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Agentic Product Evolution: An Empirical Study of How Product Strategy Must Evolve in the Age of Agentic AI
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 84 |
| Year of Publication: 2026 |
| Authors: Suhas Rumalla, John Felix |
10.5120/ijca2026926469
|
Suhas Rumalla, John Felix . Agentic Product Evolution: An Empirical Study of How Product Strategy Must Evolve in the Age of Agentic AI. International Journal of Computer Applications. 187, 84 ( Feb 2026), 15-21. DOI=10.5120/ijca2026926469
Abstract
Enterprises are aggressively pursuing Generative AI initiatives in today’s highly competitive environment. Along this journey, many are attempting to move from simple AI features to autonomous systems (aka agentic AI, agentic systems, autonomous agents). Even after making substantial technology investments, most companies still struggle to move beyond pilots and proofs of concept, with many ultimately abandoning their agentic AI ambitions. The prevailing narrative continues to attribute these failures to insufficient technology maturity. Only a small percentage, roughly 10% have managed to implement agentic AI systems successfully at scale. While, technology readiness is certainly a factor, this paper argues that the core challenge lies in a broken product strategy that has not evolved to support autonomous agents. Most product strategies were never designed to incorporate agentic behaviour, shared decision-making or autonomous reasoning into systems. Based on the observations from 10 successful enterprise agentic systems implementations, this study proposes Agentic Product Evolution (APE), a strategy model tailored for agentic AI use cases. The framework is further validated through insights from more than 25 product leaders across global technology companies that have successfully scaled agentic AI capabilities. Early adopters of the APE witnessed a drop in strategic rework by an average of 36% and a boost in their stakeholders’ confidence by up to 60% with average being 48%. This paper positions APE as a practitioner-centric playbook and researchbased contribution to the nascent field of GenAI product strategy.
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