Performance, Attention, and Authority: Rethinking AI-Enabled Enterprise Performance Management
Synopsis
This conceptual article argues that although enterprise performance management has moved from static periodic reporting to continuous real-time monitoring, finance leaders still obtain abundant data without faster or firmer decisions, locating the binding constraint in scarce managerial attention and ambiguous decision authority, and proposing a three-layer architecture of performance, attention, and authority, a five-dimension Management Attention Test (materiality, persistence, confidence, decision ability, ownership), a separation of analytical from decision authority, and governance for responsible AI use in finance, concluding that disciplined filtering and accountability serve organizations better than more AI dashboards, with AI as an analytical partner rather than a decision substi
Interpretation
The article relocates the constraint on modern enterprise performance management from information supply to managerial attention and decision authority: even after enterprises moved from static periodic reports to more continuous real-time monitoring, finance leaders have not gained faster or firmer decisions. Against the common narrative that treats data availability and dashboard coverage as markers of progress, it reframes the bottleneck as scarce attention and ambiguity about who holds decision authority. Conceptual argument: the available text is the abstract, the authors state their position with 'We contend', and no sample, data, or empirical test is reported.
It proposes a three-layer architecture of performance, attention, and authority to organize the relationship between real-time performance data, attention allocation, and decision rights. It integrates three existing discussion threads — financial planning and analysis (FP&A), human-AI collaboration, and information overload — into a single architecture rather than treating data or tools separately. A framework synthesis built on existing lines of thinking; the abstract offers no application cases or validation results.
It introduces a five-dimension Management Attention Test covering materiality, persistence, confidence, decision ability, and ownership to help executives decide which signals merit their time. It turns attention allocation from intuitive judgement into an item-by-item checklist, and folds 'ownership' together with 'decision ability' into the screening dimensions. A tool proposal; the available text lists only the five dimension names, with no scoring rules, thresholds, or pilot data.
It argues for separating analytical authority from decision authority and sets out governance requirements and an implementation path for responsible AI use in finance, concluding that disciplined filtering and accountability assignment beat adding AI dashboards, with AI as an analytical partner that is 'not a stand-in for making the call'. It confines AI to the analytical side and asks governance structures to carry decision responsibility, rather than letting automated output substitute for judgement. Normative argument and governance recommendations, framed as positions and design proposals, with no implementation-effect evidence in the abstract.
Perspective
This article addresses finance leaders and enterprise performance management teams, in organizational settings that already have continuous real-time monitoring data and need to decide which signals to prioritize; what it offers is an analytical framework and governance orientation (the three-layer architecture, the five-dimension attention test, and the separation of analytical from decision authority), useful for drafting signal-screening rules, authority allocation, and governance arrangements for AI in finance, rather than an evaluation of the technical performance of any AI system.
The available text is only the abstract, and the figures, tables, and argumentative detail of the main body are not included, so the specific criteria and thresholds of the five-dimension test, the steps for landing the three-layer architecture in an enterprise, and the operational detail of the governance and implementation path remain open questions; the applicable boundary of AI as an 'analytical partner' and the effect of the attention test within real decision cycles also await further research and practice.