Governing Agentic AI as Organizational Portfolios for Sustained Value: Evidence from a Japanese Industrial Conglomerate

Abstract

Work automation is shifting from centrally built systems to decentralized, frontline, self-service tools refined in use. Agentic AI extends this shift by enabling large language model (LLM)-based agents to interpret instructions, plan multi-step tasks and act via tools. However, value often fails to scale when similar agents proliferate across projects without coordination. Drawing on a comparative analysis of five AI projects conducted in a Japanese industrial conglomerate, this study examines how governing a System of Agents (SoA) as an organizational portfolio of reusable assets can support sustained value creation. Using traceable project artefacts, weekly review records, development logs and operational feedback traces, we identify four configurations, from model-based AI to portfolio-governed SoA. Cross-case analysis suggests that SoA adoption alone is insufficient: sustained value creation only became observable when governance shifted towards assetization, standardized logging and human intervention routines, supported by portfolio-level decision rights that facilitate reuse and learning across deployments. The article derives design implications for the governance of Agentic AI as reusable organizational capabilities, treating return on investment as one observable dimension of value.

Keywords

Agentic AI System of Agents portfolio governance assetization human-in-the-loop value co-creation

Get full access to this article

View all access options for this article.

References

Baxter

, & Sommerville

(2011). Socio-technical systems: From design methods to systems engineering. Interacting with Computers , 23(1), 4–17. https://doi.org/10.1016/j.intcom.2010.07.003

Bock

A. C.

, & Frank

(2021). Low-code platform. Business & Information Systems Engineering , 63(6), 733–740. https://doi.org/10.1007/s12599-021-00726-8

Brunet

A. P.

, & New

(2003). Kaizen in Japan: An empirical study. International Journal of Operations & Production Management , 23(12), 1426–1446. https://doi.org/10.1108/01443570310506704

Eisenhardt

K. M.

(1989). Building theories from case study research. Academy of Management Review , 14(4), 532–550. https://doi.org/10.5465/amr.1989.4308385

Grönroos

, & Voima

(2013). Critical service logic: Making sense of value creation and co-creation. Journal of the Academy of Marketing Science , 41(2), 133–150. https://doi.org/10.1007/s11747-012-0308-3

Jennings

N. R.

(2000). On agent-based software engineering. Artificial Intelligence , 117(2–3), 277–296. https://doi.org/10.1016/S0004-3702(99)00107-1

, Lee

, Frieske

, Yu

, Su

, Xu

, Ishii

, & Fung

(2023). Survey of hallucination in natural language generation. ACM Computing Surveys , 55(12), Article 248. https://doi.org/10.1145/3571730

Kohli

, & Devaraj

(2003). Measuring information technology payoff: A meta-analysis of structural variables in firm-level empirical research. Information Systems Research , 14(2), 127–145. https://doi.org/10.1287/isre.14.2.127.16019

Langley

(1999). Strategies for theorizing from process data. Academy of Management Review , 24(4), 691–710. https://doi.org/10.5465/amr.1999.2553248

10.

Lewis

, Perez

, Piktus

, Petroni

, Karpukhin

, Goyal

, Küttler

, Lewis

, Yih

W. T.

, Rocktäschel

, Riedel

, & Kiela

(2020). Retrieval-augmented generation for knowledge-intensive NLP tasks>. In Larochelle

, Ranzato

, Hadsell

, Balcan

M. F.

, & Lin

(Eds), Advances in neural information processing systems (Vol. 33, pp. 9459–9474). Curran Associates, Inc.

11.

March

J. G.

(1991). Exploration and exploitation in organizational learning. Organization Science , 2(1), 71–87. https://doi.org/10.1287/orsc.2.1.71

12.

Meskendahl

(2010). The influence of business strategy on project portfolio management and its success—A conceptual framework. International Journal of Project Management , 28(8), 807–817. https://doi.org/10.1016/j.ijproman.2010.06.007

13.

Min

, Ross

, Sulem

, Veyseh

A. P. B.

, Nguyen

T. H.

, Sainz

, Agirre

, Heintz

, & Roth

(2023). Recent advances in natural language processing via large pre-trained language models: A survey. ACM Computing Surveys , 56(2), Article 30. https://doi.org/10.1145/3605943

14.

Mosqueira-Rey

, Hernández-Pereira

, Alonso-Ríos

, Bobes-Bascarán

, & Fernández-Leal

Á.

(2023). Human-in-the-loop machine learning: A state of the art. Artificial Intelligence Review , 56, 3005–3054. https://doi.org/10.1007/s10462-022-10246-w

15.

Orlikowski

W. J.

(2007). Sociomaterial practices: Exploring technology at work. Organization Studies , 28(9), 1435–1448. https://doi.org/10.1177/0170840607081138

16.

Payne

A. F.

, Storbacka

, & Frow

(2008). Managing the co-creation of value. Journal of the Academy of Marketing Science , 36(1), 83–96. https://doi.org/10.1007/s11747-007-0070-0

17.

Shneiderman

(2020). Human-centered artificial intelligence: Reliable, safe & trustworthy. International Journal of Human–Computer Interaction , 36(6), 495–504. https://doi.org/10.1080/10447318.2020.1741118

18.

Silić

, & Bäck

(2014). Shadow IT: A view from behind the curtain. Computers & Security , 45, 274–283. https://doi.org/10.1016/j.cose.2014.06.007

19.

Too

E. G.

, & Weaver

(2014). The management of project management: A conceptual framework for project governance. International Journal of Project Management , 32(8), 1382–1394. https://doi.org/10.1016/j.ijproman.2013.07.006

20.

van der Aalst

W. M. P.

, Bichler

, & Heinzl

(2018). Robotic process automation. Business & Information Systems Engineering , 60(4), 269–272. https://doi.org/10.1007/s12599-018-0542-4

21.

Vargo

S. L.

, & Lusch

R. F.

(2004). Evolving to a new dominant logic for marketing. Journal of Marketing , 68(1), 1–17. https://doi.org/10.1509/jmkg.68.1.1.24036

22.

Vargo

S. L.

, & Lusch

R. F.

(2008). Service-dominant logic: Continuing the evolution. Journal of the Academy of Marketing Science , 36(1), 1–10. https://doi.org/10.1007/s11747-007-0069-6

23.

Wang

, Ma

, Feng

, Zhang

, Yang

, Zhang

, Chen

, Tang

, Chen

, Lin

, Zhao

W. X.

, Wei

, & Wen

(2024). A survey on large language model based autonomous agents. Frontiers of Computer Science , 18, 186345. https://doi.org/10.1007/s11704-024-40231-1

24.

Wooldridge

, & Jennings

N. R.

(1995). Intelligent agents: Theory and practice. The Knowledge Engineering Review , 10(2), 115–152. https://doi.org/10.1017/S0269888900008122

25.

Yin

R. K.

(2014). Case study research: Design and methods (5th ed.). Sage Publications.