AI is increasingly moving into the layer that keeps enterprise technology running. NTT DATA is expanding deployment of an AI-powered infrastructure operations platform designed to monitor global IT environments, predict potential disruptions and automate incident response, with Daimler Truck among the manufacturing organizations supported by the approach.
The move reflects a broader shift in enterprise AI: rather than focusing only on customer-facing applications or generative AI assistants, companies are applying machine intelligence to the infrastructure and operational systems that underpin factories, offices, networks and digital services.
NTT DATA’s platform combines real-time infrastructure monitoring, predictive analytics and intelligent automation. The company says it can provide visibility across local and global environments while monitoring tens of thousands of devices, helping IT teams identify operational issues and optimize capacity and resource utilization.
For large manufacturers, that capability can have implications beyond traditional IT service management. Production facilities increasingly depend on connected devices, communications networks, enterprise applications and digital systems. A network or infrastructure problem can therefore become an operational problem when it affects production sites or employees.
The Daimler Truck deployment provides an example of that model. NTT DATA says its platform supports global manufacturing environments through real-time monitoring, AI-driven predictive insights and automated, multi-stage incident processing. The company also describes an AI-orchestrated delivery model that combines on-site expertise with offshore execution.
The emphasis on prediction is particularly important. Traditional infrastructure operations often depend on teams responding after an alert or outage occurs. AIOps approaches instead attempt to correlate large volumes of telemetry and operational data to identify patterns that could indicate an impending failure.
That can include detecting abnormal device behavior, identifying recurring incidents, prioritizing alerts and triggering predefined remediation workflows. The objective is not simply to generate more alerts, but to reduce the amount of manual analysis required before an IT team can act.
From monitoring to autonomous operations
The infrastructure operations market is moving toward increasingly automated operating models. Gartner’s 2026 research identifies AI and infrastructure platforms as major forces shaping IT infrastructure and operations, while its research on agentic AI in I&O points to a longer-term shift toward systems capable of executing operational tasks rather than merely recommending actions.
But the transition is not automatic.
Gartner reported in April that only 28% of AI use cases in infrastructure and operations fully succeed and meet ROI expectations, while 20% fail outright. The research found that successful deployments are strongly associated with integrating AI into existing workflows and systems and securing executive support.
That makes NTT DATA’s positioning significant. Its platform is being presented not as an isolated AI tool, but as an operating layer connected to infrastructure monitoring, service management, hardware lifecycle processes and global delivery teams.
The distinction matters because enterprise infrastructure is heterogeneous. A global manufacturer can have on-premises systems, cloud workloads, network equipment, production-site technology and legacy platforms operating simultaneously. AI can only improve operations if it can access enough operational data and interact with the systems responsible for remediation.
Manufacturing raises the stakes
Manufacturing is particularly suited to this approach because IT and operational technology are becoming increasingly interconnected.
Daimler Truck’s own technology strategy includes digital and connectivity-based services, a unified electronic architecture across brands and a software-defined vehicle platform initiative with Volvo Group. That broader digitization increases the importance of resilient enterprise and production infrastructure.
In such environments, infrastructure management is no longer simply a back-office concern. Connectivity supports production facilities, employees, engineering operations and increasingly software-defined products.
NTT DATA says its platform also supports coordinated hardware upgrades and lifecycle management, an area where automation can help organizations maintain infrastructure consistency across geographically distributed locations.
The potential business value therefore extends beyond faster ticket resolution. Predictive infrastructure management can help reduce unplanned downtime, improve resource utilization and give CIO and infrastructure teams a consolidated view of environments that would otherwise be managed through separate monitoring and service-management tools.
The AI infrastructure market is expanding beyond compute
The development comes as enterprise spending on AI infrastructure accelerates.
Gartner forecasts worldwide AI spending will reach $2.59 trillion in 2026, up 47% year over year. More than 45% of that spending is expected to be associated with AI infrastructure, including AI-optimized IaaS, servers, networking, processing semiconductors and devices.
That growth creates a second-order requirement: organizations need better systems to operate the infrastructure being deployed for AI.
Gartner also forecasts worldwide spending on AI-optimized IaaS to reach $42 billion in 2026, representing 96% growth through the year.
The implication is that AI operations can become part of the AI infrastructure stack itself. As enterprises deploy more compute, networking, cloud resources and connected devices, manually managing every component becomes increasingly difficult.
This creates an opportunity for vendors including NTT DATA, Microsoft, IBM, Cisco, ServiceNow and other AIOps and observability providers to compete around predictive analytics, automation, observability and intelligent remediation.
The differentiator, however, is likely to shift from simply having an AI model to having access to operational data, integrations and governance mechanisms that allow AI recommendations to be safely executed.
Why the NTT DATA deployment matters
NTT DATA’s announcement illustrates how enterprise AI is moving into a less visible but increasingly consequential category: the systems responsible for maintaining digital infrastructure.
The company already positions its infrastructure business around secure networking, hybrid data centers and AI-ready infrastructure, including partnerships with technology providers such as Cisco, NVIDIA, Palo Alto Networks and Dell Technologies.
Its latest deployment adds an operational intelligence layer to that infrastructure strategy.
For enterprise technology leaders, the bigger question is therefore not whether AI can monitor infrastructure. It is whether AI can become sufficiently integrated with enterprise operations to predict problems, prioritize the right responses and eventually execute remediation under appropriate human and governance controls.
That is where AIOps is heading. The value will be measured less by the sophistication of an underlying model and more by whether the technology can keep increasingly complex digital businesses running reliably.
Market Landscape
The enterprise infrastructure market is entering a period in which AI is being used both to power workloads and to operate the infrastructure supporting them.
Gartner’s 2026 research says I&O leaders are increasingly expected to move from reactive support toward becoming strategic enablers of AI value, while its research on agentic AI emphasizes operational data, automation pipelines, governance and trust as prerequisites for successful deployment.
NTT DATA is competing in a landscape that includes AIOps, observability, IT service management and infrastructure automation platforms from companies such as Microsoft, IBM, Cisco, ServiceNow, Dynatrace and others. The competitive battleground is increasingly moving toward end-to-end visibility, cross-platform integration, predictive analytics and the ability to safely automate remediation.
For manufacturers, the stakes are higher because IT infrastructure increasingly intersects with production and operational technology. This makes resilience, latency, connectivity, lifecycle management and cybersecurity as important as the AI analytics itself.
Top Insights
- NTT DATA is expanding AI-powered infrastructure operations to provide real-time visibility, predictive insights and automated incident management across complex enterprise environments.
- Daimler Truck demonstrates the manufacturing use case, where infrastructure reliability increasingly affects connected production facilities, employees and digital operations.
- AIOps is shifting from alert analysis toward proactive operations, using AI to identify potential disruptions and automate portions of the response process.
- AI infrastructure creates an operational management challenge, with rapidly expanding compute, cloud, networking and connected-device environments requiring greater automation.
- Enterprise ROI will depend on integration and governance, not AI models alone, as infrastructure teams need reliable data, workflow integration and controlled automation.
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