Latest news with #HariGopalkrishnan
Fast Company
5 days ago
- Business
- Fast Company
How To Embrace Enterprise AI: A Conversation with Bank of America's Hari Gopalkrishnan
While consumer tools like ChatGPT dominate headlines, IT leaders face an even tougher challenge: keeping pace with an explosion of data demands while making the right infrastructure bets to support enterprise‑scale AI. Strategy is key; and there's no better way to implement the right AI strategy than hearing directly from the pros who have successfully made artificial intelligence work for them and their teams, and ultimately their clients. In this virtual discussion, Hari Gopalkrishnan, head of consumer, business, and wealth management technology, Bank of America, shares how to incorporate security, compliance, and resilience into your corporation's AI infrastructure. '
Yahoo
02-05-2025
- Business
- Yahoo
Morgan Stanley and Bank of America are focusing AI power on tools to make employees more efficient
Some financial institutions are prioritizing internal AI tools to enhance daily operations. For example, Morgan Stanley and Bank of America have trained staff to use AI with human supervision. This article is part of "AI in Action," a series exploring how companies are implementing AI innovations. The financial industry's approach to artificial intelligence reveals considerable pragmatism. Popular notions of generative AI, guided by the explosive growth of OpenAI's ChatGPT, often center on consumer-facing chatbots. But financial institutions are leaning more heavily on internal AI tools that streamline day-to-day tasks. This requires training programs and user-experience design that help a bank's entire organization — from relationship bankers directing high-value accounts to associates — understand the latest AI technology. Banks have long used traditional AI and machine learning techniques for various functions, such as customer service bots and decision algorithms that provide a faster-than-human response to market swings. But modern generative AI is different from prior AI/ML methods, and it has its own strengths and weaknesses. Hari Gopalkrishnan, Bank of America's chief information officer and head of retail, preferred, small business, and wealth technology, said generative AI is a new tool that offers new capabilities, rather than a replacement for prior AI efforts. "We have a four-layer framework that we think about with regards to AI," Gopalkrishnan told Business Insider. The first layer is rules-based automation that takes actions based on specific conditions, like collecting and preserving data about a declined credit card transaction when one occurs. The second is analytical models, such as those used for fraud detection. The third layer is language classification, which Bank of America used to build Erica, a virtual financial assistant, in 2016. "Our journey of Erica started off with understanding language for the purposes of classification," Gopalkrishnan said. But the company isn't generating anything with Erica, he added: "We're classifying customer questions into buckets of intents and using those intents to take customers to the right part of the app or website to help them serve themselves." The fourth layer, of course, is generative AI. Koren Picariello, a Morgan Stanley managing director and its head of wealth management generative AI, said Morgan Stanley took a similar path. Throughout the 2010s, the company used machine learning for several purposes, like seeking investment opportunities that meet the needs and preferences of specific clients. Many of these techniques are still used. "Historically, I was working in analytics, data, and innovation within the wealth space. In that space, Morgan Stanley did focus on the more traditional AI/ML tools," Picariello told BI. "Then in 2022, we started a dialogue with OpenAI before they became a household name. And that began our generative-AI journey." Given the history, it'd be reasonable to think banks would turn generative-AI tools into new chatbots that more or less serve as better versions of Bank of America's Erica, or as autonomous financial advisors. But the most immediate changes instead came to internal processes and tools. Morgan Stanley's first major generative-AI tool, Morgan Stanley Assistant, was launched in September 2023 for employees such as financial advisors and support staff who help clients manage their money. Powered by OpenAI's GPT-4, it was designed to give responses grounded in the company's library of over 100,000 research reports and documents. The second tool, Morgan Stanley Debrief, was launched in June. It helps financial advisors create, review, and summarize notes from meetings with clients. "It's kind of like having the most informed person at Morgan Stanley sitting next to you," Picariello said. "Because any question you have, whether it was operational in nature or research in nature, what we've asked the model to do is source an answer to the user based on our internal content." Bank of America is pursuing similar applications, including a call center tool that saves customer associates' time by transcribing customer conversations in real time, classifying the customer's needs, and generating a summary for the agent. The decision to deploy generative AI internally first, rather than externally, was in part due to generative AI's most notable weakness: hallucinations. In generative AI, a hallucination is an inaccurate or nonsensical response to a prompt, like when Google Search's AI infamously recommended that home chefs use glue to keep cheese from sliding off a pizza. Banks are wary of consumer-facing AI chatbots that could make similar errors about bank products and policies. Deploying generative AI internally lessens the concern. It's not used to autonomously serve a bank's customers and clients but to assist bank employees, who have the option to accept or reject its advice or assistance. Bank of America provides AI tools that can help relationship bankers prep for a meeting with a client, but it doesn't aim to automate the bank-client relationship, Gopalkrishnan told BI. Picariello said Morgan Stanley takes a similar approach to using generative AI while maintaining accuracy. The company's AI-generated meeting summaries could be automatically shared with clients, but they're not. Instead, financial advisors review them before they're sent. Bank of America and Morgan Stanley are also training bank employees on how to use generative-AI tools, though their strategies diverge. Gopalkrishnan said Bank of America takes a top-down approach to educating senior leadership about the potential and risks of generative AI. About two years ago, he told BI, he helped top-level staff at the bank become "well aware" of what's possible with AI. He said having the company's senior leadership briefed on generative AI's perks, as well as its limitations, was important to making informed decisions across the company. Meanwhile, Morgan Stanley is concentrating on making the company's AI tools easy to understand. "We've spent a lot of time thinking through the UX associated with these tools, to make them intuitive to use, and taking users through the process and cycle of working with generative AI," Picariello said. "Much of the training is built into the workflow and the user experience." For example, Morgan Stanley's tools can advise employees on how to reframe or change a prompt to yield a better response. For now, banks are focusing AI initiatives on identifying and automating increasingly more complex and nuanced tasks within the organizations rather than developing one-off applications targeted at the customer experience. "We try to approach problems not as a technology problem but as a business problem. And the business problem is that Bank of America employees all perform lots of tasks in the company," said Gopalkrishnan. "The opportunity is to think more holistically, to understand the tasks and find the biggest opportunities so that five and 10 years from now, we're a far more efficient organization." Read the original article on Business Insider Sign in to access your portfolio
Business Insider
02-05-2025
- Business
- Business Insider
Morgan Stanley and Bank of America are focusing AI power on tools to make employees more efficient
The financial industry's approach to artificial intelligence reveals considerable pragmatism. Popular notions of generative AI, guided by the explosive growth of OpenAI's ChatGPT, often center on consumer-facing chatbots. But financial institutions are leaning more heavily on internal AI tools that streamline day-to-day tasks. This requires training programs and user-experience design that help a bank's entire organization — from relationship bankers directing high-value accounts to associates — understand the latest AI technology. From AI classification to AI generation Banks have long used traditional AI and machine learning techniques for various functions, such as customer service bots and decision algorithms that provide a faster-than-human response to market swings. But modern generative AI is different from prior AI/ML methods, and it has its own strengths and weaknesses. Hari Gopalkrishnan, Bank of America's chief information officer and head of retail, preferred, small business, and wealth technology, said generative AI is a new tool that offers new capabilities, rather than a replacement for prior AI efforts. "We have a four-layer framework that we think about with regards to AI," Gopalkrishnan told Business Insider. The first layer is rules-based automation that takes actions based on specific conditions, like collecting and preserving data about a declined credit card transaction when one occurs. The second is analytical models, such as those used for fraud detection. The third layer is language classification, which Bank of America used to build Erica, a virtual financial assistant, in 2016. "Our journey of Erica started off with understanding language for the purposes of classification," Gopalkrishnan said. But the company isn't generating anything with Erica, he added: "We're classifying customer questions into buckets of intents and using those intents to take customers to the right part of the app or website to help them serve themselves." The fourth layer, of course, is generative AI. Koren Picariello, a Morgan Stanley managing director and its head of wealth management generative AI, said Morgan Stanley took a similar path. Throughout the 2010s, the company used machine learning for several purposes, like seeking investment opportunities that meet the needs and preferences of specific clients. Many of these techniques are still used. "Historically, I was working in analytics, data, and innovation within the wealth space. In that space, Morgan Stanley did focus on the more traditional AI/ML tools," Picariello told BI. "Then in 2022, we started a dialogue with OpenAI before they became a household name. And that began our generative-AI journey." How banks are deploying AI Given the history, it'd be reasonable to think banks would turn generative-AI tools into new chatbots that more or less serve as better versions of Bank of America's Erica, or as autonomous financial advisors. But the most immediate changes instead came to internal processes and tools. Morgan Stanley's first major generative-AI tool, Morgan Stanley Assistant, was launched in September 2023 for employees such as financial advisors and support staff who help clients manage their money. Powered by OpenAI's GPT-4, it was designed to give responses grounded in the company's library of over 100,000 research reports and documents. The second tool, Morgan Stanley Debrief, was launched in June. It helps financial advisors create, review, and summarize notes from meetings with clients. "It's kind of like having the most informed person at Morgan Stanley sitting next to you," Picariello said. "Because any question you have, whether it was operational in nature or research in nature, what we've asked the model to do is source an answer to the user based on our internal content." Bank of America is pursuing similar applications, including a call center tool that saves customer associates' time by transcribing customer conversations in real time, classifying the customer's needs, and generating a summary for the agent. Keeping humans in the loop The decision to deploy generative AI internally first, rather than externally, was in part due to generative AI's most notable weakness: hallucinations. In generative AI, a hallucination is an inaccurate or nonsensical response to a prompt, like when Google Search's AI infamously recommended that home chefs use glue to keep cheese from sliding off a pizza. Banks are wary of consumer-facing AI chatbots that could make similar errors about bank products and policies. Deploying generative AI internally lessens the concern. It's not used to autonomously serve a bank's customers and clients but to assist bank employees, who have the option to accept or reject its advice or assistance. Bank of America provides AI tools that can help relationship bankers prep for a meeting with a client, but it doesn't aim to automate the bank-client relationship, Gopalkrishnan told BI. Picariello said Morgan Stanley takes a similar approach to using generative AI while maintaining accuracy. The company's AI-generated meeting summaries could be automatically shared with clients, but they're not. Instead, financial advisors review them before they're sent. Training the finance workforce for AI Bank of America and Morgan Stanley are also training bank employees on how to use generative-AI tools, though their strategies diverge. Gopalkrishnan said Bank of America takes a top-down approach to educating senior leadership about the potential and risks of generative AI. About two years ago, he told BI, he helped top-level staff at the bank become "well aware" of what's possible with AI. He said having the company's senior leadership briefed on generative AI's perks, as well as its limitations, was important to making informed decisions across the company. Meanwhile, Morgan Stanley is concentrating on making the company's AI tools easy to understand. "We've spent a lot of time thinking through the UX associated with these tools, to make them intuitive to use, and taking users through the process and cycle of working with generative AI," Picariello said. "Much of the training is built into the workflow and the user experience." For example, Morgan Stanley's tools can advise employees on how to reframe or change a prompt to yield a better response. For now, banks are focusing AI initiatives on identifying and automating increasingly more complex and nuanced tasks within the organizations rather than developing one-off applications targeted at the customer experience. "We try to approach problems not as a technology problem but as a business problem. And the business problem is that Bank of America employees all perform lots of tasks in the company," said Gopalkrishnan. "The opportunity is to think more holistically, to understand the tasks and find the biggest opportunities so that five and 10 years from now, we're a far more efficient organization."
Yahoo
11-04-2025
- Business
- Yahoo
How Bank of America scaled AI
This story was originally published on CIO Dive. To receive daily news and insights, subscribe to our free daily CIO Dive newsletter. Bank of America has reached a major milestone in its AI journey: More than 90% of its 213,000 employees now leverage the Erica for Employees virtual assistant, the company said in a Tuesday announcement. The AI-powered tool was rolled out in 2020 to ease IT administrative processes when pandemic concerns drove a pivot to remote work. The company has since attributed a more than 50% reduction in IT service calls to Erica for Employees, according to the announcement. As organizations invest in emerging technologies, there are risks and — if all goes well — rewards. The progression from pilots to widespread adoption isn't automatic or assured. Development and implementation costs have to align with value. At Bank of America, the tech vetting process is grounded in practical considerations, Hari Gopalkrishnan, head of consumer, business and wealth management technology, told CIO Dive. 'We start by looking at what the customer wants,' Gopalkrishnan said. 'If you start by asking 'how can I take this cool technology to market?' you're going to spend a lot of money and it's going to fail. It has to map back to what the customer needs.' Bank of America's virtual assistant initiative began in 2018, when the company launched the first iteration of an in-app virtual assistant called Erica. The chatbot has tallied 2.5 billion client interactions since its initial deployment and now has 20 million active users, the company said. Measurable efficiency gains and user-experience improvements don't come cheap. Bank of America invests roughly $13 billion on tech annually, earmarking nearly one-quarter of that budget to new technology initiatives this year. Some of that spending supports regularly scheduled innovation sessions to workshop potential use cases. 'Teammates come together to go over what we've heard from the market, what are the cool things in tech and how we can come up with a bunch of ideas in a 48-hour cycle,' Gopalkrishnan said. 'Some of them get funded right away and some take longer.' The bank's portfolio of virtual assistants, including Erica, Erica for Employees and a pair of customized tools for its Merrill and Bank of America Private Bank units, emerged from an emphasis on deliberate innovation. 'There are many areas where we've used traditional predictive AI for years to actually deliver value, both client facing and internally,' said Gopalkrishnan. As generative AI capabilities took center stage, the bank already had processes in place to evaluate the safety, efficacy and potential value of the technology. 'There are 16 different parameters we look at to determine if a capability holds muster when it comes to responsible deployment and that has not changed,' said Gopalkrishnan. The company also has an AI oversight council to manage safety and governance. The technology's evolution, from an engineering perspective, stems from the size of large language models and the scope of their capabilities. Prior to ChatGPT, models were built from the ground up with specific functions in mind. Generative AI models are multitaskers, capable of summarization across functions and multilanguage code generation. 'The new kid in town is content-based,' Gopalkrishnan said. 'Now you get to the interesting realm of generating content, which is both exciting and, in some ways, daunting, because now you've got to worry about hallucinations.' Despite generative AI's broad potential, many organizations are struggling to register a return on their investments in the technology. As accuracy issues, safety concerns and governance complications impede adoption, the proof-of-concept process can lead to shuttered pilots. 'You can spin up something that sounds like it's cool but yields very little value to the business,' Gopalkrishnan said. 'A cool demo can end up costing you a ton of money, which is not a good outcome.' Gopalkrishnan recalls a particular technological innovation predating ChatGPT that also promised to revolutionize business processes. 'A few years ago, the world was abuzz with talk of the metaverse,' he said. 'We took a look at augmented reality, we did innovation sessions and teams came back with all kinds of cool ideas. But, early on, we realized that no customer was asking us for that.' Predictive and then generative AI coupled with natural language processing followed a more promising development curve. 'When we started our journey with Erica, we realized that real customers were having challenges navigating hundreds of features in a mobile app,' Gopalkrishnan said. 'We could see that customers actually cared about natural language processing even if they didn't call it that.' Erica's evolution helped establish a vetting framework that begins with understanding the various roles in the organization and working from there to weigh costs against value. One area that promised a return on investment in the data-intensive banking industry was coding assistants. Big banks are well positioned to reap the rewards of tools that guide engineers through legacy applications, according to Accenture. The largest industry players have moved quickly to add governance and ethical use expertise to ease the adoption process, Evident Insights found. Bank of America has already seen a 20% efficiency improvement among its coders through the use of generative AI, the company said in the Tuesday announcement. The technology was adopted with safety in mind and an eye toward ROI, according to Gopalkrishnan. 'It's not a panacea yet, but the faster we can generate valuable, meaningful code, both in terms of revenue and business initiatives that are going to take out expenses, the better we get,' Gopalkrishnan said. 'It's really for those activities that are safely reproducible.' The company's ongoing innovation push has yielded 7,400 patents and pending patent applications, more than 1,200 of which are AI and machine learning focused. While patents protect the bank's intellectual property, they can also be a moral booster. 'It's a way to reward the team for innovative thinking,' Gopalkrishnan said. 'But it also allows us to make sure people realize there's a lot of IP here and we're not flying by the seat of our pants.' 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