How can businesses improve decision making in an ever-changing commercial, sourcing and operational landscape? This is the promise of autonomous supply chain management and responsible sourcing technology, but these new systems must be adopted with a clear understanding of the risks they are intended to navigate.
In the past, many responsible sourcing workflows may have been based on poor risk intelligence and ineffective risk management processes. Unless designed carefully, autonomous systems could be an extension and even amplify this challenge.
The Evolution of Supply Chains
In recent years, cyclical and manual compliance checks, supplier audits and corrective actions have not kept up with the pace of change. The results have been predictable: Fragmented risk visibility and delayed interventions. There has been a “hope for the best” attitude towards the gap between when an issue is initially identified and controlled, and the next period that it’s checked (potentially one to two years later).
The automation of responsible sourcing activities offers a different approach: leveraging collective intelligence and linking networks of data that extend beyond the bounds of a single firm, some from industry benchmarks, adverse media scanning and proprietary data-sets. Such insights enable faster and more incisive reactions and could even pre-empt business disruptions.
Logistics and vertically integrated commodity supply chains have already come to operate with a standard that looks closer to automation. When it works, there has been a lesson: Autonomy doesn’t eliminate risk, it simply reshapes it.
Our Leapfrog Moment: Tariff Shockwaves
Recent tariff policies have catalyzed a structural reshuffling of global trade, with reshoring, nearshoring and "friendshoring" to countries considered political or economic allies. The effect of these changes has led to many unintended consequences for the vendor base, new risks for responsible sourcing, and an escalation of inventory levels.
Alongside the growth of digital trade, this has also shifted the operating model away from large, leverage-based supply chains to more transactional interactions, predicated on lower minimum order quantities and skirting de minimis requirements. The structure of supply chains has become more opaque and complex.
Fragmented supply chains ultimately increase the difficulty of monitoring compliance. Recent audit data from LRQA’s EiQ platform suggests that only 48% of companies have clear visibility of their Tier 1 suppliers, and this drops to less than 10% at Tier 2.
Decoupling from China (a strategy originally encouraged by due diligence legislation), has further complicated this picture. It is an illusion. Chinese manufacturers are not manufacturing less, they’re relocating. In Vietnam, for example, Chinese foreign direct investment surged nearly 90% over the past three years, with 28% of new projects owned by Chinese investors in 2024.
With suppliers under pressure to meet tight deadlines and low production costs, audit data shows that all sourcing markets have shown significant complexity and risk in recent years, including the United States and many European nations. Even environmental targets are impacted as grid structure and supplier maturity levels impact greenhouse gas targets when we "tier" away from China.
Combined, these shifts in supply chain geography often result in exposure to new worker or environmental risk, such as the hidden use of forced labor and child labor in some parts of India, or high levels of health and safety violations in Bangladesh. These often sit as an additional risk layer of risk on top of your existing supply chain and risk appetites.
What’s the Cost?
It’s best to think of the modern supply chain not as a straight line, but as a web. Each node — whether it’s a factory, logistics partner or digital platform — introduces potential weak points. Research from EiQ’s Supply Chain Risk Outlook report shows that even the world’s largest retailers face challenges with non-compliance at the factory level.
Tariffs accelerate these shifts, creating a perfect storm of complexity that outpaces traditional due diligence. When a tariff forces companies to change suppliers rapidly, risk assessments, training programs and compliance audits often can’t keep pace. Legacy risk management processes that are based on annual inspections struggle to detect new risks that arise when production migrates to a different countries or tier.
Autonomy as a Cure and Catalyst
Autonomous responsible sourcing systems promise a way out. With AI-powered risk assessment, dynamic supplier segmentation and real-time data feeds, organizations can move from periodic audits to continuous assurance and "parallel processing." This means handling issues of risk management with one’s historical vendor base while migrating to a new, digitally native framework for new vendors. This is how responsible sourcing might disrupt itself.
There are advantages. An autonomous sourcing ecosystem can, for instance, detect anomalies in supplier data such as a sudden drop in reported working hours, or a mismatch between energy consumption and output and trigger a proactive investigation. It can learn from global patterns and prevent repeat incidents across the network. It could even explain how correlations between risk product integrity and the financial performance of a vendor can be explained through a root-cause in responsible sourcing.
However, algorithms are only as ethical and accurate as the data they’re trained on, and a surge in low-quality or incomplete supplier disclosures can lead to false confidence and a masking of blind spots. We shouldn’t forget that some of the earlier technologies in supply chain transparency were little more than low quality self-assessment questionnaires “rubber stamped” with a label for supplier performance.
The Case for Collaboration
That’s why the future of supply chain intelligence depends as much on collaboration as automation. Open supplier relations, shared data ecosystems, interoperability between assessment standards and transparent benchmarks may be antidotes to the risk of over-automation.
The ultimate test of an autonomous supply chain isn’t how efficiently it operates, it’s how intelligently it prevents harm and manages risk. As tariffs continue to redraw the map of global manufacturing, the challenge for leaders is to ensure that speed doesn’t come at the expense of scrutiny.
Supply chain intelligence must evolve from measuring what’s easy to measure to understanding what’s essential: labor conditions, carbon intensity, human welfare and systemic resilience. And finally, we should be evaluating: is our data valuable?
By 2028, as autonomous responsible sourcing ecosystems mature, the most resilient companies will be those that leverage intelligence as a shared asset. Collective visibility and insight across supply chains will become the new determinant of trust.
Kevin Franklin serves as chief executive officer for EiQ
