Research analysis and white-space audit Self-Evolving Business Models: Continuous Algorithmic Reconfiguration vs. Episodic Strategic Renewal Your topic has the potential to become a serious contribution to strategic management, particularly at the intersection of business model innovation, organizational adaptation, dynamic capabilities, and algorithmic decision-making. But the contribution will not come simply from observing that AI-enabled firms change faster than traditional firms. The stronger research opportunity is to investigate a deeper strategic question: What happens when the mechanism that changes a firm's business model shifts from deliberate, episodic managerial intervention to continuous, algorithmically mediated reconfiguration? This raises questions about strategic agency, organizational learning, adaptation speed, the accumulation of strategic errors, and whether continuously adapting business models remain strategically coherent over time. I would initially treat this as a potential Strategic Management Journal, Organization Science, or Research Policy-level research idea—not as a validated FT50 contribution yet. The decisive task is to establish what existing research already explains, identify the unresolved theoretical problem, and formulate a mechanism that can be tested rather than merely described. 1. First, sharpen the central construct Provisional core concept Self-evolving business models A business model whose configuration changes through a recurring process of sensing, experimentation, evaluation, and reconfiguration, with some or all of these activities increasingly delegated to algorithmic systems. Proposed definition for theory development; not an established construct or a claim of novelty. The crucial distinction is between a business model that uses algorithms and a business model that can be reconfigured through algorithms. For example, an organization may use AI to forecast demand but still change its pricing architecture, customer segments, distribution channels, or revenue logic only after a quarterly strategy review. That is algorithm-assisted decision-making, not necessarily continuous business model reconfiguration. Conversely, a digital platform might use algorithms to repeatedly change prices, allocate supply, rank customers, adjust incentives, or redirect transactions based on incoming data. This is closer to algorithmically mediated reconfiguration—but only if those changes affect the business model itself rather than merely operational performance. What exactly is being reconfigured? Value capture Prices, commissions, revenue-sharing arrangements, monetization rules, and customer-level economic terms. Value creation and delivery Partner participation, resource allocation, channel configuration, matching mechanisms, and coordination across an ecosystem. The business model's underlying logic Which customers the firm serves, what it offers, how it creates va
Research analysis and white-space audit Self-Evolving Business Models: Continuous Algorithmic Reconfiguration vs. Episodic Strategic Renewal Your topic has the potential to become a serious contribution to strategic management, particularly at the intersection of business model innovation, organizational adaptation, dynamic capabilities, and algorithmic decision-making. But the contribution will not come simply from observing that AI-enabled firms change faster than traditional firms. The stronger research opportunity is to investigate a deeper strategic question: What happens when the mechanism that changes a firm's business model shifts from deliberate, episodic managerial intervention to continuous, algorithmically mediated reconfiguration? This raises questions about strategic agency, organizational learning, adaptation speed, the accumulation of strategic errors, and whether continuously adapting business models remain strategically coherent over time. I would initially treat this as a potential Strategic Management Journal, Organization Science, or Research Policy-level research idea—not as a validated FT50 contribution yet. The decisive task is to establish what existing research already explains, identify the unresolved theoretical problem, and formulate a mechanism that can be tested rather than merely described. 1. First, sharpen the central construct Provisional core concept Self-evolving business models A business model whose configuration changes through a recurring process of sensing, experimentation, evaluation, and reconfiguration, with some or all of these activities increasingly delegated to algorithmic systems. Proposed definition for theory development; not an established construct or a claim of novelty. The crucial distinction is between a business model that uses algorithms and a business model that can be reconfigured through algorithms. For example, an organization may use AI to forecast demand but still change its pricing architecture, customer segments, distribution channels, or revenue logic only after a quarterly strategy review. That is algorithm-assisted decision-making, not necessarily continuous business model reconfiguration. Conversely, a digital platform might use algorithms to repeatedly change prices, allocate supply, rank customers, adjust incentives, or redirect transactions based on incoming data. This is closer to algorithmically mediated reconfiguration—but only if those changes affect the business model itself rather than merely operational performance. What exactly is being reconfigured? Value capture Prices, commissions, revenue-sharing arrangements, monetization rules, and customer-level economic terms. Value creation and delivery Partner participation, resource allocation, channel configuration, matching mechanisms, and coordination across an ecosystem. The business model's underlying logic Which customers the firm serves, what it offers, how it creates va
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This research focuses on self-evolving business models, distinguishing algorithm use from reconfiguration, and mapping value dynamics. It audits existing literature to identify gaps, comparing innovation and AI strategies while addressing the adaptation-renewal paradox. The study aims to build and test contributions by modelling misalignments and refining research design, ensuring relevance and fit for academic publication.