The paradox of algorithmic innovation: between the hyperreality of metrics and the materiality of project results

Purpose This study develops a conceptual model to understand the growing gap between artificial intelligence (AI)-optimized innovation metrics and actual value creation in B2B contexts, termed the “algorithmic innovation paradox”. Design/methodology/approach The study uses a conceptual approach, integrating the philosophical perspectives of simulacra theory with contemporary approaches to innovation management to develop the Innovation Paradox Framework. The paper analyzes how AI systems create hyperreal representations of innovation value that are detached from material results. Findings A typology of innovation simulacra identifies four levels of discrepancy between algorithmic assessments and actual project results. Three reinforcing mechanisms sustain the paradox: organizational incentives for simulation, algorithmic reinforcement loops, and institutional isomorphism in assessment practices. The Innovation Paradox Framework demonstrates how these elements interact to create systematic gaps between measured and actual innovation performance. Research limitations/implications As a conceptual study, empirical validation across different organizational contexts and cultures is required. The rapid evolution of AI technologies necessitates continuous model refinement. Practical implications The research provides tools for organizations to validate algorithmic assessments against tangible indicators and design assessment systems balancing AI effectiveness with genuine value creation. Originality/value The study integrates poststructuralist philosophy with AI-based innovation management, offering novel theoretical concepts and practical tools to bridge the measurement-reality gap. The Material Reality Check Protocol and antifragile metrics principles represent innovative approaches to achieving material-algorithmic balance.