Deploying AI To Shed Light On The Murky Depths Of Multi-Tier Supply Chains

As artificial intelligence becomes embedded across supply chain operations, companies are well-advised to plan for heightened expectations around transparency, explainability and governance from customers, investors, stakeholders and regulators — expectations that intensify sharply when things go wrong.

Data breaches, discriminatory outcomes, human rights failures and quality and safety incidents increasingly prompt demands for clear explanations of what warnings were missed, and whether the company effectively used data and analytics to identify and mitigate the risks. This indicates a growing assumption that companies will actively use AI throughout their supply chains not merely to drive efficiency, but also to identify, predict, assess and address social and ethical risks.

Senior management, particularly at publicly traded firms, should therefore anticipate increasingly tough scrutiny of how AI and emerging technologies are being used to improve business performance while preventing, detecting and mitigating adverse consequences before they become costly failures.

Supply Chain Risk in a VUCA World

Back in the 1980s, the Army War College introduced the concept of VUCA — volatility, uncertainty, complexity and ambiguity — to describe the global risk environment. VUCA certainly describes the current challenges facing corporate leaders responsible for managing supply chains, including geopolitical disruptions to trade routes’ increasingly complex sanctions, tariffs and trade controls; and global competition for scarce natural resources. This highly stressed and dynamic environment both incentivizes and increases the likelihood of supply-chain-related misconduct such as corruption and fraud, modern slavery and human rights abuses, quality and safety failures, and risks associated with critical or conflict minerals

Fraud and corruption thrive in VUCA environments, where supply‑chain controls are strained and visibility limited. Bad actors exploit uncertainty and ambiguity to evade controls, with the goal of extracting funds from companies and directing those funds to line their own pockets and those of others, including corrupt government officials who may feel emboldened by a reduced risk of sanction. Fundamentally, the path to sustainable business success is rarely paved with supply-chain transactions involving false documents, inflated invoices, fictitious services, or criminal actors. Instead, effective compliance programs, supplemented by AI insights, can mitigate risk and help avoid the reputational, legal and financial harms associated with corruption and fraud.

Modern Slavery and Human Rights

The business-related impacts of modern slavery and human rights abuses are serious and increasing. This trend is driven by factors such as globalized supply chains, increased demand for critical minerals (for example, to support semiconductor manufacturing and data centers), along with persistent risks in longstanding areas of concern, such as garment manufacturing or electronics assembly. Governments have responded with a variety of regulatory responses, from forced labor import bans in the U.K. and U.S.; mandatory human rights due diligence laws like the Corporate Sustainability Due Diligence Directive and other domestic laws; disclosure requirements such as modern slavery acts in Canada, the U.K., and Australia; laws authorizing civil and criminal remedies for exploiting or benefitting from modern slavery, and public procurement laws prohibiting government bodies from purchasing goods tainted with forced labor.

Historically, some companies have taken the position that so long as their subcontract terms required compliance with supplier codes of conduct or similar contractual provisions prohibiting modern slavery, or if they obtained certifications from suppliers, risks could be effectively managed. However, because modern slavery often lurks deep within supply chains among opaque and hard-to-trace actors, it can too easily evade detection except by the most dedicated organizations. Today, AI has become a regular part of supply-chain due diligence and is being employed by companies as well as regulators seeking to identify links to geographies or entities associated with forced labor.

Many firms rely heavily on their supply chains to deliver materials and components that conform to required specifications and can be readily incorporated into finished products. When supply chains are stressed by VUCA or other disruptive dynamics, incentives for misconduct increase and may take the form of counterfeit parts, gray-market transactions, or falsified test and inspection documentation. If these issues go undetected and ultimately undermine the reliability or performance of end products delivered to customers, the consequences can be catastrophic to company reputation, brand and financial performance — not to mention the lawsuits and enforcement actions that will likely follow.

Critical and Conflict Minerals

As noted above, the demand for critical minerals, including those extracted in conflict zones, is expected to increase exponentially. Growing global demand for computing capacity, driven by semiconductors and energy-intensive data centers capable of powering entire cities, will only increase the pressure on the global extractive supply chain to mine and distribute these minerals at scale. Here again, end-user companies face challenges associated with critical minerals being extracted from areas of active conflict or from countries with non-transparent political and legal systems.

This dynamic materially increases the risk of corruption, modern slavery and the delivery of substandard materials that can affect product quality and safety. At the same time, the difficulties in collecting reliable information and generating valuable insights impede the ability of companies to effectively manage exposure to disruptions in critical mineral production and the resulting impacts on performance.

The Role of AI

The risks outlined above aren’t new, but the speed and pace of change in a VUCA world have accelerated; and with it, the pressure on businesses to use technology to make better and faster decisions has only increased.

Somewhat ironically, while AI is responsible in part for this acceleration, it can also be deployed to improve a company’s ability to not only manage risk effectively but also to achieve superior business results. In particular, AI is highly effective at ingesting large volumes of data and generating insights that identify correlations, relationships and other red flags indicative of heightened risk.

For example, Amazon has publicly described deploying AI‑driven systems to monitor human rights and forced labor risks across its global supplier network. The company reports that its AI tools flag “approximately nine out of every 10 high‑risk supplier sites” with “85% overall accuracy,” and enable audit reports to be processed “65% faster.”

There are a range of technology applications that can support this goal: automated platforms that assess and address supplier risk based on factors such as corruption, quality and safety, or modern slavery; LLM engines that can generate inferences from vast quantities of data; predictive analytics that can identify potential supply chain failure points before they occur, and agentic agents that can perform specific tasks similar to those performed by human operators. When implemented effectively, AI in supply chains can increase efficiency, reduce costs, improve margins and help avoid the adverse consequences of compliance and performance failures.

While the implementation of AI solutions may seem daunting, there are practical steps companies can take now to get ahead of risk and prepare for the future:

Commission an updated risk assessment. AI projects deliver the most value when they are carefully scoped and have clear objectives. To borrow an engineering concept, companies should first identify “what problem are we trying to solve,” and focus on the top five to 10 supply-chain risks that have the most material impact on the business.

Map your tech stack to your risks. Once key risks are identified, determine where AI can make a meaningful difference. This requires assessing your existing IT tools and platforms and identifying the gaps (for example, where a third-party due diligence platform performs periodic checks of suppliers but lacks continuous, real-time monitoring) and then assessing which AI tools can fill those gaps. This may include machine learning, enterprise LLM engines, or the deployment of agentic agents.

Pick a standard. There is no single, overarching standard governing AI performance and compliance. Companies should review available standards (for example, ISO 42001, NIST RMF, COSO’s GenAI standard, or OECD due diligence standards) and determine which standard, or combination of them, best aligns with their business needs. As supply-chain professionals know, high performance depends on clear requirements and standards, and AI is no exception.

Update or develop effective policies, procedures and controls. AI initiatives that positively impact business and financial performance depend on effective governance and controls. Companies should act now to establish principles for responsible AI use and deployment, and operationalize those principles through policies, procedures and disciplined controls. AI governance bodies should be put in place to identify and assess high-risk use cases, and to ensure that critical issues such as cybersecurity and appropriate human oversight are sufficiently addressed.

Prepare to be audited. Whether AI systems are operating as intended and whether management is fulfilling its obligations, particularly in publicly traded companies, will increasingly be subject to audit. This highlights the importance of selecting standards against which auditors can test processes and render a credible and defensible opinion. Audit functions can add tremendous value to organizations by holding management accountable and identifying areas for improvement and corrective action.

Companies that get busy now implementing these measures will leap ahead of their competitors in managing supply chain risk and position themselves to successfully navigate the vagaries of a VUCA world.

Jonathan Drimmer and Carl Hahn are partners in the Washington, D.C. office of Steptoe LLP., The authors gratefully acknowledge the contributions of Stephanie Sebastian, an associate of the firm, for her work on this piece.

Originally published by SupplyChainBrain.

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