Macrobond hosted a discussion at Alpha Edge North America exploring how AI, quantitative investing, and trusted data infrastructure are transforming hedge fund allocation and portfolio construction workflows.
At Alpha Edge North America, Macrobond hosted a panel discussion exploring how AI and quantitative technologies are reshaping portfolio construction and investment research workflows across institutional investing.
Moderated by Hank Rainey of Macrobond, the conversation brought together senior leaders across asset allocation and advanced investment research, including Bill Li, Director of Portfolio Completion Strategies at Mass PRIM, and Gilbert Haddad, Head of Advanced Strategies & Research at Fidelity.
While the discussion ranged widely from operational efficiency to evolving research processes one argument ran through all of it: the firms that win with AI will not be the ones with the best models. They will be the ones whose research infrastructure can actually be trusted at scale.
Across the industry, firms are racing to embed AI into investment workflows. But AI-generated outputs are only as reliable as the data feeding them, and without transparent lineage, revision-aware datasets, and auditable workflows, firms risk pushing noise, inconsistency, and governance exposure into decisions that carry real capital.
The deeper issue is structural. AI has consistently underdelivered in financial research not because the models are weak, but because domain knowledge, AI capability, and product design have never been connected into a single coherent system. Closing that gap is an infrastructure problem, not a model problem and it is the problem the next generation of research platforms will be measured against.
This is already changing how investment teams evaluate infrastructure. Used well, AI does not replace analysts; it moves teams faster through discovery, synthesis, and analysis while human judgment stays at the centre of the decision which places even more weight on the integrity of the data environment underneath it.
As AI adoption matures, firms will likely face increasing pressure to answer difficult operational questions:
These are no longer technical footnotes. They are the questions that will separate the institutions that scale AI from the ones that stall and they point to a single conclusion.
At Macrobond, we believe the future of AI-enabled research will not be defined by black-box automation. It will be defined by trusted workflows that combine human expertise with transparent, high-integrity data infrastructure. The firms that succeed will not be the fastest. They will be the ones building systems their investment teams can stand behind.
Special thanks to Bill Li, Gilbert Haddad, Institutional Investor, and everyone who joined the conversation at Alpha Edge North America.
