November 21, 2024 AI In Manufacturing: Turn Potential Into Value

Mid-market manufacturers expect high ROI on AI investments, but data and infrastructure hurdles remain critical barriers to success. How can firms turn ambition into action?

Avanade Trendlines AI Value Report 2025

Artificial intelligence (AI) is revolutionizing manufacturing, offering significant opportunities for firms to enhance efficiency, cut costs, and boost revenues. According to Avanade’s AI Value Report 2025, mid-market manufacturers—known for agility and innovation—are poised to lead this transformation. Despite limited budgets compared to industry giants, these firms exhibit bold ambitions to use AI as a lever for growth.

However, the path to success isn’t without challenges. Companies must address foundational issues—such as data quality, infrastructure compatibility, and workforce readiness—to fully realize AI’s potential. The Avanade report explores insights from 4,100 decision-makers across 10 countries and 9 industries, including manufacturing, about their AI aspirations for 2025.

The Biggest AI Opportunities in Manufacturing

AI is benefitting every stage of the manufacturing value chain. According to Avanade’s new research, 62% of firms plan to focus on AI for supply chain management, while 48% prioritize factory IT/OT troubleshooting. These areas exemplify how AI can streamline operations and boost competitiveness.

AI enhances supply chain management by predicting demand, evaluating suppliers, predicting potential disruptions, and suggesting optimal procurement strategies. AI-driven insights, drawing on near real-time data, help manufacturers maximize inventory availability and reduce waste while maintaining optimal production levels.

Factories are deploying AI for automated quality control using machine vision to reduce waste, delivering cost and environmental benefits. In addition, AI-enabled troubleshooting helps identify machine faults, resolve them quickly, and even prevent future breakdowns, minimizing downtime and operational expenditure.

Bridging the AI Readiness Gap

Despite the excitement, 74% of manufacturers report poor data quality and governance as significant barriers to AI adoption. Another 49% cite software compatibility issues. A persistent challenge for manufacturers adopting AI is the divide between IT (Information Technology) and OT (Operational Technology) systems.

Historically, these systems were developed and operated independently. IT focuses on enterprise-level data—like inventory, financials, and customer information—and OT manages factory-floor processes, including machinery, sensors, and production lines. For example, predictive maintenance relies on real-time data from OT systems, but without integration with IT systems, it cannot account for inventory availability or production schedules to prioritize interventions.

Unstructured IT/OT data is a goldmine for manufacturers. Estimates suggest that 80–90% of manufacturing data is unstructured, making it a significant yet underutilized resource to enhance decision-making and business performance. This includes:

• Sensor data: Offering near real-time insights into equipment performance, energy usage, environmental conditions, and production outputs.
• Visual data: Images and videos captured by cameras on production lines that help identify quality issues, track inventory, or monitor safety compliance to reduce waste with cost and environmental benefits.
• Maintenance logs: Technical documents, reports, and even audio transcripts containing historical knowledge about machine performance, repair patterns, and troubleshooting techniques, aiding predictive maintenance and automation.

AI models work best when trained on proprietary company data. Unlike generic datasets, proprietary data reflects the unique characteristics of a manufacturer’s operations, products, suppliers, logistic partners, and customer relationships. With proprietary data, AI becomes a strategic partner, not just a tool, helping manufacturers create unique efficiencies, improve customer satisfaction, and explore innovative revenue streams like service-based models.

Six Steps to AI Success

1. Start Small and Prioritize Use Cases
   Identify high-impact areas where AI can deliver immediate value. The optimization of supply chains and factory operations are excellent starting points for mid-market firms.
2. Adopt Industry Standards for Data Integration
   Industry standards, like ISA-95, can help unify and map IT and OT system data, providing a robust foundation for AI-driven insights. Manufacturers can use “knowledge graphs” to connect data across domains.
3. Use Digital Twins to Simulate and Optimize
   Digital twins provide real-time insights into operations, enabling manufacturers to simulate scenarios and validate decisions before implementation. These tools close data gaps and improve responsiveness to unexpected events.
4. Build Toward Autonomous Operations
   AI is driving manufacturers toward autonomy, optimizing production, predicting maintenance, and regulating quality with minimal human intervention. Only 5% of manufacturers are uncomfortable with AI making autonomous decisions, while 25% are comfortable with AI handling low-risk decisions, and 26% with high-risk decisions. The rest are conditional, underscoring the need for Responsible AI frameworks.
5. Focus on Workforce Readiness
   AI adoption isn’t just a technological shift—it’s a human one. Training employees to work alongside AI systems and fostering a culture of collaboration between AI and human intelligence is essential.
6. Invest in Data Governance and Quality
   Effective data governance, quality, and security in manufacturing ensure reliable data for decision-making, protect sensitive information, and maintain compliance with industry standards. Along with real-time data access, this enhances operational transparency and supports timely, informed decision-making.

Unlocking Business Value with AI-Driven Insights

To help firms unlock AI’s potential, Avanade’s Gemba Walk program offers tailored assessments for identifying high-value AI applications. Gemba means “the real place” in Japanese and is part of the “lean” manufacturing methodology to uncover opportunities for continuous improvement. By engaging directly with frontline workers and management teams, this hands-on approach bridges the gap between AI’s theoretical possibilities and tangible business outcomes.

Conclusion: Turning Ambition into Action

AI has the power to redefine manufacturing, but success requires more than ambition. By addressing foundational issues—data integration, governance, and workforce readiness—mid-market manufacturers can position themselves as leaders in the AI-driven industrial revolution.

Avanade’s AI Value Report 2025 highlights both the opportunities and challenges on this journey, emphasizing the importance of practical, incremental steps to ensure long-term success.

For firms ready to take the next step, Avanade’s expert-led programs provide the roadmap to make AI a practical and profitable reality.

Visit www.avanade.com/manufacturingcopilot to learn more.

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