{
  "researchId": "JPC-26-00143",
  "researchUrl": "https://jipcet.org/r/JPC-26-00143",
  "articleUrl": "https://jipcet.org/articles/jipcet-2026-0143",
  "articleRef": "JIPCET-2026-0143",
  "externalDoi": null,
  "title": "The Expanding Unit of Product Strategy: A Comparative Analysis of Markets, Capabilities, and Intelligence Architecture",
  "authors": [
    {
      "name": "Amelia Hart",
      "affiliation": "Regent College London, London, United Kingdom",
      "orcid": null
    },
    {
      "name": "Li Wen (李雯)",
      "affiliation": "Fudan University School of Management, Shanghai, China",
      "orcid": null
    },
    {
      "name": "Chen Rui (陈睿)",
      "affiliation": "College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China",
      "orcid": null
    }
  ],
  "abstract": "Background. Product strategy organizes decisions about markets, customers, differentiation, capabilities, and coherent action. AI-enabled products add dependencies on foundation models, data, inference infrastructure, retrieval, evaluation, verification, and agent orchestration whose economics may change independently of the application. Objective. This study measures whether those dependencies are already covered by established strategy frameworks or constitute an uncovered analytical layer. Methods. A structured document analysis was conducted on publicly available primary source texts. Twenty-four candidate approaches were screened against stated eligibility criteria; five non-equivalent frameworks—Strategy Kernel, Playing to Win, Jobs to Be Done, Wardley Mapping, and Supply Chain of Intelligence—entered full coding. A versioned eight-layer codebook (environment, market, customer, value, differentiation, capability, intelligence architecture, action) was applied to 40 framework-by-layer cells, each coded explicit, implicit, or absent, in two independent passes. Results. Nineteen of 40 cells (47.5%) were coded explicit. Coverage was uneven by layer: differentiation was explicit in 5 of 5 frameworks and customer progress in 2 of 5, whereas the intelligence-architecture layer was explicit in 1 of 5, implicit in 1, and absent in 3. Wardley Mapping showed the broadest coverage (6 of 8 layers explicit, no absent layers); Jobs to Be Done the narrowest (2 explicit, 2 absent). The two coding passes agreed on 34 of 40 cells (85.0%), and the 6 disagreements were resolved by returning to the source text. Conclusion. AI does not invalidate conventional product strategy, but the measured coverage gap is specific and reproducible: no established framework in the coded corpus makes intelligence production an explicit strategic object. A layered model is proposed together with falsifiable prospective tests of whether making that layer explicit improves decisions.",
  "keywords": [
    "product strategy",
    "strategic frameworks",
    "artificial intelligence",
    "intelligence architecture",
    "technology strategy",
    "Wardley Mapping",
    "Jobs to Be Done",
    "dynamic capabilities"
  ],
  "area": "Product & Innovation",
  "issue": "JIPCET 2026; 3(2)",
  "pageRange": "105–148",
  "published": "2026-07-31",
  "received": "Received 02 Jun 2026",
  "version": "1.0",
  "versions": [
    {
      "version": "1.0",
      "date": "2026-07-31",
      "note": "Version of record published after editorial decision."
    }
  ],
  "articleType": "Research Article",
  "reviewStatus": "Peer reviewed under named human editorial supervision",
  "peerReviewed": true,
  "handlingRecord": "Handled by a named JIPCET editor of record, who read the manuscript, oversaw review and approved the decision.",
  "methodology": "Research Article",
  "dataAvailability": "Available from the corresponding author on reasonable request, in line with participant and organisational agreements.",
  "codeAvailability": "Analysis procedures are documented in full in the methods section of the article.",
  "referenceCount": 19,
  "references": [
    "Adner, R. (2017). Ecosystem as structure: An actionable construct for strategy. Journal of Management, 43(1), 39–58. https://doi.org/10.1177/0149206316678451",
    "Agrawal, A., Gans, J. & Goldfarb, A. (2018). Prediction Machines: The Simple Economics of Artificial Intelligence. Harvard Business Review Press.",
    "Baldwin, C. Y. & Clark, K. B. (2000). Design Rules, Volume 1: The Power of Modularity. MIT Press.",
    "Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108",
    "Christensen, C. M., Hall, T., Dillon, K. & Duncan, D. S. (2016). Competing Against Luck: The Story of Innovation and Customer Choice. HarperBusiness.",
    "Dwivedi, Y. K. et al. (2023). So what if ChatGPT wrote it? Multidisciplinary perspectives on generative conversational AI. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642",
    "Faraj, S., Pachidi, S. & Sayegh, K. (2018). Working and organizing in the age of the learning algorithm. Information and Organization, 28(1), 62–70. https://doi.org/10.1016/j.infoandorg.2018.02.005",
    "Iansiti, M. & Lakhani, K. R. (2020). Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World. Harvard Business Review Press.",
    "Jacobides, M. G., Cennamo, C. & Gawer, A. (2018). Towards a theory of ecosystems. Strategic Management Journal, 39(8), 2255–2276. https://doi.org/10.1002/smj.2904",
    "Lafley, A. G. & Martin, R. L. (2013). Playing to Win: How Strategy Really Works. Harvard Business Review Press.",
    "Porter, M. E. (1980). Competitive Strategy: Techniques for Analyzing Industries and Competitors. Free Press.",
    "Raisch, S. & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072",
    "Rumelt, R. P. (2011). Good Strategy/Bad Strategy: The Difference and Why It Matters. Crown Business.",
    "Shrestha, Y. R., Ben-Menahem, S. M. & von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California Management Review, 61(4), 66–83. https://doi.org/10.1177/0008125619862257",
    "Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of sustainable enterprise performance. Strategic Management Journal, 28(13), 1319–1350. https://doi.org/10.1002/smj.640",
    "Ulwick, A. W. (2005). What Customers Want: Using Outcome-Driven Innovation to Create Breakthrough Products and Services. McGraw-Hill.",
    "Wernerfelt, B. (1984). A resource-based view of the firm. Strategic Management Journal, 5(2), 171–180. https://doi.org/10.1002/smj.4250050207",
    "Wardley, S. (2022). Wardley Maps. Leanpub.",
    "Arivukkarasu, A. (2026). Supply Chain of Intelligence™ (version 1.0, January 2026). Framework paper. Retrieved from https://supplychainofai.com/paper"
  ],
  "replications": "No independent replication recorded to date.",
  "corrections": "No corrections issued. Any correction is published as a new version with a dated entry in the version history.",
  "license": "CC BY 4.0",
  "fullTextHtml": "https://jipcet.org/articles/jipcet-2026-0143",
  "fullTextPdf": null,
  "publisher": {
    "name": "JIPCET Editorial Office",
    "url": "https://jipcet.org",
    "email": "editor@jipcet.org"
  },
  "journal": {
    "title": "Journal of Innovation, Product, Computing & Emerging Technologies",
    "short": "JIPCET"
  }
}