added up, take the overall probability to 73%.

The report also looked at various ways in which interactions between AI and humans can take place and identified following five modes:

• AI decides and implements
• AI decides, human implements
• AI recommends, human decides
• AI generates insights, human uses in a decision process
• Human generates, AI evaluates.

It found that the respondents reported 5% success of achieving significant financial benefit, when only one mode is used, but this probability goes up when a greater number of modes are used. The percentage improvements were 6% (with 2 modes), 15% (with 3 modes), 15% (with 4 modes) and 32% (with 5 modes).

Another way to look at the relationship between humans and AI is through the feedback methods used. In the same research (as above), three main feedback methods were analysed, namely, AI learns from human feedback, humans learn from AI, and human design AI to learn autonomously. It was found that when no feedback method was used, respondents had only 5% likelihood of achieving significant financial benefit from AI, but it goes up with 6% (when one feedback method is used), 13% (when two feedback methods were used) and 29% (when three feedback methods were used).

The above findings strongly suggest that a good “relationship” between machine and individual working together is likely to hold the key to the success of digital organizations. In their book, Daugherty and Wilson[76]  point that human element is on the ascendance, and also make a distinction between machine learning (through large volumes of data) and machine teaching (human tutoring machine in addition to training them). The advantages with machine teaching include less data to “train” machines and use of human experience and expertise, particularly in situations of high uncertainty.

The above findings suggest that the potential of digital technologies to result in financial gains is limited, if they are treated (sometimes wrongly) as “plug and play” options and limited to very specific applications. The full potential of digital technologies requires that organization – and its employees – are fully engaged in this journey, and this requires enabling the right culture supported by digitally aligned HR processes.

4)         Key ideas

The main ideas in this section can be summarised as follows:

• All organizations have organization culture, whether or not it is intentionally built. In practice, culture provides ways to understand how things are done in an organization. Further, all organization cultures, whether digital or not, are unique to the organization, and reflect the values and assumptions that guide how things are done. In all these respects, digital culture is no different from any other culture.
• Previously, firms competed in a relatively stable and predictable environments with efficiency maximisation as one of the main goals. Organization culture in such contexts was characterised by well-defined processes, low empowerment (to avoid variations in processes), top-down goal setting, and well-structured roles. Coordination was achieved through established processes, budgets and senior level review meetings.
• Since digital firms compete in a dynamic and unpredictable environment (commonly referred to as environments with VUCA features) and use digital resources to build competitive advantage, they are likely to be more effective, if the “way things are done” is aligned to the requirements of their dynamic environments and digital resources (which have different features from traditional resources). This alignment is achieved when digital firms have flexible cultures, high empowerment, distributed leadership and open communication.
• This working paper defines digital culture as “as a set of collective values, norms, expectations, relationships, processes, and other considerations – whether or not derived from overall corporate culture – that influence the purposes, ethics, practices and outcomes related to the use of “digital resources”, digital technologies, and digital eco-systems – whether internal or external – that are subject to the influence of digital firm.”
• Digital culture, as defined above, is external-oriented, because the processes followed and choices made by digital firms influence and impact the platform and eco-system partners and their behaviours.
• A distinction is made between a digital organization and digital culture. An organization could be digital, but its’ culture could be process-led and rule-driven – features, which are more aligned to stable and predictable environments.

• Digital firms do not have to build digital culture, but given the high survival and performance bar they face, an alignment of the culture with realities of external environment and nature of digital resources is likely to improve its performance.
• The relationship between a typical organizational culture and digital culture could vary, particularly in established manufacturing and service firms, which are moving up on the digital spectrum. It is one of the contributory reasons why such firms begin their digital transformation through a business unit located and managed separately from the main office.
• Surveys have found few cultural elements that correlate with digitally maturing organizations (i.e., firms that are advanced in their journey on the digital spectrum). These elements are digital savviness (Note: It is different from technical skills), a bias towards data and analytics, risk-taking, experimentation, innovation, collaboration and cross-functional workings, ethical and governance frameworks, and digitally-aligned HR processes (such as development, reward and hiring).
• The above list does not contain all elements. Leadership plays a key role in influencing culture and discussed next. In addition, there may be other elements that are not included, but their role and salience will become known as firms become more digital.

References

[1] Kane, Gerald C., Palmer, Doug., Phillips, Anh Nguyen., Kiron, David., and Buckley, Natasha. (2016) “Aligning the Organization for Its Digital Future: Findings from the 2016 Digital Business Global Executive Study and Research Project”. MIT Sloan Management Review. Summer 2016. Research Report in collaboration with Deloitte University Press.

[2] As cited in, Schrage, Michael., Pring, Benjamin., Kiron, David., and Dickerson, Desmond (2021) “Leadership’s Digital Transformation: Findings from the 2021 Future of Leadership Global Executive Study and Research Project”. MIT Sloan Management Review. January 2021, Research Report in collaboration with Cognizant.

[3] Kane, Gerald C., Palmer, Doug., Phillips, Anh Nguyen., Kiron, David., and Buckley, Natasha. (2017) “Achieving Digital Maturity: Findings from the 2017 Digital Business Global Executive Study and Research Project”. MIT Sloan Management Review. Summer 2017. Research Report in collaboration with Deloitte University Press.

[4] Kane, Gerald C., Palmer, Doug., Phillips, Anh Nguyen., Kiron, David., and Buckley, Natasha. (2017) “Achieving Digital Maturity: Findings from the 2017 Digital Business Global Executive Study and Research Project”. MIT Sloan Management Review. Summer 2017. Research Report in collaboration with Deloitte University Press.

[5] Kane, Gerald C., Palmer, Doug., Phillips, Anh Nguyen., Kiron, David., and Buckley, Natasha. (2018) “Coming of Age Digitally: Findings from the 2018 Digital Business Global Executive Study and Research Project”. MIT Sloan Management Review. Summer 2018. Research Report in collaboration with Deloitte University Press.

[6] Kane, Gerald C., Palmer, Doug., Phillips, Anh Nguyen., Kiron, David., and Buckley, Natasha. (2018) “Coming of Age Digitally: Findings from the 2018 Digital Business Global Executive Study and Research Project”. MIT Sloan Management Review. Summer 2018. Research Report in collaboration with Deloitte University Press.

[7] Daugherty, Paul R. and Wilson, James, H. (2021). Radically Human. How New Technology is Transforming Business and Shaping Our Future. Harvard Business Review Press. Massachusetts: Boston.

[8] Neeley, Tsedal, Keller, JT, and Barnett, James (2019). Case: From Globalization to Dual Digital Transformation: CEO Thierry Breton Leading Atos into “Digital Shickwaves” (A). Harvard Business School. 9-419-027.

[9] Ready, Douglas A., Cohen, Carol, Kiron, David., and Pring, Benjamin (2020) “The New Leadership Playbook for the Digital Age: Findings from the 2020 Future of Leadership Global Executive Study and Research Project”. MIT Sloan Management Review. January 2020, Research Report in collaboration with Cognizant.

[10] Kane, Gerald C., Palmer, Doug., Phillips, Anh Nguyen., Kiron, David., and Buckley, Natasha. (2016) “Aligning the Organization for Its Digital Future: Findings from the 2018 Digital Business Global Executive Study and Research Project”. MIT Sloan Management Review. Summer 2016. Research Report in collaboration with Deloitte University Press.

[11] Kane, Gerald C., Palmer, Doug., Phillips, Anh Nguyen., Kiron, David., and Buckley, Natasha. (2016) “Aligning the Organization for Its Digital Future: Findings from the 2018 Digital Business Global Executive Study and Research Project”. MIT Sloan Management Review. Summer 2016. Research Report in collaboration with Deloitte University Press.

[12] Leonardi, Paul., and Neeley, Tsedal. (2022). The Digital Mindset: What It Really Takes to Thrive in the Age of Data, Algorithms, and AI.  Harvard Business Review Press. Massachusetts: Boston.

[13] Ransbotham, Sam., Khodabandeh, Shervin., Fehling, Ronny, LaFountain, Burt., and Kiron, David.(2019) “Winning with AI: Pioneers Combine Strategy, Organizational Behaviour, and Technology”. MIT Sloan Management Review. October 2019. Research Report in collaboration with BCG.

[14] Digital Transformation of Industries (World Economic Forum White Paper: In collaboration with Accenture). World Economic Forum. January 2016.

[15] Leonardi, Paul., and Neeley, Tsedal. (2022). The Digital Mindset: What It Really Takes to Thrive in the Age of Data, Algorithms, and AI.  Harvard Business Review Press. Massachusetts: Boston.

[16] As above.

[17] Randy Bean and Thomas H. Davenport, “Companies are Failing in their Efforts to Become Data-Driven,” hbr.org, February 5, 2019, https://hbr.org/2019/02/companies-are-failing-in-their-efforts-to-become-data-driven.

[18] Ibid.

[19] Interview with Ishit Vachhrajani by MIT SMR Connections. “Developing the Data-Driven Organization: Leadership, Culture, and Learning”, MITSloan Management Review.  November 18, 2020.

[20] Davenport, Thomas H. and Harris, Jeanne G. (2017).  Competing on Analytics: The New Science of Winning.    Harvard Business review Press. Massachusetts: Boston.

[21] Ibid.

[22] Subramaniam, Mohan (2021) Are you Using the Right Data to Power Your Digital Transformation? Harvard Business Review Digital Article. December 3, 2021.

https://hbr.org/2021/12/are-you-using-the-right-data-to-power-your-digital-transformation

[23] Siren, Pontus M.A., Anthony, Scott D. and Bhatt, Utsav. (2022). Persuade Your Company to Change Before It’s Too Late. Harvard Business Review. January – February 2022.

[24] Lewis, Michale (2003) Moneyball. New York: Norton.

[25] Agrawal, Ajay., Gans, Joshua., and Goldfarb, Avi. (2018). Prediction Machines: The Simple Economics of Artificial Intelligence. Harvard Business review Press. Massachusetts: Boston.

[26] Garvin, David A., Wagonfeld, Alison B. and Kind, Liz. (2013) Google’s Project Oxygen: Do Managers Matter? Case: 9-313-110 (Boston: Harvard Business School, August 2018).

[27] Derose, Chris (2013) “How Google Uses data to Build a Better Worker”, The Atlantic, October 7, 2013.

[28] Kane, Gerald C., Palmer, Doug., Phillips, Anh Nguyen., Kiron, David., and Buckley, Natasha. (2016) “Aligning the Organization for Its Digital Future: Findings from the 2018 Digital Business Global Executive Study and Research Project”. MIT Sloan Management Review. Summer 2016. Research Report in collaboration with Deloitte University Press

[29] Davenport, Thomas H. and Harris, Jeanne G. (2017).  Competing on Analytics: The New Science of Winning.    Harvard Business review Press. Massachusetts: Boston.

[30] Kane, Gerald C., Palmer, Doug., Phillips, Anh Nguyen., Kiron, David., and Buckley, Natasha. (2018) “Coming of Age Digitally: Findings from the 2018 Digital Business Global Executive Study and Research Project”. MIT Sloan Management Review. Summer 2018. Research Report in collaboration with Deloitte University Press.

[31] Ransbotham, Sam., Shervin., Kiron, David., Gerbert, Philipp., and Reeves, Martin.(2017) “Reshaping Business With Artifical Intelligence: Closing the Gap Between Ambition and Action”,  Findings from the 2017 Artificial Intelligence Global Executive Study and Research Project”. MIT Sloan Management Review. Fall 2017.. Research Report in collaboration with BCG.

[32] Kane, Gerald C., Palmer, Doug., Phillips, Anh Nguyen., Kiron, David., and Buckley, Natasha. (2015) “Strategy, not Technology, Drives Digital Transformation: Becoming a digitally mature enterprise”, Findings from the 2015 Digital Business Global Executive Study and Research Project”. MIT Sloan Management Review Research Report in collaboration with Deloitte University Press. . Summer 2015.

[33] Kane, Gerald C., Palmer, Doug., Phillips, Anh Nguyen., Kiron, David., and Buckley, Natasha. (2017) “Achieving Digital Maturity: Findings from the 2017 Digital Business Global Executive Study and Research Project”. MIT Sloan Management Review. Summer 2017. Research Report in collaboration with Deloitte University Press.

[34] Kane, Gerald C., Palmer, Doug., Phillips, Anh Nguyen., Kiron, David., and Buckley, Natasha. (2016) “Aligning the Organization for Its Digital Future: Findings from the 2018 Digital Business Global Executive Study and Research Project”. MIT Sloan Management Review. Summer 2016. Research Report in collaboration with Deloitte University Press.

[35] Koh, Annie., Speculand, Robin., and Wong, Adina., (2020). “DBS: Digital Transformation to Best Bank in the World”. Case: SMU 816. Singapore Management University. Version: 2020-06-15

[36] Kane, Gerald C., Palmer, Doug., Phillips, Anh Nguyen., Kiron, David., and Buckley, Natasha. (2017) “Achieving Digital Maturity: Findings from the 2017 Digital Business Global Executive Study and Research Project”. MIT Sloan Management Review. Summer 2017. Research Report in collaboration with Deloitte University Press.

[37] Kane, Gerald C., Palmer, Doug., Phillips, Anh Nguyen., Kiron, David., and Buckley, Natasha. (2017) “Achieving Digital Maturity: Findings from the 2017 Digital Business Global Executive Study and Research Project”. MIT Sloan Management Review. Summer 2017. Research Report in collaboration with Deloitte University Press.

[38] Kane, Gerald C., Palmer, Doug., Phillips, Anh Nguyen., Kiron, David., and Buckley, Natasha. (2019) “Accelerating Digital Innovation Inside and Out: Agile Teams, Ecosystems, and Ethics”, Findings from the 2019 Digital Business Global Executive Study and Research Project”. MIT Sloan Management Review Research Report in collaboration with Deloitte Insights. June 2019.

[39] Kane, Gerald C., Palmer, Doug., Phillips, Anh Nguyen., Kiron, David., and Buckley, Natasha. (2019) “Accelerating Digital Innovation Inside and Out: Agile Teams, Ecosystems, and Ethics”, Findings from the 2019 Digital Business Global Executive Study and Research Project”. MIT Sloan Management Review Research Report in collaboration with Deloitte Insights. June 2019.

[40] Kane, Gerald C., Palmer, Doug., Phillips, Anh Nguyen., Kiron, David., and Buckley, Natasha. (2015) “Strategy, not Technology, Drives Digital Transformation: Becoming a digitally mature enterprise”, Findings from the 2015 Digital Business Global Executive Study and Research Project”. MIT Sloan Management Review Research Report in collaboration with Deloitte University Press. . Summer 2015.

[41] Kane, Gerald C., Palmer, Doug., Phillips, Anh Nguyen., Kiron, David., and Buckley, Natasha. (2017) “Achieving Digital Maturity: Findings from the 2017 Digital Business Global Executive Study and Research Project”. MIT Sloan Management Review. Summer 2017. Research Report in collaboration with Deloitte University Press.

[42] Kane, Gerald C., Palmer, Doug., Phillips, Anh Nguyen., Kiron, David., and Buckley, Natasha. (2019) “Accelerating Digital Innovation Inside and Out: Agile Teams, Ecosystems, and Ethics”, Findings from the 2019 Digital Business Global Executive Study and Research Project”. MIT Sloan Management Review Research Report in collaboration with Deloitte Insights. June 2019.

[43] As cited (pp 9) of Kane, Gerald C., Palmer, Doug., Phillips, Anh Nguyen., Kiron, David., and Buckley, Natasha. (2019) “Accelerating Digital Innovation Inside and Out: Agile Teams, Ecosystems, and Ethics”, Findings from the 2019 Digital Business Global Executive Study and Research Project”. MIT Sloan Management Review Research Report in collaboration with Deloitte Insights. June 2019.

[44] Page 7-8 of the article: (https://www.apple.com/careers/pdf/HBR_How_Apple_Is_Organized_For_Innovation-4.pdf)

Podolny, Joel M and Hansen, Morten, T., (2020). “How Apple is Organized for Innovation”. Harvard Business Review, November – December 2020.

[45] Iansiti, Marco and Lakhani, Karim R. (2020). Competing in the Age of AI. Harvard Business Review. January – Febuary 2020.

[46] Porter, Michael E. and Heppelmann, James E (2015). How Smart, Connected Products Are Transforming Companies. Harvard Business Review. October.

[47] Ransbotham, Sam., Khodabandeh, Shervin., Fehling, Ronny, LaFountain, Burt., and Kiron, David.(2019) “Winning with AI: Pioneers Combine Strategy, Organizational Behaviour, and Technology”. MIT Sloan Management Review. October 2019. Research Report in collaboration with BCG.

[48] Kane, Gerald C., Palmer, Doug., Phillips, Anh Nguyen., Kiron, David., and Buckley, Natasha. (2019) “Accelerating Digital Innovation Inside and Out: Agile Teams, Ecosystems, and Ethics”, Findings from the 2019 Digital Business Global Executive Study and Research Project”. MIT Sloan Management Review Research Report in collaboration with Deloitte Insights. June 2019.

[49] Mohammad, Shamim, Kane, Gerald C., and Phillips, Anh Nguyen. Cultivating a Culture of Cross-Functional Teaming and Learning at CarMax. MITSloan Management Review. Digital

[50] Kane, Gerald C., Palmer, Doug., Phillips, Anh Nguyen., Kiron, David., and Buckley, Natasha. (2019) “Accelerating Digital Innovation Inside and Out: Agile Teams, Ecosystems, and Ethics”, Findings from the 2019 Digital Business Global Executive Study and Research Project”. MIT Sloan Management Review Research Report in collaboration with Deloitte Insights. June 2019.

[51] Randolph, Marc. (2019). That Will Never Work.(London:  Octopus Publishing Group Ltd).

[52] Kanter, Rosabeth Moss. And Fox, Daniel (2017). Case: Uber and Stakeholders: Managing a New Way of Riding. Harvard Business School. 9-315-139. February 2017.

[53] Nohria, Nitin and Taneja, Hemant (2021) “Managing the Unintended Consequences of Your Innovations”.  Harvard Business Review Digital Article. January 19, 2021. https://hbr.org/2021/01/managing-the-unintended-consequences-of-your-innovations

[54] Financial Conduct Authority (2018). Transforming Culture in Financial Services. Discussion Paper DP 18/2. Retrieved from: https://www.fca.org.uk/publication/discussion/dp18-02.pdf

[55] Schrage, Michael., Pring, Benjamin., Kiron, David., and Dickerson, Desmond (2021) “Leadership’s Digital Transformation: Findings from the 2021 Future of Leadership Global Executive Study and Research Project”. MIT Sloan Management Review. January 2021, Research Report in collaboration with Cognizant.

[56] Kane, Gerald C., Palmer, Doug., Phillips, Anh Nguyen., Kiron, David., and Buckley, Natasha. (2019) “Accelerating Digital Innovation Inside and Out: Agile Teams, Ecosystems, and Ethics”, Findings from the 2019 Digital Business Global Executive Study and Research Project”. MIT Sloan Management Review Research Report in collaboration with Deloitte Insights. June 2019.

[57] Ransbotham, Sam., Khodabandeh, Shervin., Kiron, David.,  Candelon, Francois., Chu, Michael., and LaFountain, Burt. (2020) “Expanding AI’s Impact With Organizational Learning: Findings from the 2020 Artificial Intelligence Global Executive Study and Research Project”. MIT Sloan Management Review. October 2020. Research Report in collaboration with BCG.

[58] As above.

[59] As above.

[60] Huber, Celia., Sukharevsky, Alex. And Zemmel, Rodney. (2021) “5 Questions Boards Should Be Asking About Digital Transformation”. Digital Article.

[61] Simon, Roberts and Lobb, Annelena. (2017). Google to Alphabet: Ten Things We Know to Be True. Harvard Business School. Case 9-116-029. Rev. December 2017.

[62] Bock, Laszlo. (2015) Work Rules!: Insights from Inside Google That Will Transform How You Live and Lead John Murray Press. London.

[63] Jacobs, Peter, Hjartar, Klemens., Lamarre, Eric and Vinter, Lars (2020). It’s Time to Reset the IT Talent Model. MITSloan Management Review. 2020.

[64] Garvin, David A., Wagonfeld, Alison Berkley., and Kind, Liz. (2013). Google’s Project Oxygen: Do Managers Matter? Harvard Business School. Case 9-313-110. Rev. October 2013..

[65] As above.

[66] Kane, Gerald C., Palmer, Doug., Phillips, Anh Nguyen., Kiron, David., and Buckley, Natasha. (2018) “Coming of Age Digitally: Findings from the 2018 Digital Business Global Executive Study and Research Project”. MIT Sloan Management Review. Summer 2018. Research Report in collaboration with Deloitte University Press.

[67] Kane, Gerald C., Palmer, Doug., Phillips, Anh Nguyen., Kiron, David., and Buckley, Natasha. (2016) “Aligning the Organization for Its Digital Future: Findings from the 2018 Digital Business Global Executive Study and Research Project”. MIT Sloan Management Review. Summer 2016. Research Report in collaboration with Deloitte University Press.

[68] Simon, Roberts and Lobb, Annelena. (2017). Google to Alphabet: Ten Things We Know to Be True. Harvard Business School. Case 9-116-029. Rev. December 2017.

[69] Kane, Gerald C., Palmer, Doug., Phillips, Anh Nguyen., Kiron, David., and Buckley, Natasha. (2019) “Accelerating Digital Innovation Inside and Out: Agile Teams, Ecosystems, and Ethics”, Findings from the 2019 Digital Business Global Executive Study and Research Project”. MIT Sloan Management Review Research Report in collaboration with Deloitte Insights. June 2019.

[70] As above.

[71] Digital Transformation of Industries (World Economic Forum White Paper: In collaboration with Accenture). World Economic Forum. January 2016

[72] Kane, Gerald C., Palmer, Doug., Phillips, Anh Nguyen., Kiron, David., and Buckley, Natasha. (2018) “Coming of Age Digitally: Findings from the 2018 Digital Business Global Executive Study and Research Project”. MIT Sloan Management Review. Summer 2018. Research Report in collaboration with Deloitte University Press.

[73] Ransbotham, Sam., Shervin., Kiron, David., Gerbert, Philipp., and Reeves, Martin.(2017) “Reshaping Business With Artifical Intelligence: Closing the Gap Between Ambition and Action”,  Findings from the 2017 Artificial Intelligence Global Executive Study and Research Project”. MIT Sloan Management Review. Fall 2017.. Research Report in collaboration with BCG.

[74] Ransbotham, Sam., Khodabandeh, Shervin., Kiron, David.,Candelon, Francois., Chu, Michael., and LaFountain, Burt. (2020) “Expanding AI’s Impact With Organizational Learning: Findings from the 2020 Artificial Intelligence Global Executive Study and Research Project”. MIT Sloan Management Review. October 2020. Research Report in collaboration with BCG.

[75] As above.

[76] Daugherty, Paul R. and Wilson, James, H. (2021). Radically Human. How New Technology is Transforming Business and Shaping Our Future. Harvard Business Review Press. Massachusetts: Boston.