Vanguard's Jing Wang to share framework for trustworthy, scalable AI in regulated firms at MIT Tech Review

Jing Wang, Principal at Vanguard’s Center for AI and Insights, is leading a session on building scalable AI foundations at MIT Technology Review’s EmTech Future conference. This event targets organizations operating in highly regulated environments that need to align technical capabilities with strict compliance requirements. The session addresses how firms can integrate disparate elements to create cohesive AI strategies.

By the numbers

• 2009: The year Jing Wang joined Vanguard after serving as a Principal at the Boston Consulting Group.
• 3: The number of professional licenses Wang holds, including Series 7, 24, and 65, reflecting her financial industry expertise.
• 1: The single convergence of data, engineering, platforms, operating models, and governance required for scalable AI systems.

What the figures mean

Wang’s background in both technology and finance informs Vanguard’s approach to AI integration. Her team drives enterprise-wide innovation, research, and delivery, enabling the firm to translate emerging technologies into trusted solutions. This includes leveraging machine learning, generative AI, and behavioral science to enhance client experiences and improve investor outcomes. The focus remains on creating measurable business value while maintaining trust.

The session emphasizes that scalable AI requires more than just advanced models. It demands a strong foundation where data, engineering, platforms, operating models, and governance converge to work as one. This holistic approach is critical for regulated industries that cannot afford fragmented or non-compliant AI implementations. Trustworthy execution turns emerging capabilities into lasting value.

What should you do?

Organizations should evaluate whether their current AI initiatives have unified governance and data strategies. Teams usually need to align engineering efforts with compliance requirements early in the development cycle. Check whether your operating models support the integration of new AI capabilities without disrupting existing workflows. Ensure that all stakeholders understand the importance of trustworthy implementation in regulated sectors.

Operational impact

When integrating AI into regulated operations, leadership must ensure that data governance and engineering practices are aligned from the start. Teams should verify that operating models support scalable deployment without compromising compliance. Internal communications need to bridge technical teams with risk and legal departments to address governance early. Suppliers must demonstrate how their platforms integrate with existing security and data frameworks.

What to do and how to stay safe: Vanguard

• Review current AI projects to ensure data, engineering, and governance strategies are unified rather than siloed.
• Verify that operating models support the integration of emerging AI capabilities without disrupting compliance workflows.
• Engage risk and legal teams early to define trustworthy implementation standards for regulated environments.
• Assess supplier platforms for their ability to converge with existing data and governance frameworks.

Step-by-step guide: Shadow AI Explained: 8 Questions Answered for Secure Operations

General security guidance from the BitShield News newsroom. It is not confirmed advice from the organisations named in this story.

Frequently asked questions

Who is leading the AI session at the EmTech Future conference?

Jing Wang, Principal at Vanguard’s Center for AI and Insights, is leading the session on building scalable AI foundations.

What elements must converge for a scalable AI system?

Data, engineering, platforms, operating models, and governance must converge to work as one for scalable AI.

What is the goal of responsible AI implementation in regulated environments?

The goal is to turn emerging capabilities, including agentic systems, into lasting value for investors through trustworthy implementation.