Author: Bryanne Van de Kant
By Onno Wasser
What is the essence of true digital entrepreneurship and what can you do as a company or leader to realize such a digital enterprise? Anderson MacGyver’s Digital Enterprise model offers a solution. Onno Wasser leads the Technology Guild within the consultancy firm. He explains how he connects to the digital ambitions of clients from his profession.
The Digital Enterprise model places the company within the context of the market and business ecosystem and then focuses on five essential building blocks: a strong customer experience, a robust operational backbone, a scalable digital infrastructure, shared data and ‘digital smartness’. In combination, these core components ensure a culture of continuous improvement and agility, allowing the organization not only to respond to change, but also to predict and capitalize on them.
Different starting point
A ‘fit for purpose’ IT environment has always been important. However, with the pursuit of a Digital Enterprise, the starting point sometimes changes. Companies are still busy solving pain points such as outdated technology, poor integration, or excessively high IT costs. With digital ambitions, this goes hand in hand with an enticing perspective: realizing distinctive digital customer experiences, new business models, optimal processes and interoperability within chains and ecosystems.
The role of technology remains largely unchanged. Both at the customer- and employee-oriented front end and in the backbone, IT must fully align with the strategy, activities and objectives of the organization. With modernization, rationalization, and/or improvement, you still work on a suitable target landscape based on an architectural vision, strategic priorities, costs and business value. Selecting and implementing IT solutions is also part of this.
Maintaining control
Depending on the nature and objectives of the organization, you can opt for standard solutions, customization and/or sourcing at both the front and back end. In practice, more and more companies are obtaining capabilities from the market that they do not have or cannot realize themselves – the digital infrastructure in particular lends itself well to sourcing.
Regardless of what exactly you outsource, maintaining and arranging control is fundamental. The idea that an IT supplier will solve everything for you is a major pitfall. For digital transformations, as an organization, you ideally need to free up your best people.
Whether you are solving a pressing pain point or working on new digital possibilities, it often starts with getting the house in order. From a stable foundation, you can work on further developing customer experience, operational efficiency and digital intelligence. Incidentally, these are and will remain large and comprehensive transformations.
In short: appropriate solutions, enabling technology and a scalable and open digital infrastructure are crucial. Click here to learn how you can transform your organization into a Digital Enterprise and take the first step towards successful digital entrepreneurship.
By Josip Cicak
What is the essence of a true digital enterprise and what can you do as a company or leader to realize it? Anderson MacGyver’s Digital Enterprise model offers a solution. Josip Cicak leads the Strategy Guild within the consultancy firm. He explains how his professional focus on business strategies aligns with the digital ambitions of clients.
The Digital Enterprise model places the organization within the context of the market and business ecosystem, and then focuses on five essential building blocks: a strong customer experience, a robust operational backbone, a flexible digital infrastructure, shared data and digital smartness. In combination, these core components lay the foundation for a culture of continuous improvement and agility, allowing the organization not only to respond to change, but also to predict and capitalize on it.
Setting a goal
Strategy involves providing direction and taking actions to achieve a set goal. As a company, you cannot see this separately from your position and added value within the ecosystem in which you operate: partners, suppliers, customers and other stakeholders. When striving for a Digital Enterprise, strategic adjustments often need to be made on the five pillars mentioned. Preferably in coherence.
Although digital propositions and customer experiences require a lot of attention, the operational backbone must also be in order. This means standardization, cost efficiency to unlock capital needed for improving products and services with smart solutions. Everything you do must be in line with your distinctive character within the context of the business ecosystem. In other words: what do you do as an organization, for whom and for what purpose?
Holistic approach
Strategy, technology and organization must be approached in congruence. This way you can find the most suitable IT for different activities. Standard platforms and market solutions for generic business actions, and more degrees of freedom in technology and data choices for differentiating business activities – or something in between. A critical look at business activities or customer experience can lead to a strategic or organizational recalibration.
When shaping a digital enterprise, the role of leadership is crucial. As Anderson MacGyver we provide directors with all the necessary tools to tackle all strategic, technological and organizational challenges from a holistic perspective.
In short: the model provides board members with insights into the interrelated strategic building blocks. Learn how to transform your organization into a Digital Enterprise. Click here to download the whitepaper ‘How to become a Digital Enterprise’ and take the first step towards a successful future.
By Edwin Wieringa
What is the essence of a true digital enterprise and what can you do as a company or manager to realize it? Anderson MacGyver’s Digital Enterprise model offers a solution. Edwin Wieringa leads the Organization Guild within the consultancy firm. He explains how his professional scope aligns with the digital ambitions of clients.
The Digital Enterprise model places the organization within the context of the market and business ecosystem, and then focuses on five essential building blocks: a strong customer experience, a robust operational backbone, a flexible digital infrastructure, shared data and digital smartness. In combination, these fundamental components ensure a culture of continuous improvement and agility, allowing the organization not only to respond to change, but also to predict and capitalize on it.
Interrelated challenges
Organizing a digital enterprise focused on business value has three strongly interrelated challenges: executing the strategy; application of technology that meets the needs of consumers, customers and business; and combining exploitation and exploration – or ambidexterity where turnover and innovation go hand in hand.
A holistic approach, addressing all three challenges, is fundamental to achieving strategic and business goals. To this end, the organization must be aware of the added value within the business context and what needs to be done to achieve digital ambitions. You then determine the direction and roadmap for tech, data and organization. For all this, Anderson MacGyver has a proven collection of methods, techniques and metrics.
Although continuous attention must be paid to the optimal basis of back end processes and infrastructure, the most benefit can be achieved at the front end: offering the right digital products and services through the desired channels and the associated user experience. Smart application of data is crucial to improve customer processes.
Four levels
Generating the highest business value requires an effectively designed organization. This ideally happens at four levels: digitally driven leadership and competencies, management via a coordinating layer to execution, where multidisciplinary teams deliver what is needed. A control layer verifies whether all this is in line with the principles.
Digitally driven leadership and capabilities are required at all organizational levels. This includes the right working methods, culture and ‘digital appetite’. To build a bridge to the optimal application and organization of technology based on business opportunities and distinctive added value for the customer, organization and ecosystem partners.
In short: the Digital Enterprise model offers a structure to distinguish teams focused on agility from those centered on stability and efficiency. Learn how to transform your organization into a Digital Enterprise. Click here to download the whitepaper and take the first step towards a successful future.
How do you make GenAI a successful part of your overall data journey? For over 10 years, Frank Ferro has been generating business value with data and AI. After many years as a senior executive at PostNL and a temporary role as Program Director GenAI at ANWB, he will soon take up the position of Director of ICT at Amsterdam UMC. During a recent CIO Masterclass at Anderson MacGyver, he shared his vision and experiences. Here’s a summary.
In various sectors, the focus has long been on physical products and services, but attention is increasingly shifting toward data and algorithms. These are no longer just used to enhance customer experience and optimize business processes but are also becoming marketable products in their own right.
To effectively work with traditional and generative AI, three things are recommended: a safe and controlled environment, proper governance, and maximum independence from major tech players. The foundation for this is a solid data management structure and IT architecture, which organizations have often already been working on for some time.
Moreover, it is crucial to know exactly what you are doing. This usually starts with improving the necessary knowledge level within the organization. This requires genuine attention, as even a basic level of data literacy is often lacking.
This approach allows you not only to work on the required quality of data—both centrally and from within the business—but also to explain why you are using AI, where, and how. For example, when an algorithm makes decisions in a call center environment, it should be transparent what the AI is doing and for what purpose. The same applies to fraud detection applications.
In the early stages, it’s best to choose a straightforward, engaging “moonshot case” that generates visible business value for everyone. Without active involvement and support from the business, initiatives are unlikely to succeed.
Controlled Environment
A key recommendation is to use GenAI in a safe and controlled environment. Just like WhatsApp, where the data itself may be secure, metadata—such as who you contact and when—can reveal more than you might wish.
For instance, the free version of ChatGPT is not entirely watertight in this regard. However, the Enterprise version provides a secure, controlled AI environment. This allows even non-IT professionals to experiment and work with it, using their own personal and professional configurations. Examples include rewriting executive announcements or other official communications in plain language, which can save executive and communications teams significant time.
Another example is deploying a large language model (LLM) to answer customer inquiries via email at scale or to generate code for programmers—a practice already commonplace in many companies.
For most applications, humans will still remain in control for now. Microsoft Copilot, for instance, is explicitly not an autopilot. It acts as an assistant that can significantly boost productivity, but it is not error-free.
Proper Governance
Step two: ensure proper governance. Start by establishing a Data & AI Governance Board, which can assist at the executive level in making decisions about the potentially sensitive or impactful use of data and algorithms.
Equally fundamental is control over reliable data—not only to ensure process efficiency and achieve goals but also to comply with laws, regulations, and privacy and security requirements. Implement robust central and decentralized data management and quality control.
Interestingly, the “data owner” is often not the “data lord.” In other words, the person responsible for data quality is not always the one using the data to create valuable and intelligent solutions for the business.
Therefore, it is wise to embed data management within the business itself. Teams can then take responsibility for cleaning their own datasets for specific initiatives or use cases, even when data quality across the organization is not yet optimal. Waiting to start with GenAI until all company data is clean and ready may result in never getting started at all.
Meanwhile, you should work on the master data, ensuring, for instance, that all customer data is collected and accessible within a single system. For new initiatives or technical possibilities involving customers, there should always be a connection to the master data management system.
Virtual Federative Approach
A central vision and policy are necessary for an organization to reach the desired maturity level in data and AI. However, to make progress within specific business units or divisions, you can adopt a virtual federative approach. This involves assigning staff from the central tech or data department to specific business units or projects.
Over time, data governance and data management can increasingly be handled and discussed at the decentralized level—for example, during planned meetings or sessions. Initiatives with potential risks related to security, privacy, or ethics can then be escalated to the central Data & AI Governance Board.
You should also consider conducting an ethical assessment to determine whether new digital possibilities and plans align with the organization’s values and principles. Often, this will quickly clarify whether something is feasible or not. Such assessments can also help identify risk mitigation measures.
Maximizing Independence
Finally, strive for maximum independence from big tech. It’s fine to use services from Microsoft, Google, and other major players, but avoid becoming dependent on them. Instead, select services and providers that add the most value in your context, and don’t put all your eggs in one basket.
Ensure you have a control layer capable of integrating with multiple LLMs. This enables you to use the most effective LLM for each use case.
In summary, GenAI offers unprecedented opportunities to create business value but requires a well-thought-out vision and approach. Ready to take the next step? Anderson MacGyver combines years of experience with challenges in strategy, organization, data, and technology to help drive new digital developments. Want to know more? Contact Anton Bubberman to share your ambitions and challenges. We’re happy to help!
By Anton Bubberman
From opportunities in customer experience to radical efficiency improvements: AI is a game-changer if you know how to apply it. Yet, there is a risk in implementing it carelessly. How can AI be deployed in a valuable and responsible manner? Data to Value expert Anton Bubberman outlines the current state of affairs and provides companies and their leaders with tools to strategically embed AI in the modern Digital Enterprise.
Everyone has some level of experience with AI by now and therefore an opinion on it. Interestingly, opinions vary widely. Some consider it an over-hyped buzzword, while others warn that we are underestimating its power. Some see mainly opportunities in AI, others see threats—possibilities versus responsibilities.
As with many technological advancements, we are probably prone to overestimating the short-term impact and underestimating the long-term effects. Tech companies contribute to this tendency by boasting about technology still in development—it often takes years before it reaches the market. And when it does, we are no longer impressed. At such moments, we underestimate the real changes it can bring.
Take big data, for example, where the buzz from years ago has largely died down. This leads us to retrospectively label it a hype. But let’s not forget: our investments in big data have given us tools like large language models such as ChatGPT, which can read and interpret PDFs, photos, audio, and video files without opening them. And this is just the beginning.
Three Crucial Success Factors
Considering these developments, it’s well worth becoming an AI-driven Digital Enterprise. To achieve this, three fundamental success factors come into play. I like to compare these to chess, jazz, and philosophy. But first, let’s consider where we currently stand…
European legislation defines AI as a machine-based system designed to operate autonomously to varying degrees. It can adapt to circumstances to deliver diverse outputs, such as predictions, creations, recommendations, and decisions that impact physical or virtual environments.
AI becomes more powerful as its autonomy and adaptability increase. We’re moving from relatively simple rule-based computing to the holy grail: artificial general intelligence (AGI), which matches human cognitive abilities. Already, AI can independently perform complex tasks in various domains and learn to adapt to new situations.
From Robot Dog to Back Office
Some experts believe AGI is still half a century away; others think we will reach it by 2025. Until then, individuals and organizations exist on a continuum between consuming and building increasingly advanced digital intelligence. AI will embed itself more and more into the operating systems and applications of our devices and systems, linked to both personal and business data. But what steps are we taking ourselves, as leaders and as companies?
At Anderson MacGyver, we help organizations become Digital Enterprises. We have developed a model for this, based on five critical building blocks. At the core is ‘digital smartness’ alongside ‘shared data’, supported by ‘digital infrastructure’ at the base, with ‘customer experience’ and the ‘operational backbone’ flanking it on either side. Additionally, the Digital Enterprise is embedded in an ecosystem of customers, partners, and other stakeholders.
In terms of AI, the possibilities are vast: from robot dogs scanning physical production environments to intelligent front-office systems for diverse forms of human communication—social, supportive, advisory, and more, in any language. And alongside these, tools to monitor and enhance both customer experience and operational processes in the back office.
Thinking Ahead, Improvising, Philosophizing
Like chess, AI revolves around thinking ahead, planning, and evaluating. The playing field is constantly changing, as are the opportunities and threats within your organization and ecosystem. Organizations and their leaders must remain agile, always contemplating the next move.
The connection to jazz lies in the apparent ease of playing and improvising, which often disguises the long period of practice required. Beyond physical skills, one must master theoretical frameworks. Mastery demands dedication, encompassing hard skills as well as soft skills—such as interacting with other band members.
Similarly, in the digital realm, alongside technical prerequisites, an AI-driven culture is essential. This culture should critically assess outcomes for their added value and ethical dimensions. While music and jazz may be hobbies for me, AI cannot be treated as a side project within an organization. True mastery demands significant investment.
AI also requires philosophical awareness of its ethical impact. It has the potential to propel individuals, organizations, and others forward significantly, but this power comes with risks. Like a surgeon’s scalpel, AI can achieve wonders in skilled hands but cause harm when misused or mishandled. With tools like AI, one must guard against risks such as information bubbles, misrepresentation, and bias.
Building a Better World
We must remain mindful of how we use AI for our customers and other stakeholders. They need to trust us, rely on us, and know that we understand the impact it has on them. Yes, jobs will disappear, and even more jobs will change. But in capable hands—with the right policies, guidelines, mindset, and behavior—AI holds the power to achieve great things. For instance, creating an intelligent and scalable Digital Enterprise. And perhaps even a better world to live in.
As AI becomes increasingly embedded in our daily lives and business operations, it’s up to companies and their leaders to guide its application. What first step will you take today?
Anton Bubberman is a Senior Management Consultant and Guild Lead Data to Value at Anderson MacGyver. He has extensive technology and data experience across sectors ranging from healthcare to energy and finance.
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?
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.
Through our annual Digital Business Monitor, we seek to better understand the key drivers and challenges faced by digital leaders. This year’s survey results, supplemented by interviews, have revealed compelling insights.
- What can we learn from digital leaders in their digital journeys?
- What challenges are they confronted with?
- And what investment decisions do these leaders make in building their Digital Enterprise?
Download our full report to explore these findings in depth.
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:
- 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.
- 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.
- 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?
- 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.
- 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.
- 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 13th of November.
Anderson MacGyver
The core purpose of Anderson MacGyver is to harness the unrealized business value for our clients by leveraging the powerful potential of technology & data. We provide strategic advice and guidance to board members and senior management to shape and drive their digital journey.