Everyone Wants AI in Risk Management. Few Are Ready for It
As organizations race to deploy AI, the rush in third-party risk management (TPRM) may pose the biggest risk of all. The successful implementation of AI relies on structured foundations including clean data, standardized processes, and consistent outcomes. However, many TPRM programs currently lack these essential elements.
The State of TPRM Programs
While some organizations have dedicated risk leaders, defined programs, and digitized data, others manage risks in an ad hoc manner using spreadsheets and shared drives. The level of regulatory scrutiny varies significantly, with some companies operating under tight regulations while others accept higher risks. This inconsistency means that AI adoption in TPRM will not rely on speed or uniformity but rather on discipline.
Assessing AI Readiness
Not every organization is prepared for AI, and that’s acceptable. A recent MIT study highlighted that 95% of GenAI projects are failing. According to Gartner, 79% of technology buyers regret their last purchase due to inadequate planning. AI readiness in TPRM is not a simple switch but a progression that reflects how structured and governed a program is.
In the early stages, TPRM programs are primarily manual, relying heavily on spreadsheets and fragmented ownership. This lack of formal methodology leads to challenges in utilizing AI effectively, as it struggles to discern valuable insights from noise.
Building Structure for AI Integration
As TPRM programs mature, they begin to form structure: workflows are standardized, data is digitized, and accountability expands. Here, AI can start adding real value. However, many well-defined programs remain siloed, which limits visibility and insight.
True readiness is achieved when these silos break down, and governance becomes shared, allowing AI to transform disconnected information into actionable intelligence.
Understanding Unique Organizational Needs
Even with agile risk programs, organizations will not have the same path for AI implementation. Different companies manage unique third-party networks and operate under varying regulations, leading to distinct levels of risk acceptance. For instance, banks face stringent regulations regarding data privacy, while consumer goods manufacturers may accept greater operational risks.
This variability emphasizes the need for purpose-built AI solutions rather than generic models. A modular approach is recommended, deploying AI where data is robust and objectives clear, allowing for gradual scaling.
Common Use Cases for AI in TPRM
- Supplier Research: AI can evaluate thousands of vendors, identifying the most suitable partners based on risk and capability.
- Assessment: AI can analyze supplier documentation and flag inconsistencies, allowing analysts to focus on critical issues.
- Resilience Planning: AI can model the potential impacts of disruptions, aiding in contingency planning.
Responsible AI Implementation
As organizations begin using AI in TPRM, the most effective programs balance innovation with accountability. AI should enhance oversight rather than replace it. Success in TPRM is not solely about speed but about accurately identifying risks and implementing corrective actions.
Global regulations are evolving, affecting how AI is governed. The EU AI Act emphasizes transparency and accountability, while the U.S. follows a more decentralized approach focusing on innovation. This divergence adds complexity for organizations operating in multiple regions.
Getting Started with AI in TPRM
To turn responsible AI into reality, organizations must establish foundational elements:
- Standardization: Ensure clean data and aligned processes before introducing automation. Implement AI in phases, validating each step.
- Start Small: Launch controlled pilots targeting specific problems. Document performance and accountability.
- Governance: Treat AI as a risk, establishing policies for its use. Maintain transparency in all AI-driven insights.
There is no universal blueprint for AI in TPRM; each organization’s maturity and regulatory environment will shape its implementation. However, the key principles remain the same: automate what is ready, govern what is automated, and adapt continuously as technology evolves.
