JPMorgan Chase & Co., one of the world’s largest and most influential financial institutions, holds over $3.9 trillion in assets and operates in more than 100 countries. Its global scale, coupled with a relentless focus on innovation, places it at the forefront of digital transformation in banking — especially in how data and AI are reshaping financial services.

In the first part of this three-part series with Acceldata’s Mahesh Kumar, Tiffany Perkins-Munn reflected on her leadership journey, the human side of data innovation, and the impact of generative AI on the future of work. In part two, she explored the structural and cultural shifts required to enable cross-functional collaboration and enterprise-wide governance in the age of AI.

In this final installment, Perkins-Munn shares a grounded, strategic view of how organizations can prepare their data for AI, the importance of prioritization, and how learning flows across hierarchies — often from junior team members to senior leaders. She also reflects on how moments of crisis like COVID-19 can spark unexpected innovation.

Start with business objectives, not data volume

With the volume, velocity, and variety of enterprise data growing exponentially, the instinct might be to clean and prepare everything for AI. Perkins-Munn advises against this.

“Even with data cleansing, the key is prioritization. Rather than attempting to clean all the data, we should start with data sets that are most critical to the business objectives,” she says. “Assess the impact of data quality on decision-making, then focus on high-value data that will drive strategic initiatives.”

She underscores that AI can assist in automating and improving the accuracy of data cleansing, but leaders must be thoughtful about where that effort is directed. A targeted approach ensures resources are optimized and the business impact is measurable.

Cleaning every data column or table isn’t just impractical — it can dilute strategic focus.

“You want to use the least amount of information that you need to answer the question so that you don’t over-engineer,” says Perkins-Munn. “You scale it down to something manageable and focused — it makes the answer more interpretable and helps you get to outcomes faster.”

She gives a simple example — ensuring correct customer salutations — as a case where mass data cleanup is viable. But when driving outcomes tied to revenue or efficiency, the goal should be to isolate a small set of features or variables tied directly to the question at hand.

Knowledge doesn’t just flow top-down

Perkins-Munn is candid about how her own understanding of AI continues to evolve, often with the help of junior team members who grew up immersed in modern technologies. “I didn’t grow up with AI — I didn’t even grow up with the iPhone,” she says. “Having people who can help you understand, in real-world fashion, how consumers think about using AI has been very eye-opening.”

She emphasizes the importance of drawing insights from across the hierarchy — peers, direct reports, and especially those closest to emerging tools. This, she says, enriches how JPMorgan Chase understands the customer and designs more relevant, human-centric solutions.

When crisis forces innovation

Some of the most profound shifts in data and customer strategy, Perkins-Munn notes, are triggered by external disruptions. COVID-19, for instance, reshaped how JPMorgan Chase thought about engagement.

“In terms of how we think about the customer, we had to totally reframe what it meant to be a banking customer,” she explains. “People were no longer going into branches; their purchase power moved entirely online. That forced us to innovate around how to meet customers in a new world.”

The bank reimagined branch functionality, reframed service offerings, and even reconsidered how it communicated with consumers navigating broader life uncertainty. This required not just product innovation but a transformation in how data was interpreted in real time to reflect shifting consumer behaviors.

Meeting customers where they are

Perkins-Munn closes by highlighting the dual nature of JPMorgan Chase’s presence: global scale with local intentionality.

“We’re a consumer bank with a broad global presence, but we are very local in our intentionality — how we cover consumers, what we offer, how we iterate and innovate.” She adds, “It’s meaningful how we talk about AI, how we navigate storms, and how we work to understand the consumer in the context of the larger economy.”

As the organization continues to embed AI across its ecosystem, the core principles remain grounded: strategic focus, ethical use of technology, cross-functional learning, and above all, keeping the customer at the center of every decision.

CDO Magazine appreciates Tiffany Perkins-Munn for sharing her insights with our global community.