The AI agents are coming

Ultimately, we will have AI agents working with each other on functions, making agent to agent protocols a premium innovation in the near future. As of now, management firms have achieved a good level of competency in deploying AI for market research and alternative data analysis, including tasks like analyzing earnings calls, portfolio construction, and risk analysis. They are actively launching AI tools for more operational functions in data quality management and reconciliation. In AIMA’s recent survey of worldwide fund managers, 58% expect wider front-office integration of AI this year, and 90% of the investors surveyed believe Gen AI would positively impact the performance of at least some fund managers' portfolios over the next three years.ⁱ However, firms face a major challenge as they transition AI strategies from pilots to enterprise-wide implementation.

Siloed AI agents don't talk

In investment management, CTOs are chasing an AI dream: for their managers to be able to type in a seemingly simple request like, “How is my growth portfolio doing?” and receive a magic answer in the form of a chart, a phrase, or a paragraph. AI models are not standardized, and they cannot easily communicate or collaborate across the organization's data infrastructure. The future may be end-to-end AI workflows, but for now AI agents operate in silos and cannot access multiple datasets simultaneously.

AI agents need the wiring plus the context. Without it, you can ask for a growth portfolio, but the agents won’t know if that’s the name of a fund, a book, or a legal entity. It's all the other systems that are supposed to give that context to the answer. It’s in the name: model context protocol. Those firms considering rolling out AI agents into their tech infrastructure should think of MCP as an integral part of these initiatives.

MCP servers drive data interoperability in finance

Most asset management firms are by now keenly aware of the necessity of data interoperability and having a centralized, scalable data management infrastructure in place to take full advantage of the strategic asset that data has become. Once again, we are talking about the need to scale, this time in firms’ AI strategies.