Mark Zuckerberg, the founder and CEO of Meta, has said that his company would start automating the coding work of mid-level software engineers this year, seemingly a foreshadowing of layoffs.
“Probably in 2025, we at Meta, as well as the other companies that are basically working on this, are going to have an AI that can effectively be a sort of mid-level engineer that you have at your company that can write code,” he predicted. This could spell cost savings for organizations, and job losses for employees.
The increasing use of artificial intelligence (AI) is causing unease across the workforce: what will this mean for future jobs? At the moment, the pace of development of AI is faster than organizations’ efforts to react with reskilling and upskilling programs. While it makes economic sense to automate tasks that machines can do better than humans – computing, optimization, recommendations – there are things machines cannot yet do. They lack interpersonal skills or feelings and they cannot host events or drive experiences. This is where AI can either spell opportunity – or disaster – for environmental, social, and governance (ESG) frameworks. The high-energy requirements for the training and running of AI, and ways to reduce that environmental cost, are well-discussed elsewhere; here we will examine how we can vastly improve environmental outcomes by harnessing the power of society.
AI does not necessarily need to be the enemy of the people when it comes to jobs. As Siemens notes in its new report A New Pace of Change: Industrial AI x Sustainability, “Industrial organizations worldwide face similar challenges: societies are aging, we face skilled worker shortages…” AI can help organizations by harnessing the power of data to become more resource-efficient, more productive, and more sustainable. In particular, there is vast untapped potential within industrial AI to reshape industries and drive sustainability at scale. The report details ways in which AI can serve as a “supercharger” for industrial transformation, fast-tracking companies in their optimization of clean technologies, including renewable energy, energy-efficient manufacturing, and electric vehicles.
The report notes, however, that organizations have a responsibility to “balance environmental and social imperatives with their fundamental need to be productive and profitable; balancing carbon budgets and bottom lines will be a critical challenge in the years ahead.” This is a tension that organizations need to manage. So, too, will the balancing of the social impact of AI.
Given the fact that many of the tensions that emerge when AI affects the E and the S of ESG, collaborative approaches to tackle these challenges make sense. Industrial organizations can benefit from collectively using their data to address workforce and energy transition challenges. This collective use can be done in privacy-preserving ways, with techniques like federated learning, a machine learning technique that trains models using data from multiple sources without sharing the raw data, or sophisticated forms of data encryption.
“Data collaborations can also facilitate the development of AI models that predict energy consumption trends, allowing industries to align their operations with sustainability goals.”
By leveraging big data and AI, these companies can create a more integrated approach to energy management, optimizing resource use and reducing emissions. One way to facilitate this data collaboration is through the establishment of collaborative platforms, where companies can securely exchange information on energy consumption, production processes, and supply chain logistics. For example, a manufacturing firm could share its energy usage patterns with a utility provider, enabling the utility to better forecast demand and optimize energy distribution. This not only enhances operational efficiency but also supports renewable energy integration.
Additionally, cross-industry partnerships can drive innovation in energy solutions. For instance, a steel manufacturer and a renewable energy firm could collaborate to develop data-driven strategies for utilizing excess renewable energy during off-peak hours, thus reducing reliance on fossil fuels.
Data collaborations can also facilitate the development of AI models that predict energy consumption trends, allowing industries to align their operations with sustainability goals. By creating a ‘systems of systems’ approach, companies can transform isolated data points into actionable insights, leading to more sustainable practices and contributing to broader climate objectives. Ultimately, this collaborative effort can accelerate the energy transition, fostering resilience and adaptability in an evolving industrial landscape.
So far, employees have contributed with work; in the age of AI-enabled or AI-dependent workplaces, perhaps an employee’s biggest contribution will be data.
AI is only as good as its data. As this tool becomes more central to the success of industrial business, the value of the pivotal quality, contextual data will rise. The data itself becomes a commodity, and companies must explore not only whether (and how) employees should be compensated for the data they produce, but how this data can be organized and pooled for industry-wide insights. One way to organize is data collectives, a model that allows individuals to pool their data, gaining collective bargaining power with the companies that rely on analyzing that data. But the benefit is not only to employees; companies benefit from regularly updated, high-quality, and relevant data. If this contextualized data is further shared, the potential for industry-wide insights will benefit employees and employers.
The compensation for their participation in data collection for the cooperatives incentivizes employees to optimize their own data collection processes, which in turn generates better AI outcomes for the company. To realize this mutual value, organizations need to raise the overall data literacy of leadership and workers to collectively derive the best insights out of AI. This redesigns the way we work and reimagines what we consider a contribution from employees to their organization. So far, employees have contributed with work; in the age of AI-enabled or AI-dependent workplaces, perhaps an employee’s biggest contribution will be data.
If organizations are to prepare responsibly for a sustainable future workforce, they must act in two ways: first, by enabling their workers to operate in a tech-reliant world, and second by clearly delineating which tasks will be accomplished by humans, and which by machines.
Enable workers to understand tech systems, helping them to determine which tasks they should delegate, and which they should not.
So far, employees have contributed with work; in the age of AI-enabled or AI-dependent workplaces, perhaps an employee’s biggest contribution will be data.
In the second phase, organizations will have a clear delineation between tasks done by machines and those accomplished by humans, freeing the latter to do the jobs that are more emotional and experiential. The heavy lifting is done by robots; competitive selection and ideation are done by software.
Better data will help write better AI. Data collectives could play a pivotal role in helping us redefine the social contract and enabling us to design a prosperous future for us all.
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