{"id":843,"date":"2025-06-05T18:26:09","date_gmt":"2025-06-05T18:26:09","guid":{"rendered":"https:\/\/pyonnier.com\/?p=843"},"modified":"2025-12-21T18:06:31","modified_gmt":"2025-12-21T18:06:31","slug":"lia-dans-la-rd-la-promesse-vs-la-preuve-dune-mise-sur-le-marche-plus-rapide","status":"publish","type":"post","link":"https:\/\/pyonnier.com\/en\/2025\/06\/05\/lia-dans-la-rd-la-promesse-vs-la-preuve-dune-mise-sur-le-marche-plus-rapide\/","title":{"rendered":"AI in R&amp;D - The Promise vs. the Proof of Faster Time-to-Market"},"content":{"rendered":"<style>.elementor-843 .elementor-element.elementor-element-1c1f9fa8{--display:flex;}.elementor-843 .elementor-element.elementor-element-6bd0aac{--display:flex;}.elementor-843 .elementor-element.elementor-element-f55a812 .elementor-heading-title{font-family:var( --e-global-typography-primary-font-family ), Sans-serif;font-weight:var( --e-global-typography-primary-font-weight );}.elementor-843 .elementor-element.elementor-element-f55a812 > .elementor-widget-container{margin:0px 0px 10px 0px;}.elementor-843 .elementor-element.elementor-element-ef330d5 .elementor-heading-title{font-family:var( --e-global-typography-primary-font-family ), Sans-serif;font-weight:var( --e-global-typography-primary-font-weight );}.elementor-843 .elementor-element.elementor-element-ef330d5 > .elementor-widget-container{margin:0px 0px 10px 0px;}.elementor-843 .elementor-element.elementor-element-af22d65 .elementor-heading-title{font-family:var( --e-global-typography-primary-font-family ), Sans-serif;font-weight:var( --e-global-typography-primary-font-weight );}.elementor-843 .elementor-element.elementor-element-af22d65 > .elementor-widget-container{margin:0px 0px 10px 0px;}.elementor-843 .elementor-element.elementor-element-18f5593 .elementor-heading-title{font-family:\"Poppins\", Sans-serif;font-size:25px;font-weight:500;}.elementor-843 .elementor-element.elementor-element-18f5593 > .elementor-widget-container{margin:0px 0px 10px 0px;}.elementor-843 .elementor-element.elementor-element-8c2e8da .elementor-heading-title{font-family:\"Poppins\", Sans-serif;font-size:25px;font-weight:500;}.elementor-843 .elementor-element.elementor-element-8c2e8da > .elementor-widget-container{margin:0px 0px 10px 0px;}.elementor-843 .elementor-element.elementor-element-4531db0 .elementor-heading-title{font-family:\"Poppins\", Sans-serif;font-size:25px;font-weight:500;}.elementor-843 .elementor-element.elementor-element-4531db0 > .elementor-widget-container{margin:0px 0px 10px 0px;}.elementor-843 .elementor-element.elementor-element-ba65664 .elementor-heading-title{font-family:\"Poppins\", Sans-serif;font-size:25px;font-weight:500;}.elementor-843 .elementor-element.elementor-element-ba65664 > .elementor-widget-container{margin:0px 0px 10px 0px;}.elementor-843 .elementor-element.elementor-element-72332d7 .elementor-heading-title{font-family:\"Poppins\", Sans-serif;font-size:25px;font-weight:500;}.elementor-843 .elementor-element.elementor-element-72332d7 > .elementor-widget-container{margin:0px 0px 10px 0px;}.elementor-843 .elementor-element.elementor-element-3fb06b4 .elementor-heading-title{font-family:\"Poppins\", Sans-serif;font-size:25px;font-weight:500;}.elementor-843 .elementor-element.elementor-element-3fb06b4 > .elementor-widget-container{margin:0px 0px 10px 0px;}.elementor-843 .elementor-element.elementor-element-d914d6b .elementor-heading-title{font-family:var( --e-global-typography-primary-font-family ), Sans-serif;font-weight:var( --e-global-typography-primary-font-weight );}.elementor-843 .elementor-element.elementor-element-d914d6b > .elementor-widget-container{margin:0px 0px 10px 0px;}.elementor-843 .elementor-element.elementor-element-47e8ebb .elementor-heading-title{font-family:\"Poppins\", Sans-serif;font-size:25px;font-weight:500;}.elementor-843 .elementor-element.elementor-element-47e8ebb > .elementor-widget-container{margin:0px 0px 10px 0px;}.elementor-843 .elementor-element.elementor-element-43872e9 .elementor-heading-title{font-family:\"Poppins\", Sans-serif;font-size:25px;font-weight:500;}.elementor-843 .elementor-element.elementor-element-43872e9 > .elementor-widget-container{margin:0px 0px 10px 0px;}.elementor-843 .elementor-element.elementor-element-f613a6c .elementor-heading-title{font-family:var( --e-global-typography-primary-font-family ), Sans-serif;font-weight:var( --e-global-typography-primary-font-weight );}.elementor-843 .elementor-element.elementor-element-f613a6c > .elementor-widget-container{margin:0px 0px 10px 0px;}.elementor-843 .elementor-element.elementor-element-2e53813 .elementor-heading-title{font-family:\"Poppins\", Sans-serif;font-size:25px;font-weight:500;}.elementor-843 .elementor-element.elementor-element-2e53813 > .elementor-widget-container{margin:0px 0px 10px 0px;}.elementor-843 .elementor-element.elementor-element-955a226 .elementor-heading-title{font-family:\"Poppins\", Sans-serif;font-size:25px;font-weight:500;}.elementor-843 .elementor-element.elementor-element-955a226 > .elementor-widget-container{margin:0px 0px 10px 0px;}.elementor-843 .elementor-element.elementor-element-01d64f0 .elementor-heading-title{font-family:\"Poppins\", Sans-serif;font-size:25px;font-weight:500;}.elementor-843 .elementor-element.elementor-element-01d64f0 > .elementor-widget-container{margin:0px 0px 10px 0px;}.elementor-843 .elementor-element.elementor-element-4f5dc9b .elementor-heading-title{font-family:\"Poppins\", Sans-serif;font-size:25px;font-weight:500;}.elementor-843 .elementor-element.elementor-element-4f5dc9b > .elementor-widget-container{margin:0px 0px 10px 0px;}.elementor-843 .elementor-element.elementor-element-94f24d0 .elementor-heading-title{font-family:\"Poppins\", Sans-serif;font-size:25px;font-weight:500;}.elementor-843 .elementor-element.elementor-element-94f24d0 > .elementor-widget-container{margin:0px 0px 10px 0px;}.elementor-843 .elementor-element.elementor-element-4badc32 .elementor-heading-title{font-family:var( --e-global-typography-primary-font-family ), Sans-serif;font-weight:var( --e-global-typography-primary-font-weight );}.elementor-843 .elementor-element.elementor-element-4badc32 > .elementor-widget-container{margin:0px 0px 10px 0px;}.elementor-843 .elementor-element.elementor-element-66971c9 .elementor-heading-title{font-family:var( --e-global-typography-primary-font-family ), Sans-serif;font-weight:var( --e-global-typography-primary-font-weight );}.elementor-843 .elementor-element.elementor-element-66971c9 > .elementor-widget-container{margin:0px 0px 10px 0px;}.elementor-843 .elementor-element.elementor-element-247fcaf .elementor-heading-title{font-family:\"Poppins\", Sans-serif;font-size:25px;font-weight:500;}.elementor-843 .elementor-element.elementor-element-247fcaf > .elementor-widget-container{margin:0px 0px 10px 0px;}<\/style>\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"843\" class=\"elementor elementor-843\" data-elementor-post-type=\"post\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1c1f9fa8 e-flex e-con-boxed e-con e-parent\" data-id=\"1c1f9fa8\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4a3ad9bd elementor-widget elementor-widget-text-editor\" data-id=\"4a3ad9bd\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\n<p><\/p>\n\n\n\n<p><\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-6bd0aac e-flex e-con-boxed e-con e-parent\" data-id=\"6bd0aac\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f55a812 elementor-widget elementor-widget-heading\" data-id=\"f55a812\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">1. Introduction \u2013 An Enthusiasm to be Revamped<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9fc1512 elementor-widget elementor-widget-text-editor\" data-id=\"9fc1512\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>The enthusiasm surrounding AI in R&amp;D is often based on a seductive promise: that of a significant acceleration in time-to-market.<\/p>\n<p>The figures frequently put forward point to gains of 20% to 50% across the board, driven by visible use cases (generative design, simulation, document automation, etc.). However, this dominant narrative obscures a more nuanced reality.<\/p>\n<p>As Robert Cooper (Ref. 34) points out, only 18% to 36% of AI projects actually achieve the expected benefits, depending on the type of gains targeted.<\/p>\n<p>Even more worrying, 47% of projects never reach the production stage, trapped in a pilot phase with no follow-up, a phenomenon he describes as \u2018pilot paralysis\u2019.\u00a0<\/p>\n<p>This observation does not call into question the benefits of AI, but it does call into question the gap between promises and evidence. If we are to succeed, we need to understand why so many initiatives fail - and how to maximise their chances of success.<\/p>\n<p>One factor in particular stands out: unrealistic expectations. Ranked as one of the most frequent and influential causes, this anticipation bias distorts the reading of results, discourages teams and compromises strategic alignment.<\/p>\n<p>The problem is not just that AI does not deliver; it is also that it is expected to bring about transformations that no single technology can provide.<\/p>\n<p>This article offers a critical re-reading of the promise of AI as a shortcut to the market.<\/p>\n<p>The aim is not so much to curb enthusiasm as to equip R&amp;D decision-makers to derive real, measurable and lasting benefits from their AI investments.<\/p>\n<p>\u00a0<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ef330d5 elementor-widget elementor-widget-heading\" data-id=\"ef330d5\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">2. The Ambient Narrative <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-78d1514 elementor-widget elementor-widget-text-editor\" data-id=\"78d1514\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">From vendor brochures to industry keynotes, a familiar claim is often repeated (Refs. 1 to 25):<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u201cAI accelerates innovation by reducing time-to-market by 20 to 50%.\u201d<\/span><\/p>\n<p>\u00a0<\/p>\n<p><span style=\"font-weight: 400;\">The narrative typically unfolds as follows<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AI enables faster identification of optimal designs.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AI anticipates failures earlier in the development cycle.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AI automates repetitive tasks (e.g., documentation, data cleaning).<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AI improves portfolio and resource allocation decisions.<\/span><\/li>\n<\/ul>\n<p>\u00a0<\/p>\n<p><span style=\"font-weight: 400;\">While these mechanisms are conceptually sound, the causal link between AI adoption and faster market launch is seldom scrutinized\u2014and the figures cited are rarely supported by rigorous evidence.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-af22d65 elementor-widget elementor-widget-heading\" data-id=\"af22d65\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">3. Where the Evidence Falls Short <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-18f5593 elementor-widget elementor-widget-heading\" data-id=\"18f5593\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">3.1. Lack of long-term studies<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c6d0c8a elementor-widget elementor-widget-text-editor\" data-id=\"c6d0c8a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">There is no broad empirical evidence showing that companies using AI in R&amp;D consistently bring products to market faster. There is simply not enough track record available and not enough time elapsed to have long term retrospectives.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Most published case studies are short-term, focusing on proof-of-concept or pilot phases. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">Much of the current understanding of AI's impact on time-to-market is based on anecdotal evidence or vendor-reported successes (Refs. 1 to 25). <\/span><\/p>\n<p><span style=\"font-weight: 400;\">Although legitimate, these accounts often lack the methodological rigor required for generalization, underscoring the need for more systematic, long-term studies.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The most widely cited examples (Refs. 1, 3, 5) are often\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Selective, only successes are shared<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Unpublished or vendor-controlled<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Confounded by parallel organizational changes<\/span><\/li>\n<\/ul>\n<p>\u00a0<\/p>\n<p><span style=\"font-weight: 400;\">These examples highlight the prevalent use of anecdotal evidence in asserting AI's impact on reducing time-to-market in R&amp;D. For a more comprehensive understanding, further empirical studies and longitudinal analyses are necessary.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8c2e8da elementor-widget elementor-widget-heading\" data-id=\"8c2e8da\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">3.2. Limited Empirical Evidence in Academic Literature<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fee56a4 elementor-widget elementor-widget-text-editor\" data-id=\"fee56a4\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">A review of academic publications (Refs. 26 to 29) reveals a scarcity of empirical studies that rigorously assess AI's long-term effects on R&amp;D timelines. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">While there is growing interest in AI's role in innovation, comprehensive studies measuring its sustained impact on time-to-market across various industries remain limited.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">While these studies provide valuable insights into AI's role in enhancing R&amp;D processes, they also underscore the current gap in long-term, comprehensive research assessing AI's impact on time-to-market. Further empirical studies are necessary to draw definitive conclusions across various industries.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4531db0 elementor-widget elementor-widget-heading\" data-id=\"4531db0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">3.3. Predominance of Forward-Looking Expectations<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1ac2a73 elementor-widget elementor-widget-text-editor\" data-id=\"1ac2a73\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">The Examples of McKinsey (Refs. 1, 5), repeated and sometimes unreliably meaning presenting expectations as facts or track record.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ba65664 elementor-widget elementor-widget-heading\" data-id=\"ba65664\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">3.4. Challenges in Isolating AI's Impact<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a3cc195 elementor-widget elementor-widget-text-editor\" data-id=\"a3cc195\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Isolating AI's specific contribution to reduced time-to-market is complex due to the frequent concurrent organizational changes.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For example, a case study (Ref. 10) by Cherry Bekaert describes a consumer goods company's transformation of its R&amp;D processes using AI.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">While the company reported faster innovation cycles, attributing this solely to AI is challenging given other simultaneous process improvements.<\/span><\/p>\n<p>\u00a0<\/p>\n<p><span style=\"font-weight: 400;\">Moreover, companies investing in AI are also more likely to have more R&amp;D funding or operate already with better processes. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">Such advantages may, at least in part, explain the reported performance improvements, independent of AI's direct contribution. References 30 to 33 provide insights on factors that have significant impacts on R&amp;D performance metrics. These factors include:<\/span><\/p>\n<p>\u00a0<\/p>\n<p><span style=\"text-decoration: underline;\"><span style=\"font-weight: 400;\">At country level<\/span><\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><i><span style=\"font-weight: 400;\">Governmental R&amp;D Funding<\/span><\/i><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Increases in government R&amp;D funding lead to sustained growth in long-term productivity, with effects becoming significant after about eight years and persisting for at least 15 years.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><i><span style=\"font-weight: 400;\">International Talent Inflow and R&amp;D Investment\u00a0<\/span><\/i><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">The inflow of international talent increases firms\u2019 R&amp;D investment, with further confirmation from patent data.<\/span><\/p>\n<p>\u00a0<\/p>\n<p><span style=\"text-decoration: underline;\"><span style=\"font-weight: 400;\">At company level<\/span><\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><i><span style=\"font-weight: 400;\">Talent Policy and Corporate Innovation<\/span><\/i><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Implementation of talent policies significantly increases R&amp;D personnel recruitment, leading to improvements in R&amp;D investment, patent output, and R&amp;D efficiency, particularly in high-tech enterprises.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><i><span style=\"font-weight: 400;\">Process Maturity and Project Performance\u00a0<\/span><\/i><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Higher levels of process maturity in R&amp;D projects attenuate the negative effects of project risks, leading to improved project performance.<\/span><\/p>\n<p>\u00a0<\/p>\n<p><span style=\"font-weight: 400;\">While AI provides promising tools to enhance R&amp;D efficiency, these foundational factors must also be considered, as they may contribute equally\u2014or even more significantly\u2014to observed performance gains. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">In some cases, improvements in time-to-market may be primarily driven by such underlying strengths, with AI acting more as an enabler than the principal cause.\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-72332d7 elementor-widget elementor-widget-heading\" data-id=\"72332d7\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">3.5. Variability in Metrics<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-dfa9c4b elementor-widget elementor-widget-text-editor\" data-id=\"dfa9c4b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">The definition of time-to-market varies widely across industries, shaped by differing regulatory environments, technological constraints, and customer expectations. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">As a result, metrics are not standardized, making cross-sector comparisons, and attributions of improvement difficult. The table below illustrates how time-to-market is typically defined in different sectors.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0359bad elementor-widget elementor-widget-text-editor\" data-id=\"0359bad\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<table>\n<tbody>\n<tr>\n<td>\n<p><b>Sector<\/b><\/p>\n<\/td>\n<td>\n<p><b>Typical Time-to-Market Definition<\/b><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><b>Pharma<\/b><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Time from discovery to regulatory approval (often 8\u201315 yrs)<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><b>Chemicals<\/b><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Time from lab validation to commercial scale-up (3\u20135 yrs), with a pilot phase in between<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><b>Software<\/b><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Time from ideation to MVP\/public release (3\u201312 months)<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><b>Hardware<\/b><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Design to production-ready system (12\u201336 months)<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><b>Energy<\/b><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">From project origination to commissioning (5\u201310+ years)<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><b>Consumer goods<\/b><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Concept to shelf-ready product (6\u201318 months)<\/span><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-393fe06 elementor-widget elementor-widget-text-editor\" data-id=\"393fe06\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">AI may accelerate specific steps in these processes but rarely transforms the entire timeline. Moreover, few organizations invest resources to track time-to-market with the rigor required to isolate AI\u2019s specific contribution when other changes occur concurrently.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This lack of measurement consistency makes it difficult to substantiate broad claims about AI's overall impact on time-to-market.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-53692b2 elementor-widget elementor-widget-image\" data-id=\"53692b2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" width=\"800\" height=\"331\" src=\"https:\/\/pyonnier.com\/wp-content\/uploads\/2025\/06\/Capture-decran-le-2025-06-05-a-15.32.40.png\" class=\"attachment-large size-large wp-image-846\" alt=\"\" decoding=\"async\" srcset=\"https:\/\/pyonnier.com\/wp-content\/uploads\/2025\/06\/Capture-decran-le-2025-06-05-a-15.32.40.png 1154w, https:\/\/pyonnier.com\/wp-content\/uploads\/2025\/06\/Capture-decran-le-2025-06-05-a-15.32.40-300x145.png 300w, https:\/\/pyonnier.com\/wp-content\/uploads\/2025\/06\/Capture-decran-le-2025-06-05-a-15.32.40-1024x493.png 1024w, https:\/\/pyonnier.com\/wp-content\/uploads\/2025\/06\/Capture-decran-le-2025-06-05-a-15.32.40-768x370.png 768w, https:\/\/pyonnier.com\/wp-content\/uploads\/2025\/06\/Capture-decran-le-2025-06-05-a-15.32.40-18x9.png 18w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3fb06b4 elementor-widget elementor-widget-heading\" data-id=\"3fb06b4\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">3.6. Takeaway from the Literature: Misplaced Generalizations from Partial Gain <\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-082e106 elementor-widget elementor-widget-text-editor\" data-id=\"082e106\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Taken together, the literature suggests that AI\u2019s value in R&amp;D is real but often mischaracterized. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">Many authors, implicitly or explicitly, <\/span><b>make the leap from task-level acceleration to system-wide transformation,<\/b><span style=\"font-weight: 400;\">assuming that gains in analysis, modeling, or design will necessarily compress the full time to market.\u00a0<\/span><\/p>\n<p>\u00a0<\/p>\n<p><span style=\"font-weight: 400;\">Yet the evidence does not support this generalization.\u00a0<\/span><\/p>\n<p>\u00a0<\/p>\n<p><span style=\"font-weight: 400;\">Time to market is shaped by complex, interdependent systems, many of which AI does not currently influence. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">Without addressing scale-up bottlenecks, regulatory timelines, organizational inertia, or supply chain constraints, faster R&amp;D tasks do not automatically translate to faster product launches.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"> Recognizing this gap is essential to forming realistic expectations about AI\u2019s current and future role in innovation timelines. <\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d914d6b elementor-widget elementor-widget-heading\" data-id=\"d914d6b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">4. Does AI Really Shorten Time-to-Market in R&amp;D?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ce627d2 elementor-widget elementor-widget-text-editor\" data-id=\"ce627d2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">As shown in the previous section, despite the enthusiasm, many claims about AI shortening time-to-market rely on a flawed assumption: that accelerating a few tasks automatically accelerates the entire process.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">AI does prove effective in accelerating <\/span><b>specific R&amp;D tasks.<\/b><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">However, <\/span><b>time-to-market depends on broader systems<\/b><span style=\"font-weight: 400;\"> - manufacturing, regulatory approvals, supply chains, and organizational dynamics\u2014which AI has yet to meaningfully transform.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-47e8ebb elementor-widget elementor-widget-heading\" data-id=\"47e8ebb\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">4.1. Limits: The Bottlenecks AI Doesn\u2019t Remove (Yet)<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-18acf61 elementor-widget elementor-widget-text-editor\" data-id=\"18acf61\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Some of the most persistent delays in R&amp;D still lie outside AI\u2019s reach:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Regulatory approvals<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Supply chain and equipment lead times<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Manufacturing scale-up and validation<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Integration with legacy systems<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Change resistance within organizations<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Scarcity of domain-specific talent.<\/span><\/li>\n<\/ul>\n<p>\u00a0<\/p>\n<p><span style=\"font-weight: 400;\">These are structural constraints. While AI may support adjacent tasks, it does not currently transform these systems end-to-end. As a result, speeding up a few steps rarely shifts the overall delivery date unless the entire system is optimized.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-43872e9 elementor-widget elementor-widget-heading\" data-id=\"43872e9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">4.2 Gains: Where AI Actually Helps<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4b43350 elementor-widget elementor-widget-text-editor\" data-id=\"4b43350\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Where AI does have a measurable impact is in reducing <\/span><b>internal inefficiencies,<\/b><span style=\"font-weight: 400;\">accelerating <\/span><b>early-stage work,<\/b><span style=\"font-weight: 400;\"> and supporting <\/span><b>faster decision-making<\/b><span style=\"font-weight: 400;\">.<\/span><\/p>\n<p>\u00a0<\/p>\n<p><span style=\"font-weight: 400;\">Examples include:\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Data triage:<\/b><span style=\"font-weight: 400;\"> extracting faster insights from historical datasets\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Hypothesis ranking:<\/b><span style=\"font-weight: 400;\"> narrowing down what to test<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Virtual prototyping:<\/b><span style=\"font-weight: 400;\"> simulating product concepts<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Automated documentation:<\/b><span style=\"font-weight: 400;\"> speeding up admin-heavy tasks\u00a0<\/span><\/li>\n<\/ul>\n<p>\u00a0<\/p>\n<p><span style=\"font-weight: 400;\">Beyond these core gains, a growing range of applications now includes\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Generative design and simulation<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Faster molecule screening and trial design in pharma<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Natural language search of research databases, code generation and automated lab analysis<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Forecasting, risk assessment, and budget modeling<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Lifecycle and manufacturing process simulations<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Synthetic data creation and customer feedback analysis<\/span><\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f613a6c elementor-widget elementor-widget-heading\" data-id=\"f613a6c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">5. Turning AI Potential into Performance in R&amp;D<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2e53813 elementor-widget elementor-widget-heading\" data-id=\"2e53813\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">5.1. Don\u2019t Expect Blanket Time-to-Market Gains <\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fdd8b8f elementor-widget elementor-widget-text-editor\" data-id=\"fdd8b8f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul>\n<li aria-level=\"2\"><span style=\"font-weight: 400;\">Time-to-market is not a uniform, monolithic metric. It varies widely by sector and includes operations that AI has no influence on.<\/span><\/li>\n<\/ul>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Avoid setting arbitrary goals like \u201c20% faster time-to-market.\u201d\u00a0<\/span><\/li>\n<\/ul>\n<p>\u00a0<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-955a226 elementor-widget elementor-widget-heading\" data-id=\"955a226\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">5.2. Target Specific Steps, Measure Local Impact<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-00c698a elementor-widget elementor-widget-text-editor\" data-id=\"00c698a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul>\n<li aria-level=\"2\"><span style=\"font-weight: 400;\">Focus instead on specific steps or bottlenecks where AI may accelerate learning, reduce rework, or improve decision quality.<\/span><\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-01d64f0 elementor-widget elementor-widget-heading\" data-id=\"01d64f0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">5.3. To Reap the Benefits, R&amp;D Still Needs to Be Actively Managed<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-407caa5 elementor-widget elementor-widget-text-editor\" data-id=\"407caa5\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AI doesn\u2019t replace the need for R&amp;D strategy, governance, and team orchestration.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Success with AI depends on\u00a0<\/span>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Clear integration points in the R&amp;D workflow<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Alignment with the strategic priorities of the project portfolio, to support key objectives;<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Development of a culture of continuous experimentation and accountability, where teams are encouraged to test new approaches and report on their results.<\/span><\/li>\n<\/ul>\n<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AI is a multiplier, not a replacement. It magnifies both good systems and dysfunctional ones.<\/span><\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4f5dc9b elementor-widget elementor-widget-heading\" data-id=\"4f5dc9b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">5.4. Change Management and IT support as Operational Enablers<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3c51070 elementor-widget elementor-widget-text-editor\" data-id=\"3c51070\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Transitioning to AI in R&amp;D isn\u2019t a plug-and-play upgrade. It demands structural and cultural readiness. It is a transformation, not a quick fix.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Ensure IT integration: Plan early for how AI tools will access relevant data, plug into existing systems, and support decision workflows.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Lead with change management: Prepare teams to adapt by clarifying new roles, building trust in AI outputs, and embedding training into project cycles.<\/span><\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-94f24d0 elementor-widget elementor-widget-heading\" data-id=\"94f24d0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">5.5. AI in R&amp;D: Promise vs. Practice <\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c8a0bc6 elementor-widget elementor-widget-text-editor\" data-id=\"c8a0bc6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">To help navigate claims about AI\u2019s impact on time-to-market, the table below highlights common narratives, observed gaps, and key questions to ask.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5c55890 elementor-widget elementor-widget-text-editor\" data-id=\"5c55890\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>\u00a0<\/p>\n<table>\n<tbody>\n<tr>\n<td>\n<p><b>Common Claims<\/b><\/p>\n<\/td>\n<td>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AI cuts time-to-market from 20 to 50 %<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Generative AI speeds up innovation cycles<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">R&amp;D teams can move faster with fewer resources<\/span><\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><b>Observed Gaps\u00a0<\/b><\/p>\n<\/td>\n<td>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Few independent, peer-reviewed studies quantify end-to-end time to market gains<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Most case studies describe local improvements, not system-level acceleration<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Gains often rely on concurrent changes in process, governance, or tooling<\/span><\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><b>What to Clarify for a Successful Transition<\/b><\/p>\n<\/td>\n<td>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">What steps of our R&amp;D cycle are bottlenecks, and how exactly might AI help?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Where in our process can faster ideation translate to faster delivery?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Have we planned integration into workflows, training, and governance?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">How will we track impact across the full development chain?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Are we ready in terms of data infrastructure, change management, and decision processes?<\/span><\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4badc32 elementor-widget elementor-widget-heading\" data-id=\"4badc32\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">6. Conclusion - What Does a More Accurate Narrative Might Say? <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-05976b3 elementor-widget elementor-widget-text-editor\" data-id=\"05976b3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">The promise of faster time-to-market through AI is widely repeated\u2014but the proof remains limited, partial, and highly context-dependent.\u00a0<\/span><\/p>\n<p>\u00a0<\/p>\n<p><span style=\"font-weight: 400;\">This review highlights three key takeaways:\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Narratives often exaggerate impact<\/b><span style=\"font-weight: 400;\"> without disclosing scope, supporting data, or implementation details.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Time-to-market is not a universal metric<\/b><span style=\"font-weight: 400;\"> and AI rarely improves it in isolation.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Performance gains depend on orchestration,<\/b><span style=\"font-weight: 400;\">not just tool adoption\u2014integrating AI into workflows, governance, and decision systems is essential.<\/span><\/li>\n<\/ul>\n<p>\u00a0<\/p>\n<p><span style=\"font-weight: 400;\">The causes of failure in AI projects are often avoidable - \u2018mostly dumb reasons\u2019, in Dr Cooper's own words. Among the seven main reasons are some classic but all too neglected factors: poor data quality, lack of understanding of user needs, and lack of change management. As we observe in practice, these factors explain why some experts estimate the failure rate to be as high as 80%, almost twice as high as traditional IT projects a decade ago.\u00a0<\/span><\/p>\n<p>\u00a0<\/p>\n<p><span style=\"font-weight: 400;\">But the problem runs deeper than isolated missteps. Real performance gains require orchestration. Improving one step in the chain is not enough if inefficiencies resurface elsewhere. Just like a set of equipment doesn't make a working electric grid, a stack of AI tools doesn\u2019t create operational excellence on its own.\u00a0<\/span><\/p>\n<p>\u00a0<\/p>\n<p><span style=\"font-weight: 400;\">Such a level of failure should be a wake-up call. Based on the sources reviewed in this article, for AI to deliver on its promise, adoption isn\u2019t enough. It must be orchestrated. Process, governance, and strategic alignment remain the real enablers of impact.\u00a0<\/span><\/p>\n<p>\u00a0<\/p>\n<p><span style=\"font-weight: 400;\">So a more accurate narrative would read:\u00a0<\/span><\/p>\n<p><b>AI accelerates innovation by reducing time-to-market\u2014in organizations that move in sync with the algorithms.<\/b><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-66971c9 elementor-widget elementor-widget-heading\" data-id=\"66971c9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">7. References<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-247fcaf elementor-widget elementor-widget-heading\" data-id=\"247fcaf\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">7.1. Examples of publications discussion time-to-market in AI-assisted R&amp;D<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4124b11 elementor-widget elementor-widget-text-editor\" data-id=\"4124b11\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><b><\/b><\/p>\n<h3><b>1. McKinsey - \"Using AI to supercharge R&amp;D: Takeaways from the R&amp;D Leaders Forum<\/b><span style=\"font-weight: 400;\"> \"\u00a0<\/span><b> (Using AI to boost R&amp;D: lessons from the R&amp;D Leaders Forum)<\/b><\/h3>\n<p><a href=\"https:\/\/www.mckinsey.com\/capabilities\/operations\/our-insights\/operations-blog\/using-ai-to-supercharge-r-and-d-takeaways-from-the-r-and-d-leaders-forum\"><span style=\"font-weight: 400;\">https:\/\/www.mckinsey.com\/capabilities\/operations\/our-insights\/operations-blog\/using-ai-to-supercharge-r-and-d-takeaways-from-the-r-and-d-leaders-forum<\/span><\/a><span style=\"font-weight: 400;\">&nbsp;<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Highlights an expectation by 40 R&amp;D leaders of a 20 to 40% reduction in time-to-market through AI applications like generative design and simulation.<\/span><\/p>\n<p><b><\/b><\/p>\n<h3><b>2. BCG - AI-Powered R&amp;D<\/b><\/h3>\n<p><a href=\"https:\/\/media-publications.bcg.com\/BCG-Executive-Perspectives-AI-Powered-RandD-EP1-14Feb2025.pdf\"><span style=\"font-weight: 400;\">https:\/\/media-publications.bcg.com\/BCG-Executive-Perspectives-AI-Powered-RandD-EP1-14Feb2025.pdf<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">Shares the expectation that AI can reduce time-to-market by 10\u201320% and lower R&amp;D costs by up to 20%, emphasizing the need for operating model transformation. The document shares several other expectations without proof points.<\/span><\/p>\n<p><b><\/b><\/p>\n<h3><b>3. Roland Berger - \"AI in R&amp;D will lead to more innovative products and efficient processes\".<\/b><\/h3>\n<p><a href=\"https:\/\/www.rolandberger.com\/en\/Insights\/Publications\/AI-in-R-D-will-lead-to-more-innovative-products-and-more-efficient-processes.html\"><span style=\"font-weight: 400;\">https:\/\/www.rolandberger.com\/en\/Insights\/Publications\/AI-in-R-D-will-lead-to-more-innovative-products-and-more-efficient-processes.html<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">Reports that companies using AI in R&amp;D will experience accelerated innovation and up to 25% lower costs. The article doesn\u2019t share data supports this claim<\/span><\/p>\n<p><b><\/b><\/p>\n<h3><b>4. INFORMS - \"How AI is Accelerating Speed to Market\"<\/b><\/h3>\n<p><a href=\"https:\/\/pubsonline.informs.org\/do\/10.1287\/LYTX.2024.04.08\/full\/\"><span style=\"font-weight: 400;\">https:\/\/pubsonline.informs.org\/do\/10.1287\/LYTX.2024.04.08\/full\/<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">Explores how advancements in AI and generative AI are accelerating the speed of bringing products to market. No quantitative claims made in the article.<\/span><\/p>\n<p><b><\/b><\/p>\n<h3><b>5. McKinsey - \"How generative AI could accelerate software product time to market\".<\/b><\/h3>\n<p><a href=\"https:\/\/www.mckinsey.com\/industries\/technology-media-and-telecommunications\/our-insights\/how-generative-ai-could-accelerate-software-product-time-to-market\"><span style=\"font-weight: 400;\">https:\/\/www.mckinsey.com\/industries\/technology-media-and-telecommunications\/our-insights\/how-generative-ai-could-accelerate-software-product-time-to-market<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">Offers early lessons on how generative AI can improve product managers' productivity and quality, potentially accelerating time-to-market.\nThe article claims that \u201cGen AI accelerated product time to market by 5 %\u201d based on \u201c empirical research on PMs in Europe and the Americas\u201d with no further details.<\/span><\/p>\n<p><b><\/b><\/p>\n<h3><b>6. Quadrillion Partners - \"AI-Driven Product Innovation: Accelerating Time to Market\".<\/b><\/h3>\n<p><a href=\"https:\/\/www.quadrillionpartners.com\/blog-details\/ai-driven-product-innovation-accelerating-time-to-market\"><span style=\"font-weight: 400;\">https:\/\/www.quadrillionpartners.com\/blog-details\/ai-driven-product-innovation-accelerating-time-to-market<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">While the article emphasizes the potential of AI to significantly shorten time to market, it does not provide specific quantitative metrics or empirical data to substantiate these claims.<\/span><\/p>\n<p><b><\/b><\/p>\n<h3><b>7. Accenture - Reinventing R&amp;D in the Age of AI<\/b><\/h3>\n<p><a href=\"https:\/\/www.accenture.com\/us-en\/insights\/life-sciences\/the-rd-opportunity\"><span style=\"font-weight: 400;\">https:\/\/www.accenture.com\/us-en\/insights\/life-sciences\/the-rd-opportunity<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">Highlights that AI-driven R&amp;D significantly reduces drug discovery times and costs associated with bringing new therapies to market in the Biopharma Industry. It mentions specific tasks as being accelerated: \u201cAI accelerates target identification, enhancing drug discovery efficiency.\u201d, \u201cWith an AI-led discovery strategy, companies can reduce discovery cycle times by two-thirds.\u201d.\nThe basis for time-to-market reduction claims remains inaccessible to scrutiny, for instance: \u00ab Our analysis shows that if intelligent technologies are used at scale and workflows are reinvented appropriately to reduce the cost of failure and shorten discovery and development timelines, companies can bring a new medicine to market four years faster\u2026 \u00bb\u00a0<\/span><\/p>\n<p><b><\/b><\/p>\n<h3><b>8. Digital product development 2025 - PWC<\/b><\/h3>\n<p><a href=\"https:\/\/www.pwc.de\/de\/digitale-transformation\/pwc-studie-digital-product-development-2025.pdf\"><span style=\"font-weight: 400;\">https:\/\/www.pwc.de\/de\/digitale-transformation\/pwc-studie-digital-product-development-2025.pdf<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">Companies expect digital product development, including AI, to reduce time-to-market by 17%, and among them, the Digital Champions project up to a 28% reduction. However, the report provides no detail on how these expectations are formed.<\/span><\/p>\n<p><b><\/b><\/p>\n<h3><b>9. AI in R&amp;D: transforming the innovation landscape - GreyB<\/b><\/h3>\n<p><a href=\"https:\/\/www.greyb.com\/blog\/ai-in-research-and-development\/\"><span style=\"font-weight: 400;\">https:\/\/www.greyb.com\/blog\/ai-in-research-and-development\/<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">Highlights that, traditionally, bringing a drug from concept to market takes about 12 years. Gives one example of a company leveraging AI to accelerate this process, aiming to develop weight loss and diabetes drugs that could enter clinical trials in just 1.5 years.<\/span><\/p>\n<p><b><\/b><\/p>\n<h3><b>10. Case-study - Reinventing Innovation: Using AI to take R&amp;D from Art to Science - Cherry Bekaert<\/b><\/h3>\n<p><a href=\"https:\/\/www.cbh.com\/insights\/case-studies\/using-ai-to-take-consumer-goods-rd-from-art-to-science-case-study\/\"><span style=\"font-weight: 400;\">https:\/\/www.cbh.com\/insights\/case-studies\/using-ai-to-take-consumer-goods-rd-from-art-to-science-case-study\/<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">Describes a consumer goods company's transformation of its R&amp;D processes using AI, alongside other simultaneous process improvements that also contributed to the observed gains.<\/span><\/p>\n<p><b><\/b><\/p>\n<h3><b>11. How real-world businesses are transforming with AI \u2014 with 261 new stories<\/b><\/h3>\n<p><a href=\"https:\/\/blogs.microsoft.com\/blog\/2025\/04\/22\/https-blogs-microsoft-com-blog-2024-11-12-how-real-world-businesses-are-transforming-with-ai\/\"><span style=\"font-weight: 400;\">https:\/\/blogs.microsoft.com\/blog\/2025\/04\/22\/https-blogs-microsoft-com-blog-2024-11-12-how-real-world-businesses-are-transforming-with-ai\/<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">The article highlights various corporate applications of AI, including some in R&amp;D. It claims that \u201dGenerative AI is revolutionizing innovation by speeding up creative processes and product development. It\u2019s helping companies come up with new ideas, design prototypes and iterate quickly, cutting down the time it takes to get to market. In the automotive industry, it\u2019s designing more efficient vehicles, while in pharmaceuticals, it\u2019s crafting new drug molecules, slashing years off R&amp;D times.\u201d.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">However, the only concrete R&amp;D-related example provided is from Bayer: \u201cBayer built an agent using Copilot that lets their Crop Science researchers and scientists search the tool using natural language. What previously could take days to find the information, now saves R&amp;D teams 3 to 6 hours per researcher per week.\u201d<\/span><\/p>\n<p><b><\/b><\/p>\n<h3><b>12. Mc Kinsey - Generative AI in the pharmaceutical industry: Moving from hype to reality<\/b><\/h3>\n<p><a href=\"https:\/\/www.mckinsey.com\/industries\/life-sciences\/our-insights\/generative-ai-in-the-pharmaceutical-industry-moving-from-hype-to-reality\"><span style=\"font-weight: 400;\">https:\/\/www.mckinsey.com\/industries\/life-sciences\/our-insights\/generative-ai-in-the-pharmaceutical-industry-moving-from-hype-to-reality<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">McKinsey links generative AI to a strategic response to asset lifecycle compression in pharma : \u00ab Furthermore, by increasing the speed at which therapies can be developed, approved, and marketed, gen AI can help pharmaceutical companies address the issue of \u201casset lifecycle compression\u201d\u2014the decreasing amount of time companies have to capture a new drug\u2019s value. Over the past two decades, that time frame has fallen by almost 18 months: to 9.8 years, from 11.7, McKinsey research has found. \u00bb While the context is compelling, the supporting evidence for AI\u2019s direct contribution to reducing development and approval times remains largely anecdotal. No quantified gains or longitudinal studies are cited, making it difficult to assess the real extent of impact beyond illustrative examples.<\/span><\/p>\n<p><b><\/b><\/p>\n<h3><b>13. Mc Kinsey - Early adoption of generative AI in commercial life sciences<\/b><\/h3>\n<p><a href=\"https:\/\/www.mckinsey.com\/industries\/life-sciences\/our-insights\/early-adoption-of-generative-ai-in-commercial-life-sciences\"><span style=\"font-weight: 400;\">https:\/\/www.mckinsey.com\/industries\/life-sciences\/our-insights\/early-adoption-of-generative-ai-in-commercial-life-sciences<\/span><\/a><\/p>\n<p><b><\/b><\/p>\n<h3><b>14. Artificial intelligence in drug development: reshaping the therapeutic landscape - Therapeutic Advances in Drug Safety 2025, Vol. 16: 1\u201324<\/b><\/h3>\n<p><a href=\"https:\/\/journals.sagepub.com\/doi\/epub\/10.1177\/20420986251321704\"><span style=\"font-weight: 400;\">https:\/\/journals.sagepub.com\/doi\/epub\/10.1177\/20420986251321704<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">The article explicitly states that AI reduces drug development timelines, and provides some quantification examples \u201cTime to approval accelerated by 1\u20132 years\u201d (Section: Higher possibility of success), \u201c...a 12-plus month acceleration in the time it takes to conduct a trial\u201d \u2014 (Ibid.).It provides an example lacking background: Heal-X (HLX-0201): Moved a candidate to Phase II in 18 months, without comparing this to prior duration.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It provides expectations for time-to-market reduction drawn from frequently cited vendor sources:&nbsp;<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Predicted benefit: \u201cup to 50% cost reduction, 12+ months acceleration, and 20% NPV increase.\u201d (Section: Higher possibility of success). This is backed by the McKinsey reference \u201cGenerative AI in the pharmaceutical industry: Moving from hype to reality\u201d that provides no solid evidence to the claim.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Estimated savings: Drug development cost reduced by billions, timeline by 1\u20132 years. Also backed by the McKinsey reference \u201cEarly adoption of generative AI in commercial life sciences\u201d that provides no factual evidence to the claim.<\/span><\/li>\n<\/ul>\n<p><b><\/b><\/p>\n<h3><b>15. AI and the R&amp;D revolution - Financial Times, November 27, 2024<\/b><\/h3>\n<p><a href=\"https:\/\/www-ft-com.ezp-prod1.hul.harvard.edu\/content\/648046c1-7fcd-43fb-819b-841f104396d9\"><span style=\"font-weight: 400;\">https:\/\/www-ft-com.ezp-prod1.hul.harvard.edu\/content\/648046c1-7fcd-43fb-819b-841f104396d9<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">Explores how artificial intelligence (AI) is transforming research and development (R&amp;D) across various industries. It delves into the ways AI technologies are being integrated into R&amp;D processes to enhance efficiency, reduce costs, and accelerate innovation.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The only supporting material for claims of AI impact is the McKinsey study \" Using AI to supercharge R&amp;D: Takeaways from the R&amp;D Leaders Forum \".&nbsp;<\/span><\/p>\n<p><b><\/b><\/p>\n<h3><b>16. How AI-Augmented R&amp;D Is Changing the Landscape of Research Industries \u2013 Ip.com<\/b><\/h3>\n<p><a href=\"https:\/\/ip.com\/blog\/how-ai-augmented-rd-is-changing-the-landscape-of-research-industries\/\"><span style=\"font-weight: 400;\">https:\/\/ip.com\/blog\/how-ai-augmented-rd-is-changing-the-landscape-of-research-industries\/<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">This article relates other sources\u2019 expectations: \u00ab The integration of Artificial Intelligence (AI) into research and development (R&amp;D) processes is having a transformative impact on productivity.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Numerous studies and industry estimates suggest that AI can:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">improve search workflow productivity by 30 to 50 %<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">product performance up to 60 %<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">time-to-market up to 40 %\".<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">\u201cBy dramatically accelerating data analysis and thanks to Enhanced Predictive Modeling and Simulation, AI allows researchers to move from the data collection phase to actionable insights faster, leading to more efficient use of time and resources. This could contribute to the higher end of the 30\u201350% productivity boost.\u201d<\/span><\/p>\n<p><b><\/b><\/p>\n<h3><b>17. AI in Product Development: Netflix, BMW, and PepsiCo \u2013 Virtasant&nbsp;<\/b><\/h3>\n<p><a href=\"https:\/\/www.virtasant.com\/ai-today\/ai-in-product-development-netflix-bmw\"><span style=\"font-weight: 400;\">https:\/\/www.virtasant.com\/ai-today\/ai-in-product-development-netflix-bmw<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">The article emphasizes that faster time-to-market results from an integrated strategy\u2014not tools alone. However, the claim that \u201ccompanies that use AI in their product development processes can reduce the time to market by 20\u201340%\u201d is supported only by a single McKinsey publication \u201cHow generative AI could accelerate software product time to market\u201d, with no additional data or case-specific validation.<\/span><\/p>\n<p><b><\/b><\/p>\n<h3><b>18. How AI agents are reshaping R&amp;D<\/b><\/h3>\n<p><a href=\"https:\/\/www.rdworldonline.com\/how-ai-agents-are-reshaping-rd\/#:~:text=These%20AI%20agents%20operate%20more,hours%2C%20days%2C%20and%20weeks\"><span style=\"font-weight: 400;\">https:\/\/www.rdworldonline.com\/how-ai-agents-are-reshaping-rd\/#:~:text=These%20AI%20agents%20operate%20more,hours%2C%20days%2C%20and%20weeks<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Gives operational examples of how AI helps reduce workloads to humans, bottlenecks and helps reduce timelines. However, it offers no quantification.<\/span><\/p>\n<p><b><\/b><\/p>\n<h3><b>19. AI in Product Development: How Businesses Are Cutting Time-to Market by 50%<\/b><\/h3>\n<p><a href=\"https:\/\/blog.hoyack.com\/ai-in-product-development-how-businesses-are-cutting-time-to-market-by-50\/\"><span style=\"font-weight: 400;\">https:\/\/blog.hoyack.com\/ai-in-product-development-how-businesses-are-cutting-time-to-market-by-50\/<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">Relies on the now-familiar narratives about AI-induced acceleration - largely descriptive and devoid of original data. Claims are supported by the same widely cited consultancy studies as other sources, without offering additional validation or differentiated insights.<\/span><\/p>\n<p><b><\/b><\/p>\n<h3><b>20. AI Enhances R&amp;D Productivity by Reducing Physical Tests<\/b><\/h3>\n<p><a href=\"https:\/\/ip.com\/blog\/how-ai-augmented-rd-is-changing-the-landscape-of-research-industries\/\"><span style=\"font-weight: 400;\">https:\/\/ip.com\/blog\/how-ai-augmented-rd-is-changing-the-landscape-of-research-industries\/<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">AI is said to reduce the need for time-consuming physical tests, thereby enhancing R&amp;D team productivity and speeding up product launches.&nbsp;<\/span><\/p>\n<p><b><\/b><\/p>\n<h3><b>21. AI Accelerates Drug Discovery but Faces Clinical Challenges<\/b><\/h3>\n<p><a href=\"https:\/\/www.mckinsey.com\/industries\/life-sciences\/our-insights\/how-artificial-intelligence-can-power-clinical-development\"><span style=\"font-weight: 400;\">https:\/\/www.mckinsey.com\/industries\/life-sciences\/our-insights\/how-artificial-intelligence-can-power-clinical-development<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">While AI speeds up drug discovery, the benefits may be delayed if clinical development doesn't keep pace.&nbsp;<\/span><\/p>\n<p><b><\/b><\/p>\n<h3><b>22. AI Shortens Product Development Cycles in Manufacturing<\/b><\/h3>\n<p><a href=\"https:\/\/ambilio.com\/how-generative-ai-can-reduce-time-to-market-in-manufacturing\/\"><span style=\"font-weight: 400;\">https:\/\/ambilio.com\/how-generative-ai-can-reduce-time-to-market-in-manufacturing\/<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">Generative AI is reported to reduce time-to-market in manufacturing by automating design and prototyping processes.&nbsp;<\/span><\/p>\n<p><b><\/b><\/p>\n<h3><b>23. AI Transforms R&amp;D in Consumer Packaged Goods<\/b><\/h3>\n<p><a href=\"https:\/\/www.wns.com\/perspectives\/case-studies\/how-a-cpg-company-leveraged-ai-data-processing-to-improve-rd\/-?utm_source=MITwebsite\"><span style=\"font-weight: 400;\">https:\/\/www.wns.com\/perspectives\/case-studies\/how-a-cpg-company-leveraged-ai-data-processing-to-improve-rd\/-?utm_source=MITwebsite<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">A CPG company leveraged AI-led data processing to improve R&amp;D efficiency and reduce go-to-market time.<\/span><\/p>\n<p><b><\/b><\/p>\n<h3><b>24. AI Drives Faster Product Development in Tech Startups<\/b><\/h3>\n<p><a href=\"https:\/\/www.linkedin.com\/pulse\/from-concept-market-ais-role-accelerating-product-nicolas-babin-lw3ze\/\"><span style=\"font-weight: 400;\">https:\/\/www.linkedin.com\/pulse\/from-concept-market-ais-role-accelerating-product-nicolas-babin-lw3ze\/<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">Tech startups report that AI enables quicker prototyping and product iterations, leading to reduced time-to-market.<\/span><\/p>\n<p><b><\/b><\/p>\n<h3><b>25. How AI Is Accelerating Innovation In Research And Development<\/b><\/h3>\n<p><a href=\"https:\/\/www.forbes.com\/sites\/garydrenik\/2024\/06\/18\/how-ai-is-accelerating-innovation-in-research-and-development\/\"><span style=\"font-weight: 400;\">https:\/\/www.forbes.com\/sites\/garydrenik\/2024\/06\/18\/how-ai-is-accelerating-innovation-in-research-and-development\/<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">Articles like this one in Forbes continue to propagate broad claims such as \u2018AI is transforming R&amp;D and reducing time-to-market\u2019 without providing grounded empirical evidence.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It exemplifies the reliance on optimistic executive perception rather than independent performance metrics. It aligns with the broader trend of enthusiasm outpacing validation.\u201d<\/span><\/p>\n<p><\/p>\n<table>\n<tbody>\n<tr>\n<td>\n<p><b>Claim made<\/b><\/p>\n<\/td>\n<td>\n<p><b>Supporting evidence level<\/b><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><b>Generative AI speeds up ideation and reduces time to market in automotive and pharma<\/b><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Anecdotal (one example: Bayer Copilot)<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><b>AI can reduce time to market by 20\u201340%<\/b><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Not reported (repeats McKinsey projection)<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><b>AI enables up to 50% time to market reduction<\/b><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Not reported (relies on consulting studies)<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><b>GenAI can bring new drugs to market 4 years faster<\/b><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Benchmarked but proprietary (McKinsey)<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><b>Digital product development reduces time to market by 17% (28% for champions)<\/b><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Survey-based (expectations, no methodology)<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><b>AI reduced search time at Bayer by 3\u20136 hours\/week per researcher<\/b><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Anecdotal (operational efficiency, not time to market)<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><b>International talent inflows increase R&amp;D investment and innovation<\/b><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Empirical (firm-level data, patents)<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><b>Government R&amp;D funding increases productivity and innovation with lag<\/b><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Empirical (long-term macroeconomic analysis)<\/span><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><b><\/b><\/p>\n<h3><b>7.2. Academic studies<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">We have found only very few academic studies that examine AI's impact on R&amp;D timelines. While these studies provide valuable insights, it's important to note that comprehensive, long-term empirical research on this topic remains limited.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The existing literature often focuses on specific sectors or short-term outcomes, highlighting the need for more extensive longitudinal studies.<\/span><\/p>\n<p><b><\/b><\/p>\n<h3><b>26. Economic impacts of AI-augmented R&amp;D \u2013 Research Policy (2024)<\/b><\/h3>\n<p><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0048733324000866\"><span style=\"font-weight: 400;\">https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0048733324000866<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">This is a macro-growth modeling paper using micro-level AI benchmarks to make a plausible case that AI-augmented R&amp;D\u2014due to its capital-intensity\u2014could structurally raise the economy\u2019s innovation speed, but important caveats remain, including the domain-specific nature of the benchmarks, the absence of long-term empirical data across sectors, and the exclusion of non-technical factors such as governance and organizational adoption.&nbsp;<\/span><\/p>\n<p><b><\/b><\/p>\n<h3><b>27. The impact of AI adoption on R&amp;D productivity \u2013 Technovation (2025)<\/b><\/h3>\n<p><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S1049007825000144\"><span style=\"font-weight: 400;\">https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S1049007825000144<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">Focusing on the pharmaceutical sector, this paper examines the relationship between AI adoption and new drug R&amp;D productivity. It provides early empirical insights but notes that comprehensive, long-term studies are necessary to fully understand AI's impact on R&amp;D timelines and productivity. It discusses how AI can revert the long-term R&amp;D decrease in productivity.&nbsp;<\/span><\/p>\n<p><b><\/b><\/p>\n<h3><b>28. Time to Market Reduction for Hydrogen Fuel Cell Stacks using Generative Adversarial Networks \u2013 arXiv (2022)&nbsp;<\/b><\/h3>\n<p><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0378775323006626\"><span style=\"font-weight: 400;\">https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0378775323006626<\/span><\/a> <a href=\"https:\/\/arxiv.org\/pdf\/2212.11733\"><span style=\"font-weight: 400;\">https:\/\/arxiv.org\/pdf\/2212.11733<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">This study presents a novel approach using AI to reduce the development time of hydrogen fuel cell stacks. While it shows promising results in shortening specific R&amp;D processes, the paper highlights the need for broader studies to assess long-term impacts on overall time-to-market.<\/span><\/p>\n<p><b><\/b><\/p>\n<h3><b>29. AI and the future of pharmaceutical research \u2013 arXiv (2021)&nbsp;<\/b><\/h3>\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2107.03896\"><span style=\"font-weight: 400;\">https:\/\/arxiv.org\/pdf\/2107.03896<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">This paper discusses the transformative potential of AI in pharmaceutical research, suggesting that AI can significantly reduce drug discovery times. However, it also points out that more longitudinal studies are needed to validate these claims across the entire R&amp;D lifecycle.&nbsp;<\/span><\/p>\n<p><b><\/b><\/p>\n<h3><b>7.3. Reports on Factors Affecting Time to Market<\/b><\/h3><h3><b style=\"color: inherit; font-family: inherit; font-size: 1.5rem; background-color: transparent;\">7.3.1. On R&amp;D Funding and Long-Term Productivity<\/b><\/h3>\n<p><b><\/b><br><b><\/b><\/p>\n<h3><b>30. Government-funded R&amp;D produces long-term productivity gains, Federal Reserve Bank of Dallas (2024)&nbsp;<\/b><\/h3>\n<p><a href=\"https:\/\/www.dallasfed.org\/research\/economics\/2024\/0213\"><span style=\"font-weight: 400;\">https:\/\/www.dallasfed.org\/research\/economics\/2024\/0213<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">The article shows that nondefense government R&amp;D funding leads to long-term gains in innovation, productivity, and scientific capacity\u2014though with a delayed effect. These investments increase inputs like skilled researchers and patents, which can indirectly accelerate product development over time. While not addressing time-to-market directly, the findings suggest that public R&amp;D creates conditions that can shorten the time to market in the long run.&nbsp;<\/span><\/p>\n<p><b><\/b><\/p>\n<h4><b>7.3.2. On Talent Policy and Corporate Innovation<\/b><\/h4>\n<h3><b>31. Talent Policy, R&amp;D Personnel Recruitment and Corporate Innovation, Frontiers of Business Research in China (2023)<\/b><\/h3>\n<p><a href=\"https:\/\/journal.hep.com.cn\/fbr\/EN\/10.3868\/s070-008-023-0008-8\"><span style=\"font-weight: 400;\">https:\/\/journal.hep.com.cn\/fbr\/EN\/10.3868\/s070-008-023-0008-8<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">\u201cAfter the introduction of talent policy, there have been significant improvements in enterprises' R&amp;D investment, patent output and R&amp;D efficiency.\u201d These findings indicate that by bolstering R&amp;D capabilities, talent policies can enhance the efficiency and speed of the innovation process, which is directly related to reducing time to market.&nbsp;<\/span><\/p>\n<p><b><\/b><\/p>\n<h4><b>7.3.3. On Process Maturity and Project Performance<\/b><\/h4>\n<h3><b>32. Risk, Process Maturity, and Project Performance: An Empirical Analysis of U.S. Federal Technology Projects, Production and Operations Management (2015)&nbsp;<\/b><\/h3>\n<p><a href=\"https:\/\/onlinelibrary.wiley.com\/doi\/abs\/10.1111\/poms.12513\"><span style=\"font-weight: 400;\">https:\/\/onlinelibrary.wiley.com\/doi\/abs\/10.1111\/poms.12513<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">This study quantifies how internal risks\u2014such as complexity, execution issues, and contracting challenges\u2014affect a project\u2019s ability to stay on schedule and on budget, using a composite metric. Based on a large dataset of 519 quarterly observations, it shows that process maturity significantly mitigates these risks, improving schedule performance. While not focused on time-to-market per se, the findings directly inform how risk management and execution processes influence timely delivery of R&amp;D outcomes.&nbsp;<\/span><\/p>\n<p><b><\/b><\/p>\n<h4><b>7.3.4. On International Talent Inflow and R&amp;D Investment&nbsp;<\/b><\/h4>\n<h3><b>33. International talent inflow and R&amp;D investment: Firm-level evidence from China, Economic Modelling (2018)<\/b><\/h3>\n<p><a href=\"https:\/\/www.rieb.kobe-u.ac.jp\/academic\/ra\/dp\/English\/DP2019-17.pdf\"><span style=\"font-weight: 400;\">https:\/\/www.rieb.kobe-u.ac.jp\/academic\/ra\/dp\/English\/DP2019-17.pdf<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">While not explicitly focused on time-to-market, this study shows that international talent inflows boost both R&amp;D investment and innovation outputs\u2014key enablers of accelerated product development. By strengthening firms\u2019 innovation capacity, such inflows can indirectly contribute to shorter time-to-market. The findings, based on patent data and firm-level R&amp;D metrics, highlight how talent-driven capabilities play a strategic role in speeding up the innovation cycle.&nbsp;<\/span><\/p>\n<p><b><\/b><\/p>\n<h4><b>7.3.5. Additional Insight from Practice&nbsp;<\/b><\/h4>\n<h3><b>34. Dr Robert Gravlin Cooper, Plenary Session and Closing Remarks of the AI for Innovation web conference, organized par LIV.INNO, May 7th, 2025<\/b><\/h3>\n<p><a href=\"https:\/\/events.zoom.us\/ev\/Al3vzdfIFZPQ6WAnaqDgnucrHT-0BxxOei9U-4umQN6vgnDLTqln~AukZonANfnAVnrUESrWa3daFI4MyDhXb1YyCf5X-h58iHiLR97Su4YDBng\"><span style=\"font-weight: 400;\">https:\/\/events.zoom.us\/ev\/Al3vzdfIFZPQ6WAnaqDgnucrHT-0BxxOei9U-4umQN6vgnDLTqln~AukZonANfnAVnrUESrWa3daFI4MyDhXb1YyCf5X-h58iHiLR97Su4YDBng<\/span><\/a><\/p>\n<p><\/p>\n<h3><b>About the author&nbsp;<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">J\u00e9r\u00f4me Gosset is a fractional executive director of R&amp;D operations, specializing in the development and turnaround of R&amp;D performance in innovative companies.&nbsp;<\/span><\/p>\n<p><\/p>\n<p><span style=\"font-weight: 400;\">He excels at turning around R&amp;D organisations where teams are struggling to reach their full potential and developing new activities, teams and processes around innovative technologies. J\u00e9r\u00f4me is also able to lead complex strategic initiatives involving multiple partners.<\/span><\/p>\n<p><\/p>\n<p><span style=\"font-weight: 400;\">What sets J\u00e9r\u00f4me apart is his ability to navigate in highly complex environments. His diverse background in science and technology, combined with his experience in managing teams and portfolios of R&amp;D projects, enables him to make a significant contribution to his clients' growth.&nbsp;<\/span><\/p>\n<p><\/p>\n<p><span style=\"font-weight: 400;\">He is known for his inspiring vision and meticulous execution. His leadership has led to several successful transformations, generating results worth tens of millions of dollars.&nbsp;<\/span><\/p>\n<p><span style=\"font-weight: 400;\">J\u00e9r\u00f4me's unique ability to tackle business and technology challenges has earned him several board appointments.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"<p>1. Introduction &#8211; Un enthousiasme \u00e0 ajuster L&rsquo;enthousiasme autour de l&rsquo;IA en R&amp;D repose souvent sur une promesse s\u00e9duisante : celle d&rsquo;une acc\u00e9l\u00e9ration significative du temps de mise en march\u00e9 (time-to-market). Les chiffres fr\u00e9quemment avanc\u00e9s font \u00e9tat de gains de 20 \u00e0 50 % sur l&rsquo;ensemble des domaines, port\u00e9s par des cas d&rsquo;usage visibles (conception [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":845,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-843","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/pyonnier.com\/en\/wp-json\/wp\/v2\/posts\/843","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pyonnier.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/pyonnier.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/pyonnier.com\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/pyonnier.com\/en\/wp-json\/wp\/v2\/comments?post=843"}],"version-history":[{"count":14,"href":"https:\/\/pyonnier.com\/en\/wp-json\/wp\/v2\/posts\/843\/revisions"}],"predecessor-version":[{"id":867,"href":"https:\/\/pyonnier.com\/en\/wp-json\/wp\/v2\/posts\/843\/revisions\/867"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/pyonnier.com\/en\/wp-json\/wp\/v2\/media\/845"}],"wp:attachment":[{"href":"https:\/\/pyonnier.com\/en\/wp-json\/wp\/v2\/media?parent=843"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/pyonnier.com\/en\/wp-json\/wp\/v2\/categories?post=843"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/pyonnier.com\/en\/wp-json\/wp\/v2\/tags?post=843"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}