Category: Knowledge article

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 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
• Part 4: theme 2: Business process redesign requiring even more attention for people change
• Part 5: theme 3: Additional risks and different measures

Now, let’s dive into the last part: the fourth underestimated theme.   

4. Even greater challenges in accessing AI talent 

Theme four of the four underestimated themes

It is widely acknowledged that demand for data talent is higher than supply. This imbalance increases when including specific AI capabilities in the equation. 

AI relies on talent in domains that are most scarce. It concerns domains such as software engineering, data science, machine learning engineering, NLP engineering, robotics engineering, data engineering and multidisciplinary agile development. It is important to take this into account and include the following in your journey to becoming an AI-driven enterprise. 

1. Focus; do not run after abstract visions but work with the business on defining and prioritizing tangible Data-to-AI-to-Value opportunities. Direct your scarce talent towards these highest priority opportunities. 

2. Retention; Do not fall into the trap of promising the most advanced AI applications in your organization to attract talent. You will probably disappoint and quickly lose anyone who was driven by this after a while. Throwing away your recruitment investment, creating inflated costs through constant delay and handovers. Instead, be honest and clearly articulate what truly makes your organization attractive; your societal role, your working atmosphere, your maturity stage and associated opportunity to be part of something new, etcetera. Attract talent that is driven by your organization’s true characteristics and stand a higher chance of being an attractive environment for your AI talent for a longer period. 
3. Strategic sourcing; Pay attention to defining a sourcing strategy. Utilize all sourcing options to your benefit. Carefully consider where to vest your inhouse talents. Assess which external suppliers can be leveraged for which scope. Investigate options to collaborate in your eco-system if there are potential synergies and there is no commercial value in differentiation in your eco-system. 

Ready to take the next step? Our team is ready to talk to you. Contact us.

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?

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.