Why AI research really matters: An interview with Manuela Veloso

15 April 2026

Before JPMorganChase hired Manuela Veloso from Carnegie Mellon eight years ago to start an AI research team, a bank wasn’t an obvious home for an academic. Since then, every bank serious about AI has been trying to replicate the impact – and recruit the kind of talent – that Veloso’s team brought to JPMC.

Veloso stepped away from her post earlier this year, but not before building the biggest AI research team of any of the 50 banks we track. In that time, JPMC published nearly 40% of all the papers those banks published. Much of that research was translated into the biggest AI success stories inside the bank.

Now, the research model is shifting. Ownership of AI is moving out of central teams and into the lines of business. Banks are still hiring academics in droves, but the expectation of them is now less on publishing and more on building. We sat down with Veloso and asked her to reflect on how to make AI research successful inside a bank as this broader shift takes shape.

This following conversation has been edited for length and clarity

Q&A

EVIDENT: For many years, you were doing and leading AI research within academia. Banking is, by nature, corporate. Those are very different worlds: How did you get them to mesh?

When I joined JPMorganChase to establish AI Research, I began as one person and in a few years I built a team of more than 100 experts on AI and related fields, all passionate about AI and the specific finance domain. Our mission was to move beyond off-the-shelf tools to explore novel solutions for business problems towards a culture to unlock the full potential of AI within the firm. Bridging the gap between research and deployment required business and engineering champions who enthusiastically embraced change – an approach that allowed the firm to achieve significant AI successes well ahead of the coming powerful scalable GenAI. Ultimately, I believe that research in AI in particular is a vital, long-term investment for any successful business. Research questions existing solutions for improvement and enables exploration for thinking big to address apparently insurmountable problems. Beyond innovation, research never stops aiming higher, as a relentless pursuit, inevitably navigating many dead ends to reach rare breakthroughs. It is only through research that a firm achieves the breakthroughs that will truly distinguish it from its competitors.

Given the inevitable business focus on ROI, how do you see research, with its long-term exploration character, succeeding in a firm?

I can speak of my own experience and how we successfully proceeded. After a few months of acting as a listening and learning "sponge" to the business, we created seven pillars of research that were of business relevance, namely Learning from Experience, Data and Knowledge, Machine Vision and Language, Multi-Agent Systems, Recourse, Optimization, and Planning, Secure and Private AI, and Safe Human-AI Interaction. The AI research team was divided among such pillars and engaged with the related business leaders and their teams. Research solutions need to be rigorously tested, presented, and peer reviewed for their validity. Then we systematically and consistently presented such publications and solutions well adjusted to the business needs. Many solutions came directly from business requests, others came from the research own exploration of perceived challenges of the field of AI in finance. The research team has the function to listen, to keep informed of the new developments, to investigate for new methods, and to advocate to the business.The ROI comes from a tight connection between research, business, product management, and engineering.

We are seeing some changes in the way banks are pursuing AI research, specifically an uptick in university partnerships. Why still have an AI research function in-house?

Not everybody may want or need to have an AI research group in-house, but I believe that they do have to have some commitment to pure academic research. As a clear way to engage with the academic research endeavor, any bank can welcome AI summer interns as graduate students pursuing top academic AI research. Business people can challenge those Ph.D. and Master students with their most complex problems, and they can build upon the devised,proposed, introduced solutions. The universities can provide very fruitful collaborations and talent.

You mentioned before the breakthroughs that research can deliver. Can you foresee a breakthrough that will change how banking is done?

The aspect that I believe will impact everyone is the principled development and human interaction with AI agents one way or another. Companies may now face serious data management challenges as a result of the data and knowledge digitalization of the 90s. If we don’t delve well now into AI agents, at the content and use levels, in addition to the software engineering level, a few years from now, we may face a significant AI agent management problem. Who knows what all these agents will be doing? Who will know if the agents are actually doing what they declare they do? How do we close agents that are not used or that systematically produce wrong outputs? How do we build trust that the agents follow policies and align with values? The pending breakthrough is the AI control of AI agents. In a few years, the people who are going to be ahead are the people that devise AI agents that continuously learn from humans and from their own experience to control other AI agents. Otherwise it may be the wild west of AI agents. I could write many pages on all the opportunities and challenges of multiagent systems and their interaction with human workers. And of the physical world and robots. Maybe next time.

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