How are investment firms rethinking data architecture to support advanced analytics and responsibly apply AI at scale?
At Bloomberg’s 2025 Enterprise Data & Tech Summit in New York, Angana Jacob, Global Head of Research Data at Bloomberg, spoke with Carson Boneck, Chief Data Officer at Balyasny Asset Management, C.J. Jaskoll, Chief Technology Officer at Russell Investments, and Nan Xiao, Chief Technology and Data Officer at Greenland Capital Management, about how investment firms are building AI-ready infrastructure, harmonizing data systems, and looking at ROI of their AI investments.
Featured insights from the discussion panel:
On AI transforming investment firms
Carson Boneck: What AI allows us to do is extract certain heuristics that can then be emulated and replicated. In a platform like ours, we have so many great investors. I think a lot of firms, including ours, in many places, are at the task level, but we could really help them improve these tasks. We can write research reports by using the deep research agent and compiling all the various information… My hypothesis is that firms like ours are going to get completely transformed [by AI] and the firms that win are those that are able to do the best job at using AI to extract the heuristics of the very best investors.*
On measuring AI investment ROI
C.J. Jaskoll: AI is new, but change management is not. For all change at every buy-side firm I’ve ever been at, there are really two major ways that I think about ROI. Number one: operational efficiency. That means avoiding a trade error, reducing costs, and reducing risk. All of those things can be tangible and quantified. If you are able to quantify that, then you can pay for the platform you’re building or that you’ve bought.
The second one, which I think our industry has and almost no other industry has, is generating alpha. I was talking to a friend last week who asked the same question, and he said, “How do I bring this data into my firm?” I said, “One trade at your firm will pay for the entire platform and three developers.” But is ROI actually needed right now? I think we’re still in the honeymoon period. I don’t think many companies are actually thinking about ROI.
On scaling research with agentic AI
Nan Xiao: We’ve deployed [solutions that are in] production already, and a lot of things in experiments. We have research agents who do multi-steps research and come up with conviction, and go from there to generate sizing, suggestions, trading ideas, and essentially simulate outcomes. Those goals become proposals to PMs. A possible use case will also be… you can ask [AI] to think [about] things differently. Instead of having one research analyst, it’s like [having] five research analysts thinking from different angles. As a result, giving you five different proposals that you can…pick which you would like to go for or combine.
*Quotations have been edited for brevity and clarity.
