Dive Brief:
- Artificial intelligence has already boosted productivity, quality and resilience in manufacturing. However, unreliable networks and other connectivity issues frequently disrupt operations and hinder broader adoption of AI once it has been implemented, according to a new Cisco survey.
- Cybersecurity is foundational for AI-ready infrastructure, Cisco said, and 40% of manufacturers cited cybersecurity concerns as the top barrier to initial AI adoption.
- While information technology/operational technology collaboration is also “critical to AI at scale,” the report said, 43% of manufacturing organizations surveyed showed little to no such collaboration.
Dive Insight:
Manufacturers around the world are deciding when and how to adopt AI, with many of them eager to leverage it for process and efficiency improvements. But according to Cisco’s new report based on a survey of more than 350 manufacturing decision-makers at firms in 19 countries, there are several barriers to manufacturers using AI more widely in their operations.
In particular, the report cited the need for more reliable networks, stronger cybersecurity and better IT/OT collaboration to fulfill AI’s potential.
Despite these challenges, Cisco found that 59% of manufacturers surveyed reported that they had already actively deployed AI at scale. It cited a Research and Markets report showing that the global AI in manufacturing market is expected to rise from $34 billion in 2025 to $155 billion by 2030.
“Not long ago, AI on the factory floor felt theoretical. Today, it’s very real,” Samuel Pasquier, product management lead with Cisco Industrial IoT Networking, said in an email. “What the findings from this survey confirm, and what customers tell me directly, is that AI is driving meaningful gains in productivity, quality, and resilience across manufacturing. This isn’t about pilots anymore; it’s how work is starting to get done.”
Early adopters of AI in manufacturing have concentrated on “efficiency- and throughput-focused applications [that] align closely with near-term productivity and cost objectives,” the report said. These include process automation, supply chain and logistics optimization, automated quality inspection and other use cases.
At the same time, advanced adopters “are more likely to associate AI with resilience, safety, and long-term operational improvement, indicating a shift from tactical efficiency toward strategic value creation,” the report said. “AI is viewed as a practical enabler of efficiency and profitability.”
Despite its upsides, significant barriers remain to more widespread use of AI among manufacturers.
For example, many manufacturers need to modernize their networks, with 56% reporting unreliable wireless connectivity that affects AI operations. Edge compute and bandwidth — other top network requirements for AI — were also cited as challenging, Cisco said.
Cybersecurity can be a significant barrier to AI as well, especially the decision to begin implementing it in the first place. “Cybersecurity concerns are significantly limiting AI adoption by creating a ‘trust deficit’ and introducing new, complex risks that outpace traditional security measures,” the report said.
In particular, manufacturers cited data breaches or data loss, supply chain or third-party attacks and ransomware or malware attacks as top cybersecurity threats hindering AI adoption.
Nevetheless, 81% of manufacturers said they expected AI to improve their security over time. Many ranked industrial cybersecurity third in importance for AI investment, after only process automation and logistics optimization.
“While cybersecurity remains a top concern for implementing industrial AI, manufacturers increasingly see AI as part of the solution to improving their security posture by, for example, using it to improve visibility, threat detection, and operational resilience,” Pasquier said.
There is also a “clear relationship between stronger IT/OT collaboration and higher confidence in scaling AI across manufacturing operations.” the report said. It added that manufacturers who do better in this area are much more likely to confidently scale AI while retaining regulatory compliance.
“Critically, the organizations making the most progress are changing how their teams work,” Pasquier said. “Rather than running AI as isolated projects, they’re bringing IT and OT together to plan deployments, operate networks day to day, and share responsibility for performance, uptime, and security.”
Manufacturers reported “generally positive levels of collaboration” between their IT and OT teams. However, at some firms, “Organizational collaboration has not yet become aligned with the demands of scaled industrial AI, a potential constraint as AI deployments scale beyond individual sites or functions,” Cisco’s report said.
Looking ahead, the report said manufacturers are confident they can expand AI adoption over time, especially when it aligns with core operational priorities. Nearly all of them indicated they could scale AI across operations and meet compliance requirements.
In addition, 84% of manufacturers said AI already has had a significant impact on the industry, and 80% said those who do not immediately start investing in AI will fall behind.
“In the near term, manufacturers are focused on scaling results from proven AI use cases such as quality inspection, automation, and predictive maintenance, while increasingly decoupling software from hardware as specialized AI hardware becomes more expensive and scarce,” Pasquier said. “This disaggregation allows manufacturers to deploy and move AI software across different environments.
He added that over the longer term, companies will shift toward “machine‑to‑machine decisioning and more autonomous operations,” where AI optimizes processes but a human still monitors OT safety and reliability.
“Rather than a single transformation, manufacturers see AI augmenting engineers, operators, and IT/OT teams incrementally, supported by AI‑ready networks that provide the secure, reliable connectivity needed to scale disaggregated architectures and maximize the value of high‑cost AI hardware,” he said.
Cisco’s report on AI in manufacturing is part of the company’s broader 2026 State of Industrial AI report.
