ARTIFICIAL INTELLIGENCE-DRIVEN DECISION SUPPORT SYSTEMS FOR ENHANCING BUSINESS INNOVATION AND ORGANIZATIONAL PERFORMANCE

Decision support systems (DSS) have moved from spreadsheets and rule engines to machine-learning models and generative assistants that recommend, draft, and sometimes decide. Whether this shift raises innovation and organizational performance depends on how well these systems fit actual decisions and on how people and organizations use them. This paper reviews the empirical evidence on that question. It combines decision-theory foundations with firm-level productivity studies, field experiments, studies of human–AI interaction, and risk and governance literature. Five findings emerge. First, data-driven decision-making is associated with 5 to 6 percent higher output and productivity in a study of 179 large firms. Second, the best causal evidence comes from experiments. Customer-support agents using a generative assistant resolved 14 percent more issues per hour, and novices gained 34 percent. Third, gains are uneven and task-dependent: consultants using AI on tasks inside its capability frontier produced work rated 40 percent higher in quality but were 19 percentage points less likely to be correct on a task outside it. Fourth, adoption lags ambition: a survey of more than 3,000 managers found that only about one in five firms had incorporated AI into some offering or process. Fifth, risks are documented. A widely used health-risk algorithm cut the share of Black patients flagged for extra care to 17.7 percent, against 46.5 percent once the bias was corrected. Two comparative tables map the performance evidence and map risks to mitigations. The paper then proposes a decision-centric evaluation framework with a break-even condition for AI-DSS investment. The framework is a conceptual synthesis of the reviewed literature and has not been tested empirically.