Decision Intelligence: From Business Predictions to Business Outcomes, Part 2

Artificial intelligence is now mainstream. According to McKinsey, 78% of firms use it in at least one business function, 71% deploy generative AI, yet over 80% see no effect on enterprise EBIT. Decision Intelligence (DI) may be the missing layer linking analytics to outcomes. This article shows how that link works in practice.

The Prediction/Outcome Gap

Just like traditional analytics, AI systems produce predictions (e.g. forecasts, classifications, and probability scores) but organizations must still decide what to do. As James Taylor in Digital Decisioning (2019) notes, prediction is a technical achievement; decision is a managerial act that defines responsibility, trade-offs, and acceptable risk. Yet many AI initiatives stop at the predictive stage, assuming that insight alone will drive change. In practice it rarely does. The real challenge is to begin with a clearly articulated business outcome and then engineer decision processes that translate predictive insight into measurable results.

A decision is where information, rules, and judgment converge to choose an action. Each one has inputs, logic, and outputs, shaped by time, policy, and risk constraints. Taylor distinguishes strategic, tactical, and operational decisions: strategic ones set direction and rely on deliberation; operational ones are frequent, formalized, and open to automation. Recognizing these levels is essential for balancing human judgment and machine assistance.

Decision Intelligence treats decisions as engineered artefacts that are explicit, testable, and improvable. Each defines its trigger, data, logic, and resulting action, making it measurable and accountable. Yet the core of a decision is its rationale: the explicit reasoning linking prediction to action and connecting decisions to outcomes. James Taylor argues a decision without a recorded rationale is not a managed decision, since it cannot be audited, improved, or trusted. Lorien Pratt extends this idea by describing rationale as the causal chain that lets an organization learn from experience, by tracing how decisions lead to consequences, and how those consequences should, in turn, refine future choices. In Decision Intelligence, rationale is not a side product of decision-making but the essential architecture that turns prediction into accountability and learning.

Figure 1: Decisions transform inputs into actions within organizational goals and constraints, with human oversight ensuring transparency, accountability, and ethical control.

Decision Flows in Practice

A decision flow describes how analytical information becomes organizational action: how data enter a decision, how logic and judgment are applied, and how outcomes generate feedback for improvement. The idea appears under different names in the literature. Taylor (2019) writes about operational decision flows that connect models with daily business processes. Pratt (2019) introduces decision chains and networks showing how one choice influences another. Davenport, Harris and Shapiro (2010) describe analytic processes that move from data to managerial action. Despite differing terminology, all capture the same structure, namely, a deliberate sequence linking analysis, choice, and learning.

Figure 2: Human-in-the-loop systems guide, supervise, and learn throughout every stage of the AI-driven decision cycle, making end-to-end outcome engineering a critical success factor against failed AI projects.

Within Decision Intelligence this structure is treated as a design object. Mapping decision flows makes explicit (and auditable) how analytical results travel through business logic to produce measurable effects. It exposes assumptions and bias, clarifies accountability, and shows who interprets, authorises, and evaluates. Over time such mapping turns isolated analytics into governed systems of learning, where feedback continually strengthens both algorithms and human judgment.

Paradigms of Decision Intelligence

Decision Intelligence manifests in several distinct architectures, each revealing a different way that prediction, rationale, and feedback combine to produce accountable outcomes. In operational optimization, supply-chain systems forecast shortages and automatically adjust orders, then compare actual service levels and costs against expectations to refine rules and models (Taylor, 2019). Judgment-intensive support appears in AI-assisted sepsis triage, where algorithms flag patient risk but clinicians review and record the rationale behind each intervention, creating the feedback necessary for learning (Goh et al., 2021). Governed decisions dominate regulated fields such as lending, where credit-scoring models are combined with explicit approval rules and documented justifications so that each decision can be audited for fairness and risk (Addy et al., 2024). And in adaptive systems, such as urban traffic control, predictive analytics coordinate signals and incentives in real time, with emissions and congestion data feeding back to update policy and model thresholds (Shahid et al., 2025). Together these examples show how DI begins with clearly defined outcomes unites prediction, rationale, and accountability.

Orchestrating the Outcome

Decision Intelligence ensures that AI delivers business outcomes, not just predictions, by engineering decision flows that can be formalized, executed, and refined. Frameworks such as Decision Model and Notation (DMN) and Decision Process Modeling (DPM) define rules and workflows that can be simulated, optimized, and embedded in automation platforms. A DMN table linked to a rule engine can trigger actions, while simulation and feedback tools test alternatives and verify results. Human-in-the-loop oversight provides accountability through escalation limits, dashboards, and ethical checks, and low-code environments let analysts adjust decision logic within governed boundaries. Together these capabilities—formal representation, execution, feedback, and human supervision—form the building blocks of commercial Decision Intelligence platforms introduced in the next article.

References

Davenport, T. H., Harris, J. G., & Morison, R. (2010). Analytics at Work: Smarter Decisions, Better Results. Harvard Business Press.

McKinsey & Company. (2025). The State of AI in 2025. McKinsey Global Institute.

Pratt, L. (2021). Link: How Decision Intelligence Connects Data, Actions, and Outcomes for a Better World. Wiley.

Taylor, J. (2019). Digital Decisioning: Using Decision Management to Deliver Business Impact from AI. Taylor and Francis / MK Press.

Taylor, J. (2022). Real-World Decision Modeling with DMN (2nd ed.). MK Press.

Addy, N., Boateng, S. L., & Li, J. (2024). AI in Credit Scoring: A Comprehensive Review of Models and Predictive Analytics. Expert Systems with Applications, 241, 122675. Elsevier.

Goh, K. H., Wang, L., Yeow, A. Y. K., Poh, H., Li, K., Yeow, J. J. L., & Tan, G. Y. H. (2021). Artificial Intelligence in Sepsis Early Prediction and Diagnosis. Nature Communications, 12, 7150. https://doi.org/10.1038/s41467-021-20910-4.

Shahid, A., Khan, M. J., & Li, Y. (2025). Revolutionizing Urban Mobility: A Systematic Review of AI, IoT, and Predictive Analytics in Adaptive Traffic Control Systems. Electronics, 14(4), 719. MDPI.