Over the last 15 years, management’s adoption of technology has advanced much slower than technology itself. Primary challenges include holding informed discussions with senior management, shaping realistic understanding of capabilities, working with stove piped information technology departments with a restricted understanding of new technology, and acceptance of data-driven simulation results over intuition-based expectations. These challenges grow in magnitude with organizations driven by intuition-based decision makers.
Biases and knowledge gaps with modeling and simulation (M&S), artificial intelligence (AI), and machine learning (ML) among managers and leaders are evident across a spectrum of clientele. When organizations decide to move from intuition-based to data-driven decision, they encounter hurdles of biases, gaps, poor assumptions, and emotion-driven responses.
In this paper, we describe the value of tying the learning life cycle directly to the business development process from lessons learned from our own experiences. Many challenges can be addressed through organizational development theory, tools, and techniques. Others can be clarified by examination of the cognitive bias codex and personality preferences. Integrating the learning and business development cycles may be the best way to overcome the technology disillusionment curve.
Our experience dates to Synthetic Environment for Analysis and Simulations (SEAS), an agent-based M&S platform and NTSA 2004/2005 award winner in analysis. We’ve moved forward to Reference World Synthetic Information Environment (RWISE) an agent-based M&S platform enriched with AI/ML in an elastic environment. SEAS was years ahead of its time with distributed AI operating in a hybrid cloud. It stretched the imagination and challenged intuition-based decision-making in the Department of Defense, Homeland Security, and commercial users in broad range of areas. Its successor, RWISE, is a data agnostic, data driven, agent-based AI/ML platform for data ingestion, model development, and forecasting to compare multiple futures based on injecting and testing strategy actions.