Marketing Sensemaking in Publishing: Environmental Scanning with Artificial Intelligence—Faster Insights or Faster Herding?

Abstract

Publishing marketers compete in e-commerce ecosystems where discoverability is governed by search, recommendation feeds, and social amplification, compressing decision cycles for title acquisition, positioning, pricing, and integrated marketing communications (IMC). Addressing themes in artificial intelligence (AI), big data analysis, strategic planning, performance measurement, and e-commerce, this conceptual paper theorizes AI-enabled environmental scanning as a double-edged marketing capability in publishing. Building on environmental scanning (Aguilar, 1967) and AI-enabled decision support (Davenport et al., 2020), the paper proposes a Scan–Sense–Shift framework that links scanning design choices (signal breadth, source diversity, model and prompt diversity, and governance) to two competing pathways. The Insight pathway explains how AI accelerates market sensing by integrating weak signals across retailer rankings, search queries, review text, and social video, improving message–market fit, metadata quality, and search engine optimization (SEO) execution, and enabling reallocation of spend based on key performance indicators (KPIs) such as return on advertising spend (ROAS) and customer acquisition cost (CAC) (Wedel & Kannan, 2016). The Herding pathway explains how shared toolchains and reliance on highly visible signals can produce strategic convergence via informational cascades (Bikhchandani et al., 1992) and institutional isomorphism (DiMaggio & Powell, 1983), shifting portfolios toward exploitation at the expense of exploration (March, 1991). Testable propositions specify when AI scanning increases time-to-trend advantage versus when it erodes distinctiveness through copycat acquisitions, homogenized metadata/packaging, and crowded category advertising. The paper closes with governance design principles for publishers and author-entrepreneurs—source diversification, structured dissent, and exploration quotas—to capture AI speed without sacrificing publishing brands.