AI: an opportunity for reinventing third-party risk management (TPRM)

Organisations today face increased volatility and interconnected risks across extended third-party ecosystems. Disruptions - geopolitical, technological, environmental, or regulatory - can quickly ripple through these networks. Cyber incidents often stem from external providers, regulatory demands are rising, and expectations around resilience, data protection, and sustainability are growing.

At the same time, reliance on third parties for critical services and innovation has deepened. Cloud providers, fintech platforms, and specialised vendors now support core business functions, extending risks beyond direct suppliers to subcontractors and downstream partners.

Yet, many TPRM approaches remain outdated - periodic, siloed, and compliance-driven - creating a gap between operational realities and risk oversight. This makes it challenging for organisations to promptly identify emerging risks and understand their impact on broader business goals. 

Unifying the operating model: a necessary foundation

Bringing TPRM activities together under a unified operating model remains a critical enabler of consistency, efficiency, and oversight. What matters is that these activities are connected - often through a single TPRM platform or shared framework - so that vendor outreach is not duplicated, assessment methodologies are harmonised, and leadership has a consolidated view of third‑party risk across the enterprise. Supported by clear governance and ownership, this approach also strengthens accountability and decision‑making. 

In practice, achieving this level of alignment is often challenging. Responsibilities for third‑party risk are typically distributed across procurement, IT, compliance, risk, and business teams, each operating with different priorities, incentives, and metrics. Without an enterprise‑level operating model, and the technology to connect fragmented processes and systems, TPRM initiatives risk remaining siloed, reactive, and resource‑intensive.

AI: from efficiency gains to a different way of working

AI has the potential to materially enhance how TPRM activities are performed. Initial adoption typically focuses on automating manual and time‑consuming tasks, such as data collection or preliminary risk screening. While these efficiency gains are valuable, the broader opportunity lies in enabling more timely, consistent, and data‑driven risk management across the third‑party lifecycle.

AI provides practical support for different TPRM stages

• Vendor inventories can be strengthened through automated identification and classification of third parties using internal and external data sources, such as integrating AI-powered tools that continuously scan supplier databases and news feeds to detect new vendors or changes in vendor status without manual input.

• Risk assessments can be supported by analytics that draw on historical information and defined risk indicators to improve consistency, for example, by using AI to analyse past supplier performance and risk events to generate standardised risk scores and flag deviations in real time.

• Due diligence activities can be streamlined through automated analysis of documentation and external signals, such as deploying AI-driven platforms that quickly review financial statements, regulatory filings, and media coverage to highlight potential red flags before manual review.

• Contract reviews can be enhanced using natural language processing to identify risk‑relevant clauses and deviations from standards, enabling legal teams to rapidly pinpoint unusual terms or compliance gaps across large volumes of agreements without time-consuming manual examination.

• Monitoring can evolve from periodic reviews to more continuous oversight, supported by indicators that help identify emerging issues, like AI tools that track supplier news, geopolitical events, or cyber threats in real time to alert risk teams about potential disruptions as they arise.

• Incident management can benefit from analytics that highlight interdependencies and potential cascading effects, for instance by mapping supplier networks and using AI simulations to predict how a disruption at one vendor could impact others as well as the wider business.

• Training and awareness efforts can be better aligned to roles, responsibilities, and observed risk exposure by leveraging AI-driven learning platforms that tailor training content dynamically based on individual user behaviour, recent incidents, or evolving risks within the third-party ecosystem.

More advanced AI capabilities will further reshape TPRM over time. Realising this potential, however, depends on more than technology alone. It requires appropriate integration into the existing process and IT landscape, reliable data foundations, appropriate governance, and oversight, clear accountability. Despite growing interest, those are the common challenges leading to uneven AI adoption within TPRM. 

A mutually reinforcing dynamic

Centralisation and AI are mutually reinforcing rather than independent initiatives. A centralised TPRM framework provides the consistent processes, data standards, and governance required for AI‑enabled solutions to deliver value. At the same time, AI can strengthen the case for centralisation by enabling enterprise‑wide visibility, more proactive risk identification, and improved coordination across functions.

Organisations that pursue both in a coordinated manner are better positioned to move away from fragmented, checklist‑driven approaches and toward a more resilient and forward‑looking TPRM model — one that supports informed decision‑making and aligns third‑party risk management with broader business priorities.