With the subtitle of this book being ‘Your Guide to Meaningful Work in a Changing World’ the reader would assume that O’Donnell’s book would provide guidance on how we can be better at our jobs and find fulfillment with the paid work that we do. This book, however, does not really achieve this end. This is not to say that this is not a good book to read, but the reader needs to understand what they will get out of this book before reading it. This is a book that can assist the reader in acknowledging and using the gifts and talents God has given us for the work we do. The work and business examples that are provided in each chapter evolved from the research that the author conducted while completing her doctoral dissertation. This book is written by a woman who provides many examples of the challenges women have when balancing life and work. Each chapter includes many stories of leaders, employees, and entrepreneurs and their experiences with their work and work environment.
The book is written with three distinct sections, each having several chapters that expound on the focus of the section, with each chapter ending with some exercises that assist with the practical application of what was written about in the chapter. The first section is called ‘Where Do You Want To Go?’ and includes 3 chapters that discuss the challenges that most people are facing in today’s world of work. (Note that the author is writing for an audience in the developed world and does not include the challenges that someone might experience in under-developed nations). O’Donnell briefly discusses the economy, the new tools and technologies that are now used in work, and the feelings and emotions experienced by workers in today’s organizations. She proposes and challenges dysfunctional beliefs about work that Christians may have and attempts to explain what God’s calling is for our lives. Even though these chapters may set the stage for our current situation they don’t really motivate the reader to read further in the book. The saving grace of this section is Chapter 2, where the author discusses the concept of ‘lean in and let go’ and where she acknowledges that God is in control of our situations. This discussion possibly sets the stage for moving on into the second section of the book where the reader learns more about how to lean in and let go.
The second section of the book is entitled ‘Who Will You Become?’ and has 4 chapters that discuss how to be entrepreneurial in your work and business. In the author’s bio, listed on the back cover of the book, she is identified as an entrepreneur and so she has an understanding of what it feels like to start a business. Throughout the chapters in this section, she uses many of her own business experiences, in addition to the answers to questions she asked entrepreneurs while completing her doctoral research, to explain many of the concepts discussed in these chapters. O’Donnell refers to several significant topics such as the importance of building relationships, understanding creativity, and developing resilience skills. In this section of the book she utilizes many scriptural references to explain how God always uses relationships in the building of His kingdom, how creative the Lord was in the creation of the world and all that is in it, and how Jesus was resilient in all that He endured. The use of some of the biblical references, however, can appear to be forced when relating these to the topics being discussed. One example of this is in Chapter 7 where the author uses the Easter story as a demonstration for building resilience; this application to work in our current situations seems a bit contrived. Since this section of the book was focused on being entrepreneurial in your work, it may appear that this book is written for people who want to start their own businesses; this may cause some readers to feel that this book would not be a guidebook for them in making their work meaningful in a changing world.
The last section of the book is entitled ‘How Will You Get There?’ and includes 4 chapters that focus on having empathy, imagination, and taking risks. This section ends by reflecting on our past and considering how we got to where we are today. In these chapters O’Donnell re-emphasizes our need to be entrepreneurial in all we do and explains good techniques for being a successful entrepreneur. Once again, though, these chapters don’t seem to fit within the main purpose of the book, which is to make any and all work meaningful in a changing world. The thoughts in these chapters could, however, assist entrepreneurs in being more effective and successful in their entrepreneurial ventures, as they deal with their failures and successes.
In conclusion, I found the book hard to read as there seemed to be no clear focus and end goal. Most chapters were well written, but the discussions did not always hang together inside the chapter or with the following chapters. The book did not successfully provide the reader with a conclusion on how to make their everyday work meaningful. The first 3 chapters probably should appear at the end of the book, as they summarize the current situation and demonstrate how all the concepts that are discussed in the following chapters provide some of the needed skills and direction on how to better manage our current work situations. Many of the chapters read like stand-alone essays (or good talks for a conference or podcast), which are good in themselves, but don’t always lead the reader to further knowledge in how to make work matter. The integration of scripture in some of the chapters and areas of focus seemed to be forced and doesn’t provide a cohesive understanding of what was really being meant by the scripture passages being used. Overall, the best chapter in the book is Chapter 6, where there is a wonderful exposition on creativity and God’s role in our ability to create. The author provides many gems in this discussion and causes the reader to truly consider our ability to be continually creative in all we do, remembering that God is the creator and we work with Him in all we create in our lives.
Due to the structure of each chapter including many anecdotes, and the fact that the majority of the references cited were not from academic books or articles, this is not an academic treatise about the integration of faith and work. This could be a good book for a Christian book study in which people in their early career stages have a desire to better understand how to integrate their faith with their work. The questions at the end of each chapter allow for good discussion and could elicit more answers as to how to make work matter, and these answers could actually create a guide-book for creating meaningful work in a changing world.
‘Make Work Matter: Your Guide to Meaningful Work in a Changing World’ by Michaela O’Donnell was published in 2021 by Baker Books (ISBN: 978-5-40-90160-6). 234 pp.
Andrea Soberg is a retired professor of human resource management from Trinity Western University in Canada. She continues to be active within the global academic and business community by researching, writing, and assisting organizations that have a focus on business as mission.
The Centre for Enterprise, Markets and Ethics (CEME) is pleased to announce the publication of a report on the topic of non-executive directors.
Is the Non-Executive Director Worth Saving?
Richard Turnbull
Is the non-executive director (NED) an endangered species?
Does it matter?
This publication argues that the continued role of the NED matters not only to the individual director, to business and companies but also to society as a whole. The contention is that without effective NEDs, corporate governance will be weaker, companies more exposed and society less well served. If that is the case, then education is as important as law in enabling NEDs themselves, policymakers, media and wider society to understand and appreciate both the responsibilities and the limits of the NED role.
It is axiomatic that NEDs should discharge their duties competently in accordance with the law and with moral intent in the service of society. However, any lack of clarity over those duties, particularly in law, or potential exposure to regulatory action as a consequence of confusion over roles or responsibilities, will not only reinforce unrealistic expectations but also discourage NEDs from taking on this important corporate and social duty.
Non-executive directors should be reminded of their duties and responsibilities and given clarity as to society’s expectations. The answer is not further liabilities. Knee-jerk reactions to scandal are unhelpful – not all failures involve scandal and some, in the normal course of business, afford opportunity to learn lessons. We should clarify and celebrate. The NED is a bridge between business and society – ensuring proper corporate governance while playing a wider role in societal leadership. We need people of character and experience to discharge this role.
Richard Turnbull is the Director of the Centre for Enterprise, Markets and Ethics. He holds degrees in Economics and Theology and a degree of Doctor of Philosophy in Theology from the University of Durham. He is also a chartered accountant. He has authored or edited numerous books, articles and other publications in church history and business ethics, including an acclaimed biography of the Earl of Shaftesbury. He is a visiting Professor at St Mary’s University, Twickenham and a Fellow of the Royal Historical Society.
The author would also like to thank his colleagues at the Centre for Enterprise, Markets and Ethics, Andrei Rogobete (Associate Director) and Dr John Kroencke (Senior Research Fellow), who also contributed.
The Second Machine Age is Erik Brynjolfsson and Andrew McAfee’s best-known work. The book explores the profound implications of rapid technological advances, particularly in digital technologies, for society, the economy, and the labour market. Published in 2014, the book delves into the transformative effects of what the authors term the ‘second machine age,’ a period marked by exponential growth in computing power, the ubiquity of digital networks, and the rise of artificial intelligence (AI) and robotics (page 9). Through a well-structured narrative, Brynjolfsson and McAfee argue that while these technological advancements hold immense potential for economic growth and societal progress, they also present significant challenges, particularly in terms of inequality and the displacement of labour (pages 11-12). In this review we examine some of the book’s arguments, structure, and contributions to the wider ongoing discourse on technology and society.
Brynjolfsson and McAfee’s central thesis is that we are entering a new phase of technological advancement that is fundamentally different from the first machine age, which was characterized by the mechanization of manual labour through the invention of the steam engine and other machinery during the Industrial Revolution. The second machine age, in contrast, is driven by digital technologies that augment and, in some cases, replace cognitive tasks traditionally performed by humans (page 9).
Brynjolfsson and McAfee’s book is structured in a logical and accessible manner, making complex ideas about technology and economics understandable to a broad audience. The book is divided into three main sections: the first outlines the characteristics of the second machine age (chapters 1-6), the second discusses its implications for the economy and labour market (chapters 7-11), and the third offers potential solutions to the challenges posed by these technological changes (chapters 12-15).
The authors identify three key characteristics of the second machine age: (1) exponential growth in computing power, (2) digitalization, which allows information to be replicated at virtually no cost, and (3) combinatorial innovation, where new technologies are built upon existing ones, leading to rapid and often unexpected advances (page 37). The authors argue that these characteristics are driving unprecedented changes in productivity, business models, and the global economy (chapters 1-6).
A significant portion of the book is dedicated to discussing the implications of the technological changes for the labour market (chapters 7-11). Brynjolfsson and McAfee argue that while technology has always created winners and losers, the pace and scale of change in the second machine age are likely to exacerbate inequality. They point to the phenomenon of ‘skill-biased technological change’, where technology disproportionately benefits those with higher levels of education and skills, leading to a widening gap between high-skilled and low-skilled workers (page 134). This dynamic is further amplified by the ‘superstar’ effect, where a small number of highly skilled individuals and companies capture a disproportionate share of the economic gains from new technologies (page 150)
The authors also explore the potential for technological unemployment, where advances in AI and robotics could lead to the displacement of a significant number of jobs, particularly in sectors such as manufacturing, transportation, and even certain white-collar professions (page 173). However, they are careful to distinguish between short-term disruptions and long-term trends, noting that while some jobs will undoubtedly be lost, new opportunities will also emerge, particularly in areas that require creativity, complex problem-solving, and interpersonal skills (page 191).
One of the strengths of the book is its use of empirical evidence and real-world examples, the authors drawing on a wide range of data, from economic statistics to case studies of companies and industries that have been transformed by digital technologies. This evidence-based approach lends credibility to their analysis and helps to ground their sometimes abstract ideas in concrete realities.
However, a critic might argue that the book’s optimistic tone about the potential of technology to drive progress and prosperity is not sufficiently tempered by a consideration of the potential risks and downsides. While the authors do acknowledge the challenges posed by technological change, particularly in terms of inequality and job displacement, they tend to focus more on the potential benefits of innovation and less on the potential for negative outcomes, such as social unrest, the erosion of privacy and the proliferation of misinformation (e.g. fake news) in an increasingly digital world.
Moreover, while Brynjolfsson and McAfee offer several policy recommendations to address the challenges of the second machine age, such as investing in education, reforming the tax system, and fostering innovation (Chapter 13), some of the discussion around proposals such as higher tax rates, universal basic income and negative income tax may seem overly idealistic and difficult to implement in practice (Chapter 14). Some readers may find the patches of real-world naiveté throughout the concluding chapters off-putting.
Despite all this, The Second Machine Age makes a worthwhile contribution to the wider discussion on technology, economics, and society. Brynjolfsson and McAfee’s nuanced discussion of the potential for both job displacement and job creation provides a comparatively thoughtful perspective that is often missing from more alarmist accounts of technological unemployment. Their focus on the importance of education and lifelong learning in preparing workers for the jobs of the future is commendable and a valuable contribution to the wider policy debate.
In concluding, The Second Machine Age is recommended to all readers who are interested in the profound technological changes reshaping our economy and society. While the book is not without its flaws, particularly in chapters where the tone may seem overly optimistic, it remains an important contribution to the discourse on technology and society. As we continue to grapple with the impacts of AI, robotics, and other advanced technologies, Brynjolfsson and McAfee provide a useful framework for understanding the challenges and opportunities that lie ahead.
‘The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies’ by Erik Brynjolfsson and Andrew McAfee was published in 2014 by W.W. Norton & Co. (ISBN: 978-0-39-335064-7). 306pp.
Andrei E. Rogobete is Associate Director at the Centre for Enterprise, Markets & Ethics. For more information about Andrei please click here.
The history of the liberals, radicals, socialists, feminists, and Christians who advocated for free trade as the necessary accompaniment to anti-imperialism and peace is the subject of Marc-William Palen’s Pax Economica: Left-Wing Visions of a Free Trade World. Pax Economica was a term promoted by Jane Addams of the Women’s International League for Peace and Freedom, whose idealistic vision was of such a world after the catastrophe of the First World War (page 199).
Today, free trade is most often associated with neo-liberal economic thought but Palen demonstrates that its origins are rooted in nineteenth-century left-wing politics, with its advocates promoting a heady blend of peace, anti-imperialism, and free trade: a vision at odds with the powerful currents of nationalism, protectionism, and colonial expansion.
The book charts the continuous movements for free trade from the 1840s to the present day. Its scope is broad in time and space, with the core themes often intersecting with major events across the period. The vicissitudes of the drive for free trade as the harbinger of a peaceful world is prominent, and its mutability is closely considered and evaluated.
Palen reveals how, for some of its more left-wing adherents, free trade represented a challenge to imperialism and militarism. In its most idealistic form, it was held that free trade would create international bonds of union, dependence, and harmony which would make war obsolete. Suffice to say, that idealist vision has not materialized.
Nonetheless, the vision of a ‘Pax Economica’ evolved to include supranational regulation, and the establishment of post-1945 liberal institutions such as the United Nations (UN), International Monetary Fund (IMF), and the General Agreement on Trade and Tariffs (GATT) which did meet, albeit insipidly and ultimately disappointingly, their support for global governance and cooperation. Yet Palen’s work is not primarily a history of international institutions but rather a detailed study of the left-wing vision of globalism. In the main, this means a roll call of movements, pressure groups, and individuals, mostly those employing an ‘outsider strategy’ as a means of changing policy. The work is ambitious, immaculately researched, and a timely publication amid the resurgence of economic nationalism and geopolitical conflict.
The text, divided into six chapters, ranges over an extensive landscape, encompassing the anti-imperialism of free trade, Christian pacifism, socialist internationalism, feminism, and Marxism. The idealism conflating free trade, peace, and prosperity is well-delineated, and the intellectual antecedents well-identified and contextualized, with Richard Cobden, Henry George, and Norman Angell referenced throughout in multiple contexts. The geographical diaspora of free trade sentiments is a fine testimony to the vibrancy and durability of these ideas.
The book considers these developments, broadly defined, with short-hand organizing themes such as the ‘Marx-Manchester’ and ‘Marx-List’ traditions. Continuity of struggle and complexity of the tasks are keynotes of the work, from the battle against the systemic protectionism of the 1840s to the current disputes over trade liberalization and neoliberalism.
Indeed, divisions over the legitimacy of free trade principles were explicitly made with the publication of Friedrich List’s National System of Political Economy as early as 1841 at the height of the campaign for economic liberalization in Britain. Economic nationalism, as a counterpoint to free trade, features prominently, with List’s ‘infant industry’ framework and the ‘American System’ of Alexander Hamilton appearing equally, if not more, historically important, in the commercial policy of nations. The idea of tariffs as a shield against foreign competition, and more positively, as an economic development strategy, proved highly influential in the United States, Canada, Australia, Germany, and even Britain.
Free Trade has always been presented in many different guises, and Palen effectively demonstrates that it is intersectional and situational. It could be a liberal, socialist, or anti-colonial force, for the variegated ‘productive profile’ of nations meant it possessed different connotations and meanings on a country-specific basis. While viewed as a liberating measure in Victorian Britain, the same policy preferences led to it being considered by less-developed countries, such as India, Ireland, and China, as a tool of economic imperialism, used against territories which had ‘suffered under the yoke of British free-trade imperialism’ (page 109). Conversely, protectionism, while historically often reflecting the dominance of political and business elites, was often considered, especially in recent times, integral to the economic development of emerging states, and in anti-colonial national struggles. Hence the terminology of ‘Marx-List’ and ‘Marx-Manchester’ traditions as a way of understanding political economy preferences via national subjectivities and economic complexities. Undoubtedly owing to constraints of space, the book does not go far in its forensic analysis of divergent commercial policy preferences, and a particularly notable omission is the extent to which policy preferences were influenced by the fiscal demands of increasingly democratic electorates.
Chapters on free trade feminism and Christian pacifism demonstrate the continuing influence of Cobdenite ideas into the twentieth century. The final chapter takes the story up to the present day, charting the post-war Bretton Woods system, and the triumph of the Pax Americana and neoliberalism, with the caveat that economic nationalism aligned with infant-industry strategy continues to challenge the long-standing association between equity and free trade. Argentina is usefully highlighted as a case study of a nation adopting a growth strategy informed by Listian and American System ideas as ‘economic blueprints’ for development (page 196).
In a divided and unequal world, an absolutist stance for free trade has often been construed as entrenching inequality. Interestingly, free traders often reconciled these Global South infant-industry strategies as a rational, though hopefully temporary, response to Western neo-liberalism, which preached free trade but practiced protectionism. Most notable in that respect, despite the guiding principles of Reciprocity and Non-Discrimination promoted by the World Trade Organization, is the recent surge of regional trade agreements delimiting and protecting rather than expanding market access.
In some ways, the timing of the book’s publication in 2024 was unfortunate, and the idea that the ‘neo-liberal order has been placed on notice’, appears chimerical. With rising global geopolitical tensions, and war in Ukraine and Gaza, any notion of Pax Economica appears unlikely (page 222).
Nonetheless, the analysis within the book is broad-ranging, conceptually coherent, and highly informative. A particular strength is the ability of the author to convey the changing nature of free trade movements, yet while the breadth of the study is highly impressive, it does necessitate a sacrifice of depth in places.
The book is primarily an intellectual and institutional history with a plethora of organizations, acronyms, and an eclectic array of individuals. At times, it would have been useful to know how popular many of the cited organizations were, and how long they lasted. Some readers may find the numerous terms, ideologies, adjectives, and acronyms difficult to follow. Equally, the thematic approach means there is some reiteration and repetition.
Nomenclature is a little odd at times, with John Bright described as an ‘antislavery activist’ and Cobden as an ‘opponent of slavery’ (page 155). It is not that these descriptions are inaccurate but that they convey a limited view of individuals whose backstory is much wider than suggested by the description. There are also some contentious points, such as the claim that the Manchester School ‘envisaged the gradual decline of the nation-state, and with it the elimination of national rivalries and trade barriers’ (page 97). Despite its purported universalist and utopian principles, there existed many, maybe even Cobden himself, who supported free trade at least partly because it aligned with vested class and/or national interests. Self-interest could co-exist with or even be disguised by idealism. Indeed, trade agreements today, such as the USMCA, are examples of managed and negotiated free trade, which are a far cry from the voluntarist model promoted by free trade idealists portrayed within the book.
At times the book appears a somewhat breathless account (indicated by 65 pages of notes and a 20-page index) in covering so many events, times, and places but there is much to be gained from a close and careful reading of the text.
In sum, the book will interest scholars and general readers. It follows the tradition of ‘broad sweep’ history, informed by a considerable body of research and synthesis, and as such is thought-provoking, engaging, and interesting to read.
‘Pax Economica: Left-Wing Visions of a Free Trade World’ by Marc-William Palen was published in 2024 by Princeton University Press (ISBN: 978-0-69-119932-0). 309pp
Gordon Bannerman is a professor teaching Business History at Wilfrid Laurier University and the University of Guelph-Humber, Ontario. His primary research interests focus on modern British political and economic history.
This paper is part of a series of essays that seek to explore the current and prospective impact of AI on business. A PDF version can be accessed here.
The previous paper in this series looked at the impact of AI on work through the lens of Peter Drucker’s concept of the ‘Knowledge Worker’. In this paper we turn our attention to existing and emergent evidence on the impact of AI upon worker productivity. We contend that it is misguided to myopically focus on the perpetrated gains in productivity. Equal importance ought to be given to furthering our understanding of the impact of AI upon concepts of meaningful work, self-esteem and job satisfaction. The Judaeo-Christian framework discussed in the first paper in this series offers a moral basis that upholds the importance of human dignity and the intrinsic value of humanity as the sole bearers of the imago Dei (image of God).
The structure here is comprised of three parts. The first will look at both existing evidence and predictions for the impact of AI on productivity, highlighting the often-overlooked time delay between the arrival of new AI capabilities and their materialisation into beneficial productivity tools. The second section turns the attention to matters of meaningfulness, job satisfaction and employee wellbeing in relation to the use and integration of AI tools. The third and final section offers some concluding remarks on how we might begin to think about developing a morally robust symbiosis between AI and work.
‘AI productivity gains may be smaller than you’re expecting’, reads the headline of a recently published report by ING Bank.[1] In May 2023, just 10 months earlier, the Brookings Institute published a research paper titled ‘Machines of mind: The case for an AI-powered productivity boom’[2]. What is the current state of AI when it comes to productivity? Previously we have seen how knowledge worker productivity, though important, presents us with challenges of measurability and accurate prediction. It is important to note here that when talking about AI we are referring primarily to generative AI rather than infrastructure AI which began spreading in the early 2010s and operates largely behind the scenes.
At almost two years from initial public release of ChatGPT we have an emerging story of two tales: there is a dichotomy of evidence between the Macro and Micro levels when it comes to AI-driven productivity gains. Let’s briefly detail some of the existing evidence for each in part.
As of the first half of 2024 there is very little, if any, evidence of AI-driven productivity gains at the Macro level. This perhaps shouldn’t come as much of a surprise since some economists, including Charlotte de Montpellier and Inga Fechner, argue that the biggest impact on productivity growth will be seen in 10-15 years’ time. This assumes that AI does indeed lead to the much-needed complementary innovations that are expected to be dispersed across an array of different fields.[3]
The concept of ‘complementary innovations’ (i.e. innovations that follow and are enabled by the arrival of new technology) is an important one when it comes to gauging the potential impact of AI-driven productivity. General Purpose Technologies like electricity, the internet, personal computers and so on face what is known in productivity theory as a ‘J curve’ (note: ‘GPTs’ – not to be confused with ChatGPT which stands for Generative Pretrained Transformer).[4] This holds that the arrival of new GPTs counterintuitively leads to an initial decrease in short-term productivity measurements followed by a gradual increase in the medium-to-long term productivity – closely resembling a ‘J curve’. The J curve is largely due to difficulties in accurately measuring the initial GPT adoption investment in intangible capital, as economists like Erik Brynjolfsson et al.[5] point out:
As firms adopt a new GPT, total factor productivity growth will initially be underestimated because capital and labour are used to accumulate unmeasured intangible capital stocks. Later, measured productivity growth overestimates true productivity growth because the capital service flows from those hidden intangible stocks generates measurable output. The error in measured total factor productivity growth therefore follows a J-curve shape, initially dipping while the investment rate in unmeasured capital is larger than the investment rate in other types of capital, then rising as growing intangible stocks begin to contribute to measured production.[6]
So it would be reasonable to assume a degree of delay between the period of initial investment, development and adoption of AI tools, and their derived productivity increases at the Macro level. Some long-term predictions remain optimistic: Goldman Sachs estimates a 7% (or almost $7 trillion) increase in global GDP and a lift in productivity growth by 1.5 percentage points over a 10-year period – though it should be noted that this estimate is dependent upon AI’s future capabilities and adoption rates.[7] Other predictions are more conservative: Daron Acemoglu, Professor of Economics at MIT, estimates that AI-driven GDP growth is unlikely to exceed circa 0.93% − 1.16% over the next 10 years, with a total factor productivity (TFP) of no more than 0.66% over the same period.[8]
Thankfully, at the Micro level the picture is less murky. About half a dozen studies provide us with reliable data, three of which will be discussed here. The first is authored by E. Brynjolfsson, D. Li and L. Raymond and looked at the effects of using a generative AI conversational assistant (or AI chatbot) by 5,179 customer support agents.[9] This likely represents the largest generative AI-workplace study of 2023 and its findings point to some positive outcomes for AI integration within this particular business scenario.
The productivity of each customer support agent was measured in resolutions per hour (RPH). Those that worked with the assistance of the AI chatbot completed on average 14% more RPH than those who didn’t.[10] The research also found that ‘AI assistance improves customer sentiment, increases employee retention, and may lead to worker learning’.[11]
What is even more interesting is the dispersion amongst high-skilled and low-skilled workers. Figure 1 illustrates the change produced in RPH (y-axis) following AI deployment to the lowest skilled workers (x-axis, Q1), through to the highest skilled workers (x-axis, Q5). The results point to a significant productivity gain of 35% for the lowest skilled workers (Q1), but negligible change in productivity for the highest skilled workers (Q5).[12]
The study found ‘… suggestive evidence that the AI model disseminates the best practices of more able workers and helps newer workers move down the experience curve’.[13] In other words, the AI chatbot proved to be an effective tool at learning from the best resolutions for certain problems and distributing this knowledge at greater pace and with higher accuracy to the most novice and low-skilled employees. It is important to note that the AI chatbot in this particular study was designed to augment and assist each particular issue and resolution. The final decision of whether to adopt or reject the AI’s suggestions remained entirely at the discretion of the customer support agent.[14]
A second notable study by S. Peng et al. looked at GitHub’s ‘Copilot’, an AI assistant utilised in computer programming.[15] A group 95 programmers recruited via Upwork, a freelance jobs platform, were tasked with implementing an HTTP server in JavaScript as quickly as possible (though the technical details are not essential for the lay reader). Of the 95 programmers, 45 were in the treated group and 50 in the control group. Performance was measured by (A) task success and (B) task completion time. The results revealed no difference of statistical significance in (A) task success – in other words, both groups completed the challenge with a high rate of success. However, the results did show a 55.8% decrease in (B) completion time for the treated group compared to the control group. This translates to 71.17 minutes versus 160.89 minutes – a net reduction in completion time of 89.72 minutes for the treated group of programmers that utilised GitHub Copilot.[16] It is important to note however, that the study did not evaluate the quality of the code produced by the two groups, and discrepancies here may be significant for the real-world impact of relying on AI tools in programming.[17] So programmers that utilised GitHub’s Copilot finished the challenge an average of 1h 30min quicker than those who did not.
The third study worth mentioning is entitled ‘Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence’, authored by S. Noy and W. Zhang, both from MIT.[18] As the title suggests, the research took an empirical look at the effects of using ChatGPT for a variety of mid-level business-related writing tasks.[19] The study recruited 444 professionals with a higher degree of experience from fields such as data science, human resources, consultancy and marketing. They were all tasked with completing 20–30-minute assignments such as writing a more important email, a short report, press releases, an analysis of various bits of data and so on – all encounters designed to resemble a real-world work environment.[20]
Between task 1 and task 2, 50% of the participants (i.e. the treatment group), were given the possibility of using ChatGPT for their second task (neither group used AI for the first task). The results in productivity were measured in earnings per minute, with each piece of final documentation being independently evaluated for content quality, writing and originality, and assigned a score. The results reveal a substantial increase in productivity by reducing the average task completion time from 27 minutes to 17 minutes. What is perhaps more interesting is that the blind evaluations in quality produced reveal an improvement of 4.54 with ChatGPT versus 3.79 without (on a scale of 1-7).[21]
The evidence presented thus far broadly points to the adoption of generative AI tools having a positive impact on productivity. However, myopically focusing on productivity gains at the expense of other factors that are relevant to work such as meaning, self-esteem and job satisfaction, risks giving us a distorted and incomplete understanding of the multifaceted implications of adopting and integrating generative AI within the workplace. Indeed, a closer look at some of the relevant studies reveal a more complex picture. Let’s start with the concept of meaning and self-esteem.
In philosophy the relationship between work and meaning is well-established, with notable studies by Diddams and Whittington,[22] J.B. Ciulla,[23] C. Michaelson[24] and others. Within the social sciences we also find a convoluted landscape that encompasses meaningful work, drawing upon contributions from organizational studies, psychology, economics, political theory, and sociology.[25] [26] What exactly does it mean for something to take on the adjective ‘meaningful’? The etymology of the word ‘meaning’ expresses the importance or value of something.[27] To become ‘meaningful’ is to give significance, intentionality and a purpose that pervades the action or the subject in question.
Work is therefore not just a means of economic survival but also a fundamental source of self-identity, worth, and purpose. Work carries repercussions that move beyond the mere intellectual or physical act itself. C. Cordasco from the University of Manchester highlights two broad categories from which work derives meaning and self-esteem: intrinsic and extrinsic. Intrinsic factors involve pride in one’s unique personal or collective skills, a genuine interest and enjoyment in the work itself (be that physical or cerebral) and contributions to an organisation or indeed a wider field. Extrinsic factors include the ability to provide for oneself and one’s family, the recreational freedom that work provides, the affiliation with certain groups and social networks, and so on.[28]
The first paper within this series we considered a Judaeo-Christian approach to AI and work. We highlighted how this implicitly raises wider questions of purpose, meaning and a sense of calling that pervades the mere temporal dimension of work. The Judaeo-Christian perspective therefore seeks to re-evaluate of the gift and place of human agency and responsibility within creation. The foundational texts can be found in Genesis 1:28 and 2:15 where humanity is called to ‘Be fruitful and increase in number; fill the earth and subdue it. Rule […] over every living creature that moves on the ground. […] The Lord God took the man and put him in the Garden of Eden to work it and take care of it.’[29] The command here is here is one of teleological reflection through human capabilities of that which is divine: humanity is given freedom and authority to order, create, steward, and against the backdrop of original sin, also to destroy.
Judaeo-Christian teaching therefore places the concept of work as a key part of what it means to be made in the Imago Dei (the image of God), and to actively partake in the eschatological realisation of creation. Work is thus an integral element of Christ’s redemptive transformation of the individual and indeed the world. Meaning therefore, finds its ultimate source in the creator God, and this of course encompasses meaning within the realm of work. It is a distinctly human pursuit – no other species on earth searches for meaningful work. Indeed, no other species even reaches a point of asking the question: ‘Why am I doing what I am doing?’. As David Atkinson rightly points out in his commentary on Genesis: ‘To be in his image is to be aware of ourselves as his creatures’.[30]
This ability for profound self-reflection is a core characteristic of what it means to be image bearers of the divine. It informs and shapes the meaning of work: if humanity has been gifted with intellectual abilities such as creativity, problem-solving skills, discernment, a capacity to learn new skills and to avoid past mistakes, and has been entrusted with these abilities to care for and steward over creation, then anything that risks compromising these qualities warrants careful attention and scrutiny. The Judaeo-Christian perspective on meaningful work is in some sense dualistic: on one hand God is the ultimate source of purpose and meaning, and on the other, human capabilities play a role in fulfilling and partaking in the larger narrative of God’s redemption of creation.
If we turn back to AI, what is the likely impact going to be on meaningful work and job satisfaction? The evidence, while still in its infancy, is patchy. Emergent studies point to both positive and negative outcomes. S. Noy and W. Zhang found that augmentation with ChatGPT in the variety of common office tasks, ‘…increases job satisfaction and self-efficacy and heightens both concern and excitement about automation technologies’.[31] The study points out that the recorded increases in job satisfaction are likely due to a heightened sense of achievement when completing a more difficult or tedious task with the assistance of ChatGPT, and in a shorter amount of time than would have otherwise been possible.[32]
However, another study by P.M. Tang et al. cautions against an overdependence on AI systems as a leading factor in social disconnection and worker loneliness:
This coupling of employees and machines fundamentally alters the work-related interactions to which employees are accustomed, as employees find themselves increasingly interacting with, and relying on, AI systems instead of human coworkers. This increased coupling of employees and AI portends a shift towards more of an “asocial system” wherein people may feel socially disconnected at work.[33]
Similarly, C. Cordasco points out that while AI development poses a significant threat to traditional sources of self-esteem derived from work, halting AI is neither feasible nor the best solution. Instead, society should explore new ways of cultivating self-esteem that align with the evolving technological landscape.[34]
A report by Boston Consulting Group’s (BCG) Henderson Institute investigated how people can ‘create and destroy’ value with Generative AI and found that, ‘…it isn’t obvious when the new technology is (or is not) a good fit, and the persuasive abilities of the tool make it hard to spot a mismatch. This can have serious consequences: When it is used in the wrong way, for the wrong tasks, generative AI can cause significant value destruction’.[35] The study had access to over 750 BCG consultants as subjects and found that in areas such as creative product innovation, AI tools boosted productivity by 40%, but in areas like business problem solving, generative AI actually led to a 23% reduction in productivity. The report also highlighted an important trade-off when it comes to collective creativity. Whilst individual performance may be boosted by 40%, collective diversity of ideas may fall by 41%. This is largely because AI chatbots tend to produce the same or similar responses to the same specific prompts – resulting in positive outcomes at the individual level but repetitive and less diverse outcomes at the collective level.[36] The potential impact of AI tools on human creativity also seems to be an issue of concern: out of a group of 60 BCG consultants, 70% expect a negative impact on creativity, 26% do not anticipate a negative creative impact, and 4% are unsure.[37]
It is important to note that when attempting to draw conclusions about the impact of AI upon the world of work, we are (whether we like it or not), operating along several core variables, or axes. The first would be the level of automation (high) versus augmentation (low). The second represents the level of skill of the employee or group of employees in question. Here it is becoming increasingly apparent that there seems to be a positive reduction in productivity inequality, with at least in these nascent stage, low-skilled workers standing to benefit the most from AI tools. There is also a challenge of AI discernment, what some authors have called a ‘jagged technological frontier’, whereby the most successful employees and managers will learn to distinguish which tasks are best suited for AI assistance and which aren’t.[38] The third is perhaps less a variable than a recognition that the business world represents a plethora of highly distinct work contexts and scenarios where AI implementation may or may not play an important role.
All of these factors are essential when attempting to understand the impact that generative AI has upon work. Broadbrush conclusions about the impact of AI are at best generic, and at worst, inaccurate. Therefore, at least in these early stages, we have to operate on a case-by-case basis and seek to identify and understand areas where AI is a net contributor, and not a hindrance, to both productivity and matters surrounding meaningful work.
Central to the Judaeo-Christian framework is the importance of humanity as the sole image bearer of the divine, tasked with responsibilities of stewardship over nature. In fulfilling the stewardship command, humanity also has the duty of recognising and protecting distinct human attributes such as meaning, purpose, self-esteem and creativity. Emergent technologies therefore ought to be developed and harnessed in harmony with the qualities conferred by humanity’s uniqueness, not against them.
Andrei E. Rogobete is Associate Director at the Centre for Enterprise, Markets & Ethics. For more information about Andrei please click here.
[1] Charlotte de Montpellier, Inga Fechner, ‘AI productivity gains may be smaller than you’re expecting’, ING Bank, April 2024, https://think.ing.com/articles/macro-level-productivity-gains-ai-coming-artificial-intelligence-the-effect-smaller/.
[2] Martin Neil Baily, Erik Brynjolfsson, Anton Korinek, ‘Machines of the Mind: The Case for an AI-powered Productivity Boom’, Brookings Institute, May 2023, https://www.brookings.edu/articles/machines-of-mind-the-case-for-an-ai-powered-productivity-boom.
[3] Charlotte de Montpellier, Inga Fechner, ‘AI productivity gains may be smaller than you’re expecting’, ING Bank, April 2024, https://think.ing.com/articles/macro-level-productivity-gains-ai-coming-artificial-intelligence-the-effect-smaller/.
[4] Erik Brynjolfsson, Daniel Rock, Chad Syverson, ‘The Productivity J-Curve: How Intangibles Complement General Purpose Technologies’, American Economic Journal: Macroeconomics, Vol. 13(1): 333-72, (January 2021), DOI: 10.1257/mac.20180386.
[5] Ibid. p.1
[6] Ibid. p.3
[7] Goldman Sachs, ‘Generative AI could raise global GDP by 7% ‘, April 2023, https://www.goldmansachs.com/insights/articles/generative-ai-could-raise-global-gdp-by-7-percent.html
[8] Daron Acemoglu, ‘The Simple Macroeconomics of AI’, paper prepared for Economic Policy, Massachusetts Institute of Technology, (April 2024), p.4.
[9] Erik Brynjolfsson, Danielle Li, Lindsey R. Raymond, ‘Generative AI at Work’, National Bureau of Economic Research – Working Paper 31161, https://www.nber.org/papers/w31161.
[10] Ibid. p.10
[11] Ibid.
[12] Ibid. p.15
[13] Ibid.
[14] Ibid. p.9
[15] Sida Peng, Eirini Kalliamvakou, Peter Cihon, Mert Demirer, ‘The Impact of AI on Developer Productivity: Evidence from GitHub Copilot’, arXiv Accessibility Forum, (February 2023), arXiv:2302.06590 [cs.SE].
[16] Ibid. p.5
[17] Ibid. p.8
[18] Shakked Noy, Whitney Zhang, ‘Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence’, Science, Vol. 381(6654): 187-192, (July 2023), DOI: 10.1126/science.adh2586.
[19] Ibid. p.1
[20] Ibid. p.2
[21] Ibid. p.4
[22] Margaret Diddams, J.Lee Whittington, Daniel T. Rodgers, Joanne Ciulla, ‘Book review essay: Revisiting the meaning of meaningful work’, Academy of Management Review, Vol. 28(3):508-512, (June 2003), DOI: 10.2307/30040737.
[23] J. B. Ciulla, The working life: The Promise and Betrayal of Modern Work, (London: Times Books, 2000), pp.266.
[24] Christopher Michaelson, ‘Meaningful motivation for work motivation theory’, Academy of Management Review, Vol. 30(2): 235-238, (April 2005), https://doi.org/10.5465/amr.2005.16387881.
[25] Ruth Yeoman (ed.), Catherine Bailey (ed.), Adrian Madden (ed.), Marc Thompson (ed.), The Oxford Handbook of Meaningful Work, (Oxford: Oxford University Press, 2019), pp.544.
[26] Catherine Bailey, Marjolein Lips-Wiersma, Adrian Madden, Ruth Yeoman, Marc Thompson, Neal Chalofsky, ‘The Five Paradoxes of Meaningful Work: Introduction to the special Issue ‘Meaningful Work: Prospects for the 21st Century’’, Journal of Management Studies, Vol. 56(3): 481-499, (May 2019), https://doi.org/10.1111/joms.12422.
[27] Cambridge Dictionary, ‘Meaning, (July 2024), https://dictionary.cambridge.org/dictionary/english/meaning.
[28] Carlo Ludovico Cordasco, ‘Should We Halt AI to Protect Meaningful Work?’, ResearchGate, (December 2023), DOI: 10.13140/RG.2.2.22893.77288, p.7-18.
[29] The Holy Bible, (NIV Translation).
[30] David Atkinson, The Bible Speaks Today Series: The Message of Genesis 1—11: The Dawn of Creation, (Westmont, IL: InterVarsity Press, 1990), p. 37.
[31] Shakked Noy, Whitney Zhang, ‘Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence’, Science, Vol. 381(6654): 187-192, (July 2023), DOI: 10.1126/science.adh2586. p.1.
[32] Ibid. p.9
[33] Pok Man Tang, Joel Koopman, Ke Michael Mai, David De Cremer, Jack H. Zhang, Philipp Reynders, Chin Tung Stewart, and I-Heng Chen, ‘No Person Is an Island: Unpacking the Work and After-Work Consequences of Interacting with Artificial Intelligence’, Journal of Applied Psychology, Vol. 108(11): 1766–1789, (2023), https://doi.org/10.1037/apl0001103.
[34] Carlo Ludovico Cordasco, ‘Should We Halt AI to Protect Meaningful Work?’, ResearchGate, (December 2023), DOI: 10.13140/RG.2.2.22893.77288, p. 35-36.
[35] François Candelon, Lisa Krayer, Saran Rajendran, and David Zuluaga Martínez, ‘How People Can Create—and Destroy—Value with Generative AI’, Boston Consulting Group Henderson Institute, (September 2023), pp. 21.
[36] Ibid. p.15
[37] Ibid. p.16
[38] Fabrizio Dell’Acqua, Edward McFowland III, Ethan Mollick, Hila Lifshitz-Assaf, Katherine C. Kellogg, Saran Rajendran, Lisa Krayer, François Candelon and Karim R. Lakhani, ‘Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality’, Harvard Business School, Working Paper 24-013, (June 2024), p.2.
This paper is part of a series of essays that seek to explore the current and prospective impact of AI on business. A PDF version can be accessed here.
The advent of Artificial Intelligence (AI) upon the business world raises a myriad of challenges and opportunities for management theorists. The first of these is a matter of considered choice: Which are the most suitable theoretical lenses that one might apply in understanding the novel phenomena that AI represents? Might one start with Taylorism and the scientific management approach, or perhaps rather turn to Henri Fayol and his pioneering work on administrative management theory? Still, it may be wise to go back and consider Weber’s work on hierarchy and the resulting Bureaucracy Theory, or perhaps Elton Mayo’s advancements in Human Relations Theory and the creation of ‘humanistic organisations’.[1] Modern managerial thought (post-WW2) brought us the pioneering work of Joan Woodward and Contingency Theory which cannot be ignored. In the realm of psychology and the broader expansion of behavioural science and personnel management, we have Maslow’s influential Hierarchy of Needs and Douglas McGregor’s Theory X and Theory Y. Far from being exhaustive, this list illustrates the plethora of avenues that are available to the inquisitive researcher. This paper, however, elects what many might consider a less obvious choice – that is, an analysis of AI through Peter Drucker’s writings and more specifically, through his concept of the ‘Knowledge Worker’.
It is important to note from the onset that when referring to AI here we are referring specifically to Generative AI, which represents a branch of the wider field that is artificial intelligence, the main distinction being that generative AI has the capacity to learn and produce novel output autonomously.
In an article in the California Management Review during the winter of 1999, Drucker made a compelling statement:
The most important, and indeed the truly unique, contribution of management in the 20th century was the fifty-fold increase in the productivity of the manual worker in manufacturing. The most important contribution management needs to make in the 21st century is similarly to increase the productivity of knowledge work and knowledge workers. The most valuable assets of a 20th-century company was its production equipment. The most valuable asset of a 21st-century institution (whether business or non-business) will be its knowledge workers and their productivity.[2]
Throughout his lifetime Peter Drucker proved to be a prolific writer, having published some 41 books and countless articles, essays and lectures. The totality of his work amounts to over ten million words which as one scholar put it, is the equivalent of 12 Bibles or 11 Complete Works of Shakespeare.[3] No wonder then, that his alias as the ‘Father of Modern Management’ is a fitting title.
Drucker was born in Vienna in 1909 into a Lutheran protestant family.[4] His father was a lawyer and civil servant, and his mother studied medicine – both parents were considered intellectuals at the time. His house often served as a place of congregation for scientists, academics, and government officials, who would meet and discuss new ideas.[5] Yet his formative years were spent at Hamburg University, where he read international law and became heavily influenced by the works of Kierkegaard, Dostoevsky, Aquinas, Luther, Calvin and Weber. Here he developed a sense of Christian responsibility In tackling life’s challenges and made it his life mission to discover a society‘…in which its citizens could live in freedom and with a purpose’.[6] Interestingly, he was not swayed by Marxism because, in his view, the will of the collective came at the expense of the freedom and purpose of the individual: ‘there was no capacity for individual purpose in a collective society’, Drucker remarked.[7]
Amongst scholars and business executives he is perhaps best known for his work on decentralisation and a management approach that emphasises the value of employees and their contributions in achieving the shared goals of the organisation. His most celebrated theory is Management by Objectives (MBO Theory), initially presented in his 1954 book, The Practice of Management,[8] which was later refined in his 1974 magnum opus, Management: Tasks, Responsibilities, Practices.[9] Yet, Peter Drucker’s most distinguished and lasting contribution came in bringing about a novel way of understanding the field of management as an integrative whole. Previous writers such as Rathenau, Fayol, and Urwick drew connections between the varied functions of management, but it took Drucker to tie in all the strings and establish Management as a standalone discipline of study and practice.[10]
Drucker also fundamentally changed how employees were to be viewed by the company. He was the first to argue that they represent assets, not liabilities, and that within the modern economy, employee value and development is crucial to the well-being of the organisation.[11] Indeed, it is in the company’s best interest to invest and support career learning and the continual growth of its employees.
The Effectiveness of Knowledge Workers
Drucker first introduced the concept of a ‘knowledge worker’ in his 1967 book, The Effective Executive, where he defined it as ‘…the man who puts to work what he has between his ears rather than the brawn of his muscles or the skill of his hands’.[12]
He understood and foresaw the seismic shift that the well-developed economies of the West would experience in transitioning from a largely manual workforce to a predominantly knowledge-driven economy. The kickstart to all of this was, of course, the revolution in Information Technology (IT) from the 1950s onwards. Drucker explains that: ‘Today, however, the large knowledge organisation is the central reality. Modern society is a society of large organised institutions. In every one of them, including the armed forces, the centre of gravity has shifted to the knowledge worker’.[13]
What, then, makes the knowledge worker valuable? It is, put rather crudely, his or her ability to make a contribution to the firm. Unlike manual workers of the past, the knowledge worker benefits from a degree of heightened indispensability since the driving source of their effectiveness lies not in machinery or even in skill, but in the knowledge and judgement found between their ears. The concept of effectiveness becomes a key theme in Drucker’s writing on the knowledge worker: ‘…[those] schooled to use knowledge, theory and concept rather than physical force or manual skill work in an organisation and are effective only in so far as they can make a contribution to the organisation’.[14]
This raises questions surrounding the measurability of effectiveness of knowledge workers. It is important and interesting to note that throughout the 1950s the term ‘productivity’ was not yet in widespread use, hence Drucker’s reliance on ‘effectiveness’ as an early substitute. The traditional methods of measurement applied to manual work would no longer apply to knowledge work. The ‘yardsticks’ used for manual work such as industrial quality control or total output generation are ill-fitted to the knowledge worker. The knowledge worker also cannot be monitored ‘closely or in detail’, such an effort is futile for the organisation.[15] Instead, all efforts must be concentrated on the effectiveness of the knowledge worker. Drucker here usefully points out that unlike the manual worker, the knowledge worker produces immaterial things: knowledge, ideas, and concepts that remain unquantifiable in a physical sense. Instead, the ultimate task of the knowledge worker is to convert these abstract intangibles into tangible effectiveness for the organisation, this being in stark contrast to the manual worker who needn’t have to undergo this step of conversion – their contribution being already justified by the goods produced. Therefore, ‘Knowledge work is not defined by quantity. Neither is knowledge work defined by its costs. Knowledge work is defined by results’.[16]
If the knowledge worker ‘thinks’ in his or her contribution to the firm and this ‘thinking’ yields favourable results for the organisation, then surely the principal aim of the knowledge worker is to develop and grow their thought processes. In this sense they are all executives because they possess the capacity as well as the permission (given by their superiors or the company in general), to enact impactful decisions that are a direct result of their thinking.[17] Surely, then, the following challenge is one of discernment in differentiating the right decisions amidst the wrong ones. Within such a context, how might knowledge worker effectiveness be gained?
Drucker argues that it has nothing to do with personality traits: ‘Among effective executives I have known and worked with, there are extroverts and aloof, [others] even morbidly shy. Some are eccentrics, others painfully correct conformists. Some are worriers, some are relaxed. […] Some are men of great charm and warmth, some have no more personality than a frozen mackerel’.[18]
If personality traits have little to no bearing on effectiveness, or at least there is no evidence to prove the contrary, what does have an impact on effectiveness? Drucker argues that effectiveness ‘…is a habit, that is a complex of processes’. There is no silver bullet when it comes to seeking knowledge worker effectiveness, rather, it represents a collection of practices and habits that collectively amount to favourable results for the employee as well as the organization. The beauty of it is that practices and habits can be learned, meaning that any knowledge worker has the capacity to become effective.
However, as Drucker points out, ‘practices are simple, deceptively so; […] practices are always exceedingly hard to do well’.[19] There are five key practice areas that executives and knowledge workers need to master should they wish to become ‘effective’. The first is time – an effective knowledge worker knows what their time is mostly spent on and controls the allocated time that they have at work. The second is a focus on outward contribution – keeping one’s ‘eye on the ball’ so to speak. The effective knowledge worker always maintains an awareness of the overarching goal which helps direct the smaller practices and offers mental guideposts in achieving the desired outcomes. The third area is a sober awareness of one’s strengths and weaknesses. Effective workers build upon their strengths – be those inherent, personal strengths or the strengths conferred on them by their position within the organisation. The fourth area is the ability to distinguish what approaches are likely to yield the most impactful results and focus primarily on them. The fifth and final area is a fundamental understanding of the decision-making process and how to navigate it to make effective decisions. They are aware of operating within a system where too many, sometimes hasty, decisions, can lead to poor outcomes. Only a carefully thought-through strategy will result in favourable outcomes in the long-run.[20]
Drucker and Technology: The Impact of AI Upon the Knowledge Worker
What would the likely impact of AI be on the knowledge worker within such a context? The aforementioned five areas of practice offer multiple viewpoints for one to postulate how AI might augment (or replace), the daily activities of the knowledge worker.
When it comes to matters of automation and the arrival of new technologies, Peter Drucker warns against a position of extremes: technology is seldom a total panacea or an absolute disaster.[21] Indeed, in 1973 he pointed out that, ‘The technology impacts which the experts predict almost never occur’.[22] Drucker would have experienced the early hype surrounding digitalisation and the purported gifts of computing in the 50s and 60s. In some of his earlier writings he branded the computer a ‘mechanical moron’ – one that is very able at storing and processing precise data yet omits all that represents unquantifiable data, the problem of course being that it is often exactly this ‘unquantifiable data’ that becomes essential to the success of the organisation in the long-run.[23] It is often not the trends themselves that dictate a company’s future but rather changes in trends and the unique events which, at least in the early stages, are yet to be quantifiable. They are too nascent to become ‘facts’ and by the time they do become facts it is often too late. Drucker points out that the logical ability of computers represents both their biggest strength and their biggest weakness. One advantage that humans hold over the machine is their enhanced sense of perception and intuition. However, there is a serious risk that executives (i.e. all knowledge workers), might lose this sense of perception if they rely too heavily on quantifiable, computable data at the expense of unquantifiable, qualitative data.[24] This is a behavioural challenge that needs emphasising.
AI: Data versus Information
The resulting key theme that emerges in Drucker’s writing is the notion of data versus information. It’s relevance to analysing the potential consequences of AI lie within the wider scope of using software to effectively manage data. The crux of the problem is as follows: data, in its raw form, is inconsequential until it is interpreted and acted upon. Too many knowledge workers are ‘computer literate’ but not ‘information literate’: they know how to access data but aren’t adept at using it.[25]
For over half a century, Drucker argues, there has been an overwhelming focus on the ‘T’ in IT and the development of technology that stores, processes, transmits and receives data, but not enough effort has been placed on the ‘I’: What does this data mean to me? What does it mean to my business? What purpose does it serve? These are all fundamental questions that haven’t been given the prominence they deserve. [26] The main challenge is to ‘…convert data into usable information that is actually being used’.[27] This has ultimately resulted in decades of computer technology serving as a producer of data and not a producer of information. Drucker, quite rightly, points out that computer generated information has had practically no impact on a business deciding whether or not to build a new office, or a county council deciding to build a new hospital, a prison, a school and so on.[28] The computer has had minimal impact on high-level decisions in business.
Yet this is not just a failure of technology or even of some form of stubbornness amongst knowledge workers and executives; it is principally a failure of providing relevant information that is needed to perform and/or change the direction of any given task.[29] This effort is personalised and applies to each individual worker or executive. The focus then shifts from data gathering to data interpretation but, as mentioned, also to an astute discernment in organising and acting upon said data. The availability of data becomes second to the usability of data. Efforts move toward organising, interpreting, and acting upon reliable data.
As information is the principal resource of knowledge workers, Drucker suggests three broad organisational methodologies. We will briefly detail each in part and thereafter consider the potential implications of AI.
The first is called Key Event which looks at one or multiple important events that have a major contributing role towards the end performance of the knowledge worker. [30] This can be a single event or, as it is often the case, a series of key events that may direct certain outcomes. The event(s) in this case act as a ‘hinge’ upon which performance is dependent. Any executive or knowledge worker stands to benefit substantially in his or her career if they are able to identify, interpret and act upon such events.
The second methodological concept is based on modern Probability Theory and its resulting Total Quality Management (TQM).[31] This approach looks at a variety of possible outcomes that are expected to fit within a given range (i.e. withing the normal probability distribution), and singles out the outliers (those that do not meet the criteria). These exceptional events automatically move from being data (where no action is needed), to being information which necessitates immediate action.[32] This approach is useful when overseeing something like a large manufacturing process but can also be applied to the provision of services, for instance, a client going bankrupt, a deal falling through, a project yielding unexpectedly poor results, etc.
The third methodology for organising information is similar to the second and is based upon the Threshold Phenomenon and the field of perception psychology pioneered by the German physicist Gustav Fechner (1801-1887).[33] This holds that humans only perceive events to become a phenomenon once they cross a certain ‘threshold’ – and the threshold itself is subjective to each individual. In physical pain we only experience it once the stimuli are of such an intensity that they become categorised as ‘pain’. Similarly, it is the intensity and/or frequency of certain data points that lead to their recognition as phenomena. Drucker argues that accurately identifying the phenomena can assist knowledge workers (or managers, executives) in the early prediction of trends. The threshold concept is highly useful in identifying which sequences of events are likely to become trends and require immediate attention.
Conclusions: striving toward AI as a generator of useful information
How might AI assist within this context? The methodologies of organising data are effectively attempts to filter out and sieve the critical information from what otherwise is a plethora of largely useless noise. AI has a major role to play not merely in data monitoring and gathering but increasingly in extraction and accurate interpretation. Here lies the biggest challenge: which AI Large Language Model (LLM) will emerge as the most capable and useful to the knowledge executive?
The reality is that there are likely to be several dominant LLMs with differing traits and characteristics. It is becoming increasingly clear that a multimodal system will benefit from the advantage of being able to receive and work across differing types of data, including text, images, sound, and video. However, multimodality alone won’t suffice if the AI performs poorly at data interpretation and reasoning (e.g. hallucinations, general black box optimisation issues, and so on). We are currently in the nascent stages of a more in-depth, multi-layered reasoning approach with companies such as Meta and OpenAI investing heavily in the ability of AI chatbots to reason memorise, and comprehend more complex challenges. OpenAI’s chief operating officer Brad Lightcap said that in the near future, ‘We’re going to start to see AI that can take on more complex tasks in a more sophisticated way. […] I think we’re just starting to scratch the surface on the ability that these models have to reason. [Today’s AI systems] are really good at one-off small tasks, [but they are still] pretty narrow in their capabilities’.[34]
Again, for AI to have a significant impact on the decisions of knowledge workers they need to possess the capacity to provide a consistent supply of relevant, actionable information. We can already see the underpinnings of a technological infrastructure that may facilitate this: continued growth in the Internet of Things (IoT), the proliferation of AI hardware and artificial neural engines in a rising number of products and services, the consolidation of reliable datasets used to train LLMs, the fine-tuning of AI chatbots with specific characteristics and so on. This also creates a pool of moral and ethical challenges for executives: issues around data privacy, misinformation, bias, fraud, manipulation (e.g. impersonating people to promote products or services via ‘Deepfakes’), the recurring problem of AI hallucinations and so on. All of these issues require careful consideration. However, at this stage the importance of AI’s primary function as a provider of useful information cannot be understated. It may well represent the pivotal element in determining the success or failure of generative AI within business and beyond.
Andrei E. Rogobete is Associate Director at the Centre for Enterprise, Markets & Ethics. For more information about Andrei please click here.
On Thursday, 23rd May 2024 the Centre for Enterprise, Markets & Ethics (CEME) hosted an online event on the topic of Artificial Intelligence: Challenges and Opportunities.
The event was chaired by Revd Dr Richard Turnbull and our speakers were:
This paper is part of a series of essays that seek to explore the current and prospective impact of AI on business. A PDF copy of this paper can be accessed here.
The advent of Generative AI is challenging and redefining the world of work. While exacting data on its impact remain at a nascent stage, a growing number of private firms and research organisations have been quick to impart their early predictions. McKinsey & Co. estimates that Generative AI could add as much as $4.4 trillion to the global economy annually, leading to profound changes in the anatomy of work, with an increase in both augmentation and automation capabilities of individual workers across all industries.[1] Goldman Sachs believes that Generative AI could raise global GDP by as much as 7% with two-thirds of current occupations being affected by automation.[2] At the macro level AI is poised to reshape the strengths of nation-state economies. Research conducted by Oxford University and CITI Bank found that ‘The comparative advantage of rich nations will increasingly lie in the early stages of product life cycles — exploration and innovation rather than execution or production — and this will make up a bigger portion of total employment. […] Without innovation, progress and productivity will stall’.[3]
In September 2023 Microsoft launched ‘Copilot 365’, an AI-driven digital assistant that integrates Office applications such as Word, Excel and PowerPoint to enable the user to harness the capabilities of AI within their workflow. Copilot and other AI agents such as Google’s ‘Gemini’ aim to combine the use of Large Language Models (LLMs) and user generated data to greatly enhance productivity. Microsoft Chairman and CEO Satya Nadella said that ‘[Copilot] marks the next major step in the evolution of how we interact with computing, which will fundamentally change the way we work and unlock a new wave of productivity growth. […] With our new copilot for work, we’re giving people more agency and making technology more accessible through the most universal interface — natural language’.[4]
These potentially seismic changes urge us to reconsider the fundamental nature of work. They force us to step back and ask how ought humanity shape its future relationship with work. This implicitly raises wider questions of purpose, meaning and a sense of calling that pervades the mere temporal dimension of work. From a Judaeo-Christian perspective it seeks a re-evaluation of the gift and place of human agency and responsibility within creation.
The argument of this paper is therefore twofold. First, we point out that that all technological advancements, including Generative AI, should be harnessed for the benefit and enhancement of humanity. This applies in particular to work but should not be excluded from other spheres of human endeavour such as leisure or recreation. Second, we point out that, while most technological advancements are valuable, a careful and persistent degree of discernment needs to be applied in minimising the novel risks brought on by Generative AI. A central concern here is the capacity for misuse of AI (with the various facets that may entail), as well as the long-term risk that it presents of a destructive and dehumanising effect on its users.
Defining the Terms
It is worth starting with a brief conceptual analysis of some of the key terms. What do we mean by ‘work’? How are we to delineate a ‘humanising’ versus ‘dehumanising’ effect on work? Indeed, are we mistaken in assuming any intrinsic value of work in the first place? These are all pertinent questions that require much thought and attention.
In his monograph on Recovering a Theology of Work, Revd Dr Richard Turnbull rightly points out that work ’…is not a static concept’.[5] Work evolves in tandem with the ability of humans to learn, pursue and engage with it, which implies an ongoing relational change in both skill and knowledge. This creative ability is, for the Christian theologian, a reflection of the Imago Dei that is fundamental to all of humanity. Darrell Cosden, who wrote extensively on the theology of work acknowledges that ’work is a notoriously difficult concept to define’.[6] Cosden views human work as ’a transformative activity essentially consisting of dynamically interrelated instrumental, relational, and ontological dimensions’.[7] Work is therefore a multifaceted concept.
If we step back for a moment and consider a more utilitarian interpretation we find some rather crude definitions of work. The Cambridge dictionary sees it as ’an activity, such as a job, that a person uses physical or mental effort to do, usually for money’.[8] In pure physics work is ’the transfer of energy by a force acting on an object as it is displaced’.[9] This apparent dichotomy leads us to (at least), two broad and distinct dimensions of work: 1. The physical or mental activity that usually results in quantifiable economic activity; 2. Work in relation to meaning (or semantics), the presence of a personal calling and a higher purpose that serves as an ultimate goal.
Attempts to categorise the term ‘humanising’ are also likely to encounter an additional array of definitional challenges. Some dictionaries see it as ‘representing (something) as human: to attribute human qualities to (something)’,[10] others define it as ‘the process of making something less unpleasant and more suitable for people’.[11] The common denominator in attempting to describe ‘humanising’ is the intention to give something qualities that make it suitable for humans to use and understand – an effort which in and of itself no doubt suffers from a degree of subjectivity.
The last major term that we will attempt to define is ‘Generative Artificial Intelligence (AI)’. I have written elsewhere about the concept of intelligence and how it fits within AI, so a detailed discussion on the matter will not be included here. However, what is worth mentioning is that by ‘Generative AI’ we are referring to complex yet narrow AI systems that currently exist or at most are likely to emerge within the short to medium term (3-5 years). By ‘generative’ we are referring to AI systems that not only learn from new data but generate interpretable results based on said data – this includes LLMs such as ChatGPT3/4, LaMDA, Google Gemini and so on.
The Impact of Generative AI
There are competing narratives as to which technological changes of the modern era bear the greatest impact on work and productivity. The British Agricultural revolution of the 17th and 18th centuries saw a dramatic increase in crop yields and agricultural output which resulted in the population of England and Wales almost doubling from 5.5 million in 1700 to over 9 million by the end of the century.[12] The arrival of the steam engine in the second half of the 18th century and the subsequent mechanisation of labour sparked the first and second Industrial Revolutions. The change to the nature and purpose of work during this time was fundamental. Europe moved from a largely agrarian-based society to one that was driven by mass production, standardisation and the development of new skills and abilities in manufacturing and scientific discovery.
One remarkable chart worth revisiting is illustrated in the adjacent figure.[13] For over 1,800 years GDP per capita remained largely flat – and only changed in the late 19th century when both GDP per capita and global population experienced a sudden and unprecedented jump in both trajectory and scale. The change was overwhelmingly attributed to the transition of a workforce that had previously been accustomed to hand manufacturing and production to becoming almost entirely machine-driven. This in turn, allowed for more effective and precise tools, a greater understanding of chemicals and alloys, and widespread availability of these to workers that previously relied solely on manual labour. Some economic historians such as Paul Bouscasse et al. (2021) estimate that the Industrial Revolution quadrupled average productivity by each decade, from around 4% up until the 1810s to over 18% from there onwards.[14]
Large-scale industrialisation and the rise of the mechanised factory system created fertile ground for what would later become the digital revolution (i.e. the Third Industrial Revolution). The middle of the 20th century saw the arrival of the first transistor which not only paved the way for modern computing, it more fundamentally enabled the digitalisation of information. This marked a major change in the way in which information is stored and shared, and perhaps unsurprisingly, at least in retrospect, also brought profound changes for the world of work. The first through third Industrial Revolutions represent magnificent events of human advancement that altered the course of history in ways that make the absence of their fruits in contemporary life hard to imagine. Therefore, how would Generative AI fit within such a paradigm?
The scholastic body of research in this area is embryonic. The ‘Fourth Industrial Revolution’ or ‘Industry 4.0’ coined back in 2013 by former German Chancellor Angela Merkel foresaw a future where the collective power of technologies such as AI, 3D Printing, Virtual Reality (VR), the Internet of Things (IoT), and others could be integrated and used within a (predominantly) unified system.[15] Over a decade later this holistic vision has yet to fully materialise. What we are currently seeing are many of these technologies being largely used in silos rather than fully integrated systems (with a few exceptions such as smart homes). In 2020 a KPMG report found that less than half of business leaders understood what the ’fourth industrial revolution’ meant, with online searches of the term having peaked in 2019 and trending downward ever since.[16]
On one level the prophecies of the Fourth Industrial Revolution have yet to be fulfilled. Current research into the impact of AI is therefore reliant upon scarce present data and future predictions that are, more often than not, overhyped and peppered with unlikely outcomes. One more robust piece of research has been an intercollegiate effort between academics at the universities of Leeds, Cambridge and Sussex, which found that 36% of UK employers have invested in AI-enabled technologies but only 10% of employers who hadn’t already invested in AI were planning to do so in the next two years.[17] Commenting on the research, Professor Mark Stuart, Pro Dean for Research and Innovation at Leeds University Business School said that,
’A mix of hope, speculation, and hype is fuelling a runaway narrative that the adoption of new AI-enabled digital technologies will rapidly transform the UK’s labour market, boosting productivity and growth. However, our findings suggest there is a need to focus on a different policy challenge. The workplace AI revolution is not happening quite yet. Policymakers will need to address both low employer investment in digital technologies and low investment in digital skills, if the UK economy is to realise the potential benefits of digital transformation.’[18]
These apparent roadblocks will require a concerted effort on behalf of employers and employees to actively seek and develop new skills that will give organisations the capabilities required to meaningfully integrate AI systems into their workflows. As has been the case with the industrial revolutions of the past, new technologies invariably necessitate new knowledge and training. AI Prompt Engineering is an interesting example of this. Although Large Language Models (LLMs) are built to operate via NLP (Natural Language Processing), they still require specialised training when dealing with more complex challenges or troubleshooting errors. A ‘Prompt Engineer’ in this sense is a trained professional that creates ‘prompts’ (usually in the form of text), to test and evaluate LLMs such as ChatGPT.[19] Thus, a well-trained prompt engineer can extract and gain far more from LLMs than the average user.
More importantly, the skills and capabilities gap between AI systems and the end-user need to be bridged in a manner that allows for the concurrent growth of the technology as well as the flourishing of the workforce. This is all the more pertinent when we are talking about a workforce that is predicted to become increasingly reliant on AI. What generative AI has achieved thus far is to fuel a creative springboard that enabled a wider audience to imagine the possibilities (and risks) of AI tools: ranging from relatively banal features such as improved email spam filtering to uncovering disease-fighting antibodies. A report by the International Data Corporation (IDC) estimated that the use of conversational AI tools is expected to grow worldwide by an average of 37% from 2019 to 2026.[20] With the accelerated growth of Microsoft’s ChatGPT, Google’s Bard as well as other tech giants joining the conversational AI race, it is reasonable to expect that this figure may end up being higher.
Yet we do not know exactly what impact this will have upon work. There have been some early studies and working papers that suggest that AI tools are having a positive effect on employee productivity. The National Bureau of Economic Research (NBER) recently published a paper by Erik Brynjolfsson, Danielle Li & Lindsey R. Raymond which looked at a case study of 5,179 customer support agents using AI tools. The report found that,
‘Access to the tool increases productivity, as measured by issues resolved per hour, by 14% on average, including a 34% improvement for novice and low-skilled workers but with minimal impact on experienced and highly skilled workers. We provide suggestive evidence that the AI model disseminates the best practices of more able workers and helps newer workers move down the experience curve. In addition, we find that AI assistance improves customer sentiment, increases employee retention, and may lead to worker learning. Our results suggest that access to generative AI can increase productivity, with large heterogeneity in effects across workers.’[21]
It appears therefore that while there is an overall increase in productivity, a key factor in its dispersion is dependent upon the varying degrees of employee experience and skill level, with those at the lower end of the spectrum likely to benefit more that those at the top. Another study led by Shakked Noy and Whitney Zhang from MIT looked at an empirical analysis of business professionals who wrote a variety of business documents with the assistance of ChatGPT. The study found that of the 444 participants, those that used ChatGPT were able to produce a deliverable document within 17 minutes compared to 27 minutes for those who worked without the assistance of ChatGPT.[22] This translates to a productivity improvement of 59%. What is perhaps more remarkable is that the output quality also increased: blind independent graders examined the documents and those written with the help of ChatGPT achieved an average score of 4.5 versus 3.8 for those without.[23] A third preliminary study looked at the impact of ‘GitHub Copilot’, an AI tool used to assist in computer programming. The paper found that programmers who used GitHub Copilot were able to complete a job in 1.2 hours, compared to 2.7 hours for those who worked alone. In other words, task throughput increased by 126% for developers who used the AI tool.[24]
Pursuing a Theology of Work
This provokes some wider questions surrounding morality, AI and work. One pertinent question here is not just a matter of can we use AI but rather how ought we to use AI? Indeed, how are we to best integrate AI in manner that reaps the rewards and minimises the risks? If we consider the Judaeo-Christian perspective, the obligatory prerequisite to answering these questions is a scriptural understanding of the act and role of work.
In the Old Testament we find several fundamental passages in relation to work. The first and perhaps most widely cited is Genesis 1:28 and 2:15 where humanity is called to ‘Be fruitful and increase in number; fill the earth and subdue it. Rule over the fish in the sea and the birds in the sky and over every living creature that moves on the ground. […] The Lord God took the man and put him in the Garden of Eden to work it and take care of it.’[25] The command here is not just one of stewardship over creation, but a calling to reflect through human capacities that which is teleologically divine: the ability to order, create, tend to, and indeed destroy (within the premise of the fall).
God himself is portrayed as a worker: ’In the beginning God created the heavens and the earth’ (Gen. 1:1), and then in Genesis 1:27 we find that God ’created man in his own image’.[26] In this sense human work is fundamentally ’…derived from the principle of God’s work in creation’.[27] While humanity is called to mimic God’s creative pursuit, it also has the responsibility to protect and care for the gift that is creation and everything found within it. Genesis 2:15 portrays the garden as an adequate place where man can fulfil his duty and calling of work. David Atkinson in his commentary usefully points out that ‘…work is not simply to be identified with paid employment. Important as paid work is in our society, both in providing necessary conditions for adequate living standards, and in giving a person a sense of worth in his or her creativity, it is the creative engagement with the world on behalf of God that is the really significant thing’.[28] This rather Barthian perspective gives significance to work in as much as it represents a conscious partaking in the establishment of God’s kingdom through Christ. The objective is, according to Barth, ‘…the centre of God’s activity. [..] [so] the centre of our human actions as Christians must be to reflect this focus on the kingdom of God’.[29] Work therefore encapsulates the temporal and the metaphysical. Human action is not merely a bystander to the cosmic order of events but an active partaker in shaping the journey. The Genesis account of creation therefore does not delineate between secular and pious work – all work in the garden carries some degree of spiritual value. It is important to note that the distinction between the sacred and the secular in the first place can only be made in light of the fall.
This raises another key dimension in developing a theology of work, that is, the notion of calling and vocation. For Martin Luther there are two kingdoms: the temporal and the eternal. Human endeavour operates entirely within the temporal but the tension between good and evil (or sin) cuts through and is present in both, making the struggle omnipresent. The act of human calling and vocation in the temporal therefore becomes as important and relevant as it is in the eternal. There is a continuous interplay between the two, as Richard Turnbull notes: “there is no dualism here in Luther. Vocation and calling, ethics and behaviour are the ways God is served in the temporal kingdom”.[30]
If we turn to the New Testament we find a series of examples where so-called ‘secular’ work is used to advance the heavenly kingdom. In Acts Chapter 16 we are introduced to Lydia of Thyratira, a businesswoman in what was considered those days to be expensive clothing or ‘purple cloth’ (verse 14). We are told that Lydia persuaded the apostles and used her earned resources to care and provide for Paul and Silas: ‘If you consider me a believer in the Lord,’ she said, ‘come and stay at my house’ (verse 15). Paul himself, though highly educated in the Hebrew law, maintained his work as a tentmaker (Acts 18:3) and used it to not only financially support his ministry but also to minister to others through it:
‘I coveted no one’s silver or gold or apparel. You yourselves know that these hands ministered to my necessities, and to those who were with me. In all things I have shown you that by so toiling one must help the weak, remembering the words of the Lord Jesus, how he said, “It is more blessed to give than to receive.”’ (Acts 20:33-35, RSV)
As a more anecdotal observation, it is interesting to see how Paul, though a scholar, never found himself too proud to undertake manual labour. That was likely driven by his profound understanding of what true Christological self-sacrificial love and service entails – his life as presented in the scriptures embodies it fully.
Peter, Andrew, James and John were the first disciples called by Jesus in Matthew 4:18–22. By most historical accounts they were ordinary fishermen operating within a highly competitive fishing environment that were the shores of Galilee in the 1st Century A.D. It is reasonable to assume that they possessed some degree of business acumen in budgeting, preparing orders, managing stocks and so on. Indeed, Jesus himself worked as a carpenter in his family business (Mark 6:3) and one can imagine that Joseph (and likely Jesus himself) had to utilise their skills and knowledge in budgeting, drawing projects, analysing space, preparing materials and fulfilling orders to clients – there is no suggestion in scripture that this was a pro bono affair.
Neither Jesus, nor any of the disciples shied away from what would today be labelled as ‘secular work’. Quite the contrary, they embodied work as: 1. An integral part of their calling before God in the temporal; and 2. A fulfilment of their God-given gifts and abilities in utilising and developing the skills needed to carry out the work. Indeed, Christ vividly illustrated the implications of this aspect in the Parable of the Talents found in Matthew 25:14–30 and Luke 19:11–27.
Conclusions: Towards a collaborative theology of work and AI?
In the introduction we mentioned the necessity and overarching aim that all technological advancements, including Generative AI, should be harnessed for the benefit and enhancement of humanity. This applies in particular to work but also to other spheres of human activity such as family time or recreation. It is also important to note that great care and discernment needs to be applied in minimising the novel risks posed by Generative AI, such as an unhealthy reliance on the technology, disinformation, fraud, and so on. Discernment in this case refers to uncovering the right way of action amidst uncertainty.
We have also seen how Judaeo-Christian teaching places the concept of Work as a key part of what it means to be made in the image of God and to actively partake in the eschatological realisation of creation. If work therefore represents an integral element of Christ’s redemptive transformation of the individual (and indeed the world), how does AI fit within this paradigm?
One possibility is arguing in favour of AI as a tool or digital aid to humanity. Within a Judaeo-Christian framework the role of AI ought to be one that contributes to humanity’s holistic development, be that spiritual, economic or scientific. Central to this overarching view of humanity is the promotion and protection of human dignity – a core principle of Catholic Social Thought (alongside the common good, solidarity and subsidiarity). If we are to see AI as a tool for human advancement and productivity, then it becomes part of an economic system that ought to be conducive to upholding human dignity. As Mons. Martin Schlag rightly points out, ‘Economic growth, material prosperity and wealth are without doubt necessary conditions for a life in dignity and freedom but they are not sufficient’.[31] In this sense, AI should bring economic benefits whist not representing a hindrance to spiritual growth (for instance, the creation of ‘false idols’ or idolatry found in Exodus 20:3, Matthew 4:10, Luke 4:8), or indeed the promotion of scripturally antagonistic values such as greed, deceit, egotism or malice of any kind.
On a more practical level, the concrete steps of integrating such guideposts in AI development will have to come, at least to some extent, from the programme creators themselves. However, it is also equally important to emphasise a degree of personal responsibility that will invariably become necessary when dealing with powerful open-ended AI systems.
AI is then best understood as a gift of human creativity, yet one that can sometimes lead to unpredictable outcomes (such as black box scenarios within LLMs). Digital AI assistants therefore need to be utilised in a manner that is conducive to a harmonious synergy between work and AI tools. The aim here is to augment and transform work rather than replace it. Digital AI assistants ought to be just that: assistants built upon a foundation of ethical values that contribute to human dignity and flourishing. Bill Gates wrote in a recent article that, ‘…advances in AI will enable the creation of a personal agent. Think of it as a digital personal assistant: It will see your latest emails, know about the meetings you attend, read what you read, and read the things you don’t want to bother with. This will both improve your work on the tasks you want to do and free you from the ones you don’t want to do.’[32] In March 2023 Pope Francis said, ‘I am convinced that the development of artificial intelligence and machine learning has the potential to contribute in a positive way to the future of humanity. […] I am certain that this potential will be realized only if there is a constant and consistent commitment on the part of those developing these technologies to act ethically and responsibly.’[33]
The future of AI and work is important not just because of its bearing on the individual but also because of its capacity to influence societal transformations. The advent of the personal computer (PC) for instance sparked profound changes in the world of work in the 1980s-1990s. A human-centric vision of AI will require a concerted effort on the part of all parties (developers and users) to ensure that the implementation represents an enrichment to human life – and as we have seen, considerations of the meaning, value and purpose of work are of fundamental importance. Such an approach would strengthen humanity’s position to reap the rewards and mitigate the risks in a myriad of areas – from creative agency and productivity to medical and scientific discovery.
Andrei E. Rogobete is Associate Director at the Centre for Enterprise, Markets & Ethics. For more information about Andrei please click here.
[1] ‘The economic potential of generative AI: The next productivity frontier’, McKinsey & Co., https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier
[2]‘Generative AI could raise global GDP by 7%’, Goldman Sachs, https://www.goldmansachs.com/intelligence/pages/generative-ai-could-raise-global-gdp-by-7-percent.html
[3] ‘TECHNOLOGY AT WORK v6.0 The Coming of the Post-Production Society’, Oxford University Martin School, June 2021, https://www.oxfordmartin.ox.ac.uk/downloads/academic/Technology-at-Work-6.pdf
[4] ‘Introducing Microsoft 365 Copilot – your copilot for work’, Official Microsoft Blog, 16th March 2023, https://blogs.microsoft.com/blog/2023/03/16/introducing-microsoft-365-copilot-your-copilot-for-work/
[5] Turnbull, Richard, Work as Enterprise: Recovering a Theology of Work, Oxford: The Centre for Enterprise, Markets & Ethics, 2018, p. 7
[6] Ibid.
[7] Cosden, Darrell, A Theology of Work: Work in the New Creation, Milton Keynes: Paternoster theological monographs, 2006, https://www.bu.edu/cpt/2013/10/03/theology-of-work-by-darrell-cosden/
[8] ‘Work’, Cambridge Dictionary, https://dictionary.cambridge.org/dictionary/english/work
[9] ‘Work – The Scientific Definition’, University of Iowa Pressbooks, https://pressbooks.uiowa.edu/clonedbook/chapter/work-the-scientific-definition/
[10] ‘Humanise’, Merriam-Webster Dictionary, https://www.merriam-webster.com/dictionary/humanize
[11] ‘Humanisation’, Cambridge Dictionary, https://dictionary.cambridge.org/dictionary/english/humanization
[12] Richards, Denis; Hunt, J.W., An Illustrated History of Modern Britain: 1783–1980 (3rd ed.), Hong Kong: Longman Group, 1983, p. 7.
[13] Slaus, IvoI & Jacobs, Garry. ‘Human Capital and Sustainability’, Sustainability. (2011). Vol.3(1): 97-154.
[14] Bouscasse, Paul, Emi Nakamura, & Jón Steinsson, ‘When Did Growth Begin? New Estimates of Productivity Growth in England from 1250 to 1870’, NBER Working Paper Series, March 2021, https://www.nber.org/system/files/working_papers/w28623/revisions/w28623.rev0.pdf
[15] ‘Industrie 4.0’, National Academy of Science and Engineering, https://en.acatech.de/project/industrie-4-0/
[16] Markoff, Richard; Seifert, Ralf; ‘Why the promised fourth industrial revolution hasn’t happened yet’, The Conversation, 27th February 2023, https://theconversation.com/why-the-promised-fourth-industrial-revolution-hasnt-happened-yet-199026
[17] University of Leeds, ‘Workplace AI revolution isn’t happening yet,’ survey shows’, 4th July 2023 https://www.leeds.ac.uk/news-business-economy/news/article/5341/workplace-ai-revolution-isn-t-happening-yet-survey-shows
[18] Ibid.
[19] Yasar, Kinza, ‘AI prompt engineer’, TechTarget, https://www.techtarget.com/searchenterpriseai/definition/AI-prompt-engineer
[20] Sutherland, Hayley; Schubmehl, David; ‘Worldwide Conversational AI Tools and Technologies Forecast, 2022-2026’, International Data Corporation (IDC), July 2022.
[21] Brynjolfsson, Erik; Li, Danielle; Raymond, Lindsey; ‘Generative AI at Work’, NBER Working Paper Series, November 2023, https://www.nber.org/system/files/working_papers/w31161/w31161.pdf
[22] Nielsen, Jakob; ‘ChatGPT Lifts Business Professionals’ Productivity and Improves Work Quality’, Nielsen Norman Group, 2nd April 2023, https://www.nngroup.com/articles/chatgpt-productivity/
[23] Ibid.
[24] Nielsen, Jakob; ‘AI Tools Make Programmers More Productive’, Nielsen Norman Group, 16th July 2023, https://www.nngroup.com/articles/ai-programmers-productive/
[25] The Holy Bible, (NIV Translation)
[26] Genesis 1:27, The Holy Bible, (NIV Translation)
[27] Turnbull, Richard, Work as Enterprise: Recovering a Theology of Work, Oxford: The Centre for Enterprise, Markets & Ethics, 2018, p. 16
[28] Atkinson, David; The Message of Genesis, Cambridge: IVP, 1990, p. 61
[29] Ibid., p. 60
[30] Turnbull, Richard, Work as Enterprise: Recovering a Theology of Work, Oxford: The Centre for Enterprise, Markets & Ethics, 2018, p. 26
[31] Shlag, Martin; Business in Catholic Social Thought, Oxford: The Centre for Enterprise, Markets & Ethics, 2016, p. 22
[32] Gates, Bill; ‘The Age of AI has begun’, Gates Notes – The Blog of Bill Gates, 21st March 2023, https://www.gatesnotes.com/The-Age-of-AI-Has-Begun
[33] Lubov, Deborah Castellano; ‘Pope Francis urges ethical use of artificial intelligence’, Vatican News, 27th March 2023, https://www.vaticannews.va/en/pope/news/2023-03/pope-francis-minerva-dialogues-technology-artificial-intelligenc.html
Geoffrey Jones is Isidor Strauss Professor of Business History at Harvard Business School and a fellow of the Academy of International Business. He is the author of several books in the broad field of business ethics from a historical perspective.
The author offers us a fascinating and informative historical review of what he calls “deeply responsible business”, a term which provides the framework for the book but which is, perhaps, slightly overworked.
Jones uses the term “deep responsibility” to characterize the set of values of those “who have seen business as a way of improving society, and even solving the world’s problems” (page 4). He distinguishes his approach from both those who seek to rewrite the rules of the game, as he puts it, and also from the now somewhat discredited approaches of corporate social responsibility (although I could offer some defence of philanthropy in this regard). His central thesis “is that deeply responsible business leaders are motivated by a set of values that shape their practice” (page 5). Some might find that defining characteristic rather weak, but I welcome it, because it enables a proper discussion of values-based business approaches in a realistic way, dealing with character, integrity, wisdom and spirituality, without embracing neo-Marxist opposition to the market economy per se. Indeed, Jones specifically contests any idea that a manager in a for-profit business could never be virtuous.
The book brings several important and significant insights. Its most noteworthy contribution is placing the quest for responsible business into a longer historical view. Jones comprehensively demonstrates that it is not simply a recent phenomenon, but one with a long history that has exercised business leaders since industrialisation. He also helpfully places “deeply responsible business” into a global context, reminding us of the pitfalls of a simply western focus.
The book consists of ten chapters divided into three parts. The first four chapters are encompassed together under the heading “A Question of Responsibility.” Here Jones looks at some significant historical figures in business leadership and history. He covers George Cadbury, Edward Filene (the Boston businessman and pioneer of credit unions), Robert Bosch and examples from India (J.N. Tata) and Japan (Shibusawa Eiichi). This is the strongest, most insightful and interesting part of the book.
The first two chapters tell gripping stories, one of which I am very familiar with, and the other of which I knew nothing about. The first chapter deals with the story of the entrepreneurial Quaker, George Cadbury, who together with his brother, Richard, pioneered a moral approach to business. As Jones argues, given “this emphasis on trust and honesty, it is not surprising that Quaker enterprises became some of the earliest examples of socially responsible business” (page 25). Jones notes the central role of spirituality (here and elsewhere in the book in various forms), the importance of housing, welfare and flourishing of the workforce, the challenges of raising capital and the ownership structure and Cadbury’s wider commitment to the community. Virtue, wisdom and spirituality lay at the heart.
The second chapter was less familiar territory for me but I was captivated by the story. Edward Filene, born in 1860, ran the family retail business in Boston with his brother, Lincoln, and pioneered many business practices. Among his ethical approaches, he introduced employee training, paid high wages whilst seeking to keep prices low and was actively concerned with not only employee welfare, but also employee involvement. He introduced health and illness insurance and banking services for employees. Although Filene did not share their faith perspective, there are several crosscurrents here with the Quakers.
Geoffrey Jones quotes Filene that the purpose of business was to:
“serve people, not merely to support the business man concerned in it. I was not an idealist. I wanted profits. I even had a strong preference for becoming rich. Nevertheless, this discovery of what business really is did strange things to me. It made me want to serve” (page 53).
Of real interest was the story of Filene’s involvement in the development and promotion of the credit union network, which is a much more significant feature of the American financial landscape than, for example, in the UK. He was involved in the 1914 launch of the Massachusetts Credit Union and helped draft a series of eight principles of good practice (page 64). He launched the Massachusetts Credit Union Association in 1921 to promote the idea of credit unions which spread rapidly, although there was always some tension between state and federal provision. Filene was elected the first president of the Credit Union National Association in 1935, with 3,600 credit unions and 750,000 members. Those numbers had grown to 7,500 retail credit unions with 92 million members by 2010.
Part 2, “Turbulence” begins with a fascinating chapter on the history of Harvard Business School and its second dean, Wallace Donham, who had called in 1927 for business leaders to adopt what he called a higher level of responsibility (a further and helpful reminder of placing these ideas in historical context). Further chapters deal with the desire to reduce wealth disparities as an aim of business leaders, consumerism and some other matters. In these chapters the book slightly loses its way. They are the least convincing part of the book and certainly, on occasion, fall into virtue-signalling around business leaders’ personal political objectives and detracted from the really significant insights of the book. In particular, chapter 9, entitled “Social Three-Folding”, seems quite disconnected.
In Part 3, Geoffrey Jones brings us back to more contemporary debates with three chapters dealing with the rise of value driven business right through to the issues around ESG (“environmental, social and governance”) and B Corps. He provides a balanced overview of the strengths and challenges of these movements. He is particularly helpful with his supportive critique of B Corps – though there was no mention of the UK’s B Corp movement, which has made some advances.
In his conclusion Jones reminds us of the reason why his book makes a good and useful contribution:
“As we delved into the history of deep responsibility, we saw many examples of business leaders across time and space who combined making profits and pursuing positive social impact” (page 342).
Jones argues that deeply responsible business will select an industry which does no harm (though that might be easier to define in some instances than others), will engage with stakeholders with respect and humility and support communities. He notes that affecting “a single city might be less glamorous than “reimagining capitalism”, but it can greatly enhance the lives of generations of people” (page 345).
Jones should be congratulated for recognising that a values-based approach to business has a long and honourable history but is not a panacea and that there are weaknesses as well as strengths. In this he is a realist and enhances his overall arguments. He recognises the values which shape character, virtue and spirituality and the need to convince the mainstream of business rather than simply movements on the margin. This is a good book, which I recommend, albeit slightly disappointed with the middle chapters.
“Deeply Responsible Business,” by Geoffrey Jones was published in 2023 by Harvard University Press (ISBN: 978-0-674-91653-1). 431pp.
Dr Richard Turnbull is the Director of the Centre for Enterprise, Markets & Ethics (CEME). For more information about Richard please click here.
The Centre for Enterprise, Markets and Ethics (CEME) is pleased to announce the publication of The Challenge of Artificial Intelligence: Responsibly Unlocking the Potential of AI by Andrei E. Rogobete.
A PDF copy can be found here. A hardcopy of the publication can be ordered by contacting CEME’s offices at office@theceme.org
Artificial intelligence is reshaping business, security, transport, and everyday life at remarkable speed. But the rapid advance of machine learning and generative AI raises questions that go beyond efficiency and innovation: What does it mean to be truly human? What separates consciousness from computation? And what ethical framework should govern how these technologies are developed and deployed?
In this publication, Andrei Rogobete traces the evolution of AI from antiquity to the present day, before examining two contrasting case studies — the successful use of biometric identification at national borders, and the persistent difficulties facing autonomous vehicle technology. He then develops a distinctively Christian response, grounded in the biblical teaching that human beings are made in the image of God: uniquely capable of self-awareness, moral reasoning, love, and relationship with the divine. On this basis, he argues that AI — however sophisticated — cannot possess genuine consciousness or spiritual capacity, and that its development must remain in the service of human flourishing. Drawing on statements from evangelical, Catholic, and Anglican traditions, Rogobete offers practical principles for the responsible adoption of AI within an ethical framework shaped by Christian theology.
We were delighted to host a joint event on the 30th November 2023 between the Centre for Enterprise, Markets & Ethics (CEME) and the Las Casas Institute for Social Justice (Blackfriars Hall, University of Oxford), on the theme of “Finance for the Common Good”. Our speakers addressed a range of issues such as the interplay between finance, greed and morality, a history of local banking, the role of Catholic Social Teaching, and the relationship of the corn laws, Brexit and the Common Good.
Edward Hadas is Research Fellow at Blackfriars Hall, Oxford prior to which he spent 45 years in finance and financial journalism. He is the author of Money, Finance, Reality, Morality (2022).
Richard Turnbull is the Director of the Centre for Enterprise, Markets and Ethics, visiting Professor at St Mary’s University, Twickenham and author of numerous articles, papers and books relating to ethics and business in historical perspective.
Jean Pierre Casey is the convenor of the UK Chapter of the Centesimus Annus Foundation, a member of the investment committee of the Holy See and formerly head of investments for both J.P. Morgan and Edmond de Rothschild.
Cheryl Schonhardt-Bailey is Professor in Political Science and Fellow of the British Academy and was head of the department of government at the LSE from 2019-2022. Her research interests are in political economy, legislatures, deliberation and accountability and she is the author of several books.
