The bank flipped the script on how it approached data to enable artificial intelligence, said Hina Shamsi, CIO of wealth management technology.

Morgan Stanley spent years upgrading its technology and data foundation in support of artificial intelligence capabilities. The investments are starting to pay off as executives expand generative and agentic AI use cases across the business.

In the company’s wealth management division, AI use has evolved from conversational tools assisting with discrete tasks to more orchestrated systems capable of managing workflows across enterprise platforms, according to Hina Shamsi, CIO of wealth management technology at Morgan Stanley. A large part of those expanded capabilities are thanks to initial foundational investments, she said.

“We know these AI agents or agentic workflows can only be as good as the context and data we have,” she told CIO Dive.

The division made a significant pivot when it came to how they viewed data around eight years ago, Shamsi said. Initially, executives thought about data in an account-centric manner. But customers instead wanted to know about the entirety of the relationship – not just specific accounts.

The shift in focus led Morgan Stanley to meaningful machine-learning capabilities including next-best action, Shamsi said, giving advisers guidance on appropriate next steps to take with clients based on the broader portfolio of data.

Continued investments in data platforms, context and the semantic layer to support generative AI and agents remain a critical focus among tech leaders, Shamsi said.

“What that has allowed us to do is really start to focus on some of the other things, building that semantic layer that genAI and agent workflows understand,” she said.

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