In a recent LinkedIn post, Lee McCabe critically examines the current state of Artificial Intelligence (AI) adoption within investment operations, drawing a stark contrast between firms that are genuinely transforming their businesses and those merely engaging in superficial implementation. McCabe suggests that while many in the industry claim to be leveraging AI, the reality for a significant portion is far less advanced, leading to what he terms ‘expensive cosplay’.
McCabe highlights a paradox revealed in industry data. He notes,
“72% of firms say AI, Gen AI and advanced analytics are the biggest innovation opportunity. 70% say AI is already active in the front office. Yet 63% still admit they do not have a unified real time data layer across front, middle, and back office.”
This disparity, according to McCabe, points to a fundamental disconnect between aspirational AI adoption and the underlying operational infrastructure required for its effective use.
The Infrastructure Gap in AI Implementation
McCabe argues that the true challenge for many investment operations firms lies not in the AI models themselves, but in their foundational data architecture and operational platforms. He points out that despite the widespread discussion around AI, a majority of firms struggle with basic data integration and process automation. As Lee McCabe observes, “Only 17% have full straight through processing. Only 20% are operating on a single platform with instant decision support.” This lack of a robust, real-time data layer means that AI initiatives are often built on shaky ground.
Innovation Over Efficiency: A Strategic Shift
A key observation from McCabe’s post is the shift in strategic priorities within the industry. He identifies a significant trend where innovation has now surpassed efficiency as the primary driver for investment.
“The most interesting number in the whole thing is not the AI one. It is that innovation has finally overtaken efficiency as the top strategic priority, with 55% of firms now chasing competitive differentiation through innovation.”
However, McCabe humorously notes that this pursuit of innovation is hampered by outdated operational systems, describing the situation as “half the industry still has the plumbing of a provincial building society from 2004.”
Two Paths to AI: Leverage vs. Accessory
Lee McCabe delineates two distinct groups of firms based on their approach to AI. The first group, in his view, is effectively using AI as a tool to enhance their existing clean architecture, consolidated platforms, and decision-ready data. This group is positioned to gain a competitive edge. In contrast, the second group, which McCabe describes as using AI as a ‘PowerPoint accessory,’ is struggling to reconcile AI’s potential with their operational realities. McCabe elaborates,
“That second group will still be talking about ‘unlocking value’ in 18 months, usually after buying another vendor, creating another dashboard, and discovering that garbage in still produces garbage out, only faster and with better branding.”
The Cruel Reality of Undisciplined Infrastructure
Ultimately, McCabe posits that AI will indeed create market winners, but not necessarily due to the sophistication of the AI models. Instead, he argues that the firms with the most disciplined, well-integrated infrastructure will be the ones who finally reap the rewards. As Lee McCabe concludes, the firms that have invested in the ‘boring’ but essential elements of their operations – clean data, consolidated platforms, and straight-through processing – will be the ones who truly benefit. This, he suggests, is a somewhat ‘cruel outcome’ for an industry often more captivated by narrative than by operational reality.
📝 About This Content
This article is based on insights shared by Lee McCabe on LinkedIn.
📅 Originally posted on April 7, 2026 | View original post on LinkedIn →