AI is actively changing how manufacturers plan production, maintain equipment, respond to supply chain disruption and make split-second decisions on the factory floor — but it can’t do any of this without the cloud.
Across every industry, cloud is no longer mere infrastructure. It’s becoming the execution layer of AI that makes the technology work safely and at scale. But in manufacturing, the stakes are higher. AI can affect physical machines, assembly lines and operational continuity. Models that work in pilot projects can fall short when they’re not plugged into the right data and factory systems.
An NTT DATA global research report, Cloud-led innovation in the era of AI: The new rules for driving value with cloud, shows the readiness gap clearly. Only 14% of organizations surveyed (and only 10% in automotive and manufacturing) describe themselves as “cloud-evolved,” meaning that they have achieved the highest level of cloud maturity, where cloud-led innovation is embedded into core business strategy. In fact, 99% say AI, including agentic AI, has increased their need for cloud investment.
The report frames this shift through six rules for cloud value creation in the AI era. For manufacturers, three of these stand out because they’re close to the realities of the factory floor: architecture, applications and security. Two of these rules — which, as we’ll see below, speak to cloud architecture and modern applications — rank slightly higher in importance for manufacturers than the all-industry average. The third addresses cloud security.
Cloud architecture determines how far manufacturing AI can scale
Cloud architecture has become a strategic manufacturing decision. When making strategic decisions about where to place AI workloads, 52% of manufacturing organizations cite data security, privacy and compliance as leading considerations. In addition, 53% point to the need for greater control over infrastructure and customization.
These findings reflect manufacturing realities. AI workloads may need to process sensitive production data, intellectual property, supplier information or quality records. Some may need to run close to the edge because latency matters. Others may require hybrid, private or sovereign environments because of regulatory, security or operational constraints.
That is why cloud architecture cannot be reduced to a public-versus-private cloud debate. A predictive maintenance model that depends on real-time machine data has different requirements than an enterprise planning tool. A digital twin for production optimization may need to integrate OT, engineering and supply chain data from multiple systems. An AI agent supporting autonomous planning needs clear guidelines on what it can do, what data it can access and when a human must be involved.
Architecture is where intelligent operations begin. If you wait too long to decide where to place your AI workloads, you may limit the very AI use cases you’re trying to scale.
ALSO READ: Why slow AI adoption is more expensive in manufacturing
Modern applications turn AI into operational value
Manufacturing value is created in workflows: scheduling production, managing inventory, monitoring quality, maintaining assets, fulfilling orders and responding to disruption. If those workflows remain trapped in fragmented, aging or poorly integrated systems, AI has limited room to work.
Across industries, data readiness and analytics challenges are the number-one reason organizations say they are dissatisfied with efforts to build cloud-native AI applications. For manufacturers, the issue is especially acute because the data they need is scattered everywhere — from enterprise resource planning and supply chain platforms to the actual sensors on the factory floor.
Modernization, then, is not just about rewriting code or moving applications to cloud platforms. It’s also about making the business more able to sense, decide and respond. Our research also shows that 55% of manufacturing organizations say industry cloud solutions will be extremely important to their cloud strategy, reflecting a need for capabilities built around manufacturing workflows, data structures and governance.
Manufacturers should resist treating agentic AI as a modernization shortcut. Agents can interpret intent and orchestrate work, but they still need dependable systems of record, strong application programming interfaces, well-governed data and clear business rules.
Security gives manufacturers permission to move faster
Security is the number-one investment priority for cloud across industries. Security, governance, risk and compliance concerns regarding autonomous agents are also the top challenge to adopting agentic AI in cloud-based solutions over the next 12 to 18 months.
This priority is a practical consideration for manufacturers whose attack surfaces now span IT systems, OT environments, connected products, cloud platforms, edge devices, supplier networks and increasingly autonomous AI-enabled workflows. Security gaps can affect not only data confidentiality but also production uptime, worker safety, intellectual property and customer commitments.
Secure operations at scale must therefore be treated as a design principle. As manufacturers connect more assets, collect more operational data and introduce more AI into decision-making, security cannot be added after the fact. It must be embedded into architecture, identity, networking, data governance and platform operations from the start.
The fundamentals are familiar, but they matter more now: strong identity and access management, clear data protection policies, zero trust principles, continuous monitoring, defined responsibilities and consistent governance across providers and environments. In AI-enabled environments, those controls must also account for how agents access data, trigger workflows and interact with business systems.
Take the next step toward intelligent manufacturing
Ultimately, AI isn’t a magic wand you can just wave over your assembly line. It’s a powerful tool but it’s only as good as the cloud foundation it runs on.
Now is the time for manufacturers to build a secure, connected environment so AI can get to work. If you want to turn AI hype into everyday reality, it’s time to take a hard look at your cloud strategy.
WHAT TO DO NEXT
Access our report, Cloud-led innovation in the era of AI: The new rules for driving value with cloud, to dive deeper into the research.