Cost of Not Investing (CONI) in Intelligent Processes Automation

As organisations navigate complex AI-driven workplace transformations through intelligent automation of business processes, traditional investment evaluation methods inadequately capture the strategic risks of its delayed adoption. Our study introduces the concept of the Cost of Not Investing (CONI), exploring how automation delays can impact organisational competitiveness and workforce dynamics. Drawing on existing theories, such as Opportunity Cost, Real Options Theory, Dynamic Capabilities, and Game Theory, we conceptualise a framework identifying three CONI dimensions: operational inefficiencies, strategic disadvantages, and human capital impacts. We operationalise CONI with a measurement-based model that maps each dimension to auditable indicators (e.g., process mining logs, service level penalties, talent attrition premiums) and test initial perceptions from a cross-sector survey of 108 professionals in the US and EU. Respondents perceive material consequences of delay: talent migration (81%), lower productivity (77%), increased training and rework costs (72%), reduced market responsiveness (7%), and loss of early mover advantages (74%). Yet evaluation practices reveal a critical implementation gap: only 16.7% systematically incorporate strategic costs, and 33.3% report no structured method. Our contribution lies in demonstrating how CONI operates as a cascading mechanism (not merely additive costs), where delayed options reduce capability-building opportunities, which amplifies competitive disadvantages. We provide a research agenda for validating objective and longitudinal CONI estimates in IPA-intensive settings.