Managing Third-Party Risk When You Have 10,000 Suppliers - with Dean Alms of Aravo (22 min)
- Release date: 2026-02-06
- Listen on Spotify: Open episode
- Episode description:
Today's guest is Dean Alms, Chief Product Officer at Aravo. Aravo is an enterprise software company focused on third-party risk management and compliance platforms that help organizations manage vendor, supplier, and partner ecosystems across regulatory, operational, and reputational risk domains. Dean joins Emerj Editorial Director Matthew DeMello to examine how third-party risk has evolved into a data and AI-driven, board-level visibility and resilience challenge, and how automation is reshaping the way enterprises identify, remediate, and monitor risk at scale. Dean also shares practical workflow changes, including using AI to automate document ingestion and survey validation, generate corrective actions, and enable natural language access to risk data. The discussion highlights how these approaches reduce operational cost, improve data integrity, support continuous monitoring models, and help enterprise leaders protect revenue, reputation, and regulatory posture across complex supplier networks. Want to share your AI adoption story with executive peers? Click emerj.com/expert2 for more information and to be a potential future guest on the 'AI in Business' podcast! This episode is sponsored by Aravo. Learn how brands work with Emerj and other Emerj Media options at emerj.com/ad1.
Summary
- 📈 Board-Level Escalation: Third-party risk has surged to board priority due to proliferating regulations, massive fines, and supplier accountability via social media scrutiny.
- 💼 Core Business Drivers: Enterprises prioritize TPRM for compliance, liability avoidance, revenue protection, brand reputation, and cybersecurity resilience amid vast vendor ecosystems.
- 🤖 AI Workflow Automation: AI ingests documents, auto-fills surveys, detects gaps, generates corrective actions, and supports natural language queries to slash costs and boost efficiency.
- 🔄 Continuous Monitoring Shift: From episodic reviews to real-time event-driven oversight using AI for adverse media, disasters, and company events, enhancing supply chain resilience.
- 🛡️ Resilience Outcomes: AI enables proactive planning across identification, remediation, reporting, and resilience, delivering trusted insights for revenue, reputation, and regulatory protection.
Insights
Why has third-party risk management escalated from a back-office task to a board-level priority?
Time: 2:38 – 4:23
Category: AI Governance & LawsAnswer: Increasing regulatory mandates across industries and geographies, coupled with enterprises being held accountable for suppliers’ ethical lapses via social media and consumer scrutiny, have amplified fines and reputational damage, demanding governance oversight. AI enables scalable monitoring to mitigate these exposures. (Start at 2:38)
What key business drivers compel enterprises to prioritize third-party risk management?
Time: 5:04 – 6:58
Category: AI-Driven Innovation EconomyAnswer: Drivers include regulatory compliance, liability avoidance, revenue protection from supply chain disruptions, brand reputation safeguarding, and security against cyber threats like ransomware. These factors are critical in regulated sectors like finance and pharma, and consumer goods. (Start at 5:04)
How is AI transforming data ingestion and survey processes in third-party risk management?
Time: 9:27 – 11:05
Category: AI-Driven Innovation EconomyAnswer: AI automates ingestion of audited documents like SOC 2 reports to auto-populate surveys, detects discrepancies between responses and expectations, and generates corrective actions at scale. This reduces manual effort, improves data integrity, and cuts operational costs for ecosystems with thousands of vendors. (Start at 9:27)
In what ways does AI enable natural language interfaces for third-party risk insights?
Time: 11:11 – 12:05
Category: AI in Everyday LifeAnswer: ChatGPT-like interfaces allow natural language queries such as identifying suppliers near a hurricane zone or adding new contacts, democratizing access to complex data without deep application knowledge. This boosts efficiency in dynamic risk scenarios. (Start at 11:11)
What four core functions of TPRM are supercharged by AI for enterprise resilience?
Time: 14:52 – 17:40
Category: AI Governance & LawsAnswer: AI accelerates risk identification, remediation via automated actions, accurate reporting for executives and regulators, and resilience planning like backup suppliers. This reallocates human effort from data processing to strategic value, protecting revenue and compliance. (Start at 14:52)
How does AI shift third-party risk management from episodic to continuous monitoring?
Time: 18:44 – 20:12
Category: AI-Driven Innovation EconomyAnswer: AI facilitates ongoing assessment via risk intelligence, adverse media, and event triggers like bankruptcies or cyberattacks, beyond scheduled reviews. This maturity enhances resilience against unforeseen disruptions like wars or natural disasters. (Start at 18:44)