Generative AI Use Cases in Manufacturing and Engineering

Manufacturing organizations are exploring generative AI applications beyond the consumer-facing chatbots and image generators that dominate public attention. When properly implemented, large language models (LLMs) and generative AI systems can address specific operational challenges related to data access, knowledge management, and decision support.

This episode examines practical generative AI applications in manufacturing environments, based on insights from Vlad Larichev, Generative AI Lead at Accenture Industry X. Rather than focusing on theoretical possibilities, this article explores use cases currently delivering value in production environments and provides a framework for evaluating where generative AI fits in your operations.

Manufacturing data presents unique challenges that generative AI is particularly suited to address: information scattered across multiple systems, unstructured data in documents and logs, and the need for flexible analysis that adapts to changing business questions. Understanding how to apply generative AI to these challenges helps organizations move beyond pilot projects to scaled implementations.

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Understanding the Business Value of Generative AI in Manufacturing

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Generative AI's relevance to manufacturing becomes clear when examining the nature of manufacturing data and how decisions are made on the factory floor. Unlike consumer applications, the value in manufacturing comes from solving specific information access and knowledge management challenges.

The Manufacturing Data Challenge

Manufacturing organizations accumulate vast amounts of data across disparate systems:

• Structured data: MES, ERP, SCADA systems contain process parameters, production metrics, and operational data
• Semi-structured data: Maintenance logs, quality reports, and shift notes follow loose formats but lack consistent schemas
• Unstructured data: Equipment manuals, troubleshooting guides, tribal knowledge documents, and email communications contain critical information in text form

Traditional analytics approaches require predetermined questions and rigid data structures. Teams build specific queries, create fixed dashboards, and design reports that answer known questions. When business needs evolve or users need different perspectives, significant rework is required.

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How Generative AI Changes Information Access

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Generative AI, particularly large language models, offers a different approach to interacting with manufacturing data:

Flexible Query Capability: Instead of building predetermined reports, users can ask questions in natural language. The system interprets the question, understands the data structure, and generates appropriate responses.

Context-Aware Responses: LLMs can synthesize information from multiple sources, providing context that single-system queries cannot. A question about quality issues might automatically incorporate related process parameters, maintenance activities, and operator notes.

Adaptive Analysis: As needs change, the same system can answer different questions without requiring new dashboard development. The shift supervisor's information needs vary by day, product, and circumstances—generative AI adapts accordingly.

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Combining Knowledge Graphs with Large Language Models

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Yaroshev emphasizes a critical technical approach: successful manufacturing applications combine knowledge graphs with LLMs to address two complementary challenges.

Knowledge Graphs: These structures capture how manufacturing data relates—processes to equipment, quality metrics to production parameters, maintenance activities to performance outcomes. They represent the "human logic" of how information is organized.

Large Language Models: LLMs provide the flexible interface layer, translating natural language questions into structured queries against the knowledge graph. They can understand variations in how people ask questions and synthesize responses from multiple data points.

This combination solves fundamental problems:

• Knowledge graphs make institutional knowledge explicit and accessible
• LLMs make that knowledge usable without requiring technical expertise
• Together, they enable self-service analytics that adapts to actual user needs

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Practical Value Delivery

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The business value emerges in several ways:

Reduced Dashboard Development: Instead of building custom dashboards for each use case, organizations develop reusable AI systems that answer diverse questions

Faster Decision-Making: Users get immediate answers to specific questions rather than waiting for reports or dashboard updates

Accessible Expertise: Knowledge captured in documents, logs, and systems becomes available to less experienced staff through conversational interfaces

Improved Knowledge Retention: When experienced operators or technicians retire, their documented knowledge remains accessible through AI systems

Organizations should evaluate generative AI opportunities by identifying situations where information exists but is difficult to access, where decision-making requires synthesizing data from multiple sources, or where flexibility in analysis creates significant value.

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Common Implementation Challenges and How to Address Them

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Organizations implementing generative AI in manufacturing frequently encounter predictable challenges. Understanding these patterns helps in planning more effective deployments.

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Treating Industrial AI Like Consumer Applications

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Many initial implementations attempt to deploy generative AI systems similar to consumer chatbots. Users ask questions, receive answers, and the system accesses general knowledge databases or documentation.

Why This Approach Falls Short:

Consumer generative AI works well for general knowledge questions. Manufacturing requires specialized, context-specific information about particular processes, equipment, and conditions. Generic implementations cannot provide the precision and reliability that manufacturing decisions demand.

What Industrial Applications Require:

• Structured knowledge representation: Manufacturing data relationships must be explicitly modeled before LLMs can navigate them effectively
• Domain-specific context: Systems need understanding of process flows, equipment relationships, and operational constraints
• Verifiable responses: Answers must be traceable to source data with clear provenance for audit and trust
• Integration with operational systems: Gen AI must connect to MES, maintenance systems, quality databases, and other operational platforms

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Insufficient Data Foundation

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Organizations sometimes attempt to deploy generative AI before establishing adequate data infrastructure. This approach typically leads to disappointing results.

Data Requirements for Success:

Knowledge graphs provide the essential foundation. Before implementing LLMs, organizations need to:

• Map relationships between processes, equipment, products, and quality parameters
• Define clear data semantics (what measurements mean, how they relate)
• Establish consistent data quality standards across sources
• Create proper metadata and context documentation

This work is not generative AI-specific—it represents fundamental data management that supports multiple analytics initiatives. However, it's essential for effective gen AI implementation.

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Scope and Use Case Selection

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Starting too broadly leads to implementation challenges and unclear value. Attempting to "use Gen AI for everything" diffuses resources and makes success harder to measure.

Effective Scoping Approach:

Begin with specific, well-defined problems where generative AI's flexibility provides clear advantages:

• High-frequency activities: Daily or shift-based tasks where information access matters repeatedly
• Information synthesis needs: Situations requiring data from multiple systems
• Variable information requirements: Cases where standardized reports don't meet diverse user needs
• Knowledge accessibility gaps: Scenarios where expertise exists but is hard to access

Examples include shift handovers, equipment troubleshooting, quality investigations, and maintenance support—all cases where questions vary but information exists in accessible systems.

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Organizational Change Management

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Technology implementation alone doesn't ensure success. Users must understand how to interact effectively with generative AI systems, trust the responses, and integrate these tools into their workflows.

Critical Change Management Elements:

User Training and Adoption: Teams need education on:

• How to formulate effective questions
• What types of queries the system handles well
• When to escalate to human experts
• How to interpret and validate AI responses

Trust Building: Users develop confidence through:

• Transparent sourcing (showing where answers come from)
• Consistent accuracy on known questions
• Clear boundaries (system communicates what it can and cannot do)
• Gradual expansion from pilot users to broader adoption

Continuous Improvement: Systems improve through:

• User feedback on response quality
• Tracking common question patterns
• Identifying knowledge gaps
• Regular updates to underlying knowledge structures

Yaroshev shares an illuminating observation: at Accenture, internal search system usage data showed employees naturally improved their query formulation over time. Early questions were vague; later queries became specific and well-structured. Organizations should plan for this learning curve rather than expecting immediate expert-level usage.

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Industrial Requirements vs. Consumer Capabilities

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Manufacturing environments have requirements that consumer AI tools don't address:

Auditability: Manufacturing decisions often require documentation for regulatory compliance, quality systems, and continuous improvement processes. Systems must log queries, responses, and data sources.

Security and Access Control: Not all users should access all information. Systems need appropriate permissions and data isolation.

Reliability and Uptime: Production environments require consistent availability and predictable performance.

Integration Architecture: Gen AI must fit within existing manufacturing IT architecture, working alongside MES, ERP, and other operational systems.

Organizations should evaluate generative AI platforms based on industrial requirements, not consumer application features.

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Proven Use Cases in Production Environments

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Several generative AI applications have demonstrated measurable value in manufacturing settings. These use cases share common characteristics: they address frequent information access needs, synthesize data from multiple sources, and adapt to varying user requirements.

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Dynamic Shift Summaries and Handovers

Traditional shift reports follow standardized formats that may not capture relevant details for specific situations or answer the next shift's particular concerns.

How Generative AI Improves the Process:

Instead of static shift reports, operators can query for information relevant to their immediate needs:

• "Were there any quality events with Product X during the last shift?"
• "What maintenance activities were performed on Production Line 3?"
• "Did we meet production targets, and if not, what caused the shortfall?"
• "Were there any equipment alarms or unusual process conditions?"

The system accesses production data, quality logs, maintenance records, and operator notes to provide synthesized responses that directly address the question.

Business Impact:

• Faster shift transitions with immediately relevant information
• Reduced miscommunication between shifts
• Better continuity of problem-solving across shift boundaries
• Improved response to emerging issues

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Maintenance Knowledge Access and Troubleshooting Support

Experienced maintenance technicians accumulate decades of troubleshooting knowledge—understanding equipment quirks, recognizing patterns, and knowing which solutions work for specific problems. When these technicians retire or move to other roles, this knowledge typically leaves with them.

How Generative AI Preserves and Shares Expertise:

Systems can access:

• Historical maintenance logs showing previous issues and solutions
• Equipment manuals and technical documentation
• Work orders with detailed problem descriptions and resolutions
• Tribal knowledge captured in maintenance team documents

When technicians encounter equipment problems, they can query the system: "What has caused bearing failures on Equipment Unit 5 in the past?" or "Show me successful solutions for hydraulic pressure fluctuations."

Documented Results:

One manufacturer reported measurable reduction in mean time to repair by implementing AI-powered maintenance knowledge systems. Less experienced technicians could access historical troubleshooting information that previously existed only in senior technicians' experience.

Implementation Considerations:

• System must distinguish between confirmed solutions and attempted approaches
• Responses should include context (when problem occurred, what resolved it, outcome)
• Critical safety information needs appropriate highlighting and handling
• Integration with computerized maintenance management systems (CMMS) provides real-time data

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Quality Investigation and Root Cause Analysis

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When quality issues emerge during production, engineers need to quickly understand what changed in the process, identify potential root causes, and implement corrective actions. Time sensitivity is critical—ongoing production may be producing defects until the issue is resolved.

How Generative AI Accelerates Investigation:

Quality engineers can ask targeted questions synthesizing information across systems:

• "Show me all process parameters that exceeded specifications in the last 24 hours"
• "Were there any maintenance activities or changeovers before this defect pattern appeared?"
• "How does current equipment performance compare to the last time we observed similar quality issues?"
• "What raw material batches were used in affected production runs?"

The system queries production databases, quality management systems, maintenance records, and material traceability systems to provide integrated responses.

Value Delivery:

• Faster identification of root causes
• Reduced defect production during investigation
• Better correlation between process changes and quality outcomes
• Improved documentation for corrective action records

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Engineering Design and Manufacturing Guidelines

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Engineers designing new products or modifying existing designs need to consider manufacturing constraints, historical lessons learned, and design-for-manufacturing principles.

Emerging Applications:

Yaroshev notes this use case is still developing, but early implementations show promise. Engineers can query:

• Manufacturing tolerances and capabilities for specific processes
• Design guidelines for particular materials or assembly methods
• Lessons learned from previous products with similar features
• Supplier capabilities and constraints

The system accesses design repositories, manufacturing specifications, historical project documentation, and engineering standards to provide relevant guidance.

Implementation Status:

This application requires sophisticated knowledge representation of engineering relationships and manufacturing constraints. Organizations with well-documented design standards and manufacturing capabilities are beginning to see value.

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