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 

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. 
  1. 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. 
  2. 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. 


Recap: 

In this series of blog posts, we looked into the question of how the journey to being an AI-driven enterprise differs from the journey to being a Data-driven enterprise. We described how AI-driven enterprises unlock value by using digital systems that, based on data, learn and adapt and generate new video, image, text, sound and code and/or trigger actions or autonomously act. 

We shared how, like for Data-to-Value journeys, successful Data-to-AI-to-Value journeys are built on the following four good practices: 

In addition to these four good practices, the following themes require specific attention in cases where AI plays a major role in an organization’s digital transformation journey: 


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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 

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

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 

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 

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 13th 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. 
  1. Data Value Opportunities: Build tangible bridges between business value and data capabilities by defining and prioritizing Data Value Opportunities. 
  1. 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. 
  2. 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. 
  1. Business process redesign requiring even more attention for people change. 
  1. Additional risks and different measures. 
  1. 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 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.