We are still in the beginning of AI adoption, even in finance, so those early birds not only get the worms but also get to define what that means. A new report explores the structural impact AI could have in the financial sector. Read more about it below.
It is a well-known truism that if you have mad skills that no one else has, that can put you in charge or ahead. That has always been the tagline for the world of finance and the whole argument for active portfolio management. If you could crunch numbers like nobody else and had a spidey sense for things to come, you could use that to be Tom Holland or outperform the market.
Conversely, if everybody (thanks to AI) has the same information at the same time and the same tools to analyze it, maybe you are not that special anymore. And then what?
A new report by Mona Naqvi from the CFA Institute’s Research and Policy Center explores just that. The report builds a framework for investigating and understanding the structural impact AI might have on finance, and it draws up some interesting scenarios.
The first and least messy one is the Augmented Markets scenario. In this scenario AI integration enhances workflow efficiency without materially reshaping the market architecture. In other words, same as now, just faster and better. Specifically, the areas where speed can have serious impact, such as how fast funds are moved around (liquidity), how assets and prices relate to each other (correlations), and how quickly prices are set (volatility), evolve gradually and in step.
The second scenario is the Competitive Divergence one, where uneven AI adoption creates widening dispersion in performance, cost structure, and competitive positioning. The report says the defining feature of this scenario is asymmetry because there is a distinct parting between those who go all-in and benefit from lower analysis costs and scalable decisioning, and those who are left behind due to legacy systems or capital limitations.
The third scenario is called Platform Convergence, and in that one AI capability is broadly adopted through shared infrastructure, which compresses differentiation and concentrates influence within common analytical platforms. Where the two previous scenarios necessitate less changes to regulation, the report points out that this scenario entails a new(ish) systemic risk due to cognitive convergence, which it defines as the progressive alignment of model architectures, training data, and decision frameworks. If we all have the same information at the same time from more or less the same source and share the same AI architecture to respond to it, that represents a systemic vulnerability in itself.
The fourth scenario is the Model-Mediated Markets in which AI systems assume primary responsibility for signal generation, allocation decisions, and risk calibration across large segments of capital. This scenario is by far the most structurally transformative since market participation is automated and synchronized, and oversight is focused on model governance and constraint setting. Because the fiduciary responsibility is largely exercised by AI systems this scenario has extensive regulatory implications as well.
None of the scenarios are mutually exclusive or sequential or will apply across all finance, so think also about a fifth scenario called Some or All of the Above.
What the scenarios do have in common is that being special will be different. The advantage no longer stems from what you know and when you know it but will come from how well you set up your architecture to respond to it. Aspects like process quality, data infrastructure, execution consistency, research intensity, and the ability to scale are more differentiating than ever before.
And the early bird gets to define the worm, meaning that since little is set in stone, those who have formed a plan of where and how to go will fare better than those who choose to wait and see. If you want to be the early bird, but is unsure of where to begin, FRG can help you.
Source: Artificial Intelligence and the Future of Finance: A Framework for Structural Change, Mona Naqvi, CFA Institute, Research and Policy Center, July 2026
Regitze Ladekarl, FRM, is FRG’s Director of Company Intelligence. She has 25-plus years of experience where finance meets technology.
This article is part of the FRG Risk Report, published weekly on the FRG blog. To read other entries of the Risk Report, visit frgrisk.com/category/risk-report/.