ARTIFICIAL INTELLIGENCE-DRIVEN DECISION SUPPORT SYSTEMS FOR ENHANCING BUSINESS INNOVATION AND ORGANIZATIONAL PERFORMANCE
Prashant Prabhakar Mulay, Jyoti Godara, Rajesh Poonia, Prateek Agrawal, Dr. Apurv Raval, Dimple Nagpal · Acta Innovations · 3 Nov 2026
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Where this sits in the debate
- Will AI actually boost productivity and growth? · its finding: it depends (why studies differ)
Direction of the finding as read from the abstract by an AI model (AI-written).
Abstract
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.
About this publication
Unranked journal. A journal that is not on the economics or computer-science rankings this guide uses. That says nothing about one paper, but no ranking vouches for the journal’s review.
- Published
- 2026 (recently)
- Citations
- 0 (OpenAlex)
0 citations recorded by OpenAlex — too few 2026 papers in this collection for a fair comparison.
Reaches a different conclusion
Papers on the same question whose finding points the other way, as read from the abstracts by an AI model (AI-written). Why studies differ.
- Experimental evidence on the productivity effects of generative artificial intelligence
Productivity: its finding supports the claim · Shakked Noy and Whitney Zhang · 2023 - The simple macroeconomics of AI
Productivity: its finding goes against the claim · Daron Acemoğlu · 2024
Related papers
On the same debate question, key papers first; related by topic, not by method or conclusion. Browse all: Productivity.
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Erik Brynjolfsson et al. · American Economic Journal Macroeconomics · 2020 - Miracle or Myth? Assessing the macroeconomic productivity gains from Artificial Intelligence
Francesco Filippucci et al. · OECD · 2024 - AI and Growth: Where Do We Stand?
Philippe Aghion and Simon Bunel · Federal Reserve Bank of San Francisco · 2024
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