AI in Ethical Sourcing — Navigating ESG Practices in 2025

AI in ethical sourcing has emerged as a powerful force in procurement and modern business itself. As more companies strive to embed ethics and sustainability into their business fabric, AI has proven to be a resourceful tool in driving ESG procurement initiatives.

From automating supplier assessments to promoting transparency across supply chains, AI is set to become even more prevalent in assisting ethical sourcing, becoming a catalyst for ethical and sustainable business practices.

This article explores the role of AI in ethical sourcing, such as its impact on procurement, key challenges, and ways to overcome the concerns associated with AI applications in ethical sourcing.

But before you read the article, I have created a free-to-download, Productive Procurement with ChatGPT Toolkit template. It includes a PDF file that contains prompts that can help you in your ethical sourcing process. I even created a video explaining how to use the templates.

AI in Ethical Sourcing

In traditional procurement, ethical sourcing refers to the procurement of goods and services through methods that follow social and environmental standards. Ethical sourcing considers the impact of business practices on people and the planet.

At its core, ethical sourcing is comprised of fair labor practices, sustainability initiatives, community engagement, transparency, and accountability in both procurement teams and their suppliers. But how can we harness AI to create and promote a culture of sustainability and ethics in our sourcing practices?

Artificial intelligence (AI) can ensure ethical sourcing practices by amplifying its potential to assist various areas of the procurement process. 

AI-Powered Ethics in Procurement

AI in ethical sourcing is a key component in building a future of more socially responsible and modern procurement. AI-powered ethics in procurement leverages artificial intelligence, machine learning, and natural language processing to enhance ethical practices within the procurement process.

Organizations can automate and improve ethical sourcing initiatives, including supplier evaluations and compliance monitoring by harnessing AI tools. These technologies have capabilities to identify potential ethical risks, ensure transparency, and promote responsible business throughout the supply chain.

AI in ethical sourcing can be incredibly resourceful in procurement. By enabling a proactive approach to human rights and environmental concerns, AI applications in ethical sourcing can contribute significantly to ensuring ethical procurement practices.

5 Ethical Sourcing Challenges and AI-Powered Solutions

AI in ethical sourcing has the potential to amplify or even kickstart business practices with ESG standards at its core. However, like all modern trends and technologies, it comes with its own set of challenges.

Below are some of the most common challenges in AI in ethical sourcing, and ways procurement teams can overcome them.

1. Complex Supply Chains

Today’s global supply chains are diverse and complex, involving several intermediaries that may pose challenges in tracing the origin of materials and evaluating ethical practices for each phase.

For instance, while your supplier may claim 100% vegan leather sourcing, their manufacturers might not have completely ethical methods of extracting materials for producing their leather products. 

Solution: Harness AI to enable real-time tracking and monitoring of products throughout all parts of the supply chain. This will allow businesses to identify potential ethical concerns while increasing visibility and traceability in their supply chain.

AI-powered procurement platforms can analyze supplier and manufacturer databases and use real-time data analytics to evaluate sourcing practices and ensure more socially responsible sourcing decisions.

2. Ethical Certifications

Regulatory agencies may vary in credentials and reliability, leading to challenges in ethical verification and routine audits. This can lead to inconsistencies and setbacks when it comes to assessing suppliers and organizational ESG practices.

Some agencies may be credible and rigorous in their evaluation processes, while others might not be as thorough. This can make companies prone to risks of non-compliance and ethics washing or greenwashing. 

Solution: Harness AI with blockchain technology to enhance transparency in the supply chain by providing transparent historical records and past data on supplier performance. Furthermore, blockchain technology is specifically effective in verifying ethical certifications and ensuring the credibility of suppliers in adhering to ESG standards.

3. Lack of Real-time Visibility

Monitoring and addressing ethical concerns proactively may be challenging if procurement leaders lack real-time visibility into supplier operations, especially in settings where suppliers come from different areas globally.

With limited visibility into supplier processes, businesses risk overlooking ethical issues and regulatory compliance. In the scope of global suppliers, there’s no guarantee that all suppliers operate in a way that is socially responsible and environmentally sound.

Solution: Harness predictive analytics with AI to accurately assess past and real-time data. This will help predict potential risks in the supply chain, including risks in compliance, environmental concerns, and labor practices.

With AI-powered predictive analytics, businesses become better equipped to proactively handle ESG issues, such as ensuring fair wage and labor conditions, minimizing their carbon footprint, enforcing regulations and ethical standards, and others.

4. Human Rights Concerns

One of the main challenges facing businesses globally is ensuring fair labor practices and preventing human rights violations throughout all levels of the supply chain. Suppliers from different cultures and parts of the world may not adhere to international environmental, social, and governance standards.

This can make it challenging to ensure that all operations in the supply chain uphold ethical values, particularly in fair wages and labor practices as not all suppliers follow the same standards or regulations, particularly when dealing with global suppliers.

Solution: Use AI to automate compliance audits which will allow businesses to evaluate and derive insights on which suppliers remain compliant with ethical standards, and which ones fail to adhere.

Harnessing AI to automate audits can ensure a comprehensive and stringent assessment, minimizing the likelihood of traditional systems overlooking non-compliance and risks in enforcing ethical practices.

5. Varying ESG Regulations

Different regions and industries follow their own environmental, social, and governance (ESG) regulations, and this poses a challenge for companies aiming to adapt and adhere in a manner that upholds ethical values.

While organizations may adhere to their ethical core values, their suppliers, producers, logistics partners, and other intermediaries might not share the same principles. This makes it difficult to enforce ethical standards in the business

Solution: Implement AI-powered systems that can provide real-time monitoring. Furthermore, train the AI model to adapt to varying ESG regulations around the world. AI can automate data collection, analysis, and reporting, thus ensuring adherence to ethical standards.

Furthermore, using AI technologies such as machine learning and blockchain in procurement can enhance transparency and traceability, effectively contributing to ESG practices across different industries. 

Companies Who Use AI to Ensure Ethical Sourcing

While AI is a fairly new trend in sourcing and procurement itself, companies around the world have proven their commitment to ethical sourcing by harnessing AI in their ESG initiatives. Below are some examples of companies that equip AI to ensure ethical sourcing:

1. IBM’s Responsible Sourcing Blockchain Network (RSBN)

Technology companies like IBM have proven to prioritize sustainability and social responsibility in their business practices. Some concerns arose about the responsible sourcing of lithium, nickel, and copper, and improper disposal of environmental hazards.

To address this issue, IBM deployed the Responsible Sourcing Blockchain Network (RSBN), built on the IBM Blockchain Network to provide more transparency and accountability to demonstrate the ethical sourcing of cobalt.

RSBN is assured by RSC Global, and its founding members include Ford Motor Company, Volkswagen Group, LG Chem, and cobalt supplier Huayou Cobalt. RSBN enables IBM to co-create an ethical sourcing network for other industries.

2. OpenSC’s Automated Claim Verification (ACV)

OpenSC uses blockchain and AI to trace each stage of its products from sourcing to its journey to the consumer, which helps ensure ethical and sustainable practices. 

OpenSC’s technology aims to certify claims through automated ways, including the verification of low-carbon and ethical production. With solid and verifiable data, OpenSC can trace its products throughout the supply chain and provide comprehensive data to its stakeholders, including consumers and investors.

Moreover, OpenSC developed three methods of Automated Claim Verification (ACV), including certifying deforestation-free palm oil, ensuring fair payments to coffee farmers, and fishing sustainably and responsibly.

3. H&M’s AI operations make its supply chain more sustainable

H&M uses AI for demand sensing to make its supply chain more environmentally responsible. In the fashion retailer’s operations, AI traces the source of materials and products and tracks each step in the sourcing process.

Moreover, H&M uses advanced analytics and artificial intelligence for demand sensing, enabling the company to make its supply chain more transparent and ethically sound. 

By calculating and predicting how much of a product will be bought, H&M reduces its resource consumption significantly. With the help of AI, H&M builds a more sustainable and ethical supply chain by creating fewer emissions and reducing its waste output.

4. Adidas’ AI-driven supply chain analytics 

One of the many challenges faced by companies in the fashion industry is ESG issues that stem from excessive waste and production processes that heavily consume resources.

To address this challenge, Adidas has set goals to significantly reduce its use of virgin polyester by 2025, and achieve carbon neutrality by 2050. Part of Adidas’ sustainability initiatives is to harness AI-driven analytics to streamline the sourcing of its materials.

AI also helps Adidas proactively handle risks related to production waste and energy consumption. Furthermore, AI helps Adidas identify the best suppliers to align with their goals, selecting suppliers with ethical and sustainable values in mind.

5. Unilever partners with Google for responsible sourcing

Unilever is one of the largest CPG companies using AI for ethical sourcing. By using Google Earth Engine, Unilever can trace its sourcing and how certain stages affect the environment and communities in those areas.

Unilever aims to eliminate deforestation from its supply chain, with an increase in climate change concerns arising over the years. Thus, Unilever is using AI to compare satellite imagery to detect land usage over time, including agriculture, forests, pasturing, and others.

The multinational consumer goods company recognizes the importance of ethical and sustainable sourcing. By harnessing AI for its ESG initiatives, Unilever can reduce waste, source ethically, and leverage cost savings through responsible sourcing.

Conclusion

AI in ethical sourcing has increasingly become crucial in modern procurement. AI technologies can contribute significantly to businesses aiming to embed ethics and sustainability in their business fabric. 

AI can drive environmental, social, and governance (ESG) initiatives by automating supplier evaluations, ensuring supply chain visibility, and monitoring sourcing processes. To sum up, adopting AI in ethical sourcing is set to grow, effectively contributing to more responsible business practices.