As early as 2023, the Singapore government was already exploring how artificial intelligence (AI) would change work in the sustainability industry.
One example is in regard to greenwashing: the act of making false or misleading statements about what a company, or their products and services, do to benefit the environment.
It stalls progress in the sector and erodes trust and a sense of fairness when it comes to climate action.
In the case of sustainability-linked loans (SLL) in real estate, the industry has faced challenges in tracking whether such products and financing were as “green” as promised. SLLs are financial products where the interest rates are tied to sustainability performance targets.
Enter AI, with a project named Project Nova, greenlit (pun intended) by the Monetary Authority of Singapore (MAS).
The utility leveraged AI and data analytics to assess the performances of SLLs, and helped banks identify prospective green building projects, monitor real-time performance targets, and accelerate green real estate financing, through the automated extraction of sustainability insights.
Since then, AI has also begun integrating into the workflows and processes of many other workers in the sustainability industry, such as:
- Optimisation of energy usage in buildings and lowering energy costs
- Lowering data centres’ energy consumption by optimising cooling systems
- Enhancing yield and resource efficiency in vertical farming
- Calculation and assessment of emissions for corporations
We spoke to Linda Teo, Country Manager of ManpowerGroup Singapore, who shared further insights on why knowledge of AI tools improves employability in the sustainability sector.
What AI-ready skills and tools are employers in sustainability seeking in recent job descriptions?
Linda: Employers in this space often look for familiarity with AI-enabled data analytics, carbon tracking platforms, or modelling tools that support reporting and planning.
In many cases, the expected proficiency depends on the role, but it’s not uncommon to see a demand for basic to intermediate level of proficiency.
What percentage of applicants have the necessary proficiency in these tools?
The level of proficiency among applicants generally varies depending on whether the role requires general AI familiarity or exposure to more specialized sustainability tools.
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Many candidates are comfortable using common generative AI and basic analytics platforms, but fewer tend to have experience with sector-specific tools such as carbon tracking systems or modelling software used for reporting and planning.
As a result, the overall readiness can be uneven across applicant pools.
What trends can we expect in the coming years regarding AI integration in the sustainability industry, and what new tools will professionals need to master?
Looking ahead, the sustainability sector may see broader adoption of AI to improve the accuracy and efficiency of reporting, particularly as more companies work toward meeting regulatory or voluntary disclosure requirements.
AI-enabled scenario modelling and resource efficiency simulations may also gain traction as organisations look to better understand environmental risks and plan mitigation strategies.
There may also be growing interest in tools that integrate geospatial data or lifecycle analysis, which can support more holistic assessments of environmental impact.
What AI tools are employment specialists using to improve their work processes and stay current?
For sustainability-focused roles, consultants also track trends using AI-driven labour market analytics and platforms that map emerging green job families.
These tools help specialists understand evolving competency requirements and identify candidates with adjacent skills relevant to sustainability transitions.
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