From Islands of Data to Intelligent Operations: A Pragmatic AI Strategy for the Shopfloor

Many companies have developed solid AI strategies for enterprise functions. But when it comes to manufacturing - the actual core of value creation – the situation is more complex, and initiatives face more difficulties to implement.

Why is that?

Because the Shopfloor is fundamentally different. It is a highly specialized, deeply task-specific and performance-oriented environment that has grown and evolved over the years. As a result, data is typically not connected across machines, lines or factories.

While this is effective for daily operations and focused optimization - such as machine performance or specific process steps – it needs to be augmented to support real-time coordination, predictive decision-making and end-to-end system optimization.

This is precisely where the next leap in productivity and resilience will come from.

The good news: The technologies to build suitable solutions are available now. What’s needed is a pragmatic, business-driven approach - one that steadily connects today’s working systems with a clear, achievable vision for intelligent operations on the shop floor.

In this article, we outline a value-first approach to realize the potential of AI in these industrial environments. We explore why some approaches might fall short, which technologies and architectural shifts unlock new potential, and how to identify the right starting points.

We offer guiding questions and best practices to help leaders move beyond disconnected pilots and towards scalable, value-driven transformation.

The Shift from Hard-Coded, Monolithic to Adaptive, Scalable Solutions

We are entering a new phase—one where task-specific data on the shop floor can be connected, contextualized, and translated into action.

This shift is driven by the convergence of technologies that have matured over time:

• Digital twins - a virtual representation of a real-world object, system, or process - enable real-time simulation and optimization by creating a dynamic feedback loop between the physical and digital layers of manufacturing.

• Unified data models and architectures, such as IT/OT integration, Data contextualization, Unified Name Space (UNS), Event-driven architecture and Data Mesh provide solid data foundations to apply AI on manufacturing.

• Edge computing is further enhancing cloud architectures by moving processing closer to the data source on the shopfloor, thus improving responsiveness and reducing latency.

On top of these technologies, generative AI and physical AI offer entirely new paths for automation, knowledge transfer and solution design.

A particularly promising concept is the enhancement of Generative AI with Agentic AI. Agentic AI systems are composed of autonomous agents that can make decisions and act independently. Thus, they offer a fast path to advanced automation and the ability to dynamically adapt to new inputs and tasks - an ideal fit for the specialized existing tool chains in manufacturing environments.

A Better Approach to AI: The Value-First Principle

Realizing the benefits of AI requires more than tools - it requires a clear strategic direction and the willingness to build, iterate, and learn.

A common pitfall is the attempt to launch a full-scale AI transformation program - often under the assumption that replicating external success stories will deliver similar results internally.

But this approach rarely works well.

Why? Because they have difficulties in providing agility and flexibility to keep up with the short innovation cycles in Generative AI. This leads to drawn-out planning, governance complexity, internal resistance, and a growing disconnect between strategy and day-to-day execution.

We suggest instead the Value-First Principle: prioritize initiatives that deliver tangible, measurable business value early on—while being aligned with a long-term, scalable vision.

This approach offers three distinct advantages:

1. It builds internal momentum and trust through early wins.

2. It shapes governance frameworks grounded in practice rather than theory.

3. It enables a more adaptive, muscle-building process where the organization learns how to work with AI - step by step.

However, this doesn’t mean focusing on isolated use cases. Overemphasizing standalone proofs of concept (POCs) has led many companies into a patchwork of initiatives that fail to scale, overlap in functionality, and compete for the same resources.

Instead, what’s needed is a coordinated use-case portfolio with a clear model of financial, operational KPI, and strategic impact with an inherent logical sequence for implementation to gradually build a shared, modular AI backbone.

This way, the entire spectrum of AI possibilities can be explored and leveraged:

• Physical AI, such as intelligent robotics and sensor-based inspection systems, which automate manual tasks while improving accuracy and quality.

• Cognitive engineering tools that support human experts with design validation, compliance checking, or advanced simulation tasks.

• AI-powered predictive maintenance, raw material price forecasting, and autonomous quality control systems—all proven to reduce downtime, increase efficiency, and drive profitability.

These are examples for concrete, actionable entry points with limited overlap. When built as modular, context-specific agents, these can become building blocks for scalable AI in manufacturing.

Five Guiding Questions for an AI Strategy in Manufacturing

Companies should start by asking a set of fundamental, practical questions to derive a strategy that delivers results and is aligned with long-term business goals.

1. Which use cases deliver both immediate impact and strategic value? Avoid chasing abstract potential. Instead, identify where AI can solve real problems today, while also laying the foundation for broader transformation tomorrow.

2. How can existing task-specific data be reused or elevated? Much of the shop floor data is locked in highly specialized systems. With the right architecture, fragmented data can be leveraged to arrive at encompassing intelligence.

3. Are we building the right infrastructure to scale? Scalable AI doesn’t just require models. It needs robust pipelines, standardized interfaces, interoperable formats, and secure architectures—capable of connecting plants, platforms, and partners.

4. Are we investing enough in change management? AI is not just a technical upgrade—it’s an organizational shift. Even the best models will fail if they’re not embedded in real workflows. Success depends on cultural readiness, clear communication, executive sponsorship, and frontline adoption. Especially on the shop floor, people need to understand, trust, and see value in the systems they are asked to use.

5. Are current initiatives aligned with our broader company direction? Lighthouse programs help set the direction—but navigation requires a map. Review your AI initiatives regularly and ask: Are they contributing to a shared, strategic direction? Or are they scattered efforts without synergies?

Answering these questions honestly and consistently helps ensure that AI doesn’t remain a collection of disconnected pilots—but becomes a core enabler of competitive advantage in manufacturing.

Conclusion: Don’t Wait for a Master Plan

While technology continues to evolve rapidly, the fundamentals of successful AI adoption remain the same.

At Accenture, we’ve scaled over 1,000 Generative AI projects across Europe. From early-stage pilots to large-scale transformation programs like the AI Refinery, our experience consistently shows that the companies that succeed are those that move forward with clarity, pragmatism, and a focus on value.

The differentiator isn’t technology alone - it’s execution.

What works is simple, but not easy: Prioritize the right use cases. Show value early. Learn fast. Design to scale.

Don’t wait for the perfect strategy or full alignment before starting. The path to intelligent operations begins with one well-chosen step - and the commitment to keep going.

And if you’re looking to define or accelerate that path, Accenture brings a proven approach, deep industry expertise, and the ecosystem partnerships needed to turn AI into real business outcomes.

This article is a joint effort of:

Frank Rütten Kathrin Schwan Vlad Larichev Georg Brutzer Timmo Sturm Dr. Markus Rossmann Götz Erhardt Enno Danke Claudia Lang

#AI #Manufacturing #DigitalTransformation