By Smitha Shetty, Regional Director, APAC, Achilles Information Limited
In boardrooms across Asia-Pacific, the conversation around supply chain due diligence has clearly evolved. The question is no longer whether organisations should strengthen oversight, but how this can be done credibly across supply chains that span tens of thousands of suppliers, multiple jurisdictions and increasingly complex regulatory expectations. In these discussions, artificial intelligence is rarely viewed as a silver bullet. Instead, it is being recognised as a practical response to a scale challenge that traditional human-led approaches were never designed to manage.
This evolution is visible across sectors such as energy, maritime, utilities and manufacturing, where supplier networks are both extensive and deeply interconnected. What emerges consistently from discussions with procurement, sustainability and risk leaders is not resistance to due diligence but frustration with its operational limitations. Manual reviews, periodic audits and spreadsheet-driven assessments can deliver depth in isolated cases, yet they struggle to keep pace with the volume, velocity and diversity of data now required to demonstrate meaningful oversight.
Leaders managing complex global value chains point to a shared reality. Traditional third-party assessments allow only a small proportion of suppliers to be reviewed in any given year. The real value of AI lies in how it reshapes the due diligence workflow rather than replacing existing controls. By pre-processing large volumes of supplier data, AI enables prioritisation at scale. Information is scanned, structured and assessed automatically, allowing organisations to focus attention on exceptions and higher-risk cases rather than reviewing every submission manually. This does not replace human judgement. It allows expertise to be applied more deliberately, where context, experience and engagement matter most.
This shift is reflected in the practical experience of Achilles and its application of AI across global supply chains. Through AchillesAI, machine learning models are used to automate early-stage checks across supplier submissions, improving both speed and consistency. By extracting key data from policies and procedures, AchillesAI auto-fills disclosure templates with ready-to-submit facts and narrative text. It saves time, improves accuracy and helps teams meet complex regulatory requirements with confidence.
One of the most significant advantages of automation is anomaly detection. AI models can identify illogical combinations of declarations, inconsistencies between policies and supporting evidence or unusual patterns in emissions and workforce data. These signals are often difficult to detect through manual review, particularly at scale. By surfacing them early, organisations can prevent superficial or duplicated responses from passing through unchecked and strengthen confidence in the data used for reporting, assurance and decision-making. These insights are explainable, enabling teams to understand why a supplier has been flagged and how to respond appropriately.
Unstructured data remains one of the most persistent challenges in supplier due diligence. Information rarely arrives in neat or standardised formats. It is often submitted as PDFs, scanned certificates, invoices or free-text explanations produced across different geographies and levels of maturity. AchillesAI reads, classifies and extracts supplier data at scale, transforming thousands of unstructured documents into structured, auditable insights in near real time. This approach also creates a clearer audit trail, which is becoming increasingly important as regulatory scrutiny intensifies.
AI-enabled automation also has a meaningful impact on the supplier experience, particularly for MSMEs. Smaller suppliers are often asked to complete multiple questionnaires for different customers, many of which overlap in content but differ in format. This creates disclosure fatigue and can discourage engagement. Centralised data repositories, reusable disclosures and greater standardisation reduce duplication and make participation more manageable, while still maintaining the integrity and depth of information collected and helping suppliers meet complex regulatory requirements with greater confidence.
Looking ahead, industry leaders expect AI to move further upstream in the due diligence process. The emerging vision is that many checks will be performed by AI at the point of submission, allowing organisations to manage global supply chains with greater efficiency and focus. Real-time alerts can flag emerging risks as data changes, rather than relying on periodic reviews. More refined supplier segmentation can help distinguish between suppliers that require support and capacity building and those that warrant closer monitoring. Predictive risk scoring and automation will increasingly guide where limited human effort should be directed.
From these discussions, a clear theme emerges. AI is not removing responsibility from organisations. It is strengthening their ability to exercise it with greater confidence, consistency and scale. The result is a hybrid governance model where AI enhances efficiency and detection capability, while humans retain responsibility for interpretation, engagement and accountability. In this sense, AI strengthens due diligence not as a substitute for governance but as the infrastructure that makes governance feasible at scale. As supply chains continue to grow in size and complexity, the role of AI is becoming less about experimentation and more about enabling credible and defensible due diligence that stands up to both regulatory and stakeholder expectations.
