The Cost of Not Knowing: When Deep Tech Deserves a Decision
The real strategic question is not whether Deep Tech is worth adopting, but whether continuing to decide with todayʼs information is worth the cost.
7D-ICA™'s Stimulus:
“Ignoring Deep Tech can expose companies to costly errors, operational delays, and insufficient or untimely information for decision-making: the cost of not knowing.”
The Evidence:
The proposition is plausible, but not self-evident. Companies often assess new technology by asking, “What will adoption cost?” A more complete question is: “What does continuing to decide without this capability already cost?”
Deep Tech should not be treated as a synonym for every digital tool. It refers to science- and engineering-intensive innovation that must move from technical validation through industrialization and commercialization, often with long development and deployment cycles. [1]
Evidence supports caution rather than universal adoption. In diagnostics, a 2025 systematic review found that generative AI performed broadly comparably to physicians overall but performed significantly worse than expert physicians, showing that technical promise does not automatically translate into operational superiority. Meanwhile, an AI-focused review reported substantial reductions in diagnostic time in radiology and pathology settings, but such findings are sector-specific and cannot be generalized across Deep Tech. [2] [3]
OECD evidence similarly qualifies the adoption argument. Across 15 countries, firms using AI, IoT, robotics, or big data tended to be more productive than non-adopters, but part of the productivity advantage reflected complementary skills and digital capital; the study explicitly notes that association does not establish causation. The economically meaningful question, therefore, is whether a technology changes the quality, speed, or confidence of decisions that matter. [4]
Decision theory offers a useful lens. The value of information measures the expected gain from reducing uncertainty before choosing; the expected value of perfect information sets an upper bound on what better information could be worth. 7D ICAʼs proposed executive concept—Cost of Not Knowing—applies this logic commercially: it asks what errors, delays, missed interventions, or foregone options persist because better information was unavailable, too late, or not acted upon. [5] [6]
Executives should therefore assess four forms of Cost of Technological Inaction: error cost, delay cost, information cost, and opportunity cost. A technology may justify testing not because it is sophisticated, but because it could enable an earlier, more accurate, or more confident decision.
The strongest Deep Tech value proposition is rarely the technology itself. It is the economically meaningful decision it makes possible.
The cost of technology is visible; the cost of making important decisions without the information it could provide often is not.
Research note:
Supporting evidence and sources were identified with the assistance of Perplexity AI and independently reviewed by the author.
Sources & Further Reading:
Calvino, F., Costa, H., & Haerle, D. — Digital technology diffusion in the age of AI: Cross-country evidence from microdata — OECD Science, Technology and Industry Working Papers — 2026 — https://ideas.repec.org/s/oec/stiaaa.html
A systematic review and meta-analysis of diagnostic performance of generative AI models — PubMed — 2025 — https://pubmed.ncbi.nlm.nih.gov/40121370/
World Bank — Deep Tech Solutions for Emerging Markets — World Bank — https://documents1.wo rldbank.org/curated/en/161011606381111160/pdf/Deep-Tech-Solutions-for-Emerging-Markets.pdf
Fenwick, E., Steuten, L., et al. — Value of Information Analytical Methods: Report 2 of the ISPOR Value of Information Analysis Emerging Good Practices Task Force — Value in Health — 2020 — https://pmc.ncbi.nlm.nih.gov/articles/PMC7373630/
MIT OpenCourseWare — Decision Trees 2: The Value of Information — MIT — https://ocw.mit.edu/courses/15-053-optimization-methods-in-management-science-spring-2013/b68f3b255275ffa42099958e373248b1_MIT15_053S13_lec19.pdf
Reducing the workload of medical diagnosis through artificial intelligence — PMC — 2025 — https://pmc.ncbi.nlm.nih.gov/articles/PMC11813001/
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