Scaling AI is an organizational challenge as much as a technical one. Data, engineering, platforms, operating models, and governance have to fit together, especially in a highly regulated environment. A session at MIT Technology Review's EmTech Future conference examined what it takes to build that foundation and turn emerging AI capabilities into lasting value for investors.
AI depends on connected foundations
A scalable AI system cannot rest on a single tool or team. The session frames its foundation as several parts that must converge: data, engineering, platforms, operating models, and governance. Each contributes to whether an organization can put AI to work consistently and responsibly.
This emphasis on connection matters because introducing a new capability does not by itself establish how it will be used across a firm. The operating model and the technical platform have to support the work, while governance provides a way to approach implementation in a regulated setting. The source describes these elements as needing to work as one.
That is a practical shift in perspective: the question is not only what AI can do, but what organizational structure is needed to make its use sustainable. A sound foundation links the technology to the processes and oversight surrounding it.
Trust shapes how AI creates value
The session connects responsible and trustworthy implementation with lasting value for investors. In that framing, trust is not separate from business outcomes. It is part of the conditions that allow emerging capabilities to become useful in a setting where regulation is a central consideration.
The discussion includes agentic systems among the capabilities organizations may seek to implement. The description does not present these systems as a shortcut to value. Instead, it places them within the broader foundation of data, engineering, platforms, operating models, and governance.
That distinction is useful for organizations evaluating new AI applications. A capability may be promising, but its value depends on how it is integrated and governed. The conference session focuses on building the conditions for implementation rather than treating adoption as an end in itself.
Jing Wang brings an enterprise perspective
The session features Jing Wang, Principal, Center for AI and Insights, Vanguard. She leads Vanguard's Center for AI & Insights (CAI), whose work advances the firm's AI, machine learning, generative AI, and behavioral science capabilities.
Her team drives enterprise-wide innovation, research, and delivery. The stated aim is to help Vanguard translate emerging technologies into trusted solutions that enhance client experiences, improve investor outcomes, and create measurable business value. That remit puts the session's focus on scalable foundations in the context of work spanning both technology and business priorities.
Before her current position, Wang held leadership roles across Vanguard's Financial Services, Workplace Solutions, International, and Corporate Services divisions. She joined Vanguard in 2009 after several years as a Principal at the Boston Consulting Group. Her background combines leadership across the firm with earlier consulting experience.
Scaling means aligning the whole organization
The central idea is that scaling AI requires coordination across an organization. Data and engineering need platforms to support them; operating models must make the work actionable; and governance must be part of the implementation. The session's focus is how these pieces can converge in a highly regulated environment.
For investors and clients, the intended outcome is not simply wider use of AI. It is the ability to turn emerging capabilities into trusted solutions and lasting value. That depends on building the foundation before expecting individual AI systems to deliver results at scale.