Loyalty in the Age of Agents: Can Algorithms Be Loyal?

Academic Research / Scholarly ArticlesArtificial Intelligence (AI)Agentic Commerce
Written By Paul F. Accornero

 Abstract

Customer loyalty has been a cornerstone of marketing strategy for decades, built on consumer psychology, rewards programs, and emotional brand connections. However, the emergence of Al agents that execute purchasing decisions autonomously challenges fundamental assumptions underlying traditional loyalty frameworks. This paper examines whether algorithmic systems can exhibit loyalty and how this transforms loyalty strategy in Al-mediated commerce. We introduce the concept of "algorithmic loyalty" - a functional rather than emotional state where Al agents demonstrate persistent preference for brands that consistently optimize their multi-variable decision criteria. Through synthesis of loyalty theory, Al decision-making research, and platform economics, we develop a four-pillar framework for algorithmic loyalty comprising: verifiable value consistency, supply reliability, systemic interoperability, and machine-readable trust signals. Our analysis reveals that loyalty is not disappearing but transforming from psychological attachment to functional optimization. This research contributes to understanding how established marketing concepts must evolve for Al-mediated environments and provides strategic guidance for organizations navigating this transition.

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Paul F. Accornero

Paul F. Accornero is a C-suite leader, global strategist, and the author of the forthcoming book, The Algorithmic Shopper. He currently serves as the Global Chief Commercial Officer for one of the world's market-leading consumer goods companies, where he is a key architect of its global commercial strategy. In this role, he directs a multi-billion-euro business with a P&L spanning over 120 countries and is responsible for the performance of thousands of employees worldwide.

Paul stands at the intersection of classic brand building and the next frontier of commerce. His career has been defined by leading profound organizational and digital transformations for some of the world's most iconic consumer brands. For over a decade at the L'Oréal Group, he was instrumental in shaping commercial policy and strategy across the Asia Pacific region, including serving as Chief Commercial Officer for the Consumer Products Division in P.R. China. Since 2008, he has been a driving force behind the globalization of his current company, spearheading the omnichannel strategies that have successfully navigated the disruption of the digital age. His leadership has a proven track record of delivering exceptional results, including driving revenue growth exceeding.

His unique perspective is not merely academic; it has been forged through decades of hands-on operational experience and senior leadership roles on multiple continents. He has served as CEO, President, or Managing Director for major subsidiaries in the USA, Japan, and Singapore, giving him an unparalleled, ground-level view of the global commercial landscape he deconstructs in his work.

A rigorous strategic framework complements this extensive real-world experience. A graduate of the University of Queensland, Paul completed his postgraduate business studies at Harvard Business School, where he studied disruptive strategy under the world’s foremost thought leaders, including the late Clayton Christensen. This blend of C-suite practice and elite academic insight makes him uniquely positioned to write the definitive playbook for the age of AI-driven commerce.

As an active and respected industry leader, Paul is a Fellow of both the Institute of Directors (FIoD) and the Chartered Institute of Marketing (FCIM) in the UK. He is also a Liveryman of the World Traders Livery Company and a Freeman of the City of London, affiliations that connect him to a deep network of influential business leaders.

The Algorithmic Shopper is more than a book; it is the culmination of a career spent leading on the front lines of commercial evolution.

https://theaipraxis.ai
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