Mandula Moments: Risks and opportunities in an AI-driven world (Part 7)

Continuation from Part 6

Societal Impact: Who Is Helped and Who Is Harmed?
Data’s proliferation will unevenly affect diverse groups in society. I believe some will be greatly helped, while others could be put at a disadvantage. Understanding these impacts is key for leaders to maximise the positive and mitigate the negative.

Those Who Will Be Helped:

• Consumers (with better services): When used well, data can enhance customer experiences significantly. Individuals benefit from personalized products (music or movie recommendations tailored to their taste, customized healthcare based on their genetic data, etc.), greater convenience (think of map apps that use traffic data to save you time), and often lower costs (dynamic pricing and competition driven by data can push prices down). Even mundane daily tasks are easier – e.g., using a smartphone’s GPS and real-time data to navigate a new city is something that saves time and stress. In a larger sense, society benefits when data is used to solve problems: for example, data-driven research finding more effective medical treatments helps patients; climate data analysis that guides disaster preparedness helps communities; agricultural data that optimizes crop yields helps farmers and consumers with more food security. If managed inclusively, the data revolution can raise living standards by powering innovations in transport, communication, finance, and more that make life safer and more efficient.

• Businesses and Workers (with new opportunities): Companies that embrace data see growth opportunities and can create new jobs. The explosion of data has given rise to entirely new professions – data scientists, data engineers, AI specialists, digital marketers, etc. These are well-paying jobs that didn’t exist a generation ago. Moreover, traditional industries can be revitalised by data: for example, manufacturers using IoT sensor data for predictive maintenance can reduce downtime and costs, which can preserve industrial jobs by keeping companies competitive. Small entrepreneurs are helped by access to data insights that were previously the domain of large firms – now even a tiny online seller can analyse web analytics to find their niche market.

• Overall, businesses that leverage data effectively tend to outperform their peers, which can lead to expansion and more hiring. Additionally, certain sectors like healthcare or education stand to improve outcomes (as discussed) – which helps professionals in those fields deliver better results and potentially derive more satisfaction from their work by focusing on higher-level tasks while data automation oversees routine work. I believe this is how and where the global factoring industry (if prepared) can grow and prosper in the future.

• Research Communities: Researchers across fields are being helped immensely by the availability of large datasets and collaborative data platforms. For instance, the rapid development of COVID-19 vaccines was aided by researchers worldwide sharing genomic and clinical data in near real-time. In climate science, shared data from satellites and sensors helps scientists improve models and provide early warnings for extreme events, potentially saving lives. When data is openly shared (with proper privacy measures), the entire scientific community benefits by being able to validate findings and build on each other’s work, accelerating discovery.

• Underserved Populations (if data is used for social good): Data can help target resources and interventions to those who need them most. For example, analysis of poverty and income data can guide more effective allocation of social welfare or identify communities in need of infrastructure. In healthcare, data can reveal gaps in service delivery (e.g., areas with poor access to care) so that programs can be directed there. There are also initiatives to use data for humanitarian purposes – such as mapping populations after natural disasters to direct aid or using mobile data to track and contain epidemics. In these ways, the vulnerable can be helped by smart use of data, provided ethical safeguards are in place. Furthermore, greater transparency (open government data, for instance) empowers citizens and civil society to hold authorities accountable and advocate for change, which can help address social inequities.

Those Who Might Be Harmed:

• Individuals (through loss of privacy and autonomy): The most immediate harm is to individuals’ privacy. As described, if personal data is misused or falls into the wrong hands, it can lead to identity theft, financial fraud, or reputational damage. But beyond breaches, there’s a more subtle harm: the loss of control over one’s own information. People may alter their behavior knowing they are watched (the chilling effect of surveillance). Also, pervasive data collection can lead to manipulation – for example, personal data used in micro-targeted advertising or political messaging can nudge people’s decisions without them realizing it. In a sense, autonomy is at stake: if algorithms know you better than you know yourself, they can influence what you buy, watch, or even who you vote for. This asymmetry between data holders and ordinary individuals could harm consumers by limiting genuine choice (e.g., if dynamic pricing charges you more because data suggests you’re willing to pay, or if online content is filtered such that you never see certain opportunities). So, while consumers enjoy better services, they also face harm if they are not protected by rights and if data is used exploitatively.

• Marginalised Groups (through bias and exclusion): As discussed under risks, biased data can lead to discriminatory outcomes that disproportionately harm marginalized communities. For example, if loan approval algorithms are biased, minority borrowers might be unfairly denied credit. If predictive policing algorithms are skewed, certain neighborhoods might be over-policed, exacerbating injustices. If facial recognition systems have higher error rates for people of color (as many early systems did), those individuals could be wrongly identified and suffer consequences. These harms are very real and could worsen with more AI unless actively countered. Furthermore, there is a risk that the benefits of the data revolution (jobs, services) don’t reach everyone equally – digital divides mean some populations have less access to the data economy. For instance, rural or low-income communities may not have as much internet access or digital literacy, so they might not benefit from data-driven services as much, yet they could still have their data collected. If the positive uses of data (like precision medicine) focus only on those who generate the most profitable data, underserved groups might be left out of the advances. Hence, there is a societal obligation to ensure the data boom doesn’t leave the vulnerable further behind.

• Employees in Certain Roles (job disruption): While many new jobs are created, there will also be workers who lose employment or income due to automation and data-driven efficiency. For example, AI chatbots and analytics can manage some customer service or administrative roles that humans used to do; autonomous vehicles in the future could disrupt truck and taxi drivers; algorithms can perform some legal document review or journalism tasks.

• Workers in these areas may find their skills less in demand. If they are not retrained, they face economic hardship. Historically, technological revolutions do create more jobs than they destroy eventually, but when caused by data per se, but data and AI are the catalysts. It’s critical that businesses and governments anticipate these shifts and invest in upskilling and reskilling programs. Otherwise, we could see greater inequality and social discontent as certain groups feel “left behind” by the data economy.

• I have consistently written about and spoken out on the fact that our industry has historically tended to lag behind others (banking, Investment backing to name just a few) when it comes to consistently providing skilling and reskilling opportunities for all of our global factoring workforce. We simply cannot to “cut the corner” on reskilling and soft leadership training that will need to be provided without hesitation as our industry goes through the coming digital revolution and reawakening.

• Society at Large (if mismanaged): One a strictly personal note, this is my greatest concern and apprehension when it comes to the future rollout of AI and GenAI. Recent behaviors by many elected leaders in the highest offices of government only have reinforced my initial fears. We must each serve as excellent role models for all of our clients, employees, and all we interact with integrity and respect without exception. There are broader societal harms if the data explosion is not managed responsibly. For instance, if we enter a period of widespread misinformation (as noted), public trust in institutions and media could erode, harming democracy and social cohesion. If data-driven surveillance becomes too intrusive, it could stifle freedoms and human rights. If a major cyber-attack on critical data infrastructure succeeds (e.g., crippling financial systems or power grids), it could cause large-scale chaos or conflict. In essence, the stakes are high: an interconnected data-rich world has great benefits but also systemic risks (sometimes called digital systemic risk). The harm of a major failure could be societal in scale. Thus, protecting data systems and ensuring ethical use isn’t just a niche concern – it’s vital for the well-being of society as a whole.

In balancing who is helped and harmed, a key principle emerges: inclusivity and ethics. The more we can include diverse voices in designing data systems, the more likely the benefits will be widespread, and the harms minimised. For example, involving ethicists and affected communities in AI design can catch biases early; enacting strong privacy rights can protect individuals while still allowing productive data use; investing in digital inclusion can help more people participate in the data economy. The goal should be a future where data is a tool for empowerment, not oppression – where it reduces inequality by informing targeted improvements, rather than increasing inequality through unchecked use. Achieving this requires conscious effort and is not guaranteed, which is why the next section focuses on strategies to guide the use of data in positive directions.