The social dimension of the circular economy

The circular economy (CE) is gaining momentum. The CE is a more sustainable alternative to the linear ‘take-make-waste’ economy which depletes resources and turns them into (often harmful) waste. The transition from a linear to a circular economy is supported by the Dutch government, which has committed itself to a fully circular economy in the Netherlands by 2050, and the CE is also one of the building blocks of the European Green Deal. This transition is believed to reduce emissions, resource depletion, and biodiversity loss, while at the same time enabling sustainable growth and new jobs.

A transition to the CE is laudable, yet there remains a rather deafening silence when considering the social rather than environmental aspects of the CE. When the social aspects are taken into account, it is often assumed that the CE will benefit people, for instance by creating jobs. However, the CE can also have negative social aspects. For instance, recycling, an important aspect of circularity, often requires contact with unhealthy chemicals. This opens up other possible issues: Can the emphasis on technology for waste reduction also reduce jobs rather than create them? And can the local repairing and recycling of textiles come at the detriment of industries in countries such as Bangladesh, China or India? These issues highlight the complex nature of the CE and its social aspects. Based on a study of the social aspects of the CE in the Netherlands, recently published in Journal of Cleaner Production, we put people at the heart of the CE and examine how to combine environmental and social benefits. We do so through a combination of interviews with strategic leaders in Dutch circular organizations and a Delphi study with experts in the CE.   

Social values, trade-offs and synergies

Our study highlights that social benefits in the CE do exist at multiple levels, and that these different social benefits can lead to both positive relationships (i.e. synergies) and negative relationships (i.e. trade-offs) between the CE and social benefits. Social benefits within the CE are visible at three levels: micro, meso and macro.

At the micro level, examples are well-being and meaningfulness at work or the creation of unique products for customers. At the meso level, telling examples are the inclusion of people with a distance to the labor market in the value network or generating inspiration and awareness. Finally, macro level benefits evolve around global justice, equality and making the world a better place.

We show that synergies exist mostly at the micro level. Circular working and organizing are considered intrinsically more meaningful and attractive, and come with positive feelings and interactions. Succinctly stated by one circular entrepreneur: it is great fun to create value this way!”. Circularity and social benefits are also positively connected as they lead to win-win situations regarding new jobs, skills, products, services, and innovation.

However, we also find that trade-offs are experienced between circularity and macro level social benefits. Tensions are, for instance, perceived between circularity and social justice. Tellingly put by one of the CE experts consulted:

“It is fabulous that a computer or ‘smartphone’ will be repurposed, or that energy garnered from sewage will heat a neighborhood, but what about the fact that MOST of our (Dutch) consumption relies on ‘ghost acres’ elsewhere. I am thinking of devastated ecosystems where mining takes place, I am thinking of children mining coltan”

In addition, experts find it difficult to scale up the benefits of circularity to the macro level because of the current economic system, as the example below illustrates:

“Circular business is less capitalist thinking. Capitalism is based on infinite growth. And we see what this does to inequality and destruction of the earth. Thinking more holistically, that is circular.”

How to turn trade-offs into synergies  

Turning trade-offs into synergies can be fostered by a communal sharing strategy. This is a strategy that consists of personal, structural and socio-circular strategic behaviors.

Personal strategic behaviors focus on developing intrinsic motivations, meaningfulness, community building, and information sharing among stakeholders with similar idea(l)s. Structural strategic behaviors aim to create resource efficiencies, leveraging government policies, and developing stakeholder relations based on trust and cooperation. Socio-circular strategic behaviors treat stakeholders as multifaceted human beings who are embedded in broader social networks. For instance, social benefits can be achieved when viewing a stakeholder as both community member, supplier and customer, as one strategic leader explained:

“youth have nothing to do, so let them clean up junk and bring it to the community center. Then we turn it into a skateboard and then they can get skate lessons. Then it’s also a community thing, that’s part of what you could do with it.”

Overall, a more ethically motivated communal sharing strategy works well to create synergies at, and between, the micro and meso level. However, it fares less well at, or scaling up to, the macro level. The reason is that economic growth motives seem essential to reach the macro level, but the pressures of these economic growth motives appear to be particularly at odds with the interests of social and circular organizations. This is summarized in the following observation:

“Circular business is less capitalist thinking. Capitalism is based on infinite growth. And we see what this does to inequality and destruction of the earth. Thinking more holistically, that is circular.”

CE practitioners and policy makers do well to remember this complex nature of the CE. A truly sustainable CE comprises next to economic and environmental benefits also social benefits, which manifest itself at micro, meso and macro level. Moreover, as the relation between social benefits and circularity shows trade-offs and synergies, communal sharing strategies can help to overcome trade-offs and turn them into synergies.  

That VCs’ startup screening and selection decisions are biased by the attractiveness of the entrepreneur is well-established. Recent research found VCs’ subsequent capital allocation decisions also to be affected by the halo-effect. A new study, available as open access article in Frontiers in Psychology, now turns the spotlight to the attractiveness of the VC himself. The study’s results, which are based on data from 341 startup investments involving male entrepreneurs and male VCs, suggest that VCs of below-average attractiveness are more sensitive to entrepreneurs’ physical attractiveness when compared to VCs of average attractiveness. Also, the nature of this effect changes from positive in the first investment round, to negative in the second investment round. Risk-taking therefore seems to be partly driven by one’s own physical attractiveness.

How venture capital funding works

Once selected, the startup entrepreneur and venture capitalist enter into a collaborative effort aimed at ensuring the success of the startup. This phase presents the VC with a myriad of uncertainties inherent to the startup ecosystem: questions concerning the startup’s ability to fulfill its potential, the entrepreneur’s competence and reliability, and the unpredictable trajectory of the market, among others. Consequently, capital allocation follows a staged approach, gradually disbursing funds as the startup progresses. This approach allows the VC to mitigate risks by retaining the flexibility to withdraw investment and minimize potential losses. Research suggests that the manner in which capital is disbursed reflects the level of trust and confidence the VC has in the startup. Higher levels of confidence translate to larger funding disbursements with longer intervals between investments, whereas doubts prompt tighter control measures, manifested in smaller funding amounts and shorter intervals between rounds. While traditionally assumed to be grounded in rational decision-making, balancing staging costs with monitoring expenses, VCs’ funding approaches may not be entirely devoid of biases.

How does physical attractiveness affect men’s financial risk-taking behavior?

Human behavior, shaped by evolutionary pressures, revolves around the fundamental goal of reproductive success facilitated through sexual selection mechanisms. This process encompasses both intersexual selection, where individuals choose mates, and intrasexual competition, involving rivalry among members of the same sex for access to opposite-sex mates. In this context, men exhibit traits and behaviors signaling mate quality, including physical attributes like facial hair and body shape, alongside behaviors like conspicuous consumption. Conversely, women prioritize physical attractiveness in potential mates, considering it a cue for underlying qualities such as masculinity and health, while also assessing signs of commitment and economic status. Moreover, women evaluate potential partners based on economic indicators, with men’s reproductive success being linked to their economic position. Men, cognizant of these preferences, may compensate for perceived deficiencies in attractiveness by enhancing their desirability through financial success, particularly when motivated by a need to increase their attractiveness to women. This is supported by research showing that men tend to take greater financial risks when exposed to attractive male stimuli. The signaling of economic status through conspicuous consumption further underscores the importance of mate value characteristics like attractiveness and ambition in male-male interactions. Thus, these evolutionary dynamics influence not only mate selection but also financial decision-making, as individuals navigate the complex interplay between attractiveness, status, and reproductive success.

How does VCs own physical attractiveness matter to their risk-taking?

The study investigated the impact of VC physical attractiveness on financial risk-taking, hypothesizing two main effects. Firstly, it was anticipated that VCs with above-average attractiveness will exhibit greater tolerance for investment risks compared to their less attractive counterparts. This expectation is supported by prior research linking attractiveness to positive risk attitudes and higher self-esteem, which in turn correlates with increased trust, financial risk tolerance, and propensity for investment. Conversely, VCs with below-average attractiveness are expected to be more risk-averse due to lower self-esteem and a propensity to react negatively to ambiguous information. Secondly, VCs’ responses to entrepreneurs’ attractiveness are predicted to vary based on the VC’s own attractiveness level, with less attractive VCs showing heightened sensitivity to the attractiveness of the startup entrepreneur. This phenomenon is attributed to a perceived disparity in attractiveness, leading less attractive VCs to engage in compensatory behavior by taking greater financial risks and potentially experiencing threats to self-esteem. Overall, it was expected that while physically attractive VCs tend to take more risks in early-stage funding, they are less influenced by the attractiveness of entrepreneurs compared to their less attractive counterparts.

The study found that VCs, especially those with average or below-average attractiveness, tended to be positively swayed by the attractiveness of male entrepreneurs as evidenced by the first round of funding (see Figure 1). This confirms earlier research on how people assess risk in financial situations. However, this effect wasn’t seen in VCs of above-average attractiveness.

Additionally, the study looked at how this same-sex attractiveness dynamic played out over time by considering the second round of funding. Interestingly, as the funding process went on, VCs of below-average attractiveness seemed to become more cautious about attractive entrepreneurs (see Figure 2). This suggests a shift from initially positive impressions to later doubts, possibly due to what researchers call a “beauty penalty” effect. This beauty penalty idea suggests that highly attractive entrepreneurs might face extra scrutiny if they don’t meet the high expectations set by their looks.

What can we learn from this research?

Fundamentally speaking, this research’s findings suggest that VC risk-taking can be understood by using evolutionary explanations of human behavior. In more practical terms, the study’s findings may assist VCs in their risk-taking considerations. Particularly, this research may help VCs to understand the origins of their risk estimations and preferences.

The stereotype of the hardened entrepreneur

When asking entrepreneurs how they deal with crises, we often focus on the venture and how it is pulled from the brink of failure by some daring decisions. But we rarely ask how they, as a person, deal with their venture being on the brink of failure. By contrast, we can imagine employees’ fears when for instance AI threatens to replace their job. But for entrepreneurs we seem to assume the answer based on a stereotype.

That stereotype is of the entrepreneur as a tenacious go-getter in the face of adversity; a portrayal based on Silicon Valley success stories. However, for most entrepreneurs, such a stereotype can be difficult to live up to, and not fitting the stereotype can be difficult to live down.

Studying the soft side

Our open-access publication in PLoS ONE reports on our empirically study of this stereotype. We asked 300 leaders of small businesses in Flanders how hard the 2010 financial crisis hit their venture. We also asked them about their self-confidence in being an entrepreneur and about the job security they experience. By analyzing the connection between these answers we hope to promote a more empathetic view on entrepreneurs.

The first thing to realize is that the financial crisis is experienced very differently by different ventures. Some ventures could hire workers and enjoy profits as they please, while others experienced years of hardship before recovering from the crisis, similar to how small businesses are still failing today due to not being fully recovered from the Covid crisis. These differences in experience of hardship for the venture could influence entrepreneurs as a person.

What further sets our study apart is that we look at the long-term effect of the crisis. Specifically, we asked entrepreneurs about their current feelings and beliefs about themselves when the crisis was about five to ten years prior. This means we do not study how some entrepreneurs regulate their emotions to prioritize what is best for the venture, but rather focus on how the experience of hardship permanently shapes their baseline attitude.

What does the data say

We found that entrepreneurs whose venture experienced harder crisis times, still experienced lower job security years later. Note that we find this effect even though we only study ventures that survived the crisis. The entrepreneur’s self-confidence was also weakened by harder crisis times, but only slightly so.

Furthermore, we asked ourselves, does this effect on the entrepreneur as a person also impact the venture; specifically, do the affected entrepreneurs still making daring decisions? We found that self-confidence and job security are needed for the venture’s risk taking, innovativeness, and pro-activity, but job security only slightly so.

Bringing it all together, we see that ventures’ hardship in crisis times slightly reduces their daringness to this day via reducing entrepreneurs’ self-confidence and sense of job security. The relation is only weak because self-confidence is most related to the daringness while job security is most related to the crisis. However, it is striking that entrepreneurs are so affected by the financial crisis that it is still observable five to ten years later. The question arises whether the affected entrepreneurs have enough daringness to face the next crisis.

Accept not only the threat of failure, but also your fear of it

In sum, entrepreneurs are not so tough as the stereotype suggests. Do not be discouraged by people saying that you cannot be an entrepreneur if you are afraid to fail; the data show that many entrepreneurs are still affected by their past brushes with bankruptcy.

Image by Freepik

How do you respond when a team member initiates an improvement to your team’s work methods? Do you praise them for being proactive, or do you instantly roll your eyes when hearing about it? Initiating changes to improve the team’s functioning is often considered to be risky by proactive employees. Proactive behavior in a team has consequences beyond oneself – it also impacts your team members. Nonetheless, fellow team members might be unaware that their immediate response can affect whether proactive initiatives will become successful or fail to hit the mark.

Our research, published in Human Relations, reveals how team members respond to the proactive initiatives of their coworkers. The study showcases which – and why – certain types of reactions are more favorable for the team’s performance.

Proactive initiatives are more noticeable to team members than managers

With proactivity being central for organizations to thrive, managers are left wondering how they could best lend their support to their proactive employees. Particularly managers of self-managing teams receive fewer occasions to intervene or respond to their team’s proactivity. Since their teams take control of their own way of working, they often question whether their teams are proactive enough in dealing with their constantly changing work environment. According to the researchers, the decreased visibility of proactive behavior to managers requires them to accept an alternative standpoint. Managers should be more concerned about the evaluations and responses of team members instead of their own. In fact, they should not underestimate team members – the success of proactive initiatives hinges on the judgments of those who will be most impacted.

To unravel how proactive behavior takes place in a team, the researchers studied three agile software development teams responsible for improving their own performance. For five months, they observed their meetings and daily work in the office. Here, the researchers studied how team members perceived 69 of their proactive initiatives and examined whether they became successful or not. Also, they regularly interviewed team members to understand why they reacted in a particular way and what would make them change their minds.

Time for reflection

In contrast to the beliefs of their managers, the study revealed that the self-managing teams engaged in various forms of proactivity to improve their team’s functioning. For example, team members introduced additional feedback meetings with customers, set up knowledge-sharing sessions for developers, and initiated new team communication methods. Also, they proactively came up with methods to prevent (technical) problems from arising again or presented innovative ideas for a new app feature.

Getting team members on board for your proactive initiatives

While all these efforts sound valuable to the team, this does not automatically have to be the case. The researchers found that its success heavily depends on how fellow team members perceive the proactive efforts, as their involvement is usually needed to implement the ideas.

These three strategies appeared to be essential for making a proactive initiative successful:

• Be visionary. Communicating the proactive initiative to team members with a visionary or promising tone helps to uplift team members. This was because they perceived its intentions to be genuine instead of selfish. When team members felt that the initiative was self-serving and intended to decrease their own workload, they would harshly blame and reject the proactive employee.

• Be innovative. Initiatives concerning innovative ideas are most likely to be instantly admired by team members. Team members were more willing to go along with implementing team innovations and would also feel more motivated to be proactive themselves in the future.
• Be flexible. Seeing the added value is not enough for team members to appraise a proactive initiative. Proactive employees should allow their team members to negotiate changes to the initiative’s implementation. When team members felt they could fine-tune how and when they contributed, they were more willing to invest their time in implementing the change.

Positivity sparks proactivity

Proactive team members generally used team reflection meetings to communicate their initiative to their coworkers, who would immediately share their initial impressions. Afterward, the team would take action or refrain from implementing the improvement. When team members saw its effectiveness once implemented, they would feel motivated to share their own proactive initiative during the next team reflection meeting – a vicious cycle of proactivity. Thus, positive and encouraging reactions give team members the feeling that they can also make a difference and energize them to make proactive changes now and in the future.

First impressions matter

These findings highlight that the success of initiatives to improve the team depends on how team members communicate, react to, and reflect upon the proactivity. When the first impression of team members is to criticize the initiative or blame the proactive employee, the proactive goals will not be realized. Such immediate reactions negatively shape the proactive process and withhold others from taking the ‘risk’ of being proactive in the future.

Moving away from a recipe approach to collaborative and flexible incubation

People tend to associate incubators with coffee machines, printers and ping pong tables and often overlook the relational and intangible factors behind business incubation. While, these kinds of structural elements enable the replication of incubators, standardized solutions have been criticized for not being adaptive enough to local contexts. We studied how stakeholders build and reshape an incubator to be adaptive and meet the changing needs of its members. For our ethnographic study, published in the journal Small Business and Economics, we participated for four months in an incubator for social entrepreneurs. This helped us understand the details of the social practices that were central to the creation of an adaptive and collaborative incubator. 

The foundations of participation, flexibility, trust and reciprocity in an incubator

We found that entrepreneurs and incubator management perform four social practices – onboarding, gathering, lunching, and feedbacking – in which they try to find a sweet spot between entrepreneurial freedom and structured support. First, there’s onboarding – the initiation ritual that gets new members on the same page about the rules, values, and how things work around the incubator to foster participation. Second, incubator members and management come together to make shared decisions through formal gathering practice. This practice enables the incubator to flexibly adapt their services to the needs of their members. The third practice, lunching, is more than just grabbing a bite; it builds trust and fosters serendipitous encounters. And last, there’s feedbacking, where there is a purposeful co-creation of ideas, and a healthy give-and-take among members of the incubator is maintained (for a detailed description of the four practices, have a look at our full study). In other words, these social practices serve a range of purposes and have implications far beyond their surface meaning. They are foundational to the intangible, yet invaluable factors of a functional community: participation, flexibility, trust and reciprocity.

Finding the right balance

These social practices allow for multiple people involved in the incubator, from novice and expert entrepreneurs, to management and stakeholders, to shape and reshape an incubator. Ultimately, this helps keep the incubator aligned with members’ unexpected and changing needs and expectations. Each practice brings a balance between bottom-up needs and top-down structure. For example, in the onboarding practice, incubator management attempts to socialize newcomers into the incubator, but also provide space for entrepreneurs to share their stories and connect with the community. While in the gathering practice, entrepreneurs are given more agency to reshape formal support elements of the incubator. We suggest that this serves the function of preventing the incubator from being perceived as too rigid and formulaic. It’s all about finding the right balance, where the structure isn’t too strict and the freedom isn’t too wild.

Experimenting with social practices and reflecting on them

While the social practices work well for the incubator we studied, we don’t advise incubators to copy the social practices we’ve described in our study. On the contrary, we warn against the copying of practices developed elsewhere as practices need to be developed in context and in interaction with the people (their needs and desires) and the material world that are part of that context. It is important to experiment with different practices, and reflect on their effectiveness for creating desired outcomes such as participation, flexibility, trust and reciprocity. Furthermore, we hope to inspire incubators to reflect on the balance between bottom-up needs and desires and top-down structures, rather than copy them from a Silicon Valley context. We hope that this will help them to move towards a desired balance where entrepreneurs experience enough structured support, while having enough freedom to adapt the incubator to their specific and evolving needs.

In this day and age, the world is marked by rapid technological development. Subsequently, educational institutions face an unprecedented dilemma: how to tackle the power of artificial intelligence (AI) to stay ahead in the game. I had the opportunity to interview IT consultant, Bauke van der Weijden – who serves secondary schools as a client – to shed light on this matter. The conversation gave me new insights and created a clear picture of the current state of AI in education – or, perhaps more accurately, the lack thereof – as well as the elements that keep educational institutions from embracing and implementing game-changing innovation. This raises an important question: Are schools prepared and willing to educate and mentor students in this modern day of artificial intelligence?

Catching the AI Wave

One of the most striking findings from the interview was the blatant ignorance about digitalisation within schools. Many educational institutions appear to be behind in implementing AI, although this technology continues to transform sectors all around the globe. IT consultant Van der Weijden notes that “Educational institutions are very reactive”. This reactivity is demonstrated by how these institutions often play catch-up, particularly in the area of AI. This brings about a digitalisation knowledge gap since schools find it difficult to keep up with their technically proficient students. The gap in understanding places schools at a noticeable disadvantage, while educational institutions should actually be leading ahead of the curve in digitalisation and educating students for a future in which AI will undoubtedly be a crucial part.

Tackling Educational Apathy

Besides the lack of awareness, within educational institutions, there is also a reluctance that prevents the implementation of AI. IT consultant Bauke van der Weijden faces this a lot in his daily work. The staff at schools do not see the benefits of implementing AI, as they have other priorities. This lack of motivation is one of the biggest obstacles to integrating AI into educational institutions. Many teachers and other school staff appear uninterested in the notion of digitalisation, thereby also ignoring the opportunities that AI can bring to them. For IT consultants wanting to introduce AI into the educational industry, the apathy among school staff presents a significant hurdle.

AI as a Classroom Cop

Many educational institutions have yet to fully grasp the potential that AI provides in today’s quickly digital environment. Academic integrity worries have been a major factor in many institutions’ decision to introduce AI into the classroom. As a result, the widespread opinion within education is centred around employing AI as a safety precaution, a watchdog looking out for possible risks like plagiarism. Van der Weijden emphasizes that schools primarily consider AI from a security standpoint, utilizing technologies like ChatGPT to screen for potential violations of academic sincerity. But is this the real potential of AI in education? Hardly.

Exploring AI’s Potential

The application of AI in education goes beyond mere plagiarism detection. According to Van der Weijden, “There are also increasingly better AI tools,” not just for detection but also to completely alter how subjects are educated and evaluated. Schools need to be receptive to the opportunities that AI can bring. However, according to Van der Weijden, it is still unpredictable what AI will exactly bring in the future: “The applications are so versatile. All we know is that the ones who start using it first will be the frontrunners in what they do.” While students quickly adopt AI, schools frequently fall behind, behaving in response rather than inventing. Institutions of higher learning require a paradigm shift rather than just trying to “keep up with what the market will do.”

From Digital Guard to Guide

The interview with Van der Weijden sheds light on the need for educational institutions to close the digital gap. AI shouldn’t be viewed as solely a detecting tool. It should be seen as a collaborator with the potential to completely alter how we transmit and consume knowledge. The incorporation of AI into the curriculum is not only advantageous but also necessary given its capacity to offer individualized learning experiences, immediate feedback, and data-driven insights. Schools should begin educating staff members and students on the advantages and disadvantages of this disruptive technology as well as how to use it effectively to navigate and code a new curriculum.

Consultant’s Call to Action

What is the real problem? Reactivity versus Proactivity. Educational institutions can no longer afford to remain spectators in a world where AI is pervasive. Schools need to change from being reactionary institutions to becoming proactive ones. Consultants should also take on this proactive role. Before technologies like ChatGPT become commonplace among students, consultants should speak with schools about the possible uses and abuses of these platforms, as Van der Weijden accurately puts it. The difference between what students already know and what they need to know about AI might be bridged with a proactive approach. IT consultants need to do more than just share their technical insight and expertise to increase schools’ adoption of AI. They should take an active role in engaging schools in discussion about the revolutionary possibilities of AI, as well as providing practical learning approaches, like workshops and training. A practical approach will aid educational institutions in gaining the knowledge and confidence to effectively incorporate AI into their curricula.

Broadening Horizons

Thus, to entirely tackle the power of AI, schools should change their perspective. It’s time to embrace AI as an ally, a tool that can not only discover errors but also improve the educational environment, rather than viewing it only as a danger or a gatekeeper. It’s an appeal to lead, create, and establish a standard for the future rather than merely keeping up with the times. The future of education with AI is not just promising—it is exhilarating—from where we stand right now.

Acknowledgement Statement

This blog is part of the student writing competition in Management Consulting Master Program at the School of Business and Economics.

Disruption has been a fearful term that is used more and more often when talking about the transformative force of Generative artificial intelligence (GenAI) in Professional Service Firms (PSFs), under which Consultancy firms. Why fearful? The popularity of natural language processing (NPL) technologies – which is a type of Generative AI – is growing increasingly. It has the astonishing ability to generate novel content (images, text, etc.) to answer complicated questions and give solutions with such a speed that the human mind cannot match. GenAI systems make use of models that have been trained on extensive datasets to comprehend the patterns and relationships found in the data. This enables the tool to only produce solutions and answers that are similar to the training data. As a result of this fearful disruption, consultancy firms are increasingly adopting this state-of-the-art technology to assist them in their daily work. Certainly, this will have an impact on the status quo of consultancy firms and their corporate hierarchy. However, is this new technology so advanced that it will replace the trust in human expertise completely? Today, public and in-house language tools are progressively used in consultancy, but not all ethical and practical implications are yet overcome. To analyze this in more detail, a Senior Consultant operative in the Netherlands was asked to share her insights and point out what exactly is fearful using the two types of GenAI.

Public/Open Language Tools

Changing the status quo, is there a transformative paradigm?

The use of publicly owned and openly used language models such as ChatGPT, Bard, Quillbot or Agros Multilingual are growing for consultants’ daily tasks, such as paraphrasing, enhancing sentence structure, translations and the writing of introductions. Indeed, these technologies greatly help consultants do the same amount of work, in a shorter amount of time. You need less and less people to complete the tasks: The status quo of consulting is bound to change. The interview with a senior consultant confirmed this transformative paradigm: “Consultants will likely lose their jobs; as a result, layers of the corporate hierarchy will get thinner, but they won’t disappear.” Your level in the corporate hierarchy is a representation of your responsibility, based on your expertise and build-up trust, in the firm and by your clients.

The operational gap

Clearly, an operational gap should be identified between the rather simple and efficient tasks that GenAI is allowed to perform, and the tasks for which the human eye remains essential. Ethical considerations are the reason that this gap still exists. The use of GenAI remains an ethical uncertainty, as its trustworthiness cannot be determined or guaranteed. This is the reason that the public GenAI operates with a confidentiality risk. At the firm of an interviewed expert: “Open language tools, such as ChatGPT and Google Translate are not allowed to be used. Because it can give too much client information, even sharing the smallest bits, is not allowed.”

All data you enter, you are giving consent to become available training data, on which other answers and possible solutions generated by that tool will be based. In addition, opacity is the lack of transparency that AI algorithms are able to provide, regarding generating operations and reasoning to their users. This contradicts directly a core responsibility of the consultant, being able to explain your sources and information at all stages of their consulting research. A connecting hazard to this is the mixture of factual and non-factual data that makes the credibility of information generated through language tools questionable. Evidently, the use of open GenAI is still quite limited in presumably taking over all tasks of a consultant. The human eye is required to be the controlling factor ensuring the trustworthiness and accountability of the firm towards clients.

Private/in-house Language Tools

In-house language tools:

“Bullshit in, is bullshit out…” as was quoted by the interviewed expert. She reflected upon the tendency of PSFs to make large investments in GenAI in-house tools. For instance, PwC has invested 1 billion dollars in their own (pilot) ChatGPT and EY 1.4 billion dollars in an AI platform and announced future spending in their language model tools. Manifestly, creating a more contained, secured, transparent, and controllable GenAI tool that can solve most security and confidentiality issues, while generating solutions on valid data. As confirmed by the interviewed expert, this disruption would hypothetically have a great impact on the transformative paradigm as it could close the operational gap. Noteworthy, the ability to have this great impact will be limited to the individual large (international) PSFs that have abundant sets of data and the funds to create, manage and improve these language models continuously. But again, not without (ethical) risks.

A limited investment

All language models will be as big or limited as their dataset itself is. This means that the quality of your in-house generated solutions will be based and trained on the companies’ data only. Hence, the idea of bullshit in is bullshit out. To add, external factors and awareness are excluded to keep the data contained, limiting the ability to research or find undiscovered information, a quality for which a public language tool like ChatGPT is explicitly valued. Most disappointingly, it will depend on client confidentiality agreements whether their data can be shared (anonymously) with the in-house tool. “With current strict security and confidentiality agreements, sometimes team members are not allowed to view all data available” let alone all employees of a large (international) consultancy firm. Despite the promising efforts by large PSFs, overcoming a predominant confidentiality problem with an in-house trustworthy GenAI tool is not yet guaranteed.

In conclusion, the application of Generative Artificial Intelligence (GenAI) in consulting raises both the prospect of a paradigm shift and ethical issues. Public language tools improve productivity and effectiveness but pose concerns about confidentiality and transparency. Large corporations are investing a lot of money in in-house technologies to address the operational gap and offer solutions that are transparently and securely regulated. The phrase “bullshit in, bullshit out” perfectly captures the problem of only using data that is internally confined. In-house GenAI technologies have a lot of work to do before they can be fully trusted and accounted for, despite their potential. To maintain its credibility while negotiating the changing context of human-machine collaboration, the consultant industry must still strike a balance between productivity and ethical considerations, to ensure its trustworthiness.

Acknowledgement Statement: This blog is part of the student competition in Management Consulting Master Program at the School of Business and Economics.

The world as we know it today is developing at a rapid pace, where technological advancements appear like wildflowers, transforming our technological landscape. One of the most actual developments has been that of Artificial Intelligence (AI), with tools like OpenAI’s chatbot ‘ChatGTP’ gaining a bigger user base by the day, including consultants not shying away from using it. With AI being able to perform lightning-fast idea formulation and data analytics on big data, it has become interesting for consultancies to explore developing their own AI tool.

However, as with every new and hot topic that seems to conquer our world and revolutionize it with its benefits, also AI comes with certain complications. Firstly, a complication that is often not spoken about is the energy usage of AI. The development and maintaining of AI systems comes not only with financial costs but also with environmental costs. Secondly, the aspect of human factors should not be overlooked. Especially with the work of consultants, in which thoroughly considered solutions are formed through a deep and detailed understanding of the problem and situation at hand.

So, an important question arises when thinking of the future of consultancies in light of AI implementation. Should consultancies explore the possibility of using AI to take over their workload and take the environmental effects for granted? To answer this question, I interviewed Ton Metselaar, experienced management consultant at a successful business and technology consulting firm. The conclusion is that we cannot let this rapid development run on its own without looking at the consequences. Every innovation should be assessed on its environmental impact and ways to limit the energy usage should be explored. Furthermore, the human factor in consultancy work is crucial due to their expertise and relationship with the client and this should stay at the core of the value they bring to clients.

Exploring the benefits AI can offer

The speed and completeness of the answers AI bots currently provide is impressive, to say the least. The convenience of having a little AI helper when doing your work can help to initiate ideas and save time when doing routine tasks. But the future lies further, where consultancies will try to reap the benefits of using AI to analyse huge chunks of data within seconds, where normal data analysis would take significantly longer. These ways of saving time come with saving costs as well of course, making it interesting to explore the possibilities.

Unravelling the environmental threats of AI

Yet, where people picture a world where AI can perform all their tasks and we do not have to do any work ourselves anymore, it is not without drawbacks. Like other technological advancements, AI models have a certain carbon footprint. The humongous amount of data that is stored by data center servers requires a lot of energy and water usage to run the servers, equipment, and the cooling systems. Ton Metselaar explains that it’s like calculating the transactions for Bitcoin, which is known to consume large amounts of energy. AI models produce CO2 emissions in three ways. The initial training of the model is the first and most energy consuming way, with researchers calculating the CO2 emission at 626,00 tons of CO2 which can be compared to the CO2 emissions of 119,000 cars (Strubell et al., 2019). Furthermore, the carbon footprint of keeping the generative models running and letting people interact with it are slightly below these emissions. The last way is through the updating and tailoring of the model to a consultancies’ specific information, which uses the least amount energy.

Analysing the importance of the human factor

Next to the fact that AI has certain drawbacks, it also contains flaws. As Ton Metselaar accurately puts it: “AI can make a lot of suggestions and have ideas on how to make things simpler but as a model it can never be completely without mistakes”. AI might interpret certain factors wrong or miss out on contextually important details which can lead to wrong outcomes and bad solutions. It is therefore not a reliable source. In addition, the absence of human empathy and understanding is also lacking with AI since they are unable to genuinely understand the client concerns. Clients always seek an emotional and human understanding that AI cannot offer. This can make clients feel like they are treated as data points rather than individuals with unique requirements and specific concerns to be addressed. AI cannot establish trust or build reputation with clients through meaningful conversations, active listening, and the human touch that comes from genuine interactions, which are the cornerstones of client relationships.

Proposing a sustainable way of AI usage

But what should our future look like then?

Ton Metselaar continues to explain that for a good consultant, it is essential to be an expert in their field. Therefore, it is vital that they can carefully listen to what a client needs and what specific situation they are in. Subsequently, this information should be mindfully analysed to come to the optimal solution. Ultimately, this needs to be properly communicated to the client, with which the relationship and the maintaining of that relationship is indispensable. All these aspects show that AI cannot replace the human factors consultants bring to the table. Furthermore, with our world being threatened by drastic environmental issues, “We should not only save the planet, but also save ourselves. The climate will likely kill us before it will kill the planet”, as Ton Metselaar alarmed in the interview. When looking in further detail at the solutions we should not aim for every consultancy to build their own specialised AI model but rather build on existing ones to limit energy usage. Moreover, it is needed to incorporate AI usage in the monitoring of a company’s emissions to be aware of its magnitude. To really seek for responsible improvement, environmental-friendly AI developments should be sought instead of creating completely personalized ones.

Reference list

Kumar, A., & Davenport, T. (2023, July 20). How to Make Generative AI Greener. Harvard Business Review. https://hbr.org/2023/07/how-to-make-generative-ai-greener?autocomplete=true

Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and Policy Considerations for Modern Deep Learning Research. Proceedings of the AAAI Conference on Artificial Intelligence, 34(09), 13693–13696. https://doi.org/10.1609/aaai.v34i09.7123

Acknowledgement Statement

This blog is part of the student writing competition in Management Consulting Master Program at the School of Business and Economics

AI is changing the world

AI is changing the world. Maybe, experiencing these changes from the inside, as citizens of the game changer superpowers, we don’t really realize the violent impact of disruption on our daily life. The violent impact will come when, while cooking dinner for our beloved family we realize that an Artificial Intelligence, linked to the watch of our children, through monitoring pulse rate, body heat and oxygen in the blood, will be able to recommend the best cartoon for them to watch or the best vegetable to eat. And just at that point we will really think “ok, what’s happening here?”. But for now, seeing these changes in “rallenty”, reading about one “small” innovation at a time (just because we can’t read simultaneously more than one word at a time), doesn’t really seem to shake us. Well, long story short, the world (and the business world firstly) is changing and it’s changing fast.

When speaking about innovative practices, people can lag behind, Businesses can’t

We, as people, can surely remain back a few steps without risking bankruptcy. Businesses can’t. Nevertheless, when the businesses on which we focus are consultancy societies that, in order to survive, formulate strategies to make other societies stand out, within a sea of competitive societies backed up by other societies. In this mess,it is inevitable that consultancy firms must not only keep up with innovation, but even direct and guide it. Regarding this last statement I’m pretty sure they will. How? Well for two main reasons, they are constantly immersed in a network of stakeholders (clients and partners) who are informed or need to be informed regarding cutting-edge technologies and trends and because of their human-centered nature (with relatively low fixed assets) that allows them to implement new methodologies or sell new products in a fast and nimble way. Now, when we speak of implementing new methodologies and selling new products, what are we referring to? Well, in this article, we are referring to implementing and selling Artificial Intelligence languages.

How can new languages be created? A chat with Carlo

In order to understand how new languages are created, I interviewed my long-time friend Carlo, Master of Science in Machine Learning and Research Engineer at CISPA Helmholtz Center for Information Security. In simple words Carlos’s daily job involves adjusting leaks and improving the state of the art of AI and deep learning models. I interviewed Carlo in order to understand what it means for Consultancy Firms to create their own AI language and what are the variables involved. Speaking with Carlo I understood that there are two main protagonists in this process, the data (the oil) and the model (the engine). While the parameters of the models can be obtained and used by all, since societies such as Meta make them public and useable (we are speaking of pre-trained, large and generalized language models), the data needed to make the model speak the “consultancy language” (through the “fine-tuning” process) is more difficult to be found. For this reason it is more likely that big companies such as PwC, EY, KPMG, that already have tons of terabytes of in-house data can more easily get the engine to work. Small companies instead, are obligated to download it from public online sources such as Kaggle or Wiki English that still provide great quantities of good quality data. This doesn’t mean that small companies can’t do it, it just means that for them it’s probably going to be more costly. So once the consultancy company in object has on the one hand Meta’s pre-trained AI model and, on the other hand, the data, Carlo takes the field. His role is to use the data collected making the model speak the strategic consulting or marketing consulting or HR consulting language (depending on the data collected), delivering a product that can increase the efficiency of work within these companies. And how does he do this? Through the fine-tuning; a process that consists in training the pre-trained, large and generalized language model and making it better suited for a particular application.

Deep diving the apparently impossible process

This process involves the use of more than 20 GPUs (Graphic Processing Units, the billions of operations needed to train these models are performed on specialized computers built for quick and parallel processing). Once the model has been trained to serve a particular mansion or “speak” a particular language, the model can be prompted and used to generate new text or voice. What happens in this second step (called inference or prediction) is often referred to as a black box, since the human mind can’t really understand what is going on under the hood and why exactly the model is generating a single specific output (but this is another story). So once the fine tuned model is created, it needs to be put in production. Putting a fine-tuned model into production is a complex process. Through CI/CD pipelines, which means Continuous Improvement and Continuous Development, the model is updated and trained with data and questions, and every time a new output is generated, feedback on that output is feeded back to the model, to ensure the full lifecycle of the CI/CD pipeline. This helps ensure the model works effectively and doesn’t give unwanted answers (such as racist answers or information on how to conduct illegal activities). Finally, after completing this phase, the model is ready to be used by the consultancy firms (while needing continuous training with new and updated data).

The outputs of the impossible process, the case of Lilli

This process, which seems long and expensive, is actually doable, thanks to the work of specialized engineers and consultants, such as Carlo, that concludes the interview making his own prediction: “I think the major consultancy companies, in five years, will all have ad hoc fine tuned AI models integrated into their systems. Their employees will use them to boost their performance working in a more efficient and data driven environment.” And someone already started! McKinsey, this year launched “Lilli,” an own generative AI solution that aggregates all Mckinsey’s knowledge and capabilities in one place. The data used to train Lilli relies on more than 100.000 documents and interview transcripts that the firm collected during years of hard work. For Adi Pradhan, an associate partner who specializes in technology strategy and transformations, Lilli is “a thought-sparring partner” ahead of meetings and presentations. He uses Lilli to look for weaknesses in arguments and anticipate questions that may arise, to tutor himself on new topics and make connections between different areas on the projects.

Acknowledgement Statement

This blog is part of the student writing competition in Management Consulting Master Program at the School of Business and Economics.

References

• How Professionals Adapt to Artificial Intelligence: The Role of Intertwined Boundary Work, Journal of Management, 2023
  [https://onlinelibrary.wiley.com/doi/epdf/10.1111/joms.12936]
• How professional services firms dodged disruption, MIT Sloan Management Review, 2023
  [https://sloanreview.mit.edu/article/how-professional-services-firms-dodged-disruption/]
• Consultants Emerge as Early Winners in Generative AI Boom, The Wall Street Journal, 2023
  [https://www.wsj.com/articles/consultants-emerge-as-early-winners-in-generative-ai-boom-8df71d38]
• Killing Strategy: The Disruption Of Management Consulting, CBINSIGHTS, 2020
  [https://www.cbinsights.com/research/disrupting-management-consulting/ ]
• Revolutionizing Decision-Making: How AI and Data Analytics are Transforming Management Consulting Practices, Medium.com, 2023
  [https://medium.com/accelerated-analyst/revolutionizing-decision-making-how-ai-and-data-analytics-are-transforming-management-consulting-94b98a8b1e1b]
• Meet Lilli, our generative AI tool that’s a researcher, a time saver, and an inspiration – McKinsey Blog, 2023
  [https://www.mckinsey.com/about-us/new-at-mckinsey-blog/meet-lilli-our-generative-ai-tool]

Think of organizations as intricate networks of communication channels—meetings, reports, calls, workshops, and other interactions. Communication channels are especially important in organizations that are organized into specialized units or departments. They help to align with strategic objectives, ensuring that each unit’s efforts contribute to the organization’s overarching goals. How this network is structured significantly shapes what leaders and other members find crucial, how they direct their day-to-day efforts, and where they focus their attention. The “pipes and prisms” of this attention architecture determine when, where, and how decisions are discussed and made, involving specific individuals and adhering to certain protocols.

While this organizational architecture might seem unchanging and dictated by upper management based on their vision and preferences, our study published in the journal Strategic Organization reveals that these ‘pipes’ aren’t rigid. They respond not only to top management teams but also to other organizational actors, adding dynamism to our understanding of attention architecture and the agency of mid-level actors.

Strategic shifts within an organization lead to changes in attention architecture

When an organization alters its strategy as a consequence of new technologies, emerging players, or shifts in the external environment, the attention of leaders naturally shifts too, resulting in changes to attention structures. However, such changes can bring both positive and negative outcomes for those who work with communication channels – mid-level employees. The communication channels they contribute to can gain or lose its importance. This was evident in our study of professional strategists at Ericsson (large telecommunication firm) who, despite losing their central role initially due to strategic transformation, managed to regain their position. Two strategies aided their resurgence.

Expanding and Connecting ‘Pipes’

First, the group we studied reinvented some of the communication channels. They explored digital tools, recognizing the potential of involving new actors in established practices, such as understanding industry trends. Traditionally, this task was performed by experts. Mid-level employees also harnessed existing connections with core customers, proposing a Customer Engagement Group to align with top management’s focus on customer satisfaction. This experimental approach aims to add novel elements to existing channels, often by broadening participation through digital tools like crowdsourcing or online communities. Such experimental thinking is the underlying logic of reinvention – what is the novel aspect we can add to the existing channel? Extending the number of participants involved in a communication channel could be one of the promising ways to reinvent or expand the existing ‘pipe’. The availability of digital  tools like crowdsourcing or online communities and chats are useful in it.

Reviving and Repurposing ‘Pipes’

Another way to align communication channels with the evolving architecture is by revitalizing outdated channels. We called this renewal. To reintegrate old practices, it’s essential to identify gaps or areas where communication channels can bring value. Networking skills are crucial here. We observed how forming alliances with groups entrenched in the new attention architecture is the key. The group of professional strategists partnered with the technologists, who managed essential communication channels for top management decisions. The professional strategists’ strategic knowledge and industry expertise complemented the technological prowess of the technologists, successfully reintroducing joint strategy meetings into the new communication architecture. This approach of repurposing channels involves identifying how and when channels can be useful in addressing new strategic challenges. The selective reuse of familiar practices is the underlying logic of renewal. It is important to consider: what are the core communication channels for Top Management Team? Who is the core player in them? And what value can we add by partnering with those players? The ability to evaluate the new communication structure and clear value proposition for potential partners plays a pivotal role in reviving old ‘pipes.

Organizational shifts might be an opportunity for different professional groups to regain their importance

Any transformation that an organization undergoes in its structure, culture, processes, strategies, or other fundamental aspects present opportunities for diverse professional groups to reclaim significance. The story of Ericsson’s strategists exemplifies how organizations can adapt to significant changes through flexibility and innovation. When traditional structures are disrupted, various employee groups, from strategy professionals to HR, Finance, and Marketing, can maintain their importance by creatively redesigning communication channels. As technologies like AI increasingly challenge professional roles, the ability to reinvent and renew communication channels empowers employees to strengthen their position, amidst major organizational changes.