A New Organizational Unit That Admits Something Uncomfortable

EY announced this week that it is creating what it calls an "AI Value Realization Office," a dedicated organizational unit whose explicit mandate is to ensure that EY's substantial AI spending actually produces measurable returns. The framing is notable: EY's leadership concluded that no existing department was capable of owning this function. Finance could not own it. IT could not own it. Strategy could not own it. The firm's response was to create an entirely new structural entity. This is not a rebranding exercise. It is an organizational admission that AI governance sits outside the competence boundary of every current function in the firm.

That admission deserves more theoretical scrutiny than it has received in the coverage so far.

When Existing Schema Structures Fail

Classical organizational theory treats the division of labor as a solution to competence allocation problems. You assign tasks to the unit best equipped to handle them. The problem EY is diagnosing is that no unit has the schema to handle AI governance - not because of resource constraints, but because the structural features of AI-mediated workflows do not map onto the categorical systems that existing departments were built around. Finance reads cost-benefit ratios. IT reads system specifications. Neither reads the feedback loops between model behavior, workflow adaptation, and output quality that determine whether AI spending generates value.

This is precisely what Kellogg, Valentine, and Christin (2020) identified in their review of algorithmic work: organizations routinely underestimate how much algorithmic systems invert the standard assumption that competence precedes deployment. Firms invest in AI systems before developing the organizational capacity to interpret what those systems are doing or why outputs vary. EY's new office is, functionally, an attempt to build that interpretive capacity retroactively, after the investment has already been made.

The Structural Problem Is Not Coordination, It Is Schema

The coverage of EY's announcement frames this as a coordination problem. Who is responsible for AI ROI? The new office will coordinate across functions to answer that question. But coordination assumes that the relevant information exists somewhere in the organization and simply needs to be routed correctly. The more difficult possibility is that the information does not yet exist in a usable form inside EY, and that no amount of coordination will produce it until someone builds an accurate structural model of how their AI deployments actually work.

Gentner's (1983) structure-mapping theory is useful here. Analogical reasoning transfers well when the relational structure of a source domain maps cleanly onto the target domain. EY's existing departments are trying to apply schema from domains - cost accounting, system administration, project management - whose relational structure does not transfer to AI value assessment. The variables that predict AI value (model behavior under distribution shift, user adaptation patterns, feedback loop dynamics) are structurally different from the variables those departments were designed to track. The new office, if it succeeds, will have to build a genuinely new schema rather than adapt an old one.

What Wall Street's AI Investments Suggest by Comparison

Recent reporting on Wall Street banks including JPMorgan, Citi, and Goldman Sachs shows a parallel pattern. These institutions are investing billions in AI while simultaneously acknowledging that workflows and organizational culture are being reshaped in ways that are not yet fully legible. The governance question - who evaluates whether this is working and by what criteria - remains largely unresolved across the industry. EY's move to create a dedicated office is one response to that unresolved question. It is structurally more honest than the alternative, which is to pretend that existing reporting lines are adequate.

Hatano and Inagaki (1986) drew a distinction between routine expertise and adaptive expertise that applies directly here. Routine expertise performs well on familiar problem types. Adaptive expertise generates solutions when the problem type itself is novel. Every large organization deploying AI at scale right now is facing a novel problem type: how do you evaluate a system whose outputs are probabilistic, whose behavior shifts as users adapt to it, and whose value is distributed across workflows in ways that resist clean attribution? Routine financial and operational expertise will not answer that question. EY is, perhaps unintentionally, institutionalizing the recognition that adaptive expertise is required.

The Deeper Implication

What EY's decision reveals is that the organizational theory of AI governance is still being written in real time. The firm is not implementing a known solution. It is creating a structural experiment. Whether the AI Value Realization Office develops genuine schema for understanding AI-generated value, or whether it becomes a rebranded cost-tracking function with new terminology, will depend on whether its staff can build structural understanding that does not yet exist in standard management education. That is a harder problem than the announcement suggests, and it is the problem that organizational theory should be focusing on right now.