Smart, scalable, and ready: Why AI is changing manufacturing operations

Artificial intelligence (AI) has quickly shifted from being a buzzword to a top priority for manufacturers. What was once an experimental technology is now recognized as a key driver of performance and resilience.

In fact, according to our recent AI in Manufacturing report, nearly eight in ten manufacturing leaders expect AI to become essential to their operations within the next five years. That figure rises to 88% among organizations with more than 10,000 employees. For these enterprises, AI has become something truly foundational.

But there’s a gap between vision and reality. While 79% see AI as essential to future success, only 22% have fully implemented it. The rest remain in trial phases, pilot programs, or early adoption stages. This disconnect presents a clear opportunity: manufacturers that accelerate adoption now stand to gain competitive ground while others wait.

In this article:

• The value of AI for manufacturers
• The main barriers preventing AI adoption in manufacturing
• The influence of digital maturity on AI adoption
• Scaling AI without disruption requires the right strategy, right now
• What manufacturers want from AI partners
• How to start: Focus on problems, not platforms
• Summary

The value of AI for manufacturers

According to the report, 97% of manufacturing leaders say they already see or expect value from AI adoption within the next two years. These benefits include performance gains, increased productivity, and stronger workforce support.

The most expected outcomes include:

• Improved productivity and performance (55%)
• Cost reductions (47%)
• Enhanced sustainability and energy efficiency (42%)
• Improved working conditions (41%)

These priorities reflect the growing role of AI as a holistic enabler—improving not just efficiency but also worker safety, sustainability goals, and resilience in volatile markets.

Crucially, the power of AI lies in its range. Manufacturers aren’t adopting AI for a single purpose—they’re using it to solve diverse challenges across the value chain.

According to the report, AI is most often applied to:

• Predictive maintenance (59%)
• Quality control (53%)
• Data analysis and reporting (47%)
• Workforce support and assistance (41%)
• Production planning and scheduling (39%)
  

Each of these use cases helps reduce delays, cut costs, and improve decision-making. For example:

• Predictive maintenance identifies wear before failure, reducing downtime.
• AI-powered computer vision detects defects in real time, improving product quality.
• Smart scheduling tools dynamically adjust workflows based on supply chain changes or machine availability.

Crucially, these are not hypothetical benefits—they’re already being experienced by early adopters.

One of the standout findings in the report is AI’s growing role in supporting frontline workers. In fact, 69% of respondents say improving workforce support is a primary motivation for adopting AI.

AI is helping teams:

• Perform tasks more accurately with guided digital workflows
• Reduce errors through real-time feedback
• Complete complex assignments faster, even with limited training

As the report explains:

“AI is no longer seen solely as a tool for back-office analytics or automated machinery. It’s becoming a real-time assistant to frontline workers, providing contextual information and intelligent support in the moment.”

This reflects a significant shift in how manufacturers view technology. Rather than replacing workers, AI is helping them perform at a higher level, even in high-pressure environments.

The main barriers preventing AI adoption in manufacturing

As the report notes, only one in five manufacturers claim to have fully integrated AI across their operations. The rest are either experimenting, partially implemented, or still evaluating where to begin.

Despite clear interest and early gains, several barriers continue to slow AI adoption across manufacturing.

The top three cited in the report are:

• Integration into existing IT and OT systems (49%)
• Data and cybersecurity (45%)
• Lack of digital maturity or required infrastructure (44%)

These barriers are especially pronounced in large enterprises managing legacy equipment, siloed data systems, and global operations. For AI to deliver real ROI, it has to integrate smoothly—without triggering high implementation costs or extended downtime.

Concerns about cybersecurity are however legitimate, especially when AI is integrated with sensitive operational systems. But our research emphasizes that secure-by-design AI tools are already addressing this head-on.

Among leaders with successful AI implementations:

• More than 80% use role-based access control.
• Two-thirds use local data processing (edge computing) to reduce cloud exposure.
• Most emphasize encryption and audit trails to maintain compliance and transparency.

As the report states:

“Security is not a barrier—it’s a design requirement. AI solutions must meet or exceed the security standards of the environments they serve.”

This reflects a maturing approach to AI governance. Rather than seeing security as a blocker, successful organizations are baking it into their selection and deployment processes from the very start.

 

The influence of digital maturity on AI adoption

The report makes clear that digital maturity is a key predictor of AI success. Organizations with more advanced digital infrastructure are much more likely to deploy and scale AI quickly and effectively.

For example:

• 55% of digitally mature companies say they’ve already implemented AI at scale.
• In contrast, 76% of less digitally mature organizations say they are only in pilot or trial phases.

Digital maturity enables faster integration, more accurate data inputs, and better alignment across departments. It also shortens the time to value by enabling clearer KPIs and performance tracking.

This highlights a valuable insight: investing in foundational digital infrastructure isn’t just about modernization—it’s about creating the conditions for successful AI deployment, and indeed, continued growth.

Scaling AI without disruption requires the right strategy, right now

A common theme across the report is that manufacturers want realistic, manageable adoption paths. There is little appetite for full-scale overhauls or high-risk transformations.

Instead, successful adopters are pursuing:

• Modular implementation: Start with one use case, prove ROI, and scale.
• Hybrid deployment: Use edge computing where latency matters, and cloud where flexibility is key.
• Secure integration: Ensure AI works seamlessly with MES, ERP, and legacy equipment.

This approach aligns with what the report calls “AI by design”—solutions developed specifically to coexist with real-world operational technology.

As noted in the report:

“Manufacturers are seeking partners who understand the complexity of industrial environments—and can provide AI tools that integrate, scale, and deliver value without disrupting operations.”

This practical focus is critical. It reduces resistance, accelerates rollout, and creates visible wins that drive long-term confidence.

And crucially, now is the time to act. Because while some organizations are still evaluating use cases, the ones that have already moved forward are enjoying clear advantages.

According to the report:

• 44% of large enterprises say AI is already generating tangible performance improvements.
• 30% have achieved reductions in operating costs.
• 27% have improved workforce enablement metrics.

These organizations report not only internal benefits, but external ones—among them, faster delivery times, improved customer satisfaction, and greater agility in responding to supply chain disruptions.

This early success sends a clear message: adopting AI early—strategically and incrementally—creates lasting advantages.

What manufacturers want from AI partners

Choosing the right AI partner matters. According to the report, manufacturers are looking for providers who:

• Understand the complexity of industrial operations.
• Offer tools that integrate with existing systems.
• Provide fast time to value.
• Deliver scalable, secure solutions.

They also want clarity—no hype, no inflated promises, and no hidden costs. Solutions have to come with transparent KPIs, measurable ROI, and ongoing support.

That’s why many manufacturers are choosing to work with partners like TeamViewer. Our solutions are purpose-built for real-time, industrial environments—and backed by proven results.

How to start: Focus on problems, not platforms

A key takeaway from the report is that the most successful AI projects begin with specific, well-defined use cases—not with platform shopping.

As the report advises:

“Organizations that start with a clear operational challenge—such as reducing inspection errors or minimizing machine downtime—are more likely to deliver successful AI outcomes.”

This is an important shift. Instead of chasing features, manufacturers are choosing solutions that deliver measurable improvements to their most pressing problems.

For example:

• If training time is too long, AI-guided instructions can accelerate onboarding.
• If downtime is costly, predictive maintenance can reduce reactive repairs.
• If quality issues affect yield, real-time defect detection can boost consistency.

Each win builds internal momentum—and justifies the case for broader adoption.

Summary

AI adoption in manufacturing is about resilience, agility, and workforce enablement. Manufacturers that move now, with a focused plan and the right support, can lead the way.

The technology is ready. The use cases are proven. And the data shows clear returns. What matters now is execution—starting with a challenge that matters, choosing the right partner, and scaling based on results.

Learn how TeamViewer can support your AI journey—from frontline guidance to predictive maintenance and everything in between.