Where is the fault in the system of digital transformation and what requirements does this give rise to for the use of artificial intelligence?

The perspective of a key account manager in the consulting and ICT industry.

Daron Acemoğlu, one of the world’s most influential economists, writes in his May 20, 2021 essay »AI’s Future Doesn’t Have to Be Dystopian«: AI can be used to increase human productivity, create jobs and shared prosperity, and protect and strengthen democratic freedoms – but only if we change our approach. ^[²²^]

My thesis: We need a paradigm shift from the classic, Tayloristic approach of foreign-organized automation, monitoring and control to a collaborative approach of self-organized automation, monitoring and control – which not only promotes productivity, agility and innovation, but also ensures structural and cultural stability. In other words: Not the foreign-organized replacement of human intelligence by artificial intelligence, but the intelligent, self-organized networking of human intelligence by means of artificial intelligence creates the necessary conditions for an ongoing, timely and efficient transformation process and becomes the new basic innovation and key technology of the social-ecological and economic transformation!

(Friedrich Schieck – 10/2023)

What is the status quo in digital transformation?
What are the causes of this paradoxical development?
A hypothesis on the past and the future
Nature is showing us the way
From externally organized to self-organized digital transformation
What questions need to be answered?
My preliminary conclusion
My statement
Sources

What is the status quo in digital transformation?

Productivity, agility and innovative capability are central prerequisites for the profitability, competitiveness and future viability of companies and economies. However, in recent years we have experienced an unprecedented increase in the dynamics of change in business, politics and society, which has led and continues to lead to a loss of productivity, agility, innovative capability as well as structural and cultural stability.

Studies and statistics show that productivity growth ^[¹^], agility ^[²^] ^[3^], innovative capacity ^[⁴^] ^[⁵^], and structural and cultural stability ^[⁶^] all declined in Germany between 2003 and 2022. At the same time, spending on external consulting ^[⁷^] and information technology ^[⁸^] more than doubled. Paradoxically, however, the enormous increase in these expenditures for consulting and digitization does not seem to be bringing about the desired improvements.

This spending on consulting and digitization should actually improve the profitability, competitiveness and future viability of companies and organizations. Paradoxically, however, the exact opposite is happening.

As early as 1987, Nobel laureate Robert Solow summed up the phenomenon of the productivity paradox of information technology when he stated,

»You can see the computer age everywhere except in productivity statistics.« Even after 35 years, there are still only hypothetical explanations available for the phenomenon of the information technology productivity paradox. ^[9^]

Erik Brynjolfsson, Daniel Rock, and Chad Syverson of the MIT Sloan School of Management Erik Brynjolfsson, Daniel Rock, and Chad Syverson of the MIT Sloan School of Management describe in their 2017 working paper, »Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics«, that we live in an age of paradoxes. Although systems using artificial intelligence are matching or surpassing human performance in an increasing number of areas and are seeing rapid advances in other technologies, measured productivity growth has fallen by half in the last decade, and real income has stagnated for the majority of Americans since the late 1990s. ^[¹⁰^]

Boris Ewenstein, Wesley Smith, and Ashvin Sologar of McKinsey, in their 2015 paper »Changing Change Management,« conclude that 70 percent of change programs fail to achieve their goals – largely due to employee resistance and lack of management support ^[¹¹^]. Other studies, such as »Productivity has fallen significantly in knowledge-intensive business services« by Alexander S. Kritikos, Alexander Schiersch and Caroline Stiel of the German Institute for Economic Research from 2021, show a significant decline in productivity in knowledge-intensive business-related services between 1995 and 2014, by over 40 percent – with only a slight subsequent recovery. ^[¹²^]

A recent study by Zippia, »20 Incredible Productivity Statistics [2023]: Average Employee Productivity in the United States« dated November 2, 2022, sums it up: according to Zippia’s research, the average office worker is productive for only 31 percent of an average eight-hour workday, or two hours and 53 minutes. A good productivity percentage, on the other hand, is between 70 and 75 percent, according to Zippia. This means that in the U.S., the percentage of non-value-added work per office worker is approximately 39 to 44 percent of daily work time ^[¹³^].

If we assume that average employee productivity at an office workplace in Germany is also around 31 percent, 29 percent of working time is spent on necessary breaks, etc., and the total cost per office workplace and working hour is an average of 40 euros, then 26 million office workplaces result in non-productive labor costs of around 765 billion euros per year.

This corresponds to about 19.1 billion working hours, 2.4 billion working days or 10.4 million skilled workers.

In view of this, the question arises as to where the fault lies in the system of digital transformation, whether and how digital technologies and artificial intelligence can counteract this paradoxical development, and which investments are at all target-oriented and economically sensible in this context.

What are the causes of this paradoxical development?

There is probably a consensus today that only a timely and efficient adaptation of products and services as well as of organizational, information and process structures to changing framework conditions and customer needs will ensure the profitability, competitiveness and future viability of companies. However, if current developments in the economy, politics and society are taken into account ^[¹⁴^] ^[¹⁵^] ^[¹⁶^], it is foreseeable that the pressure to adapt to changing framework conditions will increase even more dramatically.

Whether it is the climate, financial, diesel, refugee, corona, supply chain, Ukraine or energy crisis, whether it is inflation, recession or the increasing competitive pressure due to globalization, digitalization and automation – all change processes result in companies having to adapt their products, structures and processes to changing framework conditions and new customer needs promptly and efficiently at ever shorter intervals.

This means that the higher the change dynamics, the more frequently and quickly a company must adapt to changing framework conditions and customer needs, with the logical consequence that the time available for structural-organizational adaptation decreases, while the effort and time required for this increases – which in extreme cases results in an adaptation gap between the time available and the time required.

In addition, there is the following contradiction: The more employees are involved in the adaptation or transformation process, the greater the time required for non-value-adding organizational and communication processes between all those involved. If, on the other hand, employees are not included in the change process, the time required can increase even more drastically: namely, if employees block the change process.

In my opinion, this dilemma between the dynamics of change and the necessary effort to adapt, with the accompanying loss of productivity, agility and innovative capability as well as structural and cultural stability, is evident in almost all structural organizational change processes – whether in the day-to-day operational business of employees and managers or in transformation projects for strategic realignment, restructuring, process optimization and digitization.

The sociologist Armin Nassehi once aptly described this with the metaphor in a lecture: “The transformation of a social system is like repairing a defective combustion engine while it is running. I think this dilemma is evident today in all areas of business, politics and society, whether in a meeting, a debate or a public discourse.

The consequence is not only that investments and personnel costs increase and productivity decreases, but also that the agility, flexibility and innovative capacity of the organization deteriorate, and employee motivation and corporate culture suffer.

In this context, the following question arises: What influence does the increasing, non-value-adding organizational and communication effort (bureaucratic effort) of employees and managers in day-to-day operations and in the implementation of transformation and change management initiatives have on productivity, agility and innovation capability as well as on employee satisfaction and the culture of a company?

The impact on productivity, agility, and innovativeness.

Productivity: When employees and managers have to spend more time on organizational and communication tasks, there is less time for value-adding activities. This leads to delays, error rates and lower efficiency. This can affect job satisfaction and lead to higher stress as well as higher sick leave and turnover.
Agility: High organizational overhead can lead to employees spending more time planning and coordinating instead of acting quickly and effectively. This can lead to delays and inefficient processes and affect the agility of the company.
Innovation capability: the time and energy spent on structural organizational adjustments reduces creative work on new products and services. Employees and managers must focus on short-term administrative tasks instead of long-term strategic innovation.

Impact on employee satisfaction and corporate culture

Employee satisfaction: The high administrative workload can cause frustration and overwork, which lowers job satisfaction. Employee morale and loyalty suffer from lack of recognition. Employees feel alienated and undervalued, leading to lower engagement and higher turnover.
Corporate culture: The high level of bureaucracy fosters a culture of slowdown and demotivation. Resistance to change is encouraged. Employees are less motivated to develop and implement innovative ideas.

Studies, statistics and publications from the last three decades make it clear that despite increasing investments in measures for strategic realignment, restructuring, process optimization and digitization, the associated change management and the use of new agile methods and artificial intelligence, the non-value-adding organizational and communication effort of all those involved in transformation projects has increased rather than decreased and in many places there has been a loss of productivity, agility and innovative capability as well as structural and cultural stability.

The first question that arises here is: Can the conventional methods & tools still be effective at all due to the constantly growing change dynamics? Or is a so-called adaptation gap between the time available and the time required for a transformation already pre-programmed?

Digital transformation should be understood as a continuous, ongoing process. Therefore, in my opinion, the proportion of non-value-adding workload for organization and communication on the part of employees and managers can serve as an indicator of whether the methods & tools used are actually efficient and economically viable.

Because of the increasing dynamics of change, it can no longer be assumed that after a classic restructuring or transformation project – at some point in the future – the non-value-adding organizational and communication workload will decrease and increasing productivity, agility, and innovative capability as well as structural and cultural stability will result.

Which brings up the second question: Which methods & digital technologies firstly ensure a truly timely and efficient adaptation of products, structures and processes to changing framework conditions and customer requirements, secondly involve all employees and managers in the change process, and thirdly reduce the non-value-adding organizational and communication effort of all participants in the ongoing transformation process?

My thesis: The cause for the failure of transformation and change management initiatives lies less in the behavior or lack of agile mindset of the employees or the corporate culture, but rather in the classic approach of foreign-organized automation, monitoring and control of structures and processes.

The cause for the failure of transformation and change management initiatives lies less in the behavior or lack of agile mindset of the employees or the corporate culture, but rather in the classic approach of foreign-organized automation, monitoring and control of structures and processes.

A hypothesis on past and future

I hypothesize that due to the increasing dynamics of change, the proportionate, non-value-adding organizational and communication effort of employees and managers has increased by at least 20 percent over the past ten years – and this despite high investments in strategic realignment, restructuring, process optimization and digitization, as well as the use of new agile methods and artificial intelligence. If the adaptation or transformation process takes place over the next ten years with the same methods & tools, I believe that the non-value-adding organizational and communication effort will double again.

If I am correct in my hypothesis, then everyone can work out for themselves what consequences this could have for productivity, agility and innovative capability, right through to the economic and ecological sustainability of a company – and even the entire economy.

Every solution approach must therefore be measured by the extent to which it involves all employees and managers in the transformation process and, at the same time, reduces the non-value-adding organizational and communication effort of all participants in the ongoing transformation process instead of increasing it.

Nature shows us how

Evolution in nature shows us how it works: What does not adapt promptly and efficiently to its environmental conditions dies out! In other words, if more time is needed for adaptation than there is time available for change, then an adaptation gap between available and needed time arises.

Whether plants, animals or humans – they all inform, communicate and interact with their natural or social environment and try to adapt to the changing conditions in a timely and efficient way by self-organization, to reshape or to evolve.

Whether plants, animals or humans - they all inform, communicate and interact with their natural or social environment and try to adapt to the changing conditions in a timely and efficient way by self-organization, to reshape or to evolve.

I am convinced that this paradigm can be equally applied to the increasing dynamics of change in the economy, politics and society of the current time, but with the difference that digital I&C technologies and artificial intelligence are influencing the information, communication and interaction process for adaptation and reshaping to an ever greater extent, both positively and negatively.

U.S. sociologist Gerhard Lenski developed an ecological-evolutionary social theory whose key concept is “communication.” According to Lenski, nothing changes a social system as much as the question of how people communicate with each other, generate information, pass it on and multiply it. It gives rise to claims and expectations, raises questions of justice, and in this way transforms entire societies. ^[¹⁷^]

From foreign-organized to self-organized digital transformation

In the discussion about digital transformation and the use of artificial intelligence (AI), crucial challenges and misunderstandings can be identified. These issues are of great relevance for CEOs and CIOs of companies as well as for professionals from the consulting and ICT industries.

The problem in the current Digital Transformation system

As already described, it is the general and established opinion of many analysts, consultants and academics that the cause of the failure of transformation and change management initiatives lies in the traditional behavioral patterns and a lack of agile mindset among employees and managers, as well as in the culture of the company. Employees would simply have to develop a new agile awareness of knowledge sharing and collaborative working [18] and learn how to handle the new digital technologies properly.

This perspective reminds me of citizenship classes in the former GDR, where people were taught to conform to a certain way of thinking. In today’s world, many representatives of the consulting and IT industry unfortunately still believe that the problem lies with the employees and that the proven methods and tools for adapting to change can be applied unchanged.

They don’t seem to realize that the way companies adapt to changing conditions and customer needs may be the root cause of the failure of transformation and change management initiatives. Over the past 35 years, there has been little change in the traditional approach of “foreign-organized as-is mapping, analysis, restructuring, optimization and digitization.”

Whether it is a matter of measures for strategic realignment, restructuring, process optimization and ERP implementation or the use of agile methods [19] and approaches to digital business transformation [20] – the probability of an adaptation gap between available and required time has demonstrably increased from year to year due to increasing change dynamics and an increasing non-value-adding organizational and communication effort.

It is my conviction that these traditional, foreign-organized methods and digital technologies that have served as a foundation for the last 35 years have become obsolete. The ever-widening adaptation gap between available and required time is becoming increasingly counterproductive from a business and economic perspective.

The paradigm shift to self-organized transformation

I think we need a paradigm shift: towards self-organized transformation methods and digital technologies that promote rather than hinder the information, communication and interaction process for self-organized adaptation and redesign.

Already in 2008, Prof. Klaus Fuchs-Kittowski (Leibniz-Sozietät der Wissenschaften zu Berlin e.V.) posed two central questions in this sense with his publication »Self-organization and design of informational systems in social organization« ^[²¹^]:

Can we create conditions that do not hinder the self-organization of social systems, and perhaps even create conditions that possibly promote self-organization? (S.179)^[²¹^]
With appropriate promotion of self-organization, including appropriate introduction of modern information and communication technology, its proper embedding in concrete work processes and in the complexity of social organization, can we perhaps steer the development of social systems in a desired direction? (S.180) ^[²¹^]

In this paper, Prof. Fuchs-Kittowski addresses questions about the methodology of ICT system design in social organization from the perspective of various concepts of self-organization (specifically second-order cybernetics as well as evolutionary systems theory). I interpret the two questions as challenging the prevailing view of “Culture First, Technology Second” and seeing the causality between ICT system design and social systems evolution the other way around.

Artificial intelligence as the key to transformation

It is my conviction that digital information and communication technologies as well as artificial intelligence have the greatest influence on individual self-organization and thus on employee behavior and corporate culture in our fast-moving times. To date, however, these technologies have tended to restrict individual self-organization through externally organized automation, monitoring and control rather than promoting it.

Companies therefore need a fundamentally new approach in the way they can promptly and efficiently readapt their organizational, information and process structures to the constantly changing framework conditions in order to drastically reduce the increasing, non-value-adding organizational and communication effort of employees and managers and to increase productivity, agility and innovation capability as well as structural and cultural stability.

Companies therefore need a fundamentally new approach in the way they can promptly and efficiently readapt their organizational, information and process structures to the ever-changing environment in order to drastically reduce the increasing, non-value-adding organizational and communication effort of employees and managers and to increase productivity, agility and innovation as well as structural and cultural stability.

Daron Acemoğlu, one of the world’s most influential economists, writes in his May 20, 2021 essay »AI’s Future Doesn’t Have to Be Dystopian« ^[²²^]:

AI can be used to increase human productivity, create jobs and shared prosperity, and protect and strengthen democratic freedoms-but only if we change our approach. ^[²²^]
Greater automation reduces the amount of work, while new tasks increase it. Measuring the sum of the work-related consequences of new AI technologies via their impact on the employment rate is therefore a promising way forward. ^[²²^]
Automation and monitoring remain the focus of AI development, and some large companies with a strong focus on algorithmic automation are having an outsized impact on the direction of this technology. ^[²³^]
Consistent with this, my recent research with David Autor, Joe Hazell, and Pascual Restrepo suggests that much of current AI is in automation mode, not the more collaborative mode envisioned by Brynjolfsson and Nachman. ^[²³^]

I think the contributions in this forum make it clear where the real challenges of today’s AI developments lie. I would rather agree with the optimistic perspective of Lama Nachmann in »Beyond the automation – Only Approach« ^[²⁴^] and Erik Brynjolfsson in »Augmentation, not Automation« ^[²⁵^] that only a change of approach in AI development – from automation mode to collaborative mode – will increase human productivity, create jobs and shared prosperity and protect and strengthen democratic freedoms. ^[²⁶^]

From my point of view, this means, as I said, that only a paradigm shift can remedy the situation: away from the classical, Tayloristic approach of foreign-organized automation, monitoring and control, to a collaborative approach of self-organized automation, monitoring and control – which not only promotes productivity, agility and innovativeness, but also ensures structural and cultural stability!

In other words: Not the foreign-organized replacement of human intelligence by artificial intelligence, but the intelligent, self-organized networking of human intelligence by means of artificial intelligence creates the necessary conditions for an ongoing, timely and efficient transformation process and, in my view, becomes the new basic innovation and key technology of the social-ecological and economic transformation!

Not the foreign-organized replacement of human intelligence by artificial intelligence, but the intelligent, self-organized networking of human intelligence by means of artificial intelligence creates the necessary conditions for an ongoing, timely and efficient transformation process ...

What questions need to be answered?

Tracy Major (MIT Senior Associate Director) writes in her article »It’s time to rechart the course of technology. Here are 4 ways to start« ^[²⁷^] to the new book »Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity« ^[²⁸^] by MIT economists Daron Acemoglu and Simon Johnson, that they decry the economic and social damage caused by the concentrated power of business. They would show how the enormous advances in computing over the last half century can become empowering and democratizing tools. For example, Acemoglu and Johnson write that it is nevertheless possible to make digital technologies work for people and increase productivity, so investing in technologies that help people can also be good business.

In this excerpt, the authors call for the development of a powerful new narrative about shared prosperity and offer four ways to reshape the course of technology so that it complements human capabilities:

Improving the productivity of workers in their current jobs
Creating new tasks using machine intelligence that augments human capabilities
Providing better and more usable information for human decision making
Building new platforms that bring together people with different skills and needs. ^[²⁷^]

I think Daron Acemoglu and Simon Johnson hit the nail on the head with these four points. However, I wonder what, for example, a family business owner who is responsible for 25,000 employees is supposed to do with this statement? He would have to know what the concrete scope of action is. Which specific use cases and processes should be supported by artificial intelligence, what the design requirements are for a multimodal, context-aware and interactive AI system, which algorithms and data should be used to train the AI, and which data model and system architecture will create the necessary conditions.

A CEO or CIO is therefore more likely to ask which investments in which initiatives, measures, and technologies will guarantee a sustainable increase in productivity, agility, and innovative capability, reduce costs, and ensure the necessary structural and cultural stability in the company. The basic technologies, such as OpenAI’s ChatGPT, Google’s Bard, Aleph Alpha’s Luminous, or Mistral 7B from the company Mistral AI are certainly available, but opinions differ widely today on the right design approach for using them in an economically sensible and ethically sustainable manner!

My preliminary conclusion

My preliminary conclusion is that every company must first develop an awareness that the problem with the failure of transformation and change management initiatives lies less in the behavior or mindset of employees and managers than in the methods and digital technologies used, which do not even permit timely and, above all, efficient transformation in the face of increasing change dynamics. In other words, any solution approach must be measured by the extent to which it involves all employees and managers in the transformation process, while at the same time reducing, rather than increasing, the non-value-adding organizational and communication efforts of all stakeholders in the ongoing transformation process.

That is why I believe it is imperative, as a first step, to question the classic, externally organized approaches to automation, monitoring and control and their consequences for the profitability, competitiveness and sustainability of companies. This means that studies are needed that transparently show how the proportionate, non-value-adding organizational and communication effort of employees and managers has changed over time and what impact this has on productivity, agility, innovative capacity, and structural and cultural stability in the company.

In my opinion, such studies would not only reveal the actual causes of the productivity paradox of digitization and the failure of transformation and change management initiatives, but would furthermore reveal necessary fields of action for an AI-supported, collaborative approach to self-organized transformation.

In this sense, in my view, there is a need for studies that build on each other and provide empirically verifiable answers to the following exemplary questions:

How has the productivity or the non-value-adding organizational and communication effort of employees and managers (office workspace) changed over the past ten years? And how high were the necessary internal and external investments in consulting and digitization?
Which information, communication and interaction processes (use cases) can be attributed to the non-value-adding organizational and communication effort of all employees and managers alike and should form the basis of an empirical survey?
What are the causal relationships between change dynamics, productivity, agility and innovation capability as well as structural and cultural stability in companies and other organizations?
Which methods, tools and digital technologies promote and which hinder a timely, efficient and ongoing adaptation or transformation process, taking into account an increasing change dynamic?
What are the requirements for a fundamentally new, collaborative approach to method & AI technology that enables self-organized automation, monitoring and control by employees and managers?
Which patterns, rules and algorithms could enable collaborative automation, monitoring and control self-organized by employees and managers? And what data and design model for a multimodal, context-aware, and interactive AI system will create the necessary conditions?
What quantitative and non-quantitative benefits could result from such a paradigm shift, from foreign-organized to self-organized, AI-supported transformation in terms of productivity, agility, innovation capability, and structural and cultural stability in the company?
What disruptive effects would such a new approach have on the consulting and ICT industry? Where will the consulting and ICT focus shift, and what opportunities and risks does this pose for traditional consulting & ICT companies as well as for startups that bring such transformative AI technology to market?
Can such a collaborative AI approach to self-organized networking or self-organized automation, monitoring and control be applied as a blueprint to any social system, regardless of its size and complexity?

My statement

With this post, I hope to initiate a discussion that will provide answers to current and future challenges of digital transformation and the future role of artificial intelligence in companies and other social systems. To this end, there will be further, more in-depth answers to each of the aforementioned questions in my blog, which will not only take into account current studies and publications, but also include the hopefully extensive contributions to the discussion, which should find space here on this page.

If there are any questions or comments about this post, please feel free to send them to me via the reply form.

Friedrich Reinhard Schieck / BCM Consult – 11.10.2023

E-Mail: fs@bcmconsult.com, fs@bcmconsult.com

Website: www.bcmconsult.com