Purpose This paper addresses the gap between data-rich environments and decision-poor organizations by introducing the Adaptive Market Intelligence Loop (AMIL) as a structured decision architecture for real-time strategy execution. It argues that firms do not lack data but lack disciplined mechanisms to convert signals into decisions and continuously refine them. The paper positions AMIL as a managerial system that integrates signal capture, intelligence structuring, strategic response and feedback recalibration, enabling organizations to move from episodic planning to continuous, adaptive strategy. Design/methodology/approach The paper adopts a conceptual and practice-oriented approach grounded in dynamic capabilities, sensemaking and organizational learning literatures. AMIL is developed as a decision architecture that operationalizes sensing, interpreting and responding within a continuous loop. A transactional dataset (UCI Online Retail) is used as an illustrative proof of concept to demonstrate how signal patterns can be structured into actionable decisions. The analysis is not intended to establish causal claims but to show how the architecture may be applied in a real data environment. Findings The paper finds that decision quality is constrained less by data availability and more by the absence of structured intelligence routines. The AMIL architecture clarifies how signals can be translated into decisions through disciplined structuring and iterative feedback. The illustration highlights that ambiguity, not data scarcity, is the primary barrier to effective decision-making, and that value emerges when signals are organized into interpretable patterns and linked to actionable responses. The propositions suggest that structured loops enhance consistency, adaptability and learning in dynamic environments. Research limitations/implications The study is conceptual and supported by an illustrative dataset, which limits claims of generalizability and causal inference. The demonstration reflects one empirical context and one interpretation of signal patterns, and does not test the propositions formally. Future research should examine the AMIL architecture across different industries, decision contexts and organizational settings, and explore its interaction with governance structures, leadership practices and digital infrastructures. Empirical validation through longitudinal and multi-context studies would strengthen its theoretical and practical contribution. Practical implications The paper provides managers with a structured approach to operationalizing real-time strategy through disciplined decision cycles. It outlines how organizations can move beyond dashboards and analytics outputs to build routines that convert signals into decisions and continuously refine them. AMIL enables firms to align data, interpretation and action as it clarifies roles, inputs and feedback mechanisms. The framework supports more consistent decision-making under uncertainty and helps organizations design adaptive processes that respond to changing market conditions without relying on fixed planning cycles. Social implications The paper highlights how structured decision architectures can improve transparency, accountability and responsiveness in organizations operating in uncertain environments. By making decision processes explicit and auditable, AMIL reduces reliance on informal judgement and unstructured intuition, which can introduce bias and inconsistency. More disciplined signal interpretation can support fairer resource allocation, clearer justification of strategic choices and improved organizational learning. In data-rich contexts, the ability to convert signals into accountable decisions has broader implications for governance, trust and responsible management practice. Originality/value The paper introduces the AMIL as a novel decision architecture that operationalizes real-time strategy through a structured and auditable cycle. Unlike existing approaches that separate data analysis from decision execution, AMIL integrates signal capture, interpretation, response and feedback into a single managerial system. The paper contributes by translating dynamic capabilities and sensemaking into a practical design that organizations can implement. Its value lies in clarifying how firms can move from data accumulation to disciplined, repeatable decision-making under conditions of uncertainty.
Imo Enang (Thu,) studied this question.