Rewiring Industry: How GenAI Can Pull Manufacturing Back Into Profit

Europe’s industrial manufacturers are caught in a bind. Profitability is slipping, productivity has plateaued, and traditional cost-saving levers have lost their edge. But now, Generative AI is emerging as more than a technological trend—it’s fast becoming a serious strategy for turning the sector’s fortunes around.

Industrial manufacturing in Europe is under pressure. For decades, productivity and profits grew hand in hand, fueled by waves of innovation from lean manufacturing to automation. But since 2010, something has shifted. Productivity growth has nearly flatlined—rising just five percent in the last 15 years—while costs, especially labor, have surged. The result is a profitability squeeze that spans the sector, from machinery and equipment makers to automation technology suppliers.

This new normal is pushing companies to ask hard questions about where the next wave of value will come from. And increasingly, eyes are turning to Generative AI. Once seen as a futuristic concept or niche software feature, GenAI is now being recognized for what it could truly be: a tool to reshape how industrial manufacturers design, produce, and compete.

A new joint study from Strategy& and VDMA Software and Digitalization makes the case. Surveying 247 companies and evaluating 45 practical GenAI applications, the study finds clear, quantifiable opportunities to improve operating margins—up to 10.7 percentage points across the sector. That equates to a €28 billion profit boost for Germany’s manufacturing industry alone, if executed effectively.

Yet that “if” looms large.

So far, most manufacturers have treated GenAI as an IT or support function play—rolling out chatbots, automating documentation, or experimenting with software tools. These initiatives are valuable but limited. They don’t hit the real profit drivers. As the study shows, the biggest financial impact from GenAI comes when it’s applied directly to core business functions—think R&D, sales, production planning, or supply chain management.

In sales, for instance, GenAI can personalize offers, anticipate market demand, and dynamically adjust pricing, unlocking real revenue growth. In R&D, it can accelerate prototyping and design, shorten time-to-market, and reduce material waste. Even in areas like production and logistics, predictive capabilities and data-driven recommendations can streamline workflows, reduce downtime, and shrink costs.

Despite this, only 7% of surveyed companies have implemented GenAI company-wide, and fewer than one in three have even deployed a single use case in a real-world setting. There’s a disconnect between potential and practice—and it’s costing the industry.

What holds manufacturers back isn’t just technical complexity. According to the study, the biggest hurdles are poor data quality, a lack of skilled talent, and uncertainty over where to invest. Many companies are still waiting for clearer use cases or broader industry adoption before making bold moves.

But that wait-and-see strategy comes with a risk. As more companies adopt GenAI, the competitive advantage it offers will narrow. The early adopters—those who embed GenAI into core processes now—are likely to capture the greatest gains in both profit and market share. Later movers may find themselves catching up, not leading.

That’s why the report argues for a deliberate, top-down GenAI strategy. Not every business process needs GenAI—but the ones that do must be prioritized, tested, and scaled. This means identifying high-impact opportunities, setting clear objectives, and creating internal “incubators” that can move fast, free from legacy roadblocks.

GenAI also opens the door to something more profound than operational tweaks. It invites a rethink of how industrial companies structure their business models altogether. New ways of designing products, serving customers, and optimizing workflows become possible when GenAI is integrated deeply, not just layered on top.

For example, automatically generated product designs based on customer input could radically shorten design cycles. Predictive insights about supply chain disruptions—drawn from unstructured global data—can help firms act before problems hit. Even the onboarding of new employees or the customization of marketing campaigns can be done at a scale and precision that simply wasn’t possible before.

These aren’t futuristic concepts. They’re already being piloted by leading firms—and they work. But the shift from pilot to scale requires executive commitment, not just experimentation.

The message is clear: the promise of GenAI in industrial manufacturing is no longer hypothetical. The tools exist, the use cases are proven, and the economic upside is compelling. What’s missing is bold execution.

For companies willing to act now, the opportunity is twofold. They can claw back lost productivity and profit—and just as crucially, they can secure a leadership position in the next chapter of industrial innovation.

Delay too long, and that window closes. As GenAI matures and becomes commonplace, its ability to differentiate will diminish. Those who wait will inherit a commoditized tool. Those who lead will build the future.

Four Foundations for a Winning GenAI Strategy

Strategic Focus: Prioritize high-impact core functions over support use cases.

Data Quality: Clean, connected data is non-negotiable.

Organizational Readiness: Create an incubator team with decision rights.

Execution Discipline: Track use cases against real financial KPIs.

Presented at Hannover Messe 2025

This landmark study, conducted by Strategy& in partnership with VDMA Software and Digitalization, was officially presented at Hannover Messe in March 2025. The event marked a turning point in how the industry views GenAI—not as an experiment, but as a new operating paradigm for Europe’s manufacturing core. The message from Hannover is loud and clear: the GenAI moment is here. Those who lead now will define the next era of industrial competitiveness.

You can download the study at GenAI in industrial manufacturing | Strategy&