Harder Than the Task: From Technical to Organizational Difficulty in AI Benchmarking

Organisations deploying AI face a scoping problem: which tasks can be automated, which should remain with humans, and which are best shared between the two. Aggregate benchmark scores provide little insight into where systems will succeed or fail in practice, while human judgements of model capabilities quickly become outdated. We introduce a pipeline that profiles agents and tasks using a shared set of core cognitive capabilities. Cognitive capability profiling infers an agent's capabilities from performance on a benchmark battery annotated for the cognitive demands of each item. Task requirements weighting elicits from domain experts the relative importance of these same capabilities for their work. As both use a common set of cognitive dimensions, they can be updated independently as models and roles change, and combined to estimate AI suitability at the level of a domain, organisation, role, or individual duty. We validate capability recovery on synthetic agents, profile six AI systems, and elicit task requirements from 410 employees across six occupational domains. AI systems differed more across cognitive dimensions than across model families, while workplace activities converged on a shared cognitive core. The resulting scores provide a comparative scoping tool for identifying promising candidates for piloting and areas where current systems are unlikely to be well suited. We discuss extending the framework to profile human workers alongside AI systems, moving from AI suitability towards human-machine task allocation.

Agent benchmarks have attracted substantial attention; yet, benchmarks for AI agents performing knowledge work are still mostly described only by their tasks, environments, and metrics. Starting from the definition of knowledge work, this paper derives four other design choices that often remain implicit. Three research threads on how knowledge work is organized each yield one choice: studies of professions, situated action, and boundary objects and coordination, while validity theory provides the fourth. Following this reasoning, we propose a benchmark representation centered on the work being performed. Based on this representation, we propose reporting practices for four largely missing fields: represented activity, tested setting, required work product, and evaluated result. Benchmarks designed in disparate ways thereby share a common description and can be compared side by side. To support the reporting of work activities, we cluster O*NET task statements into 18 shared work activities through unsupervised learning paired with human review. We then use this representation to analyze cases from three popular knowledge work benchmarks: GDPval, OfficeQA Pro, and APEX-SWE. Across all three benchmarks, the scope covered by the evaluated result is narrower than that of the required work product. We audited the tests of all 25 public APEX-SWE Integration tasks, independently coded by two authors. We found from the test code that in 13 of these tasks, a deliverable can alter established content from the source data and still pass all tests. We synthesize these four fields into a one-page record, accompanied by a worked example and a fillable template.

Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not translated into economically meaningful deployment across many professional domains. We argue that this gap is largely an evaluation problem: widely used benchmarks lack sustained performance measurement on real and economically valuable workflows. This paper introduces Agents' Last Exam (ALE), a benchmark designed to evaluate AI agents on long horizon, economically valuable, real world tasks with verifiable outcomes. Developed in collaboration with 250+ industry experts, ALE covers non-physical industries defined with reference to O*NET / SOC 2018 (the U.S. federal occupational taxonomy). It is organized around a task taxonomy with 55 sub fields grouped into 13 industry clusters covering 1K+ tasks. Current results show that the hardest tier remains far from saturated: across mainstream harness and backbone configurations, the average full pass rate is below 1%. ALE is designed as a living benchmark: its task pool grows continuously as new workflows and industries are onboarded. More broadly, ALE is intended not merely as another leaderboard, but as an instrument for closing the gap between benchmark success and GDP relevant impact.