Empower your workforce with AI in manufacturing operations

Current manufacturing reporting systems serve as living, breathing connections between the factory floor and the broader business. But even with all the available data and defined metrics, these same systems often fail to provide actionable insights.

System constraints include high data volumes, rigid reporting structures, limited system access, siloed business functions and poor user interfaces. In fact, a recent NTT DATA report, Feet on the floor, eyes on AI: Do you have a plan or a problem?, shows that 92% of manufacturers say outdated infrastructure is critically hindering GenAI initiatives. This leaves users struggling with ad hoc solutions ranging from manual data extractions to my favorite, Microsoft Excel pivot tables with embedded VBScript calculations.

In response, manufacturers have continually invested in reporting systems. They have integrated sensors, programmable logic controllers, historians, distributed control systems, manufacturing execution systems, enterprise resource planning systems and supply chain data with varying degrees of success.

Obstacles to valuable insights

Yet, legacy processes, platform limitations and fragmented data sources continue to stand in the way of providing real-time access to critical data insights. The NTT DATA report finds that only 41% of manufacturers strongly agree they have invested adequately in data storage and processing capabilities to support GenAI. This is despite the fact that 59% acknowledge a key lesson learned from existing AI deployments — that high-quality, diverse and clean data is vital for GenAI models to be effective.

As a result, many IT and OT teams find it difficult to provide timely insights that support critical business decisions in manufacturing operations. Empowering the workforce to make real-time decisions on complex business processes requires a different approach.

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Creating interactive manufacturing dashboards requires much more than simply revamping existing reporting systems with an improved user interface. Organizations need to weave AI into the evaluation process with conversational AI technology. This means moving beyond displaying data in a new format to interpreting the results, learning from the model and delivering actionable guidance to plant managers and manufacturing teams. This is the formula for reducing the time spent “digging” for data and building the next manual or automated report.

Sample benefits in manufacturing reporting:

• Dynamic recommendations for resource allocation, schedule adjustments and operation issues
• Immediate visibility of production flow, equipment health and workforce performance, across disparate systems and processes
• Empowering in-depth Pareto analysis in real time on critical business decisions

5 ways AI-infused dashboards drive manufacturing performance

While every facility has unique needs, AI-powered dashboards consistently deliver value in five key areas:

1. Unifying data and breaking down silos

Manufacturing data is often scattered across machines, sensors and business systems. Nearly two-thirds of manufacturers report that useful data is either lacking or improperly formatted. AI-driven dashboards bring these sources together and automatically cleanse and integrate data. This provides accurate and timely insights, eliminating the manual effort and errors that come with disconnected data silos.

2. Delivering real-time operational intelligence

AI-enabled dashboards highlight emerging issues as they happen, whether it’s a dip in throughput, a spike in scrap rates or subtle quality trends. According to recent survey data from the Manufacturing Leadership Council (MLC), it’s a timely capability, as 71% of manufacturers cite a lack of proof that AI drives better decision-making as a key obstacle to adoption — underscoring the need for real-time, actionable intelligence that can clearly demonstrate ROI.

3. Enabling predictive maintenance and quality control

AI analyzes patterns in equipment and process data to forecast potential failures and identify subtle quality trends. This enables proactive maintenance and quality interventions, reducing unplanned downtime and minimizing waste.

4. Supporting optimized resource allocation

With a holistic, data-driven view, the system recommends how to reallocate labor, materials and assets in real time to meet changing production demands. Scaling these capabilities, though, remains a challenge, with the MLC report showing that 66% of manufacturers are struggling to scale pilot AI projects into production.

5. Accelerating decision-making and enabling continuous improvement

AI-powered dashboards do more than report on what’s happened — they actively support decision-making by surfacing trends, uncovering root causes and tracking the impact of process changes over time. This creates a cycle of continuous improvement, where every operational decision is informed by insight rather than instinct.

From data to action: Real-world impact

Imagine a factory running multiple lines, 24x7, producing thousands of units each hour. A single percent improvement in yield or reduction in scrap can translate into significant cost savings and revenue gains. But these gains are only possible if manufacturers can identify and address issues in real time. Such transformations are gaining momentum, with 68% of manufacturers now viewing AI as essential to growing their business, according to the MLC study.

With AI-infused dashboards, actionable insights reach the right people instantly. This includes a maintenance manager alerted to a vibration anomaly, a supervisor notified of a shift in quality metrics or an operations leader prompted to adjust production schedules in response to supply chain changes.

Across the industry, manufacturers leveraging these solutions report measurable benefits:

• Faster response times to operational issues
• Reduced unplanned downtime and maintenance costs
• Improved quality and yield
• Enhanced workforce empowerment and adoption, with intuitive, user-friendly interfaces that minimize training requirements

Best practices for unlocking value

To fully realize the benefits of AI-powered dashboards, manufacturers should focus on a few key best practices:

• Don’t boil the ocean: Define clear business objectives that are aligned with specific goals and defined use cases, whether it’s increasing throughput, reducing downtime or improving quality.
• Ensure data quality:Reliable AI insights depend on accurate, consistent data. Regular audits and data governance help maintain integrity.
• Integrate with existing systems:Seamless connectivity with current hardware and software is essential to avoid disruption.
• Invest in workforce enablement:Engage teams early, provide training and leverage intuitive tools to drive adoption and change management.
• Choose the right platform(s): Every solution provider is marketing AI as a core strength regardless of in-house breadth and depth. Spend the time to evaluate vendors holistically, including strategic, functional and technical components, as they relate to your company’s infrastructure and growth objectives.
• Choose experienced partners:Working with a trusted provider ensures smooth implementation, support and alignment with business needs.

Overcoming challenges on the path to transformation

While the benefits are compelling, the journey to AI-powered operations is not without obstacles. High upfront investment, integration with legacy equipment and workforce skill gaps are common hurdles. Failing to invest in advanced digital tools can further leave manufacturers at a disadvantage — stuck reacting to problems rather than driving performance. And it leaves the workforce without the insights and capabilities they need to contribute successfully.

These challenges can be overcome with thoughtful planning, targeted upskilling or reskilling and the right technology partners. Embracing AI-powered dashboards gives your workforce the tools to lead. When empowered with real-time data and AI-driven insights, manufacturing teams can make faster, smarter decisions and help shape the future of manufacturing from the ground up.

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This article includes contributions by Logan Carpenter, Consultant at NTT DATA Services.