JPMorgan Chase has rolled out a generative artificial intelligence assistant to tens of thousands of its employees in recent weeks, the initial phase of a broader plan to inject the technology throughout the sprawling financial giant.
The program, called LLM Suite, is already available to more than 60,000 employees, helping them with tasks like writing emails and reports. The software is expected to eventually be as ubiquitous within the bank as the videoconferencing program Zoom, people with knowledge of the plans told CNBC.
Rather than developing its own AI models, JPMorgan designed LLM Suite to be a portal that allows users to tap external large language models — the complex programs underpinning generative AI tools — and launched it with ChatGPT maker OpenAI’s LLM, said the people.
“Ultimately, we’d like to be able to move pretty fluidly across models depending on the use cases,” Teresa Heitsenrether, JPMorgan’s chief data and analytics officer, said in an interview. “The plan is not to be beholden to any one model provider.”
The move by JPMorgan, the largest U.S. bank by assets, shows how quickly generative AI has swept through American corporations since the arrival of ChatGPT in late 2022. Rival bank Morgan Stanley has already released a pair of OpenAI-powered tools for its financial advisors. And consumer tech giant Apple said in June that it was integrating OpenAI models into the operating system of hundreds of millions of its consumer devices, vastly expanding its reach.
The technology — hailed by some as the “Cognitive Revolution” in which tasks formerly done by knowledge workers will be automated — could be as important as the advent of electricity, the printing press and the internet, JPMorgan CEO Jamie Dimon said in April.
It will likely “augment virtually every job” at the bank, Dimon said. JPMorgan had about 313,000 employees as of June.
ChatGPT ban
The bank is giving employees what is essentially OpenAI’s ChatGPT in a JPMorgan-approved wrapper more than a year after it restricted employees from using ChatGPT. That’s because JPMorgan didn’t want to expose its data to external providers, Heitsenrether said.
“Since our data is a key differentiator, we don’t want it being used to train the model,” she said. “We’ve implemented it in a way that we can leverage the model while still keeping our data protected.”
The bank has introduced LLM Suite broadly across the company, with groups using it in JPMorgan’s consumer division, investment bank, and asset and wealth management business, the people said. It can help employees with writing, summarizing lengthy documents, problem solving using Excel, and generating ideas.
But getting it on employees’ desktops is just the first step, according to Heitsenrether, who was promoted in 2023 to lead the bank’s adoption of the red-hot technology.
“You have to teach people how to do prompt engineering that is relevant for their domain to show them what it can actually do,” Heitsenrether said. “The more people get deep into it and unlock what it’s good at and what it’s not, the more we’re starting to see the ideas really flourishing.”
The bank’s engineers can also use LLM Suite to incorporate functions from external AI models directly into their programs, she said.
‘Exponentially bigger’
JPMorgan has been working on traditional AI and machine learning for more than a decade, but the arrival of ChatGPT forced it to pivot.
Traditional, or narrow, AI performs specific tasks involving pattern recognition, like making predictions based on historical data. Generative AI is more advanced, however, and trains models on vast data sets with the goal of pattern creation, which is how human-sounding text or realistic images are formed.
The number of uses for generative AI are “exponentially bigger” than previous technology because of how flexible LLMs are, Heitsenrether said.
The bank is testing many cases for both forms of AI and has already put a few into production.
JPMorgan is using generative AI to create marketing content for social media channels, map out itineraries for clients of the travel agency it acquired in 2022 and summarize meetings for financial advisors, she said.
The consumer bank uses AI to determine where to place new branches and ATMs by ingesting satellite images and in call centers to help service personnel quickly find answers, Heitsenrether said.
In the firm’s global-payments business, which moves more than $8 trillion around the world daily, AI helps prevent hundreds of millions of dollars in fraud, she said.
But the bank is being more cautious with generative AI that directly touches upon the individual customer because of the risk that a chatbot gives bad information, Heitsenrether said.
Ultimately, the generative AI field may develop into “five or six big foundational models” that dominate the market, she said.
The bank is testing LLMs from U.S. tech giants as well as open source models to onboard to its portal next, said the people, who declined to be identified speaking about the bank’s AI strategy.
Friend or foe?
Heitsenrether charted out three stages for the evolution of generative AI at JPMorgan.
The first is simply making the models available to workers; the second involves adding proprietary JPMorgan data to help boost employee productivity, which is the stage that has just begun at the company.
The third is a larger leap that would unlock far greater productivity gains, which is when generative AI is powerful enough to operate as autonomous agents that perform complex multistep tasks. That would make rank-and-file employees more like managers with AI assistants at their command.
The technology will likely empower some workers while displacing others, changing the composition of the industry in ways that are hard to predict.
Banking jobs are the most prone to automation of all industries, including technology, health care and retail, according to consulting firm Accenture. AI could boost the sector’s profits by $170 billion in just four years, Citigroup analysts said. Â
People should consider generative AI “like an assistant that takes away the more mundane things that we would all like to not do, where it can just give you the answer without grinding through the spreadsheets,” Heitsenrether said.
“You can focus on the higher-value work,” she said.
— CNBC’s Leslie Picker contributed to this report.
Fixed-income markets recorded significant re-pricing during the week ending July 25, 2026, as a convergence of strong labor market metrics and surging energy costs drove U.S. Treasury yields higher across all maturities. The benchmark 10-year Treasury yield climbed toward 4.70%, reaching its highest point in several months. Institutional bond investors rapidly adjusted portfolio durations as expectations for near-term interest rate cuts by the Federal Reserve faded in response to inflation concerns.
The upward shift in sovereign yields reflects a broader fundamental reassessment of global monetary policy. Earlier in the quarter, money markets had priced in a series of rate reductions designed to support economic activity. However, with initial jobless claims falling to 187,000 and crude oil breaching $100 per barrel, fixed-income traders are pricing in a ‘higher-for-longer’ interest rate environment. The inversion between short-term Treasury bills and long-term bonds narrowed, indicating a shift toward term premium expansion.
Rising Treasury yields present both challenges and opportunities for institutional wealth managers. While commercial lenders and mortgage origination volumes face headwinds from elevated borrowing costs, fixed-income investors are locking in attractive real yields on high-quality sovereign and investment-grade corporate bonds. Institutional debt issuers, conversely, are recalibrating their capital structures, opting for shorter-term refinancing instruments or private credit facilities to avoid committing to elevated long-term coupon rates.
Navigating the current bond market landscape demands strict duration management and credit selection. Wealth advisors recommend maintaining flexible fixed-income allocations, combining short-duration Treasuries with inflation-protected securities (TIPS) to shield capital against potential energy-driven inflation spikes while earning dependable nominal income.
Private credit markets reached a pivotal milestone during the week ending July 25, 2026, as non-bank direct lending consortiums captured a record share of middle-market corporate debt originations. With commercial banks maintaining conservative credit standards and public bond yields remaining elevated, corporate borrowers are increasingly turning to private fund managers for customized capital solutions. This expansion marks a permanent structural shift in enterprise finance, establishing private credit as a primary pillar of institutional corporate liquidity.
The acceleration of private credit deals is driven by speed, deal certainty, and flexible terms. Unlike traditional syndicated bank loans that require lengthy underwriting, credit rating approvals, and public roadshows, private direct lenders can structure tailored financing packages within days. Middle-market firms facing upcoming debt maturities are utilizing private debt facilities to execute recapitalizations, strategic acquisitions, and growth capital deployments without risking execution delay in public markets.
However, financial regulators and central bank supervisors are scrutinizing the sector’s rapid growth. Supervisory agencies are evaluating potential systemic risks associated with non-bank leverage, valuation transparency, and liquidity mismatches during economic downturns. Despite regulatory interest, major pension funds, insurance firms, and sovereign wealth entities continue to expand capital allocations to private credit funds, attracted by reliable floating-rate yields that outperform public fixed-income benchmarks.
As private credit matures into a dominant asset class, corporate chief financial officers must evaluate non-bank lenders alongside traditional banking relationships. Direct lending partnerships provide valuable balance sheet resilience, enabling companies to secure flexible financing terms even during periods of public market turbulence.
The landscape of institutional debt markets is undergoing a profound structural shift on July 21, 2026, as major corporate issuers and commercial banks rapidly accelerate the deployment of tokenized debt instruments. Data published by leading capital market consortiums indicates that primary issuances of digital commercial paper and tokenized corporate bonds have reached record volumes this month. By moving legacy debt origination, underwriting, and secondary distribution onto permissioned distributed ledgers, corporate treasurers are unlocking unprecedented operational flexibility and instantaneous cross-border liquidity.
The adoption of tokenized debt is fundamentally altering how enterprise balance sheets manage short-term liquidity needs. Traditional corporate bond settlement cycles historically required multi-day clearing processes involving numerous intermediaries, custodial entities, and clearinghouses. Through programmable smart contracts on distributed ledgers, issuers can now execute atomic settlement—enabling continuous, 24/7 access to institutional capital pools. This instantaneous clearing mechanism drastically reduces counterparty risk, eliminates costly settlement friction, and allows treasury teams to dynamically optimize working capital in real time.
A major catalyst driving this institutional migration is the establishment of comprehensive digital asset regulatory frameworks across major financial hubs. Clear legal guidelines regarding ledger-based securities ownership have provided institutional compliance officers with the regulatory confidence necessary to transition multi-billion-dollar liquidity facilities onto digital platforms. Furthermore, the integration of automated regulatory reporting directly into token smart contracts simplifies ongoing compliance audits, ensuring that secondary market trades automatically enforce investor accreditation limits and tax withholding requirements.
For chief financial officers and institutional portfolio managers, tokenized debt represents a fundamental evolution in fixed-income strategy. Companies that embrace ledger-based debt structures gain direct access to a broader, global base of digital-native institutional investors while substantially reducing borrowing overhead. As ledger interoperability continues to improve across global exchanges, tokenized debt is poised to become the standard infrastructure for global corporate finance.