From Operational Closure to Autonomous Value Realization: Intent-Aware Finance, Agentic Governance, and the Autonomous Enterprise
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2026
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| _version_ | 1866901394366136320 |
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| author | Mukherjee, Jaita |
| author_facet | Mukherjee, Jaita |
| contents | <p><span class="TextRun SCXW105049914 BCX8"><span class="NormalTextRun SCXW105049914 BCX8">Enterprises increasingly deploy artificial intelligence, observability platforms, automation toolchains, and policy engines to improve operational performance. Yet many programs still </span><span class="NormalTextRun SCXW105049914 BCX8">fail to</span><span class="NormalTextRun SCXW105049914 BCX8"> convert technical intelligence into sustained enterprise value. A first-order bottleneck is the Insight-to-Action Gap, where analytics do not reliably result in safe operational execution. A second-order bottleneck </span><span class="NormalTextRun SCXW105049914 BCX8">emerges</span><span class="NormalTextRun SCXW105049914 BCX8"> after operational closure: the Intent-to-Impact Gap, where executed actions are not consistently aligned with financial intent, risk appetite, or portfolio-level value realization. This article synthesizes prior work on the autonomous enterprise, operational intelligence, agentic decisioning, and autonomous value realization into a unified journal-scale framework. We introduce the Intent-Aware Autonomous Enterprise Architecture (IAAEA), a multi-loop control system that connects telemetry, context fusion, intelligence generation, decision orchestration, runbook execution, value-aware financial agents, governance controls, and feedback learning. The paper formalizes operational and financial decision loops, defines decision-trace requirements across technical and financial domains, proposes a maturity model for bounded autonomy, and offers implementation patterns for integrating observability, IT service management, CI/CD, policy-as-code, and financial planning signals. We further provide a rigorous evaluation blueprint that spans operational outcomes, value realization, governance quality, and learning efficacy. The resulting synthesis reframes enterprise autonomy not as isolated automation, but as policy-bounded, evidence-driven, and value-accountable execution at scale.</span></span><span class="EOP SCXW105049914 BCX8"> </span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20044683 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | From Operational Closure to Autonomous Value Realization: Intent-Aware Finance, Agentic Governance, and the Autonomous Enterprise Mukherjee, Jaita artificial intelligence <p><span class="TextRun SCXW105049914 BCX8"><span class="NormalTextRun SCXW105049914 BCX8">Enterprises increasingly deploy artificial intelligence, observability platforms, automation toolchains, and policy engines to improve operational performance. Yet many programs still </span><span class="NormalTextRun SCXW105049914 BCX8">fail to</span><span class="NormalTextRun SCXW105049914 BCX8"> convert technical intelligence into sustained enterprise value. A first-order bottleneck is the Insight-to-Action Gap, where analytics do not reliably result in safe operational execution. A second-order bottleneck </span><span class="NormalTextRun SCXW105049914 BCX8">emerges</span><span class="NormalTextRun SCXW105049914 BCX8"> after operational closure: the Intent-to-Impact Gap, where executed actions are not consistently aligned with financial intent, risk appetite, or portfolio-level value realization. This article synthesizes prior work on the autonomous enterprise, operational intelligence, agentic decisioning, and autonomous value realization into a unified journal-scale framework. We introduce the Intent-Aware Autonomous Enterprise Architecture (IAAEA), a multi-loop control system that connects telemetry, context fusion, intelligence generation, decision orchestration, runbook execution, value-aware financial agents, governance controls, and feedback learning. The paper formalizes operational and financial decision loops, defines decision-trace requirements across technical and financial domains, proposes a maturity model for bounded autonomy, and offers implementation patterns for integrating observability, IT service management, CI/CD, policy-as-code, and financial planning signals. We further provide a rigorous evaluation blueprint that spans operational outcomes, value realization, governance quality, and learning efficacy. The resulting synthesis reframes enterprise autonomy not as isolated automation, but as policy-bounded, evidence-driven, and value-accountable execution at scale.</span></span><span class="EOP SCXW105049914 BCX8"> </span></p> |
| title | From Operational Closure to Autonomous Value Realization: Intent-Aware Finance, Agentic Governance, and the Autonomous Enterprise |
| topic | artificial intelligence |
| url | https://doi.org/10.5281/zenodo.20044683 |