Letter from the Editor: The AI productivity story is easy to tell until you sit inside the institution building it
- Today’s analysis examines the widening gap between the AI capabilities being developed and the language used to describe them.
- If AI's role is to make banking more efficient, productivity matters. If its role is to redefine decision-making and workflow, productivity may be too narrow a lens.
The ‘Letter from the Editor’ series features exclusive insight and opinion-driven analysis from Tearsheet editor Sara Khairi. The focus is on linking ideas, questioning assumptions, and tracking shifts across both mature and emerging trends in financial services.
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Issue # 6
The AI story in financial services is usually told as a productivity story: output rises, headcount becomes more efficient, workflows compress, and banks enter a phase where complexity is finally “managed” rather than accumulated. In this version, AI is not disruptive so much as it is smoothing the edges of an already familiar efficiency curve.
But the closer you look at how this is being implemented inside institutions, the clearer it becomes that the real change is in how capabilities are organized and managed across the organization. More and more, the language used to describe these systems carries greater weight than the systems themselves.
We see this in the way AI is being rolled out across institutions such as J.P. Morgan, Goldman Sachs, Morgan Stanley, and Citi. Whether in advisor copilots in wealth management or generative AI tools embedded in analyst workflows, the lens remains consistent: support, instead of substitution. Yet the vocabulary that surrounds these deployments often carries its own subtext – terms like “streamlining,” “lower-value work,” and “operational simplification” imply a reshaping of hierarchy rather than just a refinement of process.
That gap between what is being built and how it is being described is where today’s analysis is focused.
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