Category
Decision Intelligence Platform
Gartner designated Decision Intelligence "transformational" in the 2025 AI Hype Cycle and published the inaugural Magic Quadrant for Decision Intelligence Platforms in January 2026. Autonomy is the first purpose-built DI platform for supply chain, covering all six decision domains through one Decision Stream.
What Is Decision Intelligence?
"Decision intelligence is the discipline of turning information into better actions at any scale."
Kozyrkov unified three sub-disciplines that traditionally operate in silos:
- Applied Data Science, Analytics, statistics, ML for extracting insights
- Social Science, Group dynamics, stakeholder perspectives, cognitive bias
- Managerial Science, Goal alignment, process management, organizational structure
"Anything we think we know, our knowledge, our insights, our impressions, they only really begin to matter when they drive our actions."
Gartner formalized this into a platform category, defining Decision Intelligence Platforms as software that supports, automates, and augments decision-making by bringing together data, analytics, knowledge, and AI while enabling collaboration across decision modeling, execution, monitoring, and governance.
The Gartner Magic Quadrant
In January 2026, Gartner published the inaugural Magic Quadrant for Decision Intelligence Platforms, validating DI as a distinct software category.
"Leaders combine strong execution with a clear, forward-looking vision for decision-centric architectures. They deliver comprehensive capabilities across the decision life cycle - modeling, orchestration, monitoring and governance - while integrating advanced AI techniques such as generative AI and agentic AI."
Leaders
FICO, SAS, Aera, Quantexa
Horizontal DI platforms
Hype Cycle Rating
Transformational
Gartner's highest impact, June 2025
Current Penetration
5-20%
2-5 years to mainstream
Gap
No SC-native DIP
MQ leaders are all horizontal
The Decision Lifecycle
Gartner's four critical Decision Intelligence capabilities, implemented natively for supply chain in Autonomy.
Decision Modeling
Define what decisions exist and how they work
A sequential decision framework models every decision with five elements: State (inventory, backlog, pipeline), Decision (order quantity, allocation), Exogenous Information (demand, lead times), Transition Function, and Objective (dollar-denominated outcomes).
Implemented: 11 agent definitions, state decomposition across physical, information, and belief dimensions
Decision Orchestration
Coordinate execution flows across agents and systems
A 6-phase decision cycle coordinates 11 agents per site. The Agentic Authorization Protocol (AAP) handles cross-functional trade-offs at machine speed - 25+ negotiation scenarios across manufacturing, distribution, procurement, and finance.
Implemented: Signal propagation, urgency coordination, authorization surfaces
Decision Monitoring
Track outcomes, detect drift, measure quality
Hourly outcome collection scores every decision against actual results. Calibrated likelihood intervals track certainty. Forecast quality scoring measures probabilistic accuracy. Drift triggers detect when agents need retraining.
Implemented: Outcome collection, likelihood calibration, quality scoring, drift detection
Decision Governance
Ensure compliance, auditability, and trustworthiness
Override effectiveness is tracked statistically per user and decision type. Likelihood bounds on every decision provide mathematical guarantees. The Escalation Arbiter routes persistent drift to higher reasoning tiers. Full audit trail from decision to outcome.
Implemented: Override tracking, authority boundaries, escalation log
Agentic AI Meets Decision Intelligence
The supply chain industry is converging on autonomous agents as the execution mechanism for decision intelligence.
"Agentic AI represents a revolution from robotic process automation (RPA) as the AI agents will autonomously complete tasks without relying on explicit inputs or predefined outcomes. Agents will continuously learn from real-time data and adapt to evolving conditions and complex demands."
"Agentic AI has the power to transform entire workflows and challenge existing business processes."
Gartner, May 2025
50%
of SCM solutions will use intelligent agents by 2030
Kaitlynn Sommers, Sr. Director Analyst
BCG, September 2025
17%
of total AI value already comes from agents, rising to 29% by 2028
The Widening AI Value Gap
Gartner, August 2025
40%
of enterprise apps will include task-specific AI agents by 2026
Up from <5% in 2025
Three-Level Maturity Progression
Autonomy's progression from Support to Automation is governed by measured decision quality, not arbitrary trust thresholds or time-based milestones.
Decision Support
Human in the loop. The system provides data, insights, scenarios, and reports. All decisions require human input. This is traditional BI and planning software.
Decision Augmentation (Copilot)
Human on the loop. AI agents generate recommendations with impact analysis. Humans inspect and override if needed. Every override is captured with reasoning and scored against outcomes. This is where most customers start.
Decision Automation (Autonomous)
Human out of the loop. AI agents execute autonomously within established guardrails. Full auditability. Humans focus on governance, exception inspection, and strategic decisions. Progression governed by calibrated likelihood, override effectiveness scores, and decision quality metrics.
"It is possible to automate demand planning to the point that 90% of the process is handled without human involvement."
The Autonomous Supply Chain
McKinsey's supply chain practice quantifies the impact of AI-driven autonomous planning across hundreds of implementations.
+4%
Revenue growth
McKinsey, Knut Alicke
-20%
Inventory reduction
McKinsey, Knut Alicke
-10%
Supply chain costs
McKinsey, Knut Alicke
"Autonomous planning is a continuous, closed-loop planning approach built on a fully automated technology platform, designed to optimize S&OP processes in real time."
Decisions as Digital Assets
In traditional planning software, decisions are implicit. They're the output of a planning run, buried in a supply plan or MPS schedule. You can see the result, but not the decision that produced it, why it was made, what alternatives existed, or what happened afterward.
Autonomy treats every recurring supply chain decision as a first-class digital asset with:
- Defined inputs and triggers - What state caused this decision?
- Explicit logic and constraints - What model produced it? What guardrails apply?
- Clear ownership and authority - Which agent type? What authority boundary?
- Measurable outcomes linked to actions - What actually happened?
- Feedback loops for continuous improvement - How does this outcome improve future decisions?
Decision Mapping
Every agent maps decisions through a clear causal chain:
- Decision Levers - Actions the agent can take (order quantity, transfer amount, release/defer)
- Externals - Factors outside control (demand variability, lead time uncertainty, supplier reliability)
- Intermediaries - Leading indicators along the chain (fill rate, days of supply, pipeline position)
- Outcomes - Ultimate measurable goals (total cost, OTIF, inventory turns)
This makes the causal chain transparent: how does this specific order quantity decision flow through inventory levels, service rates, and ultimately to dollars saved or lost?
The Trust Equation
Autonomous decisions require systematic trust-building, not blind faith in algorithms.
"Across the enterprise, we're seeing massive ambition around AI, with organizations starting to pivot from experimentation to integrating AI into the core of the business with a focus on scale and impact."
"The organizations succeeding with AI aren't just investing in automation and algorithms, they're investing in their people."
Deloitte, 2026
74%
expect moderate+ agentic AI use within 2 years
Deloitte, 2026
21%
have mature governance for autonomous agents
BCG, September 2025
5%
"future-built" for AI. 60% are laggards
BCG, September 2025
2x
revenue growth for AI leaders vs. laggards
Four Interdependent Technologies
Gartner's 2025 Hype Cycle for Supply Chain Planning identifies four technologies as "interdependent levers of change, not individual trends." Autonomy implements all four natively.
Decision-Centric Planning
Innovation Trigger
The organizing principle. Shift from periodic batch planning to continuous decision execution. Every recurring choice modeled as a repeatable decision asset.
Agentic AI
Innovation Trigger
Autonomous agents specialized in different areas interact seamlessly, creating integrated supply chain views. Autonomy deploys 11 per site today.
Autonomous Planning
Trough - Slope of Enlightenment
Demands a cultural shift from people-centric to decision-centric. Measured maturity progression governs the transition.
Explainable AI
Slope of Enlightenment
Proven but underused. Every Autonomy agent decision includes reasoning with evidence citations and likelihood scores. Ask Why at any point.
"Autonomous planning has passed the peak of inflated expectations."
Market Context
of SCM solutions will use intelligent agents by 2030
Kaitlynn Sommers, Gartner, May 2025
of Global 500 will apply DI practices including decision logging by 2026
Gartner CDAO Survey
Inaugural Gartner Magic Quadrant for Decision Intelligence Platforms published
Pidsley, Idoine, Herschel, Quinn, Carlsson
25% of CDAO vision statements will become "decision-centric" surpassing "data-driven"
Gartner Prediction
Autonomy vs. Horizontal DI Platforms
Gartner's MQ Leaders (FICO, SAS, Aera Technology, Quantexa) are horizontal platforms that require extensive customization for supply chain.
| Capability | Horizontal DIPs | Autonomy |
|---|---|---|
| Decision Modeling | Generic business rules | Domain-specific sequential decision framework for supply chain |
| Decision Execution | Rules engines, workflow | Real-time specialized agents (<10ms) |
| Decision Monitoring | BI dashboards | Calibrated likelihood + quality scoring + drift triggers |
| Decision Governance | Audit logs | Causal AI - counterfactual override evaluation |
| Supply Chain Domain | Bolt-on or absent | Native (35 AWS SC entities, 8 policy types) |
| Agentic AI | Early / experimental | 11 production agents per site, multi-site coordination |
| Probabilistic Planning | Limited | 21 distributions, Monte Carlo, forecast quality scoring |
| Learning from Overrides | Basic | Causal AI - learn from impact, not correlation |
The Convergence
Every major research firm and consultancy has arrived at the same conclusion: the future belongs to platforms that make decisions, not just insights.
Cassie Kozyrkov, Google
Decisions as the unit of value. Instrument decision quality, not just data.
CEO, Data Scientific. Formerly Google's first Chief Decision Scientist.
Pidsley et al., Gartner
Decision Intelligence Platform as a new software category with four lifecycle capabilities.
Inaugural Magic Quadrant, January 2026.
Sam Ransbotham, BCG & MIT Sloan
AI shifting from instrument to actor. 76% of executives see AI as coworker.
Professor of Analytics, Boston College. 9th annual AI study.
Knut Alicke, McKinsey
Quantified impact: +4% revenue, -20% inventory, -10% supply chain costs.
Partner & Head of Supply Chain Europe.
Nitin Mittal, Deloitte
Trust as the gateway. 10x value correlation with systematic trust-building.
Global AI Leader. State of AI in the Enterprise, 2026.
Kaitlynn Sommers, Gartner
50% of SCM solutions will include agentic AI by 2030. Agents will continuously learn.
Senior Director Analyst, Supply Chain Practice.
See Decision Intelligence in action
Watch how decisions flow from modeling through execution to measured outcomes.