Managing Enterprise Capability Hubs for Better ROI

The COVID-19 pandemic and accompanying policy steps caused economic disturbance so stark that advanced statistical methods were unneeded for lots of questions. Unemployment leapt greatly in the early weeks of the pandemic, leaving little room for alternative descriptions. The effects of AI, nevertheless, might be less like COVID and more like the internet or trade with China.

One typical method is to compare outcomes between more or less AI-exposed workers, firms, or markets, in order to isolate the impact of AI from confounding forces. 2 Direct exposure is normally defined at the job level: AI can grade homework however not handle a classroom, for instance, so instructors are thought about less uncovered than workers whose whole job can be performed from another location.

3 Our technique combines information from 3 sources. Task-level direct exposure estimates from Eloundou et al. (2023 ), which measure whether it is theoretically possible for an LLM to make a task at least twice as fast.

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4Why might real usage fall brief of theoretical capability? Some jobs that are theoretically possible may disappoint up in usage since of model constraints. Others may be sluggish to diffuse due to legal constraints, specific software application requirements, human verification steps, or other obstacles. Eloundou et al. mark "License drug refills and offer prescription details to drug stores" as fully exposed (=1).

As Figure 1 shows, 97% of the tasks observed across the previous 4 Economic Index reports fall into categories ranked as theoretically possible by Eloundou et al. (=0.5 or =1.0). This figure reveals Claude usage dispersed across O * web jobs organized by their theoretical AI exposure. Jobs ranked =1 (completely possible for an LLM alone) account for 68% of observed Claude usage, while jobs ranked =0 (not feasible) account for just 3%.

Our brand-new measure, observed direct exposure, is indicated to measure: of those tasks that LLMs could theoretically speed up, which are really seeing automated use in expert settings? Theoretical capability includes a much broader range of jobs. By tracking how that gap narrows, observed direct exposure offers insight into economic changes as they emerge.

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

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We then adjust for how the job is being carried out: fully automated applications receive full weight, while augmentative usage receives half weight. Lastly, the task-level coverage procedures are balanced to the profession level weighted by the fraction of time invested in each task. Figure 2 reveals observed exposure (in red) compared to from Eloundou et al.

We calculate this by first averaging to the occupation level weighting by our time fraction step, then balancing to the occupation classification weighting by overall employment. The measure reveals scope for LLM penetration in the majority of jobs in Computer system & Mathematics (94%) and Office & Admin (90%) professions.

The coverage shows AI is far from reaching its theoretical capabilities. For circumstances, Claude currently covers just 33% of all tasks in the Computer & Mathematics classification. As abilities advance, adoption spreads, and release deepens, the red area will grow to cover heaven. There is a large uncovered area too; many tasks, of course, remain beyond AI's reachfrom physical farming work like pruning trees and operating farm equipment to legal tasks like representing clients in court.

In line with other information revealing that Claude is thoroughly used for coding, Computer system Programmers are at the top, with 75% protection, followed by Customer support Agents, whose primary jobs we increasingly see in first-party API traffic. Lastly, Data Entry Keyers, whose primary job of reading source documents and entering data sees significant automation, are 67% covered.

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

A regression at the occupation level weighted by current work finds that growth projections are rather weaker for jobs with more observed direct exposure. For every single 10 percentage point increase in coverage, the BLS's growth forecast drops by 0.6 percentage points. This provides some validation because our procedures track the individually derived price quotes from labor market experts, although the relationship is small.

Each strong dot reveals the average observed exposure and projected employment change for one of the bins. The dashed line reveals a basic linear regression fit, weighted by current employment levels. Figure 5 programs attributes of employees in the top quartile of exposure and the 30% of workers with no exposure in the three months before ChatGPT was launched, August to October 2022, using information from the Current Population Study.

The more disclosed group is 16 percentage points most likely to be female, 11 portion points more most likely to be white, and practically twice as likely to be Asian. They earn 47% more, on average, and have higher levels of education. For instance, people with academic degrees are 4.5% of the unexposed group, however 17.4% of the most uncovered group, a nearly fourfold distinction.

Scientists have taken different methods. For instance, Gimbel et al. (2025) track modifications in the occupational mix using the Current Population Study. Their argument is that any crucial restructuring of the economy from AI would appear as modifications in distribution of jobs. (They find that, up until now, changes have actually been typical.) Brynjolfsson et al.

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( 2022) and Hampole et al. (2025) use job posting information from Burning Glass (now Lightcast) and Revelio, respectively. We focus on unemployment as our priority result since it most straight records the potential for economic harma employee who is unemployed desires a job and has actually not yet discovered one. In this case, job posts and work do not always signify the requirement for policy actions; a decrease in job posts for an extremely exposed role may be combated by increased openings in a related one.