Decision-Making Theory is concerned with the reasoning underlying our choices among alternatives, for best possible outcomes. Determined by logic but also desires, beliefs, moral and social values, decisions can be multi-diverse, even controversial as per the Data-evidenced outcomes. Fueled by these Data that Digital revolution enriched us with, Decision-Making Theory and Artificial Intelligence (AI) became deeply connected, both in concept and application. Hybrid models favor Human to AI teaming for better and real-time decision making. AI contributing by using Big Data analytics, expected utilities, Bayesian networks, decision trees, expert systems, to mimic decision processes, identify alternatives, estimate outcomes, and select optimal actions. Humans by injecting their empathy, multi-Intelligences, ethical judgment and above all Ulysses ingenious spirit. New methods of data-driven heuristics emerge, based on utilities and probabilities, algorithms, behavioral modeling and fairness for ethics to account for AI's role. Shifting finally from Rational to Data-Driven Decisions.

Moreover, since Real World situations are usually subject to Chaos Theory, pure certainty exists only in Cybernetic-automated conditions. Patterns of dynamical systems highly sensitive to initial situations prevail, and most decisions are forced to be made under uncertainty or risk (in the best case), due to first time occurrence or limits in time, information, and cognitive capacity.

Behavioral economists showed that most decisions (~60–70%) involve risk-based on known probabilities with known volatility and risk profiles, especially in fields like healthcare, economics, and operations. As per the decisions under Uncertainty (~20–30%) common in public policy, strategic management, and innovation, information is usually incomplete, controversial, or future conditions are unpredictable. In Healthcare decisions for instance, risk is always high, uncertainty medium to high and certainty always low. This distribution innates to high entropic systems and dysregulated homeostatic mechanisms, highlighting the challenges for both Human & Artificial Intelligences to combine evidence-based/data driven algorithms with innovative / judgment-based reasoning, particularly as AI continues to influence decision-making frameworks.

Reinventing Governance by Increasing reliance on AI and mostly on “black boxes” such as deep learning models where “optimal” decisions are not pre-defined but learned from data, traditional models are challenged, and concerns are raised about opaque logic, accountability gaps, cybersecurity and alignment with social needs values and priorities. Hence, a new field of research and innovation is emerging and there is a clear need for further investigation into the modern practices and regulatory policies.