As one of the world’s leading wealth management firms with trillions in client assets, Morgan Stanley Wealth Management continues to leverage data and AI to redefine how investment strategies are shaped, decisions are made, and client experiences are personalized. With more than 16,000 financial advisors and a vast digital ecosystem, the firm’s analytics leadership plays a critical role in driving this transformation from within.

The first part of this three-part series explores how Morgan Stanley’s “One Firm” strategy is driving transformation through connected data, enterprise intelligence, and a client-first mindset. Part two details how AI is being deployed across the organization through effective tools and a disciplined governance framework that ensures responsible innovation.

In this final installment, Atul Dalmia, Chief Analytics Officer at Morgan Stanley Wealth Management, speaks with Rohit Choudhary, CEO of Acceldata, about cultivating teams that evolve with AI, the changing skill sets shaping the analytics function, and the leadership mindset required to thrive in this age of accelerated intelligence.

From “How to use AI?” to “What problem are we solving?”

Dalmia leads a 150-person global analytics team dedicated to enhancing business outcomes through data-driven decision-making. For him, the guiding principle remains consistent: start with the problem, not the technology.

“I often joke about the fact that we almost go with the approach of ‘How do I use AI?’ But the question should be, ‘What business problem am I solving, and how can AI help me do it better?’” he says.

The focus, Dalmia explains, is always on applying analytics and AI to make current decisions smarter and faster. “We start off with what problem are we solving? How is this going to be able to help me drive better outcomes for the business? How do I get better facts?”

He emphasizes that the evolution of AI tools simply allows teams to act faster and more intelligently. What matters most is nurturing a mindset oriented toward outcomes — translating business needs into analytical frameworks, telling compelling stories through data, and driving actionable results.

A blend of legacy expertise and fresh talent

Dalmia’s approach to building AI-ready teams hinges on a blend of evolution and augmentation. “It’s partly about getting new talent and partly making sure that organizations which already have a lot of inbuilt skills can evolve and train on the new AI tools,” he says.

According to him, the institutional memory and contextual understanding that existing teams possess are invaluable. The key is to augment existing strength with new tools and fresh thinking rather than replacing it.

“If you just let go of what’s in the past, you’ll miss out on the advantages and the value,” Dalmia cautions.

Redefining roles in the AI era

As AI continues to reshape how organizations operate, he believes the real transformation lies not in replacing roles but in redefining how every role evolves.

“Clearly, people who are using machine learning and decision science continue to be evolving, but people who fundamentally understand how data comes together, who are able to feed and understand data processes and platforms, are going to be as important,” he says.

Dalmia points out that asking the right questions — what analytics teams do best — will become even more valuable.

For him, every function within analytics and data organizations retains value; what matters is how teams evolve their functions to stay relevant and efficient.

Leadership in the age of intelligent acceleration

With more than 25 years of experience in analytics, Dalmia has seen the field evolve from manual models to machine learning at scale. Yet, he insists that certain fundamentals remain constant: understanding the business, deriving insight, and communicating it effectively to the leadership.

What has changed, however, is speed and accessibility. “In the future, everybody should be able to get the same level of information I get today, faster,” he notes. As technology accelerates, the ability to think critically and ask the right questions becomes the most valuable skill.

“The value creation moves more towards the thinking, the strategy, and the partnerships,” says Dalmia. “Even with people starting out, you want people who can think, who can ask the right questions.”

He adds that as automation handles more operational tasks, human partnership and collaboration become even more central to success. “Maybe the argument is that in the future, working with people, the human relationship becomes even more important.”

AI complements, not replaces

In an industry often wary of automation, Dalmia is quick to clarify a major misconception: “There’s a perception, especially in the financial services industry, that AI is going to take up everything that humans are doing today. I think it’s going to be something that complements.”

He states that every technological leap has sparked fears of human redundancy, yet humans continue to thrive by adapting.

For Dalmia, the coming phase of AI adoption will be judged by measurable business impact. “It’s going to be about the use cases, and making sure we can scale the use cases in this space,” he says. While many successes today focus on efficiencies, the true measure will be revenue generation and commercial value. “You want to be able to generate revenue and commercial outcomes, show that investment in AI can lead to strong commercial outcomes.”

Always start with the business problem

The cornerstone of Morgan Stanley’s analytics culture, Dalmia concludes, is outcome orientation. “It’s the first question that I mentioned, which is, what are you solving for? What’s the business problem at hand?” he emphasizes. “The day we start thinking and chasing the technology, that will be a failure for us.”

CDO Magazine appreciates Atul Dalmia for sharing his insights with our global community.