Explainable AI for Industrial Decision Support 2026
Explainable AI for Industrial Decision Support 2026
XAI for industrial decision support spans interpretable ML, hybrid AI architectures, and generative explanation interfaces. This dataset covers 14 patents and ~50 literature records from 2013 to 2026.
From Black-Box Models to Transparent Industrial Intelligence
XAI for industrial decision support addresses the ‘black box’ problem: deep learning and ensemble models achieve high predictive accuracy but offer no intelligible rationale for their outputs, creating trust barriers in operational adoption. The dataset spans 14 patents and approximately 50 literature records, covering publication dates from 2013 to 2026.
Core mechanisms identified include local explanation methods such as SHAP and LIME applied to time-series and sensor data, knowledge graph and ontology-based reasoning, hybrid AI architectures combining physics-based models with ML, human-machine interface layers surfacing explanations via dashboards and natural language, and federated privacy-preserving XAI for multi-site deployments.
The field is bifurcated between post-hoc explanation tools applied to existing black-box models and inherently interpretable model architectures designed for transparency from the ground up. Application domains include manufacturing, energy management, aerospace, enterprise strategy, and supply chain operations across Industry 4.0 environments.
Among retrieved records, AVEVA Software leads with 4 filings across IN, CN, and WO jurisdictions, followed by Rockwell Automation with 3 filings across US and EP. India accounts for 9 of 14 patents in this dataset, reflecting both academic IP activity and multinational filing strategies in the Indian jurisdiction.
Innovation Phases and Technology Cluster Distribution
The dataset follows three discernible phases: a Foundational Phase (2013–2018) establishing infrastructure, a Development Phase (2019–2022) with explicit XAI framing, and an Acceleration Phase (2023–2026) integrating generative AI and LLMs. Four technology clusters structure the patent activity in this dataset.
XAI Technology Clusters — Patent & Literature Records in This Dataset
Hybrid AI and local explanation method clusters account for the largest concentrations of records in this dataset, each supported by multiple patent filings and literature references spanning 2021–2026.
↗ Click bars to exploreXAI Patent Filings by Innovation Phase — Retrieved Records Timeline
The Acceleration Phase (2023–2026) shows the highest patent filing activity in this dataset, with at least 7 IN-jurisdiction filings and multiple generative AI integrations appearing in 2025–2026 records.
↗ Click bars to exploreKey Industrial Domains for XAI Decision Support
XAI decision support activity in this dataset spans manufacturing, energy management, aerospace, and enterprise strategy — each with distinct stakeholder requirements, explanation depth, and architectural approaches.
Manufacturing & Process Industries
The largest concentration of XAI activity in this dataset addresses manufacturing operations including quality control, demand forecasting, predictive maintenance, and production scheduling. Lovely Professional University’s 2025 IN patent unites IoT data collection, ML-driven forecasting, and XAI dashboards in a single architecture. The STARdom architecture (2021) and human-centric AI for Industry 5.0 (2022) both frame XAI as a core requirement for trusted manufacturing.
ManufacturingEnergy Management
The 2023 literature record on Intelligent Decision Support for Energy Management proposes a tailored explainability methodology that customizes explanation depth based on stakeholder profile — distinguishing between operational engineers, facility managers, and executive decision-makers. This multi-stakeholder XAI personalization is unique to the energy domain among retrieved results.
EnergyAerospace & Defense
The 2023 paper on Sustainability-Driven Green Innovation describes an intelligent DSS for aerospace engineers and managers integrating NLP-based data analysis and multi-domain optimization. The 2019 IIoT-based decision support architecture for aeronautics demonstrates IIoT-enabled predictive maintenance and simulation for aerospace manufacturing.
AerospaceEnterprise Strategy & Supply Chain
The 2026 IN filing for an Enterprise Decision Intelligence Platform explicitly targets C-suite and strategic planning functions, combining IIoT sensor streams with transparent ML for supply chain, manufacturing equipment, and smart infrastructure management. The 2026 IN Generative AI-Driven Adaptive MCDM Framework addresses multi-criteria engineering decision-making with transparent justification of autonomous decisions.
Enterprise AIKey Patent Assignees in XAI Industrial Decision Support (Retrieved Records)
In this dataset, AVEVA Software, LLC accounts for 4 filings across IN, CN, and WO jurisdictions — the highest filing count among retrieved records — followed by Rockwell Automation Technologies with 3 filings across US and EP. Large industrial automation incumbents represent the dominant filing concentration in retrieved records, with a secondary layer of academic and startup activity visible in India.
Top Assignees by Filing Count — XAI Industrial Decision Support (Dataset Snapshot)
↗ Click bars to exploreAVEVA Software, LLC
AVEVA Software holds 4 filings in this dataset across IN, CN, and WO jurisdictions — the highest count among retrieved records — with filings spanning 2024–2025. Key patents include the Hybrid AI-driven decision support system for real-time predictive industrial plant asset optimization (IN, 2025; CN, 2025) integrating physics-based simulation, probabilistic risk models, and ML within a single explainable DSS. The WO filing covers a prescriptive intelligent system for mobile industrial workers (2024).
United StatesRockwell Automation Technologies, Inc.
Rockwell Automation holds 3 filings in this dataset across US and EP jurisdictions, spanning 2021–2026. Patents include Progressive Contextualization and Analytics of Industrial Data (EP, 2021), an Integrated Generative AI Framework for Analytics using HMI Assistance (US, 2025), and Generative AI Industrial Automation Augmented Remote Support Services (EP, 2026) — the last using AR with generative AI to surface contextual support for field workers interacting with physical industrial assets.
United StatesFive Directional Shifts in XAI for Industrial Decision Support
The most recent filings (2024–2026) in this dataset reveal five identifiable directional shifts, spanning generative AI explanation interfaces, RAG-enhanced decision support, federated XAI, enterprise-scale strategic AI, and MCDM with transparent generative AI.
Generative AI as Explanation Interface
Rockwell Automation’s 2025 US and 2026 EP filings both embed generative AI models within industrial HMI systems to produce natural language explanations and recommendation narratives. The 2026 EP patent specifically uses augmented reality with generative AI to surface contextual support for field workers interacting with physical industrial assets. R&D teams in industrial software must treat natural language explanation generation as a core product capability by 2026.
RAG-Enhanced Manufacturing Decision Support
Morale AI’s 2026 US patent deploys retrieval-augmented generation within a manufacturing management DSS, converting natural language queries and unstructured data into semantic vectors processed by LLMs, with structured outputs for operational decisions. This represents one of the earliest patent filings in this dataset to explicitly name RAG as an industrial XAI mechanism. The approach lowers the technical barrier for non-expert users to engage with AI recommendations.
Post-Hoc Explanation vs. Inherently Interpretable Architectures
Click any row to explore further.
| Dimension | Post-Hoc Explanation (e.g. SHAP/LIME) | Inherently Interpretable Architecture |
|---|---|---|
| Definition | Explanation tools applied to existing black-box models after training | Model architectures designed for transparency from the ground up |
| Representative Examples | ABB interactive SHAP/LIME time-series explanation system (WO, 2023); AVEVA hybrid AI maintenance DSS with GUI-rendered explanations | XAI-KG knowledge graph DSS for manufacturing (2021 literature); Ontology-based intelligent DSS for business model design (2021 literature) |
| User Interaction Model | Passive explanation display or interactive editing of inputs and simulation-based scenario testing (ABB 2023 patent) | Structured domain knowledge captured via ontologies; user feedback iteratively improves model and recommendations |
| Application Fit | Anomaly detection, predictive maintenance, quality control applied to existing deployed models | Demand forecasting in manufacturing, business model design, sustainability assessment |
| Generative AI Integration | Rockwell Automation (2025 US, 2026 EP) and Morale AI (2026 US) embed LLMs and RAG within post-hoc explanation pipelines | CH Maheswara Rao MCDM framework (2026 IN) creates novel alternatives and transparently justifies autonomous decisions |
| Multi-Stakeholder Support | Energy management literature (2023) customizes explanation depth by role: engineers, facility managers, executives | Ontology-based systems capture role-specific domain knowledge but fewer patent examples in this dataset |
| Federated / Privacy-Preserving | Privacy-preserving XAI literature (2022) addresses federated post-hoc explanations; fXAI (2023) proposes shared insights without data centralization | No federated inherently interpretable architecture patent identified in this dataset |
| Patent Activity in Dataset | Multiple patents: ABB (WO, 2023), Rockwell (US, 2025; EP, 2026), Morale AI (US, 2026), AVEVA (IN/CN, 2025) | Primarily literature-based in this dataset; fewer patent filings for pure interpretable architectures |
Frequently Asked Questions: XAI for Industrial Decision Support
Modern deep learning and ensemble models achieve high predictive accuracy but offer no intelligible rationale for their outputs. In industrial environments, this creates trust barriers that impede operational adoption by operators, engineers, managers, and regulators who need to understand, audit, and act on AI-generated recommendations.
The dataset spans 14 patents and approximately 50 literature records, covering publication dates from 2013 to 2026. This represents a snapshot of innovation signals within this dataset only and should not be interpreted as a comprehensive view of the full industry.
India (IN) is the dominant jurisdiction by filing count, with 9 of 14 patents in this dataset. This spans assignees from academic institutions (Lovely Professional University, Chaitanya Bharathi Institute of Technology), individual inventors, and multinational subsidiaries such as AVEVA Software.
The four clusters are: (1) Hybrid AI with physics-based and probabilistic models; (2) Local explanation methods such as SHAP and LIME applied to industrial sensor and time-series data; (3) Knowledge graph and ontology-based decision support; and (4) Generative AI and LLM-augmented explanation interfaces.
AVEVA Software, LLC is the most active assignee in this dataset with 4 filings across IN, CN, and WO jurisdictions (2024–2025), covering hybrid AI maintenance optimization and prescriptive intelligence for mobile industrial workers. Rockwell Automation Technologies holds 3 filings across US and EP (2021–2026).
Federated XAI for privacy-preserving multi-site deployments is identified as a near-term patent opportunity. The fXAI concept — proposed in a 2023 literature record — has no equivalent in the patent filings retrieved in this dataset, representing an uncontested patent space as industrial consortia and multi-plant enterprises demand XAI without centralizing sensitive operational data.
Data and insights on this page are based on a limited patent and literature dataset and are for reference only. Figures may not represent the complete technology landscape.