5.1 Envision a Digital Strategy

The main purpose of every organization – private or public – is to create, deliver, and capture value. This value originates from two concepts. The first one is the business model , which defines how an organization creates and captures value in economic, social, cultural, or other contexts.² The second is the organization’s operating model, which defines the particular way an organization delivers value to its customers – the “plan to get it done” if you wish. In other words, the business model defines the theory while the operating model captures the practice of an organization’s value creation process. Digital transformation is about rethinking and rearchitecting both the business and operating model by leveraging the power of digital technologies. It usually targets either (1) an increase of the operational excellence by streamlining internal processes or (2) an enhancement of the customer experience [8] to transform a traditional to a digital business and operating model.

5.1.2 Envision a Digital Strategy: Derive a Digital Operating Model

Once a digital business model has been defined, it is important to ask yourself about how you are going to deliver value to your customers and which operating model best supports this value creation process. This is the point where digital technologies come into play. The best way to understand the characteristics of digital operating models is to compare them to traditional ones that are not based on digital technologies and centered around data. The fundamental difference between a traditional and digital operating model arises due to the particular way it leverages data. Traditional operating models use data as a necessary evil to support their operations without deriving economic value from them. As the number of products, services, customers, and the amount of data associated with them increase, traditional operating models are ultimately limited by their inability to manage complexity. For traditional business models that are not centered around data, it simply becomes impractical and too costly at some point to achieve data coherence⁶ across the organization. This is particularly challenging if the different divisions evolved into functionally separated business units over time with their own databases and legacy systems. The distribution of data to decentralized databases implies that decision making in traditional operating models is often based on information intransparency and thus on incomplete and inadequate assessments of decision options.

A digital operating model, on the other hand, overcomes those obstacles and leverages digital technologies to analyze (vast amounts of) data and identify possible decision options that allow for more consistent and objective decision making as shown in Figure 5-2 schematically [9]. In other words, digital technology allows for extracting valuable insights from data that complement human judgment and thereby enable better decisions – some decisions may even be evaluated and executed automatically in a digital operating model by employing smart contracts, for example.

Furthermore, a digital operating model embraces the advantages of exponential scale, scope, and learning associated with digital technologies. Scale in this context refers to delivering as much value to as many customers as possible at minimum cost. Scope describes the range of activities, that is, the variety of products and services, a company offers.⁷ Digital learning determines an organization’s ability to continuously improve and innovate its processes, products, and services iteratively.

5.2 Select the Right Technology Stack

Once you have defined the cornerstones of your digital business and operating model, the selection of the most suitable technology stack that perfectly supports them is particularly important since each digital technology has certain strengths and weaknesses that determine its applicability to a certain business idea. A tentative comparison of the three technologies with respect to eight key domains of digital transformation is shown in Figure 5-3. This figure only provides a coarse guidance and highlights some differences between the three digital technologies introduced in this book – it is certainly not comprehensive in its domains and presentation.

But independently of which digital technology stack you are going to use, you may either consider (1) building up your own digital capabilities in-house or (2) collaborating with external partners and cloud computing vendors. The first option is best if you aim to leverage your digital in-house expertise as a competitive advantage on a medium or long term. Otherwise, the best option is to subscribe to an external cloud computing service, which is less time consuming and invest intense in most cases.

Cloud computing was briefly introduced in Chapter 1, “Digitalization and Digital Transformation,” already and generally refers to a computational hardware and software infrastructure that can be accessed via Internet on demand. It builds on a technique called virtualization that was released with IBM’s CP-40 operating system in 1970 for the first time [18]. Virtualization allows for simulating a (private or public) environment or “partition” including dedicated computational processing power, network, and storage from a physical hardware system that can be shared with other users. The US National Institute of Standards and Technology defines cloud computing as a “model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction” [19]. The idea to rent computational resources that are provisioned, managed, and made accessible to users by a third party was in fact very revolutionary, which is why it is not surprising that cloud computing soon became a multibillion-dollar business. Cloud computing services generally fall into four main service categories that are important to consider when subscribing to a particular vendor. They differ in the technical involvement and autonomy of the customer as depicted in Figure 5-4 schematically and explained in the following:

1. 1.

   Infrastructure-as-a-service or IaaS is the lowest service level with the highest customer autonomy. IaaS comprises all infrastructure building blocks, such as data servers, storage, networks, and virtual machines.⁸ All resources are provisioned and offered by the cloud vendor on demand within a pay-as-you-use pricing model.

    
2. 2.

   Platform-as-a-service or PaaS offers both ready-to-use hardware infrastructure and software development tools that enable users to build, test, and deploy their own cloud applications – all computational resources are administered and maintained by the respective cloud vendor in this service category too.

    
3. 3.

   Software-as-a-service or SaaS is the third service level and refers to a hosted cloud infrastructure that offers users completely developed software applications together with the required hardware on demand. Users can simply log in via a web browser, upload the data to be processed, and use different applications on the vendor’s software and hardware stack to process and analyze the data further. This service category is particularly interesting for users, who cannot build up their own resources, such as software, hardware, and personnel.

    
4. 4.

   Business-process-as-a-service or BPaaS allows to fully outsource stand-alone business processes, such as the payroll management (including the payslip entering and legal filings) and payment process. This service category is designed for companies that do not like to build up their own digital in-house capabilities and outsource particular business processes completely.

    

Most cloud computing vendors offer elaborately made web interfaces that allow for an easy configuration of the IT infrastructure and fast customization of tools for a particular use or business case. The development of more advanced software tools and applications does, however, require profound knowledge about some of the most common and mainly open source programming languages in data science, such as Python¹⁰ and R. The different commands available in programming languages are often grouped together in certain packages and libraries for convenience. The most important packages that can be used to implement the three digital technologies introduced in this book in your own organization are shown in Table 5-1 for your reference.

5.3 Digitize the Core

The selected technology stack provides the underpinning technologies to digitally transform the core of an organization. According to the “context vs. core” model¹¹ proposed by the American business strategist and consultant Geoffrey Moore, the core is what creates a competitive advantage, differentiation in the market, and ultimately wins and retains customers. Context consists of everything else including finance, sales, and marketing. In his book entitled Dealing with Darwin [24], Geoffrey Moore illustrates this concept exemplarily by referring to the American golf champion Tiger Woods. Tiger Woods’ core business is golfing, and his context business is marketing. While marketing generates money, there is no marketing (the context) without golfing (the core).

A very good example and role model for a digitized core is Microsoft and its business unit “Core Services Engineering and Operations” or CSEO.¹² CSEO is at the heart of Microsoft and has more than 5,500 employees globally who work across a broad spectrum of the value chain ranging from releasing products and running retail stores to human resources. Its core mission is to build products and tools that empower employees to drive productivity in their particular roles independently of their business unit by connecting everybody within the organization to a central data catalog, common software components library, and shared algorithm repository. By digitizing its core, Microsoft can rapidly digitize, enable, and deploy digital products and services across all business units to drive efficiencies and innovative business outcomes for the entire company. In their 2020 bestselling book Competing in the Age of AI, the two American economists Marco Iansiti and Karim Lakhani at Harvard Business School call such a digital operating model that relies on a highly integrated modular software and data platform AI factory. Furthermore, they explain:

[...] while production was industrialized, analysis and decision-making remained largely traditional, idiosyncratic processes. [...] The AI factory is the scalable decision engine that powers the digital operating model of the twenty-first-century firm. Managerial decisions are increasingly embedded in software, which digitizes many processes that have traditionally been carried out by employees [29].

In other words, an AI factory captures decision making as an industrial process driven by big data analytics [30]. Data-driven decision making systematically converts information into valuable business predictions, insights, and choices to guide and even automate certain business processes. This is at the heart of digitizing the core of a company and implementing a digital operating model. Marco Iansiti and Karim Lakhani explain further:

Four components are essential to every factory. The first is the data pipeline, the semiautomated process that gathers, cleans, integrates, and safeguards data in a systematic, sustainable, and scalable way. The second is algorithms, which generate predictions about future states or actions of the business. The third is an experimentation platform, on which hypotheses regarding new algorithms are tested to ensure that their suggestions are having the intended effect. The fourth is infrastructure, the systems that embed this process in software and connect it to internal and external users [31].

5.4 Identify Pilot Projects

The digital transformation of an organization cannot be finished over night from one day to the other, of course – it is a transformation journey rather than a sprint. Furthermore, incumbent organizations often need to continue running a legacy business while transforming organizational units and processes digitally. This is why it is crucial to start your digital transformation journey with pilot projects¹⁴ to create enthusiasm across the organization and drive its digital transformation. Innovative digital organizations, like Microsoft or Google, do always launch multiple pilot projects to increase the odds of getting a win.

In practice, pilot projects often face one major challenge that is in fact the main reasons why digital transformations fail [34]. This challenge is related to scalability – the second selection criterion – and called proof-of-concept trap. It refers to projects with an infinite pilot phase, which prevents the successful rollout of prototypes across the entire organization in time. Projects of that type turn into an endless “science experiment” that deploys overly complex solutions to showcase technical prowess even if a simple solution is available and may quickly return value. This is partly related to the fact that digital pilot projects often innovate in a very artisanal way without any established processes and delivery plans. The proof-of-concept trap can be avoided by focusing consequently on a concrete business purpose and making a reasonable compromise between innovation and industrialization. Successful pilot projects explore promising prototypes for innovation and concepts for their industrialization together at the same time rather than sequentially. This approach allows for a rapid rollout of successful features as soon as the first simple prototype version of a product or service has been established – just think about Tesla’s autopilot and its active learning strategy discussed in the previous chapter, for instance.

Vittorio Colao, the CEO of Vodafone Group, compares pilot projects with boats on the sea and framed the associated leadership challenge nicely with the words:

There are big new winds blowing – in data analytics, automation and artificial intelligence – and they will not blow exactly in the same way across all of the organization. In my fleet some boats will gain speed, while others have smaller sails and won’t capture the same momentum. The question is whether you allow each boat to go at its own cruising speed – as we did in the beginning – or if you want to align the fleet and wrap it into a big program, as we are now trying to do. Aligning the boats is helpful for the organization, but you also risk forcing them into a linear speed that ends up being blown away by disruptors [35].

5.5 Empower Management and Employees

Another very crucial aspect of digital transformation regards the continuous empowerment of employees by education and training. This measure cannot be overstated since – simply put – people make change happen [36]. Digital transformation requires technical skills that legacy organizations often lack. Digital pilot projects rely on multidisciplinary and agile innovation teams,¹⁶ in which different talents complement each other – diversity is the key to success here [37]. The skills required for a digital product development team (or “squad”¹⁷) to be successful range from data science – the skills to turn data into valuable insight and action – to project management, product design, software development, agile methodologies (including design thinking [39], blue ocean strategy [40], and scrum [41]), marketing, and storytelling [42]. An insightful framework for building successful project teams in the digital age is given in, for example, [43].

The third option is particularly important since organizations cannot learn everything at once. They rather have to focus and develop their digital core competencies in-house and hire everything else from external sources. This is why it is crucial for organizations to determine and prioritize the data skills they foremost need in advance [45].

Due to the increasing importance of artificial intelligence for its business, the American software company Adobe, for example, recently launched a six-month machine learning training and certification program for its more than 5,000 engineers worldwide. However, the particular skills you need on your digital pilot projects will critically depend on how deeply you plan to get into digital technologies and associated tasks, such as software development and programming. The good news is that many cloud computing vendors and open source platforms offer software tools and online trainings for free – digital technology and knowledge are slowly getting democratized.

Empowering employees is also about developing digital leaders and transforming the role of management. According to Marco Iansiti and Karim Lakhani,

management as supervision, especially of employees performing routine tasks, is finally over. In an AI-powered operating model, managers are designers, shaping, improving and (hopefully) controlling the digital systems that sense customer needs and respond by delivering value. Managers are innovators, as they envision how these digital systems will need to evolve over time. Managers are integrators, as they work to connect disparate digital systems and identify new connections between the firm’s operating model and the customers it serves. And managers are guardians, as they work to preserve the quality, reliability, security, and responsibility of the digital systems they control [29].

Eric Schmidt, Jonathan Rosenberg, and Alan Eagle point out further that

The primary job of each manager is to help people be more effective in their job and to grow and develop. [...] Managers create this environment through support, respect, and trust. Support means giving people the tools, information, training, and coaching they need to succeed. It means continuous effort to develop people’s skills. Great managers help people excel and grow. Respect means understanding people’s unique career goals and being sensitive to their life choices. It means helping people achieve these career goals in a way that’s consistent with the needs of the company. Trust means freeing people to do their jobs and to make decisions. It means knowing people want to do well and believing that they will [46].

5.6 Shape the Organization and Structure

The structure of an organization mirrors the architecture of its products and services [47]. This empirical observation became known as mirroring hypothesis or Conway’s law named after the American computer scientist Melvin Conway, who introduced the idea in 1967 by stating: “Any organization that designs a system (defined broadly) will produce a design whose structure is a copy of the organization’s communication structure” [48]. In other words, interrelated tasks, such as the development of complex products and services, are best performed by cross-functional and highly integrated teams that focus on developing end-to-end customer features rather than a single function or component of the overall product or service only. This is why digital operating models require agile and highly interconnected organizational structures without any isolated silos. Reshaping the organizational structure to establish new ways of collaboration is thus another crucial dimension of digital transformation.

5.7 Establish an Open Innovation Culture

Establishing an agile and open business culture is crucial for completing a digital transformation successfully. In this context, the term business culture refers to the collective capability of an organization to create value by innovation for its customers and employees, which is also why it is sometimes referred to as innovation culture in this particular context. Inspired by the MIT sociologist Edgar Schein – one of the world’s leading scholars in organizational culture – Clayton Christensen defines this integral part of any organization as follows: “Culture is a way of working together toward common goals that have been followed so frequently and so successfully that people don’t even think about trying to do things another way. If a culture has formed, people will autonomously do what they need to do to be successful” [52]. In other words, culture is a unique combination of internal rules and workflows that allow employees to complete certain tasks that recur in their daily work frequently. The more often they solve the task successfully, the more instinctive the workflow becomes. A business culture is thus not created overnight but rather formed through repetition. In order to establish a new business culture successfully, it is consequently not enough to communicate what the culture is. Instead, the management of an organization – as a role model for its employees – needs to adhere to the culture and constantly reinforce it by making decisions and prioritizing projects that are in alignment with it. This is an important point to keep in mind when transforming a traditional and establishing a new innovation culture.

5.7.1 Establish an Open Innovation Culture: Adopt Agile at Scale

While a traditional business culture is best suited for well-established business processes and the exploitation of existing revenue streams, digital innovation cultures are best in exploring new processes and creating new revenue streams based on digital technology. An open innovation culture fosters a much more agile, collaborative, and integrated approach to innovation with a strong customer- and product-centric view. Instead of relying on sequential, inflexible, and often slow product development processes, a digital culture cultivates the emergence of new ideas and empowers employees to deliver results faster by employing a less linear and more iterative approach to product development based on customer feedback. Using agile methodologies across the company – which is sometimes referred to as agile at scale [57] – is thus not a goal in itself but rather a means to an end.

In addition, an open innovation culture motivates employees by giving them freedom and space to explore novel ideas in accord with Frederick Herzberg’s famous motivation theory¹⁹ [53]. Eric Schmidt, Jonathan Rosenberg, and Alan Eagle once noted in this context:

You need to [...] go beyond “traditional notion of managing [employees] that focuses on controlling, supervising, evaluating and rewarding/punishing” to create a climate of communication, respect, feedback, and trust. [...] The path to success in a fast-moving, highly competitive, technology-driven business world is to form high-performing teams and give them the resources and freedom to do great things [46] .²⁰

5.8 Leverage the Ecosystem

From our previous discussion, we know that the iterative integration of customer feedback into the product or service development process is vital for establishing an open innovation culture focused on the customer – at Amazon, for instance, more than 90% of its innovations are actually triggered by customer feedback. Moreover, engaging with customers systematically is also inevitable for developing personalized products and services that are tailored to meet specific customer needs and expectations. In his 2016 Letter to Shareholders, Jeffrey Bezos noted in this context:

There are many advantages to a customer-centric approach, but here’s the big one: customers are always beautifully, wonderfully dissatisfied, even when they report being happy and business is great. Even when they don’t yet know it, customers want something better, and your desire to delight customers will drive you to invent on their behalf [56].

But customers are not the only stakeholders of your ecosystem that are important to engage with during a digital transformation.

Such partnerships can help you to leverage the full diversity of your ecosystem, create more attention, get valuable feedback from customers, source external ideas, and attract new digital talent. You may also think about collaborating with innovative startup companies or academia for the same reason. Successful digital organizations do also employ a whole range of other initiatives to better position themselves in their particular ecosystem. Such initiatives range from organizing software and hardware development contests or hackathons to visiting researcher programs, summer schools, and conferences and to opening an innovation hub in Silicon Valley or other highly innovative geographic places.

Ultimately, I think that digital transformation provides various opportunities for any organization operating in the private and public sector. The emergence of digital technologies, such as quantum computing, blockchain technology, and artificial intelligence, is framing a mandate for thought leaders to embrace the full potential of digital technology to realign organizations and prepare them for the digital future ahead of us. Digital transformation and its support technologies present valuable and equally unique opportunities for all organizations to differentiate and defend their products and services against the steadily increasing number of competitors.

I hope that the concepts and frameworks presented in this book will inspire new ideas and digital thinking and help you to complete your own digital transformation journey successfully. The best of digital technology is – for sure – still to come.

5.9 Key Points

5.10 Further Reading

5.11 References