AI and the Erosion of Social Capital: Impacts on Older & Younger Worker Value
The advent and buzz around the business possibilities of AI make today’s world of organisation design an exciting place to be. Certainly, based on the extensive media coverage surrounding the AI world, there is a feeling of a ‘wild west frontier’ enveloping its application within the commercial world. If you believe the papers, one day AI is a catalyst for a workforce Armageddon, another day, it is a hierarchy crusher, then a productivity lifesaver and the next a massive job creator.
The ostensible justification for business investment in AI is the belief it will accelerate growth and productivity opportunities. Yet, whilst the AI industry might be advocating how this technology is heralding strategic disruption, at a macro-change level the preoccupation with chasing productivity improvement through more tightly integrating human capital and technology has a ‘two dimensional’ feel about it. From an organisational design perspective this opens another productive line of enquiry – to what extent does the pre-occupation with increasing the integration of human capital and technology generate a significant opportunity cost in the erosion of organisation social capital? What impacts might this have on longer-term organisation innovation and growth capabilities? If organisations deliberately or otherwise choose to deplete their social capital investment, the question becomes how then does this impact the value of the older and younger worker?
A Brief Social Capital Primer
Whilst social capital was first conceptualised by Pierre Bourdieu in 1985, from a business management perspective, the idea of social capital took hold in 1998 through the academic work of Nahapiet and Ghoshal. They defined social capital as the sum of actual and potential resources embedded within, available through, and derived from networks of relationships. Over time these resources have come to be commonly understood as the existence of norms, reciprocity, and trust enabling individuals and groups to collaborate, coordinate and achieve common goals. Social capital was originally understood as an outcome of individual interactions within close-knit largely homogenous groups.
However, further work by Putnam identified the concept of bridging social capital which referred to connections and relationships across diverse social groups and networks allowing the exchange of information, ideas, and resources beyond the boundaries of close-knit groups. Promoting collaboration and co-operation, these ties help facilitate innovation, diversity and social inclusion thereby, in a work environment, enhancing the capability of workforces to address complex challenges and pursue common goals. The major element of social capital is that it emphasises collectivism rather than the individualism and agency of economic theory, so popular in today’s management literature.
Why the existence of social capital matters in a business context is because of consistent empirical evidence that it makes a difference to performance. The presence of social capital is associated with:
Better innovation as ideas travel through relationships.
Better knowledge sharing because people are willing to help each other.
Greater resilience resulting from networks helping organisations adapt during disruption.
The push for the recognition of the importance of social capital to an organisation acts to offset the traditional view that individual talent ie human capital is the driver of organisation success. My PhD found the talent management literature overestimates the importance of talent and human capital on organisational performance whilst neglecting the performance contributions of social capital. However, academic literature is now increasingly arguing organisation success is more connected with relationship quality and network structure. The economist John Kay emphasises the successful 21st century corporation will emphasise the building of the collective intelligence in the firm. He posits, in the modern world successful commercial relationships are not simply instrumental and transactional, as perhaps they have become understood to be, but are social and embedded in a wider framework of communities and teams. This then leaves open the question of how the current AI mania supports social capital acquisition and usage in driving improved business performance?
What We Understand About the Current AI Stampede
Every day brings more media stories about AI, more surveys, more reports, more expert opinions and ultimately more confusion as to the actual state of AI play within industry. What is emerging is a variety of corporate motives, approaches and debates surrounding integrating AI within their organisations. At the heart of the issue is the rationale for the increasing number of layoffs within corporations.
Certainly, there is no debate that AI is here to stay. The World Economic Forum reports 41% of companies worldwide expect over the shorter term to reduce their workforces over the next five years because of the rise of AI. Currently, large organisations appear to be using AI to flatten hierarchies with both middle management and entry-level roles targeted. The rationale for this remains unclear.
A survey of 350 global business executives with an annual revenue of at least $1 billion by the research and advisory firm Gartner found that many have reduced their workforce irrespective of AI adoption. Companies such as Oracle, Microsoft and Meta have declared they need to cut headcount to free up cash to invest in organisations structured around AI. Still others are adopting a new practice of ‘AI washing’ which is using AI as the reason to explain layoffs driven more by competitive factors than technology concerns.
However, smaller companies appear to be following a different path to the larger companies in their AI implementation approach. A 2026 Upwork Research Institute study of small to medium businesses (SMBs), found these SMBs have largely avoided using lay-offs as their major transition strategy. Instead, they are focusing on experimentation, flexing their workforces and using AI to change the scale equation to manage disruption surrounding them. Early indications are revealing these SMBs developing stronger innovation capabilities as a result.
Despite increasing AI investments, as yet, companies are reporting little improvement in productivity or performance. McKinsey report while 88 percent of organisations are now experimenting with AI, 81 percent do not report any meaningful bottom-line gains. Another 2026 study published by the National Bureau of Economic Research found that among 6,000 CEOs, chief financial officers, and other executives from firms who responded to various business outlook surveys in the U.S., U.K., Germany, and Australia, the vast majority see little impact from AI on their operations. Nearly 90% of firms said AI has had no impact on employment or productivity over the past three years.
There currently appear two competing rationales for AI implementation initiatives – a desire for greater organisation efficiency or making organisation’s more resilient and sustainable. Large companies with a cost-cutting focus are seeing flattened organisation hierarchies as an opportunity to improve business efficiency, whilst those with a strategic emphasis and focus on a structural reset of the workforce understand the need to maintain talent pipelines to reinforce business sustainability.
Some preliminary lessons for organisation designers seem to be emerging in these early stages of industry AI adoption. These include:
A primarily cost- cutting mentality to AI implementation focused on headcount reduction through organisation flattening, if not part of a thoughtful design process is likely to lead organisations down a path of questionable value and limited returns.
Those companies applying a considered and longer-term perspective to integrating AI into their organisations are likely to be characterised by:
balancing automation with investment in workforce capability.
adopting skills as the shared language connecting business strategy, technology and people.
understanding AI by itself won’t deliver real competitive advantage but will come from how effectively organisations retrain, redeploy and empower their people to work alongside it.
protecting the pipeline of skilled and valuable employees, recognising its benefit for developing networks, ideas, resilience and sustainability.
In essence, the companies successfully integrating AI into their workforces and support structures will be those who understand AI value is amplified through integrating both human and social capital with its application to driving business productivity and growth initiatives. Developing a collective workplace mentality will be vital. Technology on its own will not deliver breakthrough thinking to accelerate growth. Economists already know this. They understand the intangible human elements involving imagination and idea generation are the real drivers of economic growth.
An Economic Growth Model
An engaging exploration of the notion of economic growth by the academic Daniel Susskind described how investigation into the causes of sustainable economic growth identified the primary driver as ongoing ‘technological progress’. However, the bigger challenge for researchers was to better understand what drove this type of progress. The first breakthrough came in the late 1980s with economists identifying the value of human capital. There was the realisation that when people invested in themselves through more education and training, the intangible stock of human capital grew, allowing the workforce to become more productive, thereby facilitating growth.
However, the challenge economists ran into in trying to explain the factors underpinning continuous growth was the inevitability of the physical issue of diminishing returns. Physical resources such as land, capital or human skills were always finite, complicating understanding of why growth was not only sustainable but increasing over time. This led to the second major economic breakthrough in understanding the foundations of growth, the importance of the world of intangible ideas.
What differentiates the intangible idea from the tangible objects of physical resources is that with them there is no sense of diminishing returns. Ideas do not compete physically with other resources; they can be re-used without limit and at no further cost. They are also cumulative with the possibility earlier ideas can be further developed. What economic researchers discovered as they analysed the notion of economic growth from the time of the Age of Enlightenment in the 18th century was that growth was the outcome of the hustle of inventors and entrepreneurs in competitive pursuit of the profits that might come from discovering new ideas.
As we know, ideas can be either individually generated or the outcome of group interaction, in other words the result of both human and social capital. Applying this model of growth to the world of AI suggests that human generated ideas are going to be critical for its ongoing evolution and effective contribution to a better world. This also suggests that creating an environment that promotes a rich and diverse range of ideas will ultimately be beneficial to an organisation. In such an environment workers of all ages can play a critical role in shaping how AI can productively serve the business. The business that invests in its social capital as an enabler of ideas to drive its AI investment will not only be smart by building collective intelligence but also more resilient.
How Older and Younger Workers Can Help Generate Social Capital
The current AI implementation approach is savage on employees of all ages with the swathe its cutting through job roles particularly from middle management positions downwards. No one is being spared. On face value, employee age seems immaterial. However, a case can be mounted that AI implementation appears more discriminatory to both younger and older workers. There is the strong suggestion, as previously mentioned, that entry level professional and administrative positions are under threat, making it difficult for young workers to get a first solid step on the career ladder. Older workers are being swept up in organisation restructuring programmes and then face the extra challenge of AI driven recruitment processes actively discriminating against them. You only need to observe, certainly in the US, the mounting number of successful legal challenges by older workers against well-known big businesses demonstrating deliberate ageism and discrimination in their employment decision-making. Right now, it is hard to argue against the proposition that AI is disproportionately eroding the value of both younger and older workers.
The last 40 years has seen the deliberate breakdown of the collective workforce mindset and the promotion of work-place individualism. Coincidentally over this same period we have witnessed a stagnation of productivity, anaemic growth and increasing issues surrounding employee well-being. There is a major risk that AI implementation accelerates the individual atomisation of the workforce. The possibility of smaller more homogenous corporate workforces becoming the norm, perhaps, is not a great recipe for creating a rich environment to stimulate imagination and create the ideas necessary to extract real economic value from AI investments.
We maintain that business in its discomfort with the idea of the collective could be acting against its own best interests. When we refer to the idea of the collective worker, we understand it being more than a static body of workers and rather more as a social process in which skills, techniques, knowledge and ideas can be passed on from generation to generation. We see age diversity as a potentially powerful contributor to the development of a healthy organisational social capital. We understand this as a mutually beneficial outcome for company and employee alike in the era of AI.
We are not saying, that just because older workers have more life experience to draw from they have more social capital. We are saying though, they can play a distinct role in creating, maintaining and transmitting social capital within organisations What we know is that having older workers in the workforce is a positive for business performance. Increasing research evidence, admittedly in a pre- AI era, is supporting this view:
Organisations with a 10% higher share of workers aged 50+ are 1.1% more productive (OECD 2020).
Increasing age diversity has a positive effect on company productivity, (Backes-Gellner & Veen, 2013).
Age-diverse firms outperform age-homogeneous firms in productivity (De Meulenaere, Boone & Buyl (2016); Wegge, J., Jungmann, F., Liebermann, S., Shemla, M., Ries, B. C., Diestel, S., & Schmidt, K.-H. (2012).)
Older workers contribute to incremental innovation implementation success whilst younger workers contribute more to radical innovation (Njøs, R., Jakobsen, S.-E., Wiig Aslesen, H., & Fløysand, A. (2020)).
Older workers improve customer service and relationship-based services outcomes.
The value of older worker involvement within the workforce is normally found in team-level productivity gains more than individual output. Teams with older workers see increased productivity of younger workers, faster onboarding speeds of new team members and heightened levels of knowledge transfer. Older workers help facilitate social capital development through their tenure and institutional memory. The benefit of longer careers is larger internal networks to be accessed, stronger ties throughout the organisation and a respected credibility that encourages other to seek their counsel. Institutional memory allows the brokering of knowledge through individual mentoring or acting as a bridge between teams.
In terms of idea generation, Carl Honore has observed that inventors tend to peak in their late 40s and remain productive in the second half of their careers. He quotes the statistic of the average age for patent filing as 47, with the most lucrative ones coming from those over 55. We also know the over 50s are starting companies faster than any other age group and being successful. And let’s not forget the opportunities experience and wisdom offer. The neuroscientist, Daniel Levitin notes, the advantage of years of experience is older people having a vast warehouse of solutions allowing them to synthesise more information and potentially offer more solutions. In the workplace, an important way this wisdom plays out is in the capacity for holistic or systems thinking that allows one to get the ‘bigger picture’ of something by combining a wide variety of information quickly. So, including older workers in the workforce and enabling the flow of social capital within the organisation seems a powerful way to increase potential returns from corporate AI investments.
Final words
If idea generation is a foundational driver of tech based economic growth, then creating work environments that are conducive to stimulating idea creation become critical to increasing returns from AI implementation initiatives. And work environments which invest in social capital development have proven innovative capabilities as relationship development, network creation and trust building encourage knowledge and idea sharing. Counter-intuitively whilst AI appears to emphasise an increasing hollowing out and reduction in managerial, professional roles and administrative roles, leading to the increasing atomisation of the workforce, we suggest a better AI investment return may be created through developing a stronger collective mentality around workforce management. We accept this is a challenging perspective in the existing business world where speed, transactional thinking and executional excellence are the dominant operational requirements.
We also suggest that the existing AI implementation focus is deliberately or unknowingly eroding valuable social capital reserves. And as these reserves are depleted, so the value of older and younger workers appears further eroded; this at a time when the value of having the capability to generate both volume and diversity in ideas will be advantageous. Denying work opportunities for younger and older workers we believe will compromise organisation idea development capability and strong social capital investment. We’re at a loss to understand how this ultimately builds resilient and sustainable organisations.
Emerging knowledge integration research is revealing that the combination of work experience and new knowledge is delivering the highest innovation output. Unstated in this observation is the likely role social capital is playing as ‘the glue’ to make it all stick. We also recognise the rapidly increasing complexity of operating environments companies are now operating in. We understand AI is and will generate business problems that may be difficult to crack without people having the ‘grey hair’ that comes with the deep experience and knowledge of a seasoned subject expert. We remain optimistic that smart companies will embrace workers of all ages in their AI journey and understand the benefits that accrue from investing in initiatives building social capital mechanisms and reserves. Creating a ‘collective intelligence’ capability we see as a future business game changer.
References
Nahapiet, J. & Ghoshal, S. (1998). Social Capital, Intellectual Capital, and the Organizational Advantage. Academy of Management Review (23): 242-266
Putnam, R. D. (2000). Bowling Alone: The Collapse and Revival of American Community. Simon and Schuster.
Kay, J. (2024). The Corporation in the 21st Century: Why Almost Everything We Are Told About Business is Wrong. Profile Books. Great Britain.
Gannon, B. & Roberts, J. (2020). Social capital: exploring the theory and empirical divide. Empirical Economics 58: 899–919
Susskind, D. (2024). Growth: A Reckoning. Penguin Books. London. UK.
McKinsey. (2022). Network effects: How to rebuild social capital and improve corporate performance.
McKinsey. (2026). The State of Organizations Report
Honore, C. (2018). Bolder: Making the most of our longer lives. Simon & Schuster. Great Britain
Levitin, D.J. (2020) Successful Aging: A Neuroscientist Explores the Power and Potential of Our Lives. Dutton. USA
Angelo, J. (2026). ‘AI isn’t paying off in the way companies think. Layoffs driven by automation are failing to generate returns, study finds.’ Fortune. (May 11)
Burlacu, G. (2025). ‘Our data shows that companies of 500 and fewer workers mostly avoided the AI layoffs. They’re making AI work for them.’ Fortune. (Dec 19)
World Economic Forum. 2025 Future of Jobs Report
