4 Steps to Transform the "Middle Office" with AI.

Bibliographic Details
Title:	4 Steps to Transform the "Middle Office" with AI.
Authors:	Wilson, H. James, Sehgal, Chetna, Zimmerman, Michael, Arsanjani, Ali, Abderholden, Blaise, Priestas, Jimmy
Source:	Harvard Business Review Digital Articles; 8/20/2026, p1-8, 8p
Subject Terms:	Artificial intelligence, System integration, Reengineering (Management), Risk managers, Ethics & compliance officers, Outlier detection, Legal compliance
Abstract:	The article focuses on transforming the “middle office” of companies—responsible for exception-heavy tasks like contract reviews, risk management, and compliance—through improved integration of artificial intelligence (AI). Despite widespread AI adoption, many organizations fail to realize productivity gains because AI tools are not fully integrated into decision-making processes, causing saved time to be lost in managing exceptions. Drawing on research and experiments by Accenture and Google, the article outlines four steps to close this adoption-to-benefit gap: identifying processes with high exception rates, building AI systems that learn during operation, employing metrics that measure true returns such as exception resolution and escalation reduction, and redeploying expert human labor to higher-value tasks. Emphasizing the middle office as a critical and underutilized area for AI value, the article argues that focusing AI efforts here can yield significant, immediate returns and build foundational capabilities for future front-office innovations. [Extracted from the article]
	Copyright 2026 Harvard Business Publishing. All Rights Reserved. Additional restrictions may apply including the use of this content as assigned course material. Please consult your institution's librarian about any restrictions that might apply under the license with your institution. For more information and teaching resources from Harvard Business Publishing including Harvard Business School Cases, eLearning products, and business simulations please visit hbsp.harvard.edu. (Copyright applies to all Abstracts.)
Database:	Complementary Index

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Header	DbId: edb DbLabel: Complementary Index An: 196557616 RelevancyScore: 1147 AccessLevel: 6 PubType: Periodical PubTypeId: serialPeriodical PreciseRelevancyScore: 1147.42517089844
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Items	– Name: Title Label: Title Group: Ti Data: 4 Steps to Transform the "Middle Office" with AI. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wilson%2C+H%2E+James%22">Wilson, H. James</searchLink><br /><searchLink fieldCode="AR" term="%22Sehgal%2C+Chetna%22">Sehgal, Chetna</searchLink><br /><searchLink fieldCode="AR" term="%22Zimmerman%2C+Michael%22">Zimmerman, Michael</searchLink><br /><searchLink fieldCode="AR" term="%22Arsanjani%2C+Ali%22">Arsanjani, Ali</searchLink><br /><searchLink fieldCode="AR" term="%22Abderholden%2C+Blaise%22">Abderholden, Blaise</searchLink><br /><searchLink fieldCode="AR" term="%22Priestas%2C+Jimmy%22">Priestas, Jimmy</searchLink> – Name: TitleSource Label: Source Group: Src Data: Harvard Business Review Digital Articles; 8/20/2026, p1-8, 8p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22System+integration%22">System integration</searchLink><br /><searchLink fieldCode="DE" term="%22Reengineering+%28Management%29%22">Reengineering (Management)</searchLink><br /><searchLink fieldCode="DE" term="%22Risk+managers%22">Risk managers</searchLink><br /><searchLink fieldCode="DE" term="%22Ethics+%26+compliance+officers%22">Ethics & compliance officers</searchLink><br /><searchLink fieldCode="DE" term="%22Outlier+detection%22">Outlier detection</searchLink><br /><searchLink fieldCode="DE" term="%22Legal+compliance%22">Legal compliance</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The article focuses on transforming the “middle office” of companies—responsible for exception-heavy tasks like contract reviews, risk management, and compliance—through improved integration of artificial intelligence (AI). Despite widespread AI adoption, many organizations fail to realize productivity gains because AI tools are not fully integrated into decision-making processes, causing saved time to be lost in managing exceptions. Drawing on research and experiments by Accenture and Google, the article outlines four steps to close this adoption-to-benefit gap: identifying processes with high exception rates, building AI systems that learn during operation, employing metrics that measure true returns such as exception resolution and escalation reduction, and redeploying expert human labor to higher-value tasks. Emphasizing the middle office as a critical and underutilized area for AI value, the article argues that focusing AI efforts here can yield significant, immediate returns and build foundational capabilities for future front-office innovations. [Extracted from the article] – Name: Abstract Label: Group: Ab Data: <i>Copyright 2026 Harvard Business Publishing. All Rights Reserved. Additional restrictions may apply including the use of this content as assigned course material. Please consult your institution's librarian about any restrictions that might apply under the license with your institution. For more information and teaching resources from Harvard Business Publishing including Harvard Business School Cases, eLearning products, and business simulations please visit hbsp.harvard.edu.</i> (Copyright applies to all Abstracts.)
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RecordInfo	BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 1 Subjects: – SubjectFull: Artificial intelligence Type: general – SubjectFull: System integration Type: general – SubjectFull: Reengineering (Management) Type: general – SubjectFull: Risk managers Type: general – SubjectFull: Ethics & compliance officers Type: general – SubjectFull: Outlier detection Type: general – SubjectFull: Legal compliance Type: general Titles: – TitleFull: 4 Steps to Transform the "Middle Office" with AI. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wilson, H. James – PersonEntity: Name: NameFull: Sehgal, Chetna – PersonEntity: Name: NameFull: Zimmerman, Michael – PersonEntity: Name: NameFull: Arsanjani, Ali – PersonEntity: Name: NameFull: Abderholden, Blaise – PersonEntity: Name: NameFull: Priestas, Jimmy IsPartOfRelationships: – BibEntity: Dates: – D: 20 M: 08 Text: 8/20/2026 Type: published Y: 2026 Titles: – TitleFull: Harvard Business Review Digital Articles Type: main
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