Vanguard’s Data Chief on Embedding AI and Data Across the Enterprise

In today’s artificial intelligence (AI)-driven economy, data is more than an asset — it’s a strategic imperative.

That’s the belief of Ryan Swann, Vanguard’s chief data analytics officer. In this exclusive interview with PYMNTS, Swann explains how data is at the forefront of any AI-powered digital transformation. He shares how the mutual fund giant is using data and AI not only to gain insight, but also to build enterprise-wide agility and accelerate value creation for itself and its clients.

Data and analytics have a seat at the C-suite table at Vanguard, a shift that Swann said enables the company to embed intelligence into decision-making at all levels.

“It is allowing us to bring insights to all of our executives about what’s happening with our clients. What are they saying? What do they need? How does this show up in the data?” Swann said. “It allows us to inform and influence the strategy and what we should focus on next. It also allows us to accelerate our business strategy.”

Moreover, “Data is really the way of our clients are communicating to us,” Swann explained. “We’re a digital organization; we don’t have bricks and mortar, so the majority of our clients interact with us through data, through transactions, through clicking around on our website, maybe calling our call center, which is just another type of unstructured data.

“That data allows us to understand what our clients need and how we should respond,” the executive said.

Making Data Sing

As companies try to do more with AI and generative AI, “it comes down to really great data, to having AI-ready data,” Swann said.

But it is the business context that makes the data sing. “When you call a financial institution, you want to know that they understand you, that they know the last three times, why you called,” Swann said. Using data and AI “allows us to do that in a very effective and personalized way.”

Swann’s office oversees the entire data lifecycle — from engineering and management to advanced analytics, machine learning and behavioral science.

Central to Swann’s approach is a hub-and-spoke model that bridges the technical and business sides of the company.

“You want your data analysts, your data scientists, your data engineers to be connected to the business” side, Swann said. “But at the same time, you want to have some kind of horizontal capability that allows you to connect the dots. Why? Because the data flows across business lines.”

To foster cross-functional collaboration, Swann said sitting together and sharing the same objectives and key results (OKRs) matter.

“You walk in the office where the business sits, and you can’t really tell who’s tech, who’s data, or who’s [on the] business” side because they sit together, he explained. “Their OKRs are the business OKRs … and the budget that supports that team is managed by the business.”

Read more: The Two Money Mindsets Shaping How Consumers Manage Their Finances

Measuring Data Strategy’s ROI

The second piece that’s important is to keep the workforce continually learning because AI advancements come “very fast,” Swann said. “We have to enable our data analysts, our data scientists, and our data engineers to continue to grow and develop.”

“You need that community of practice that’s going to allow you to learn from each other, find synergies horizontally, but also upskill our crew,” Swann continued. “Learning and development is a big part of that.”

The result is the ability to “create value for the client, reduce risk, reduce inefficiencies and cost avoidance,” he added.

Swann emphasized that one of the hardest parts of data transformation revolves around people and culture. “Most enterprises don’t have a technology issue,” he said. “They have a people and process, change management issue because of the legacy way of doing things.”

That’s why Vanguard began upskilling all employees — or crew — on data literacy and AI well before generative AI became a mainstream topic. “We’ve always said, ‘Hey, your data is important. You need good quality data.’ And now it’s even 10 times more important,” he said.

But how does Vanguard measure the effectiveness of its data strategy?

Swann said the company created a Value Measurement Office to do just that: It evaluates the success of its initiatives in four areas — revenue generation, cost savings, cost avoidance (not incurring the expense in the first place), and risk reduction.

For example, Swann said an AI model could look through the data to coach the sales team into performing better. It could recommend the three best actions to take, the next three people to talk to, and the three topics they should talk to them about. By A/B testing these recommendations, the team tracked the performance improvements and worked with finance to quantify the gains.

These and other initiatives have brought value to Vanguard. “Last year alone, we were up over $300 million of incremental value” across revenue generation, cost efficiencies, cost avoidance and risk reduction, he said.

Deploying AI Agents

As for agentic AI, Vanguard is deploying agents that let users do things like pull data from databases in natural language and conduct data lineage checks, which avoids a costly undertaking.

Swann said Vanguard has data processes going back decades, and the company must now create lineage to trace where the data comes from and where it goes.

“So when there’s a data quality issue, we know where to look,” Swann said. “Generative AI is helping us do that.” In one case, he said, generative AI saved Vanguard 30,000 hours of work.

One tool Vanguard recently rolled out was a generative AI summarization tool for wealth advisers that summarizes market commentary for clients. Other client-facing efforts include using AI for financial planning, tax optimization, and hyper-personalization. “People’s lives are complex, and so our multi-goal solver really helps them,” Swann said.

Internally, the company operates chat-based tools for developers and employees to access information and documentation, dubbed “search and summarize,” Swann said.

For fellow business leaders, Swann offers the following lessons when deploying generative AI:

AI-ready data is nonnegotiable: “Turning your data into AI-ready data is probably one of the most important things organizations can do.”

Buy vs. build strategically: For commoditized services, buy. But where your data is your IP, “this is where the hybrid or build phase comes in.”

Invest in culture and talent: “This is a change management process. You need to upskill your … employees and find ways to give them an opportunity to experiment, test and learn.”