About a decade ago, Artificial Intelligence (AI) seemed like something in the future. Another perspective held by many is its technicality, which involves machine learning, predictive analytics, and sophistication in data centers. In the same way, the practice of the word “sustainability” is seen as an ambiguous task and is usually viewed as a buzzword for corporate public relations and carbon pledges by many. Fast forward to today, both AI and sustainability are colliding in fascinating ways. Although AI involves data at its center, it is no longer just about crunching data but also about teaching people to think greener and act smarter. It is therefore right to say the machines are also helping us save the planet, one employee at a time. From Policy to Practice: Why Green Competencies Matter From Policy to Practice: Why Green Competencies Matter I once had the privilege of working in an organization where I was part of a team proposing a more robust way to improve the organization's sustainability practices. During the project, we discovered that the core of organizational sustainability should revolve around integrating environmental, social, and economic responsibility into their operations and decision-making. When research was done on organizations that are thriving well in their sustainability practice, it was discovered that most organizations boast with some version of a sustainability policy and mindset, and each of those organizations has its targeted years for its carbon neutrality, that is, Net-zero emissions, with the majority targeting 2030. However, there’s a vast difference between a policy on paper and the actions of thousands of employees to actualize these policies, and that’s where green competencies come in.  We need to think of green competencies as the practical toolkit for sustainability, which highlights how waste can be reduced, choosing eco-friendly materials, and designing more efficient systems. But the problem is how people can be trained in these skills on a large scale. Unfortunately, the traditional means of training cannot keep pace at the rate at which technologies and regulations are evolving. The next big question is whether AI can help train employees to be more sustainable. The good thing is that AI can, and it's already doing so in some surprising ways. AI as the New Green Mentor AI as the New Green Mentor Let's start with how AI works and how it works best. AI works by learning data patterns and making predictions from the learnt data, and with improved output over time. So when trained on high-quality and diverse data, guided by clear goals, AI results in accurate outcomes, especially when continuous monitoring is in place. Therefore, when AI is applied to workforce training, the systems can easily analyze job roles, performance metrics, and even previous learning outcomes. It therefore builds personalized learning paths from there and, in this case, focuses on sustainability. For example, Unilever rolled out an AI-powered learning platform that delivers custom sustainability lessons to its global workforce. As part of their initiative, digital twin technology and AI were utilized to transform their factories into World Economic Forum (WEF) Lighthouse facilities, which shows their commitment to sustainability. The AI was able to track progress, test employees' understanding, and automatically determine future lessons. As a result, a number of their employees learn Unilever’s sustainability goals without having to be trained by humans. Siemens Energy even went further in their AI sustainability deployment, as they also developed smart factories that utilize AI and digital twin technologies to optimize their production processes, thereby reducing their energy consumption and improving their workers' training. As a result, Siemens employees were able to make real-time production decisions and experiment with materials to see how the choices of the materials impact energy consumption. In each of these cases, AI's capacity to teach, guide, and nudge workers toward greener behavior in ways that are engaging was achieved. Predicting and Reinforcing Green Behavior Predicting and Reinforcing Green Behavior Beyond training, AI can forecast behaviour. Using predictive analytics, it can analyze which employees or departments are most likely to adopt sustainable practices and which are likely to resist them. This isn’t about surveillance; it’s about insight. For example, if a system notices that employees who complete a certain training module are also more likely to follow waste reduction protocols, it can push that module to others. It’s a quiet revolution in workplace learning: the company itself learns how to teach better. AI also makes sustainability measurable. The same systems tracking employee progress can feed directly into ESG (Environmental, Social, and Governance) reporting, a yardstick used by organizations to measure and disclose organizational performance on sustainability and ethical issues. The Human Element in Machine-Led Learning The Human Element in Machine-Led Learning Of course, there’s a fine line between guidance and control. The flipside of AI-powered training is that it can easily lead to micromanaging if not properly implemented. Additionally, the concept of data privacy and unethical algorithmic output can also pose challenges. Therefore, while still relying on human mentors and team engagement to deepen understanding, there is a need to strike a balance by automating content delivery and using AI to provide feedback. The inclusion of the human element (Human-in-the-Loop) is to checkmate the social, ethical, and emotional balance. This is because the role of human interference can not be jettisoned, as no algorithm can replace empathy or collective purpose, which humans provide. Challenges and Scaling the Green Classroom Challenges and Scaling the Green Classroom In the past, creating sustainability content for thousands of roles and regions used to take months, but today, generative AI does it in hours. The common generative AI Tools like ChatGPT and Gemini, among others, and in some cases, some customized company tools have been proven to produce localized training materials such as videos, quizzes, and policies which are tailored to job roles or languages. Although these tools are not perfect, they are confronted with the common challenges that AI-enabled systems face. One of which is bias in the dataset, which may lead to insensitive training materials. Bias in datasets can lead to insensitive training materials. Another is poor management of data privacy which can erode employee trust. Lastly, over-dependence on automation can make learning challenging. Despite these challenges, when proper human input is applied to AI algorithms, the AI-powered sustainability training will be successfully designed, keeping pace with changing technologies and regulations. The Future: Green Skills for a Digital Planet The Future: Green Skills for a Digital Planet AI will continue to shape our decisions, behaviors, and industries, and in the coming years, we will likely see AI-driven sustainability training moving beyond corporate organizations into being a national concern. Therefore, governments and international organizations should prepare to deploy AI systems to make life easier for people. One such avenue is to explore AI-powered upskilling platforms, which would sensitize citizens about green jobs in sectors such as energy, transportation, and agriculture. With this, AI will make it possible to train millions quickly, in multiple languages, and across different skill levels.

The Algorithm That Cares: How AI is Coaching Humans to Save the Planet

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