Firms today face rapidly changing and complex environments that managers and leaders must navigate carefully because confronting these changes is directly connected with success and failure in business. In particular, business leaders are adopting a new paradigm of planning, dynamic adaptive plans, which react adaptively to uncertainties by adjusting plans according to rapid changes in circumstances. However, these dynamic plans have been applied in larger-scale industries such as wastewater management in longer-range time frames. This paper follows the dynamic adaptive plan paradigm but transfers it to the technology management context with shorter-range action plans. Based on this new paradigm of risk management and technology planning, we propose a risk-adaptive technology roadmap (TRM) that can adapt to changing complex environments. First we identify risk by topic modeling based on futuristic data and then by sentiment analysis. Second, for the derived risks, we determine new and alternative plans by co-occurrence of risk-related keywords. Third, we convert an existing TRM to network topology with adaptive plans and construct a conditional probability table for the network. Finally, we estimate posterior probability and infer it by Bayesian network by adjusting plans depending on occurrence of risk events. Based on this posterior probability, we remap the paths in the previous TRM to new maps, and we apply our proposed approach to the field of artificial intelligence to validate its feasibility. Our research contributes to the possibility of using dynamic adaptive planning with technology as well as to increase the sustainability of technology roadmapping.

当今的公司面临着快速变化和复杂的环境，管理者和领导者必须谨慎应对，因为面对这些变化直接关系到业务的成败。特别是，企业领导者正在采用一种新的规划范式，即动态适应性计划，通过根据环境的快速变化调整计划来对不确定性做出适应性反应。然而，这些动态计划已在更大规模的行业中得到应用，例如更长期的废水管理。本文遵循动态自适应计划范式，但将其转移到具有短期行动计划的技术管理环境中。基于这种新的风险管理和技术规划范式，我们提出了一个适应不断变化的复杂环境的风险自适应技术路线图（TRM）。首先，我们通过基于未来数据的主题建模，然后通过情绪分析来识别风险。其次，对于派生的风险，我们通过与风险相关的关键字的共现来确定新的和替代的计划。第三，我们将现有的 TRM 转换为具有自适应计划的网络拓扑，并为网络构建条件概率表。最后，我们估计后验概率并通过贝叶斯网络根据风险事件的发生调整计划来推断它。基于此后验概率，我们将先前 TRM 中的路径重新映射到新地图，并将我们提出的方法应用于人工智能领域以验证其可行性。我们的研究有助于将动态自适应规划与技术结合使用，并提高技术路线图的可持续性。