Managing Enterprise Capability Centers for Better ROI

The COVID-19 pandemic and accompanying policy procedures caused economic disturbance so plain that sophisticated statistical methods were unneeded for numerous questions. Joblessness leapt greatly in the early weeks of the pandemic, leaving little room for alternative descriptions. The impacts of AI, nevertheless, might be less like COVID and more like the internet or trade with China.

One common method is to compare results in between basically AI-exposed workers, companies, or industries, in order to separate the impact of AI from confounding forces. 2 Exposure is usually specified at the task level: AI can grade research however not manage a class, for instance, so teachers are considered less unwrapped than workers whose entire task can be performed from another location.

3 Our technique combines information from 3 sources. Task-level exposure quotes from Eloundou et al. (2023 ), which measure whether it is theoretically possible for an LLM to make a job at least two times as quick.

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Some tasks that are theoretically possible might not show up in use because of design restrictions. Eloundou et al. mark "License drug refills and offer prescription info to pharmacies" as totally exposed (=1).

As Figure 1 programs, 97% of the jobs observed throughout the previous four Economic Index reports fall under classifications ranked as in theory practical by Eloundou et al. (=0.5 or =1.0). This figure shows Claude use distributed throughout O * NET tasks grouped by their theoretical AI exposure. Tasks rated =1 (totally feasible for an LLM alone) account for 68% of observed Claude usage, while jobs ranked =0 (not possible) account for simply 3%.

Our brand-new procedure, observed exposure, is meant to measure: of those tasks that LLMs could in theory speed up, which are in fact seeing automated use in expert settings? Theoretical capability encompasses a much broader variety of jobs. By tracking how that gap narrows, observed direct exposure supplies insight into economic changes as they emerge.

A job's direct exposure is greater if: Its tasks are theoretically possible with AIIts tasks see considerable usage in the Anthropic Economic Index5Its tasks are carried out in job-related contextsIt has a fairly greater share of automated use patterns or API implementationIts AI-impacted jobs make up a bigger share of the overall role6We give mathematical information in the Appendix.

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The task-level protection measures are averaged to the occupation level weighted by the fraction of time spent on each task. The step reveals scope for LLM penetration in the bulk of tasks in Computer & Math (94%) and Office & Admin (90%) occupations.

Claude currently covers just 33% of all jobs in the Computer system & Math classification. There is a big uncovered area too; numerous tasks, of course, stay beyond AI's reachfrom physical farming work like pruning trees and operating farm machinery to legal tasks like representing clients in court.

In line with other data revealing that Claude is thoroughly used for coding, Computer Programmers are at the top, with 75% coverage, followed by Client service Agents, whose primary jobs we significantly see in first-party API traffic. Data Entry Keyers, whose main job of checking out source files and going into data sees significant automation, are 67% covered.

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At the bottom end, 30% of employees have zero protection, as their jobs appeared too rarely in our data to fulfill the minimum threshold. This group includes, for example, Cooks, Motorbike Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Space Attendants.

A regression at the occupation level weighted by present work finds that development projections are somewhat weaker for jobs with more observed exposure. For every 10 percentage point boost in coverage, the BLS's development forecast come by 0.6 portion points. This provides some validation because our procedures track the separately obtained estimates from labor market analysts, although the relationship is minor.

step alone. Binned scatterplot with 25 equally-sized bins. Each solid dot reveals the average observed direct exposure and predicted work change for one of the bins. The rushed line reveals an easy linear regression fit, weighted by present work levels. The little diamonds mark specific example occupations for illustration. Figure 5 shows characteristics of workers in the top quartile of exposure and the 30% of workers with absolutely no exposure in the three months before ChatGPT was launched, August to October 2022, utilizing data from the Present Population Study.

The more unwrapped group is 16 portion points more likely to be female, 11 percentage points more likely to be white, and nearly twice as likely to be Asian. They earn 47% more, usually, and have higher levels of education. For instance, individuals with academic degrees are 4.5% of the unexposed group, but 17.4% of the most discovered group, an almost fourfold difference.

Researchers have taken different techniques. Gimbel et al. (2025) track modifications in the occupational mix using the Present Population Survey. Their argument is that any crucial restructuring of the economy from AI would appear as modifications in circulation of tasks. (They find that, so far, changes have been average.) Brynjolfsson et al.

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( 2022) and Hampole et al. (2025) utilize task publishing information from Burning Glass (now Lightcast) and Revelio, respectively. We focus on joblessness as our top priority outcome since it most directly records the capacity for financial harma worker who is unemployed wants a job and has not yet discovered one. In this case, job posts and work do not always signal the need for policy responses; a decline in job posts for an extremely exposed role may be combated by increased openings in a related one.