Morgan Stanley’s Jeff McMillan, head of firmwide artificial intelligence, is focused on developing and deploying AI throughout the operations of the $212 billion financial institution. 

The New York-based FI  appointed McMillan to the newly created position March 14, he told Bank Automation News. McMillan previously served as chief data and analytics officer for eight years at the FI, according to his LinkedIn profile. 

“Morgan Stanley created this role to ensure the appropriate AI strategy and governance are in place,” McMillan told BAN.

In an interview with Bank Automation News, McMillan discussed his priorities, where Morgan Stanley aims to deploy AI and the potential impact of gen AI on the financial services industry. What follows is an edited version of the conversation: 

Bank Automation News: What is the overall strategy at Morgan Stanley?

Jeff McMillan: Through our unique partnership with OpenAI, Morgan Stanley has early access to their new products and AI experts to create a solution unique to our needs. Our first use case in our wealth management division, the AI at Morgan Stanley Assistant, brings Morgan Stanley’s expansive intellectual capital to the hands of our advisers in seconds and in an easily digestible format. Think of it as having our chief investment strategist, chief global economist and global equities strategist on call — 24 hours a day.

We have successfully deployed the AI Morgan Stanley Assistant to our financial advisers — fully rolled out in September 2023.  

BAN: What are some use cases being explored? 

JM: To optimize efficiency, we will roll out AI in Morgan Stanley Debrief, a tool that acts as an AI-enabled assistant taking notes on an adviser’s behalf in meetings with clients, summarizing key discussion topics and surfacing action items. The tool works by transcribing client meetings, provided the client gives their explicit prior consent. After the meeting, it summarizes key points, creates an email for the financial adviser to edit and send, and then saves a note into Salesforce. It’s important to note that Debrief does not share any information with third parties and every client will have the opportunity to consent to the technology prior to use. If a client does not feel comfortable, the technology will not be used for the meeting.  

BAN: What are your short- and long-term goals for AI at Morgan Stanley?  

JM: We are identifying near-term use cases that every business area can engage with, learn and deliver value. To this end we are also planning on developing and deploying a series of firmwide training modules customized to different roles. Much of this is about demonstrating the value and challenges these tools present to all employees and getting them thinking creatively about the future. 

In the long term, AI at Morgan Stanley is going to be an interaction layer that sits between our employees (advisers, bankers, sales force, etc.) and all the tools and information they currently have access to. The goal is to reduce the complexity of our platform and make everything more seamless by using language to get what you need as opposed to the historical forms of menus, search and a lot of clicking around.  

Ultimately, you’ll be able to use just your voice. The AI can create proposals, evaluate alternative market scenarios, rebalance portfolios, build financial spreadsheets, and help in a variety of repetitive operational or administrative tasks. And I want to reiterate the value here is about helping people do a better job for their clients by making them smarter and more efficient.  

I am hopeful that AI will free up more time to do the things we enjoy which are working with our clients to help them solve their complex challenges and to be engaged, as opposed to less. It’s not going to happen immediately. But we’ve been very deliberate about mapping out the different building blocks. 

BAN: How is Morgan Stanley looking to implement generative AI? 

JM: As part of my new role, I’ll work closely with teams across Morgan Stanley to leverage gen AI in a control-forward, scalable way. To that, their use cases are best served if they fall under one of five buckets:

1. Search function: Allow users to access structured and unstructured content, as well as data and analytics that will use natural language prompts.  
2. Summarization: Allow employees to digest, classify and summarize input documents or transcribed audio/video. 
3. Interpret and evaluate applications: Analyze, apply logic and draw conclusions on text or audio content. 
4. Generate solutions: Create original content based on reference materials and prompts in text or image format. 
5. Translation: Provide the ability to translate content across 53 languages. 

BAN: What impact will generative AI have on financial services? 

JM: Generative AI marks a new era of innovation, with the promise to unlock new business capabilities, pioneer methods of value delivery, and potentially broaden companies’ range of products and services. It empowers organizations to create new products and technologies with significantly less friction. Further, companies will be increasingly rated and valued by their ability to leverage and integrate AI to drive operational efficiency and productivity.   

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