Category: Knowledge article

The rules of the game have changed. A few years ago, companies could still slumber and adopt a wait-and-see attitude, but those times are over: organizations that do not transform, lose. But how can you win? The key to this success lies in the combination of the Digital Enterprise model and the Multimodal approach: the ‘winning formula’. Read on quickly.  

Why the Digital Enterprise? 

A Digital Enterprise goes beyond just implementing technology. It is a strategic approach in which you seamlessly integrate business activities, technology and data to be flexible, efficient and customer-oriented. This requires strategic leadership. But why is this transformation essential? 

• A head start on competitors 

Companies that use digital possibilities smartly are at the forefront and make the difference in speed, innovation and performance. In short: as a Digital Enterprise you remain relevant. 

• Improve customer experience 

Customers expect a consistent and distinctive experience, from product to service and from app to store. A Digital Enterprise makes this possible by using all its channels and services in the right way. 

• Operational excellence 

A Digital Enterprise builds an operational backbone that is scalable, efficient and connected: ready to deliver, and ready for innovation. 

The five building blocks of a Digital Enterprise (customer experience, operational backbone, digital infrastructure, shared data and digital smartness) together form the foundation on which companies build their future. Want to know more? Check out our page on Digital Enterprises here. 

What is Multimodality and why is it essential? 

Not all business activities are the same. Some are stable and need to be efficiently organized, while others need to be dynamic and distinctive. Multimodality is a practical framework, developed by Anderson MacGyver, that has been proven in practice and is scientifically substantiated. The model divides business activities based on their dynamics and distinctiveness. Organizations use it, among other things, when making important strategic choices, complex transformations, sourcing issues and to create focus and alignment between disciplines. 

The Multimodal model divides business activities into four categories, each of which we have given a color: 

• Common (green): Generic, stable activities that in most cases do not differ much from similar activities in other organizations. These are focused on efficiency and reliability, such as administration, purchasing or other supporting processes. 

• Adaptive (blue): Dynamic, generic activities that are not necessarily very distinctive, but must be continuously adapted to changes in the market or technology, such as marketing. 

• Specialized (orange): Stable activities that require very specific expertise and/or resources, such as the integration or maintenance of complex infrastructure, or implementation of specific legislation and the like. 

• Distinct (purple): Unique and dynamic activities, such as product innovation or customized customer solutions, that distinguish you from the competition. 
   

Want to know more about Multimodal? Read our whitepaper on Multimodality. 

Why is this combination a winning formula? 

The power of the Digital Enterprise model lies in the transformation from reactive to proactive management of digital success. In a clear manner: per building block. But without Multimodality, you cannot optimally use these building blocks. The Digital Enterprise model ensures that you know where you need to go, and Multimodality offers the right approach to get there. Together they form an indispensable combination that simplifies the complexity of digital transformations. The entire transformation process is guided by our passionate consultants, with practical and interactive tools. 

“The Digital Enterprise model ensures that you know where you need to go, and Multimodality offers the right approach to get there.“

Ready to apply the winning formula? 

The Digital Enterprise and Multimodality together form the winning formula for companies that want to remain relevant. If you want to get started practically with a strong vision, you can already do the following: 

Download the whitepaper ‘How to become a Digital Enterprise’ and learn everything about the five building blocks of the Digital Enterprise. 

Download the whitepaper ‘Multimodality‘ and learn how to optimally organize business activities. 

Contact Gerard Wijers or Edwin Wieringa and share your ambitions and challenges without any obligation. We are happy to help you! 

The rules of the game have changed. Are you ready to win? 

Artificial intelligence (AI) is a captivating subject that resonates widely. While experimenting with ChatGPT and image generation can be thrilling personally and professionally, translating AI into large-scale business value proves to be more challenging. The recent CIO Masterclass by Anderson MacGyver provided actionable insights in this area, with contributions from Management Consultant Anton Bubberman and Frank Ferro, who spent the past decade overseeing Analytics, Data Insights, and GenAI at PostNL.

An informal survey among the attendees revealed that everyone had experience with ChatGPT. When moderator Fiep Warmendam asked participants to share their last-used prompt with the person next to them, it became clear that the tool is primarily useful for personal tasks—such as planning an exciting holiday destination, complete with the best routes, or selecting a new phone or other potential purchases.

Smartwatches, on the other hand, appeared to be less popular. Warmendam confessed she avoids letting her running routines be dominated by an abundance of data: “This likely influences your behavior and decisions. I fear it could take the joy out of running—I don’t want to lose the human touch.” This risk can also apply in business. However, data and intelligence can add value in other areas, like predicting delivery times for meals, groceries, or packages.

This set the stage for the insights and experiences shared by the two specialists. “In line with Roy Amara’s Law, we tend to overestimate the short-term impact of AI while underestimating its long-term effects,” noted Anton Bubberman. The senior Management Consultant is also Guild Lead Data to Value at Anderson MacGyver. Has extensive relevant data experience in sectors ranging from healthcare to energy and finance.”

Cognitive Skills

Under the ironically yet compelling title “Create a clickbait title for my AI-vision talk”, Bubberman introduced the concept of AI, which becomes more powerful as autonomy and adaptability increase. Ultimately, we are moving toward artificial general intelligence (AGI), which matches human cognitive skills. This would allow AI to independently perform complex tasks across diverse domains and adapt to new situations. However, since we are far from the AGI phase, human oversight and monitoring of AI remain essential.

Bubberman outlined three success factors for scalable and potentially value-creating AI deployment within organizations, using analogies from chess, jazz, and philosophy.

Chess is all about planning, foresight, and continuous evaluation. “Circumstances and opportunities are constantly evolving, and organizations and leaders must adapt. You must always think ahead to the next move on the chessboard.”

The connection with jazz is that while playing music and improvising might appear effortless, it often follows a long period of practice. Beyond technical skills, it requires an understanding of theoretical frameworks and foundational principles. “Dedication is necessary to master an instrument. It involves hard skills but also soft skills, such as interacting with other band members.” In the digital domain, a culture driven by AI is essential, alongside technical prerequisites, with attention to ethical considerations.

Finally, philosophy highlights the dual-edged nature of tools. A surgeon’s scalpel can perform miracles but, in the hands of an unskilled or malicious individual, it can cause disaster. Similarly, AI carries risks such as polarization, information bubbles, misinformation, and bias—particularly when data is incorrect or human oversight fails to address potential negative impacts. “In the right hands, AI has the power to positively change the world,” Bubberman concluded.

Lessons Learned

Frank Ferro reflected on his decade of experience in realizing business value with data and AI. He began his presentation with a cloud of personal data—trivia and relevant details that only gained meaning after verbal explanation. From his birth to the year 2025, when after nearly 17 years at PostNL and a temporary role as Program Director GenAI at ANWB, he will take on the position of CIO at Amsterdam UMC.”

Ferro is a recognized frontrunner in adopting and implementing new technologies. At PostNL, the focus gradually shifted from physical services to leveraging data and algorithms. “Our vision was that data would eventually deliver value,” he explained. This transformation was pivotal in positioning PostNL as a ‘postal tech company,’ emphasizing the importance of in-house data and technology capabilities.

PostNL’s IT strategy has long relied on principles fostering a flexible architecture to adapt to new developments, including AI. The company has consistently stayed ahead of the curve, from fully embracing the cloud in 2013, launching a Data & Insights Competence Center and Advanced Analytics in 2017, to applying GenAI in 2024.

All of this was driven by developments where the volumes of mail and parcels continually shifted places. Data and intelligence were essential to optimize the use of available physical assets. Furthermore, control over the delivery process gradually shifted from the sender to the recipient. The importance of accurate data was further highlighted by the changing relationships with supply chain partners, who were also seeking to capitalize on critical information for their own benefit.

A Successful Journey

PostNL has undergone a successful journey overall. According to Frank Ferro, several aspects remain crucial in this process. Ownership of data initiatives must lie with the business, and organizations should start small and at a manageable scale before industrializing algorithms on a larger scale. Authorized access to high-quality data and embedding robust data governance are also essential.

Ferro also elaborated on the federated structure of internal data capabilities, designed to operate as closely as possible to the business. He highlighted the accelerating impact of a dedicated GenAI task force, all with the aim of creating value as effectively and rapidly as possible.

Aside from data-related content, the closing Q&A raised the question of how leaders and organizations determine which aspects of data and AI to manage in-house and which to delegate to partners. Distinctive processes and activities appear to be the key factors in this decision: ‘Your own intellectual property and what sets you apart from the competition,’ Bubberman and Ferro agreed. ‘Of course, consultants can help clarify this.

Want to know more about becoming an AI-driven enterprise? Read our blog series: How do we become an AI-driven enterprise?

By Tim Beswick

We are often asked whether becoming an AI-driven enterprise requires something different than becoming a data-driven enterprise. In this series of blog posts, Anderson MacGyver shares her point of view on this topic. For those who want to start from the beginning, you can read part 1: How do we become an AI-driven enterprise?, part 2: Data-to-AI-to-Value journey, part 3: theme 1: The generative / general-purpose AI model buzz, and part 4: theme 2: Business process redesign requiring even more attention for people change. Now, let’s dive into part 5: the third underestimated theme. 

3. Additional risks and different measures 

Theme three of the four underestimated themes

Additional compliance requirements 

It is likely that organizations that are already on the journey of leveraging data to create business value, are already aware and making advancements in governing. Governing data is aimed at, amongst other drivers, ensuring compliance with applicable legislation. 

When additionally pursuing utilization of AI to unlock business value, you need to consider the EU AI Act. Given its intent and due to the broad definition of an AI system in the EU AI Act, it is inevitable you will have to make this legislation part of your norms and additional measures are likely to be required by most organizations. 

At the bare minimum, it requires all organizations to have oversight and transparency with regards to their usage of AI. The EU AI Act classifies AI systems into four different risk levels: unacceptable, high, limited and minimal risk. Each class has different regulations and requirements for organizations developing or using AI systems. Even if you expect to fall into the lowest risk categories only, you need at least oversight and transparency regarding all AI systems that you use. Without this you are not able to assess in what category your AI’s fall and with that if and which regulations apply to each of your AI systems. 

Having oversight and transparency is therefore a bare minimum and requires a mechanism to identify, administer and classify your AI systems. Having this oversight and transparency may lead to the conclusion that your AI systems fall into risk categories where significant additional measures are required

Additional or other measures 

In many organizations management of for instance privacy, security, regulatory, ethics and operational risks rely for at least a part on humans. In many cases an AI fulfils part of the role that a human traditionally fulfilled. This implies that possibly the human is no longer there to fulfil the measures that have been stipulated to manage the risk. 

Let us again look at the example of driving a car to make this more tangible. 

To achieve an acceptable risk around operating a car we rely on measures that are attached to humans like being healthy, being sober, the driver not being excluded by the insurance companies and having a valid driving license. 

So, what happens if (parts of) operating the vehicle shift to an AI? To keep it simple, let us ignore the legal implications as AI under current law does not have personality. 

Can we simply consider that AI to be a replacement of the human and apply the same measures to control risk? If so, what defines a healthy AI and who attests to this, probably not your GP? Should we register and classify an AI’s historical behaviour to enable exclusion? Does an AI need to do a driving test? Or do we need to go back to the drawing board and reassess risk and implement additional and / or completely different measures? For instance, in this example of autonomous driving, limit the autonomy, and with that the role of the AI, by retaining a human factor in the process? Or in the future accept that AI control cars and implement an overarching control layer that supersedes the individual cars?

We discuss the last theme separately in the next blog post. So, stay tuned!

Follow us on LinkedIn to be notified when we publish a new blog: Anderson MacGyver LinkedIn. 

Leading organizations distinguish themselves through their approach to digital opportunities and issues. The main difference from laggards is that leaders of truly digital organizations do not blindly pursue trends and developments, but approach them strategically and proactively. This is the difference between the so-called ‘Catch-up Enterprise’ and the Digital Enterprise. 

Catch-up: surviving instead of thriving 

Many companies still operate as Catch-up Enterprises. They wait until external pressure forces them to embrace new technologies. This race to catch up often leads to reactive decisions that sometimes help them keep their heads above water, without really making progress. The focus is more on survival than on growth and innovation. 

This type of company only adopts technologies when they have no other choice. Or they have no higher plan, which leads to thoughtless and reactive ‘snacking’ on often irrelevant digital solutions within the organizational context. Catch-up then means ‘add a little ketchup and eat!’ While a healthy basis for sustainable growth is lacking. 

Digital: vision and action hand in hand 

The leaders of a true Digital Enterprise have a completely different approach. Here, digital technology is not seen as a necessity for survival, but as a force for thriving. These organizations proactively integrate technology into their core strategy, with every step aimed at creating efficiency, increasing competitive advantage and exploiting opportunities. 

Their success rests on five building blocks: a strong customer experience, a robust operational backbone, a flexible digital infrastructure, shared data and so-called ‘digital smartness’. In combination, these core components ensure a culture of continuous improvement and agility, allowing the organization not only to respond to changes, but also to predict and capitalize on them. 

Anderson MacGyver developed the Digital Enterprise model to help companies develop (further) digitally. In addition to the five building blocks, it places the organization within the context of the digital ecosystem of customers, partners and other stakeholders. The idea is that companies and their leaders can work better and more deliberately on their digital development through insight and visualization. 

Dare to choose sustainable growth 

It is up to you as a leader to shape the future of your organization. Are you in the sometimes tempting, but reactive Catch-up mode? Or are you ready for the transition to sustainable digital growth and development? The path to proactive digital strategies starts with the right insights and tools. 

Learn how to transform your organization into a Digital Enterprise. Read our whitepaper ‘How to become a Digital Enterprise?’ or contact us. Take the first step towards a successful future. 

By Tim Beswick

We are often asked whether becoming an AI-driven enterprise requires something different than becoming a data-driven enterprise. In this series of blog posts, Anderson MacGyver shares her point of view on this topic. For those who want to start from the beginning, you can read part 1: How do we become an AI-driven enterprise?, part 2: Data-to-AI-to-Value journey, and part 3: theme 1: The generative / general-purpose AI model buzz. Now, let’s dive into part 4: the second underestimated theme. 

2. Business process redesign requiring even more attention to people change 

Theme two of the four underestimated themes

People change activation is a key success factor in any digital transformation journey. It is always important to understand where and how a more extensive use of data (insights) impacts people, and to put a deliberate effort into guiding the resulting people changes. However, in cases where AI is a major part of the journey, the people impact is typically even bigger. Let us look into why the implementation of AI has such a large people impact. 

Using more Business Intelligence (BI) better is often a part of becoming more data-driven. Data insights created through BI are typically an additional piece of information that people use in an existing business process. The people involved need to learn how to use these insights. Also, it is common that organizations need to step up their efforts in creating the right data as input for BI. This implies that people need to change, better understand, and pay more attention to creating good data as the key ingredient for accurate insights. 

On top of these types of people changes typically required for successful implementation of data analytics, implementation of AI results in a business process redesign. A process redesign that changes the role of the humans involved. In extreme cases, it makes the human redundant. More often, it leads to a shift of the role of the human. The individuals involved need to be supported and coached to make this shift of their role to accommodate the new business process. If the individuals involved do not make this shift, the AI is either redundant, or conflicts arise in the business process at hand, and the AI does not deliver value. 

Besides described people change on an individual level, there is a more group dynamics-related phenomenon that requires attention. It is not uncommon that AI is directly associated with redundancy, which can lead to group resistance to AI-driven change. Overcoming this resistance after it emerges is hard and time-consuming. You are better off avoiding this initial reaction. 

This already starts in the early planning stages. Abstract business strategies that include loose statements on AI result in hard-to-change preconceptions and resistance. Close business involvement in defining and communicating laser-sharp focused Data & AI Value Opportunities creates clarity for the people involved. This is instrumental in making a good start on working the transformation together with the people who will actually ensure the embedment of AI in your business.

We discuss the other 2 themes separately in the next two blog posts. So, stay tuned! 

Follow us on LinkedIn to be notified when we publish a new blog: Anderson MacGyver LinkedIn. 

By Tim Beswick

We are often asked whether becoming an AI-driven enterprise requires something different than becoming a data-driven enterprise. In this series of blog posts, Anderson MacGyver shares her point of view on this topic. For those who want to start from the beginning, you can read part 1: How do we become an AI-driven enterprise? and part 2: Data-to-AI-to-Value journey. Now, let’s dive into part 3 in this blog: the first underestimated theme. 

1. The generative / general-purpose AI model buzz 

Theme one of the four underestimated themes

It is hard to escape the buzz around AI. To a large extent, this is caused by the tremendous advances in and uptake of freely and commercially available generative and/or general-purpose AI solutions, which kicked off in a big way with ChatGPT. On one side, this is helping data professionals to get attention from business and executives. The advancement and potential value are real. On the other, more negative side it is leading to short-sighted perspectives on what AI can be for an organization. 

Many technology and data teams are being pushed by business users into focussing their, often scarce, resources towards fulfilling business demand for the obvious generic and probably not most impactful use cases for generative- and / or general-purpose AI. Often without accurately assessing where the highest yield of efforts would be. Exactly in these situations, it is important to take a step back. Structurally engage to understand business priorities and create a common view on where and how data and AI contribute most to business value. Not in abstract terms, but in tangible wording, such as: The marketing manager uses a generative AI solution to reduce effort in redirecting marketing communications messages, reducing human effort and, with that, saving €200K per year. 

If this €200K per year efficiency gain is a strategic priority, go for it! If it is merely a nice to have and there are other more transformational opportunities to create value with data and AI, go for those! We are not aiming to debunk the value potential of generative- and / or general-purpose AI; we believe it can be real. We are cautioning for getting distracted by the buzz and becoming shortsighted. Engage with your businesses and flush out true business value beyond the buzz and prioritize.

We discuss the other 3 themes separately in the next three blog posts. So, stay tuned! 

Interested in further insights into this topic? Join our CIO Masterclass on becoming a scalable, AI driven enterprise on the 13^th of November. 

By Tim Beswick

We are often asked whether becoming an AI-driven enterprise requires something different than becoming a data-driven enterprise. In this series of blog posts Anderson MacGyver shares her point of view on this topic. For those who want to start with part 1, you can read it here: How do we become an AI-driven enterprise? Now, let’s dive into part 2 in this blog. 

Common good practices

You may recall an earlier series of blog posts: Did we just meet the modern Don Quixote?, where we discussed the following four common good practices: 

1. Vision, Goals and Strategy: Activate and focus the change effort by ensuring that all stakeholders have a clear and collective understanding of a relevant vision, goals and strategy. 

2. Data Value Opportunities: Build tangible bridges between business value and data capabilities by defining and prioritizing Data Value Opportunities. 

3. Data Value Delivery & Data Foundation: Use the prioritized Data Value Opportunities as the guiding stars for balancing Data & AI Value Delivery and Data Foundation efforts. 
   • If you focus your budget and efforts too much on Data Value Delivery, you may end up with solutions that do not meet requirements, fail to comply with regulations, cannot be integrated in your architecture, do not scale for production usage, do not provide sufficiently accurate insights et cetera. Great experiments in a dark and cold cellar, that will never see the light of day. 
   • If you put too much emphasis on the Data Foundation, you will end up doing lengthy and costly work without making an impact on the business. You spend a lot of time and budget without visible impact, you will lose momentum, business will lose their focus and limit their contribution, executives will become impatient and eventually the plug is pulled. 
4. Undercurrent of people change: Pay attention to the undercurrent of people change by deliberately working on addressing people related aspects such as motivations, emotions, beliefs, behaviors, symbols and rituals.

What is specific for Data-to-AI-to-Value journey?

AI requires specific capabilities in knowledge domains such as Machine Learning, Artificial Neural Networks, Natural Language Processing, Computer Vision, Cognitive Computing and Autonomous Systems. We will not delve into the details of these, but rather investigate the extent to which the earlier mentioned common good practices apply to a Data-to-AI-to-Value journey. 

Putting it very straightforward: the stated good practices are equally applicable to situations where AI is a major focus in your transformation journey. We have found that there are four additional themes that are often underestimated and will prove to be pivotal in your journey towards being an AI-driven enterprise: 

1. The generative- / general-purpose AI model buzz. 

2. Business process redesign requiring even more attention for people change. 

3. Additional risks and different measures. 

4. Even greater challenges in accessing AI talent.

We discuss each of these themes separately in the next four blog posts. So, stay tuned! 

Interested in further insights into this topic? Join our CIO Masterclass on becoming a scalable, AI driven enterprise on the 13^th of November. 

By Tim Beswick

We are often asked whether becoming an AI-driven enterprise requires something different than becoming a data-driven enterprise. In this series of blog posts Anderson MacGyver shares her point of view on this topic. 

Data-driven enterprise

It is good to start with looking at what a Data-driven or an AI-driven enterprise is. Anderson MacGyver takes a business value centric view and defines a Data-driven enterprise as an organization that unlocks the business value of data in any of the following three ways: 

1. Enabling digital systems with the exchange of data: Today’s society relies on systems exchanging data. The importance of the exchange of data between digital systems has superseded the importance of spoken and written words between humans. Without the timely exchange of the right data between digital systems, an organization and our society come to a standstill. Data-driven enterprises ensure that the digital systems in their organization and eco-system have the right data, of the right quality, at the right time. 

2. Deriving commercial value from data: Data on itself can have commercial value. Data-driven enterprises derive monetary value from their data by trading data either for a direct monetary reward or in exchange for other goods or services. 

3. Creating insights through analysis of data: Data analysis provides insights that can be used for improving customer intimacy, strengthening business control, driving operational efficiency and for the development or as part of new products & services. Data-driven enterprises unlock value from data by using data-driven insights to optimize their business processes and offerings. 

AI-driven enterprise 

So how does an AI-driven enterprise differ from a Data-driven enterprise? Let us first look at what an Artificial Intelligence is. Anderson MacGyver uses the following terms to define an AI. An AI is a digital system that: 

• Has the ability to learn and adapt. 

• Can generate output in the form of new data and content (video, image, text, sound, code). 

• Triggers or autonomously take actions. 

What does this mean in relation to the three types of value from data? 

1. Exchange of data between digital systems: An AI can be used to optimize the processes that drive the timely availability of data for digital systems. This does not result in new types of value in this domain. 

2. Commercial value: An AI can be used to generate new data and content that has commercial value. AI becomes the core production engine of your commercial goods that contain data, video, image, text, sound or code. 

3. Insights: AI can provide dynamic data-driven insights and if desired autonomously act. By autonomously acting an AI often improves process efficiency. The AI generated insights are used for improving customer intimacy, strengthening business control, driving operational efficiency and for the development or as part of new products & services. In these kinds of applications, AI is an extension of using data driven insights to optimize your business processes and offerings. 

An analogy to a smart car can help to make the point on dynamic insights and ability to act autonomously more tangible: 

• A route planner that calculates the best route to your destination based on a static map and ignoring current traffic situation is not an AI. 

• A route planner that dynamically recommends the best route based on the current and predicted traffic situation is not an AI if the recommendations are based on predefined rules (Note: rules can be defined using historic data-driven insights). 

• A route planner that dynamically recommends the best route based on the current and predicted traffic situation is an AI if the recommendations are based on predicted future traffic patterns, the system continually learns from historical data and optimizes the route based on multiple, complex factors. 

• A system that controls your autonomous vehicle taking the most advantageous route into account, must be an AI if you would want to safely make it past even the first junction. It would have to respond to the ever-changing circumstances on your route. 

Autonomously acting is not an absolute phenomenon. It comes in many forms. From a simple trigger in the form of a recommendation to operating and controlling systems without any human intervention. 

Summarizing the above, an AI-driven enterprise is an organization that leverages Artificial Intelligence to unlock business value by using digital systems that, based on data, learn and adapt and: 

• Generate new video, image, text, sound and code. 

• Trigger actions or autonomously act. 

Data analytics is typically used as part of an AI system. This implies that an AI-driven enterprise is an extension of a Data-driven enterprise. 

Data-to-AI-to-Value 

Becoming a Data-driven or and AI-driven enterprise is a journey. Anderson MacGyver uses the terms Data-to-Value and Data-to-AI-to-Value for an organization’s journey to become a Data-driven or an AI-driven enterprise. 

In the next blog post we share the good practices that we learnt to apply in these journeys: Data-to-AI-to-Value journey. 

Interested in further insights into this topic? Join our CIO Masterclass on becoming a scalable, AI driven enterprise on the 13^th of November. 

By Gerard Wijers  

What exactly constitutes the essence of a Digital Enterprise? And what can you do as a leader or board of directors to become such a tech- and data-driven organization? Anderson MacGyver co-founder and Nyenrode lecturer Gerard Wijers explains what is needed for distinctive customer experiences, new business models and an optimal operational backbone. All to claim a unique position within the market and business ecosystem.  

My vision of the Digital Enterprise is based on my academic work at Nyenrode, combined with the in-depth knowledge and experiences I have gained since 1991 after my PhD in IT Strategy, including  as a strategic consultant in the field of technology and data. Since our founding in 2013, as Anderson MacGyver, we have helped many of our clients become a Digital Enterprise. 

When I started as a consultant, tech and data were still by no means a boardroom topic. Over the years, that changed and our profession became more and more interesting. We now see that with a dedicated officer, the subject is increasingly gaining a permanent position at the boardroom table. Together with people from operations, commerce and finance, the decisions are made. 

Partly because of this broader engagement of leaders, organizations are becoming increasingly digital. In this article, I not only talk about the specifics of modern digital organizations, I also offer tools for becoming such an organization. 

Although the importance of digitalization varies enormously between companies and sectors, sooner or later virtually all organizations will have to deal with digitalization. Technology and data are used to differentiate, to operate more efficiently, to cooperate better within chains or ecosystems, and to respond flexibly to the needs and demands in social and economic dynamics. 

Three Pillars 
Digitalization requires a holistic view of multiple, interconnected aspects. Starting with the so-called “digitization” of the operational backbone – ranging from an ERP implementation to full automation of the production environment. Based on all my experiences with clients, I dare say that for many organizations, this domain accounts for about 40 percent of IT investments. 

Around 2014, when the term “digital” began its rise, this was still mostly about the outside of the organization. This included digital marketing, use of social media and the right digital channels such as apps, websites and so on. This ‘digitalization’ of the customer – and employee – experience in the front office is seen mostly in B2C companies. 

In addition, modern digital leaders create space and capabilities for innovation and the realization of new digitally based business models. Often, organizations start by turning their existing products, solutions into ‘digital’ services. Deployment of technology and data causes people to start thinking about issues such as long-term customer value and thus the introduction of, for example, subscription models and other forms of value creation. 

The three pillars – operational backbone, digital customer experience and value-based innovation – have so-called ‘enabling technology’ as their common basis . This is the digital version of the ‘firm infrastructure’ coined by Michael Porter, which consists of support items such as buildings, communications, administration, energy supply and physical security. 

This preconditional technology does not provide direct business functionality, but without solutions for areas such as integration, data platforms, identification, information security, you can do nothing at all as an organization. Enabling technology is growing rapidly and accounts for 30 to 40 percent of annual IT costs. 

Digital Evolution 
All of this does not make you a digital enterprise, however. A clear evolution of technology and data can be seen over the years. This started in the 1990s as IT management, which in a decade later gave way to the dynamics of demand and supply – where IT started to have its own processes and SLAs. As tech and data became very distant from the business, the step to outsourcing became smaller and smaller. 

As a result, well defined ownership became important. Agile-IT led to the rise of product owners, process owners and other owners some 15 years ago – even at the board level. By now, the more tech-savvy a board is, the more digital the organization. Sectors such as media, travel and retail are really leading the way right now. Some 50 to 70 percent of their revenue is based on digital activities. 

Since 2015 we have been in the era of digital business, with a greater importance of ecosystem dynamics in the last five years, where, for example, within a specific sector you use specialized and often powerful digital platforms. Think of Independer in the world of insurance, or Bol.com in retail and sites where you can book cheap airline tickets and vacations. 

It is important to know one’s position and added value within the network or ecosystem in which one operates. 

Top 5 pains & gains 
With our annual Digital Business Monitor, as Anderson MacGyver, we measure the state of affairs within European organizations. From this we distill a top 5 of pains & gains for board members. One of the most important gains is scalability. The idea behind it is that software is more scalable than people. Organizations are in a better position to grow through digitalization. 

Another gain is achieving operational excellence in the backbone – accounting for roughly 40 percent of all tech investments last year. Also important are an optimal customer and employee experience and realizing a responsive value-oriented organization in which Agile principles are applied at scale. 

Most of the pain is in an overly complex and outdated IT landscape. Also at play are too high costs due to inefficient operations. A word of warning: be well aware of where you are going to cut! Anyone who wants to improve margins or achieve higher sales will really have to invest more in IT. The high-performing organizations prove this. 

Other pain points include compartmentalized management rather than an integrated, holistic view of the organization. Attracting, developing and retaining digital talent also requires attention. Outsourcing driven by digital ambitions often has scarce talent as its trigger.  

Last but not least, poor data quality and the lack of a good data foundation. After all, data is increasingly at the core of the digital enterprise. 

Digital Enterprise 
But what is the core of a Digital Enterprise and what can you do as a leader or board of directors to make it happen? Before that, a word about the dynamics on the outside and inside… 

A unified and distinctive customer experience requires an integrated mix of digital propositions, products, channels and services. Organizations that do this well include Albert Heijn, Rituals and DPG Media – roughly half of their IT investments go toward optimal customer contact. Here it is important to be able to change quickly and energetically in order to respond optimally to current needs. 

There is also the extensive and relatively slow operational backbone, which needs to be automated, standardized and simplified. This stable and well-integrated IT supports the primary operational business processes and staff functions such as finance, HR and procurement. The pain of technical debt and legacy is especially felt here. Companies that focus heavily on having a good backbone include LeasePlan, CZ and Spie. 

Digital infrastructure was also already discussed under the heading of enabling technology. As mentioned, 30 to 40 percent of total IT spending goes to integration and data platforms, IoT and IT4IT. Organizations with a major stake in their digital infrastructure are Tennet and Schiphol Airport. 

Central elements 
Many organizations and their leaders are aware they are not getting the most out of their software and data. To change this, they can make the right tools and data widely available within the organization so that different functions and departments can run artificial intelligence and algorithms on them. In this way, they can become digitally smarter. Efforts in this area tend to lead to more and better insight into customers, products, processes and operational exploitation of these insights into direct action. 

Characteristic of the digital enterprise are this digital smartness and organization-wide shared data. As with the digital outside, inside and infrastructure, these are not things you approach in isolation. For maximum results, you address everything as much as possible in interconnection. with the digital front end, back end and infrastructure. Frontrunners in this area include PostNL and TVH. 

Recommendations 

My recommendation is to start thinking like a digital enterprise: holistically and end-to-end. Based on governance and personal leadership, create business strategies with explicit digital ambitions, leveraging technology and data to differentiate yourself from the competition. 

Moreover, build strategies that are scalable. Meanwhile, keep the overall IT landscape as simple and fit for purpose as possible. Place ownership and responsibility within multidisciplinary teams. Ensure tech and data capabilities within management teams and the board. Also recognize that there is no one type of IT, but embrace a multimodal approach. 

That multimodality means that different aspects of business operations require different kinds of IT. The Operating Model Canvas (OMC) helps determine which IT best fits which business activity. Across the axes of dynamics (from stable to responsive) and differentiation (generic versus specific), our multimodal model has four modes: distinctive versus common, and adaptive versus specialized. 

Conclusion: digital is everybody’s job. Everything you do with technology and data must be scalable and fit for purpose. That requires ownership and leadership – especially within boards of directors – in addition to a multidisciplinary approach. 

What’s next?

Want to find out more about the Digital Enterprise? Take a look at our other page: Stop playing catch-up, start becoming a Digital Enterprise
Want to get more insights? Jump to our pain: a complex legacy IT landscape and technical debt
Over the coming months, we will be sharing fundamental insights and experiences. Don’t wait to dive into this exciting theory! You can also contact Gerard via email or text to spar.

Last year, our Digital Business Monitor surveyed digital leaders to understand their strategic decisions on technology and data. We found varying levels of digital integration in organizations: some use technology and data to support the business, while others have digital ingrained in their operations and is thus an integral part of doing business. In the latter the business is digital minded and collaborate with tech and data experts in multidisciplinary teams to drive value. 

Challenges of digital leaders
Top digital challenges cited by all leaders were complex legacy systems and business-technology collaboration. But differences between our groups also emerged: “supporting the business” organizations faced digital talent shortages and lack of digital savvy leadership, possibly due to a preference for digitally-driven companies. Meanwhile, “doing digital business” leaders encountered issues with poor data quality, suggesting a heightened need for effective data management amidst intensified collaboration with business (and even partners) to deliver value. 

Do you want to know more about the Digital Enterprise? Check out our page: Stop playing catch-up, start becoming a Digital Enterprise