Manufacturing Organizations Doubled AI Investment Yet Only 37% Fully Prepared to Operationalize AI

Learn more about Riverbed

While 87% of manufacturing leaders and technical specialists report that ROI from their AIOps initiatives has met or exceeded expectations, only 37% say they are fully prepared to operationalize AI at scale, according to The Future of IT Operations in the AI Era, a report from Riverbed.

With 62% of AI projects still in pilot or development stages, and 90% of respondents agreeing that improving data quality is critical to AI success, the findings highlight a sector eager to leverage AI to streamline operations, reduce costs, and navigate increasingly complex global supply chains, yet still working to close the gap between ambition and enterprise-wide AI execution at scale.

As organizations in the manufacturing sector aim to advance their AI journey, there are several significant barriers hindering wide-scale adoption. While more than half (57%) of manufacturing organizations express confidence in their AI projects, and the vast majority agree that improving data quality is critical to success, persistent data quality challenges remain a central obstacle. Almost half (47%) lack confidence in the accuracy and completeness of their organization's data to be able to deliver the right outcomes, and only 34% rate their data as excellent for relevance and suitability. These gaps highlight a clear disconnect between leadership optimism and the technical realities of implementation.

"The manufacturing industry is investing heavily in AI to transform IT operations, and our survey results show that nearly nine in ten companies in this sector (87%) are already meeting or exceeding ROI expectations from their AIOps investments," said Richard Tworek, CTO at Riverbed. "However, many still face major challenges, including gaps in readiness and preparedness, as well as data quality issues which are hindering progress. As a data-driven company, we're helping our manufacturing customers close these gaps with safe, secure and accurate AI built on high-quality real data; delivering practical AI-powered solutions that enable organizations to scale AI across the enterprise."

Tool consolidation a top IT priority for manufacturers

Amid changing processes and varying priorities, manufacturers have pursued an array of IT tools to support shifting goals. The research found that, on average, organizations in this industry currently use 13 observability tools from nine different vendors. In response, 95% of manufacturers are consolidating tools to cut down on sprawl in an effort to reduce costs, streamline operations, and optimize efficiencies across IT operations.

Vendors will be well-served to continue exploring their tools' capabilities, with 91% of manufacturing organizations considering new tools as they look to consolidate. The top capabilities and drivers manufacturing leaders are actively considering when consolidating tools include enhancing tool integration and interoperability (48%), reducing vendor management overhead (47%), and improving IT productivity (46%).

Unified communication in need of reform

With AI and remote work set to transform manufacturing organizations worldwide, the survey found enthusiasm for unified communication tools and their integration into operations.

  • The research revealed that 42% of employees use UC tools throughout their work week and 66% of manufacturing respondents say that these tools are essential to operating effectively on a weekly basis.
  • Despite growing adoption, these tools still have significant room for improvement. Less than half (45%) are satisfied with UC tools' performance, and 42% of manufacturers report experiencing issues with video calls, messaging platforms, and more.
  • The top three challenges organizations face with UC tools include limited visibility (51%), dropped calls (42%), and integration challenges with other enterprise systems (38%). 

Adoption of OpenTelemetry across manufacturing

Manufacturing leaders surveyed also report their views on OpenTelemetry (OTel) and its place within their organization. The research found that 44% have fully implemented OTel, with a further 42% adopting it, and overall, 97% agree that cross-domain OpenTelemetry correlation is critical to their observability strategy. The vast majority (93%) say that OTel is a foundation for future initiatives such as AI-driven automation and 37% cite that OTel is already a mandate in their organization, indicating a substantial interest in this technology.

AI data movement and network performance

With data already identified as a key factor to critical success in the implementation of AI initiatives, 91% of manufacturing respondents cited the movement and sharing of data as important to their organization's overall AI strategy, with 31% stating it's critical and foundational to how they design and executive AI. To further support AI initiatives, 75% of manufacturing respondents plan to establish an AI data repository strategy by 2028.

Respondents also confirmed their top three considerations when enabling their organization to move and scale data effectively were:

  • Network performance and ability (96%) 
  • Cost of data movement and storage (94%) 
  • AI model proximity to data, and interoperability between environments (both 93%)

Additionally, as manufacturing organizations strive to stay competitive, ensuring superior network efficiency and robust data security is a top priority, as 79% report that network performance and security are essential to their AI strategy.

Methodology: The survey polled 1,200 business decision-makers, IT leaders and technical specialists across seven countries and multiple industries, including the Manufacturing sector. The research was conducted by Coleman Parkes Research in July 2025.

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Manufacturing Organizations Doubled AI Investment Yet Only 37% Fully Prepared to Operationalize AI

Learn more about Riverbed

While 87% of manufacturing leaders and technical specialists report that ROI from their AIOps initiatives has met or exceeded expectations, only 37% say they are fully prepared to operationalize AI at scale, according to The Future of IT Operations in the AI Era, a report from Riverbed.

With 62% of AI projects still in pilot or development stages, and 90% of respondents agreeing that improving data quality is critical to AI success, the findings highlight a sector eager to leverage AI to streamline operations, reduce costs, and navigate increasingly complex global supply chains, yet still working to close the gap between ambition and enterprise-wide AI execution at scale.

As organizations in the manufacturing sector aim to advance their AI journey, there are several significant barriers hindering wide-scale adoption. While more than half (57%) of manufacturing organizations express confidence in their AI projects, and the vast majority agree that improving data quality is critical to success, persistent data quality challenges remain a central obstacle. Almost half (47%) lack confidence in the accuracy and completeness of their organization's data to be able to deliver the right outcomes, and only 34% rate their data as excellent for relevance and suitability. These gaps highlight a clear disconnect between leadership optimism and the technical realities of implementation.

"The manufacturing industry is investing heavily in AI to transform IT operations, and our survey results show that nearly nine in ten companies in this sector (87%) are already meeting or exceeding ROI expectations from their AIOps investments," said Richard Tworek, CTO at Riverbed. "However, many still face major challenges, including gaps in readiness and preparedness, as well as data quality issues which are hindering progress. As a data-driven company, we're helping our manufacturing customers close these gaps with safe, secure and accurate AI built on high-quality real data; delivering practical AI-powered solutions that enable organizations to scale AI across the enterprise."

Tool consolidation a top IT priority for manufacturers

Amid changing processes and varying priorities, manufacturers have pursued an array of IT tools to support shifting goals. The research found that, on average, organizations in this industry currently use 13 observability tools from nine different vendors. In response, 95% of manufacturers are consolidating tools to cut down on sprawl in an effort to reduce costs, streamline operations, and optimize efficiencies across IT operations.

Vendors will be well-served to continue exploring their tools' capabilities, with 91% of manufacturing organizations considering new tools as they look to consolidate. The top capabilities and drivers manufacturing leaders are actively considering when consolidating tools include enhancing tool integration and interoperability (48%), reducing vendor management overhead (47%), and improving IT productivity (46%).

Unified communication in need of reform

With AI and remote work set to transform manufacturing organizations worldwide, the survey found enthusiasm for unified communication tools and their integration into operations.

  • The research revealed that 42% of employees use UC tools throughout their work week and 66% of manufacturing respondents say that these tools are essential to operating effectively on a weekly basis.
  • Despite growing adoption, these tools still have significant room for improvement. Less than half (45%) are satisfied with UC tools' performance, and 42% of manufacturers report experiencing issues with video calls, messaging platforms, and more.
  • The top three challenges organizations face with UC tools include limited visibility (51%), dropped calls (42%), and integration challenges with other enterprise systems (38%). 

Adoption of OpenTelemetry across manufacturing

Manufacturing leaders surveyed also report their views on OpenTelemetry (OTel) and its place within their organization. The research found that 44% have fully implemented OTel, with a further 42% adopting it, and overall, 97% agree that cross-domain OpenTelemetry correlation is critical to their observability strategy. The vast majority (93%) say that OTel is a foundation for future initiatives such as AI-driven automation and 37% cite that OTel is already a mandate in their organization, indicating a substantial interest in this technology.

AI data movement and network performance

With data already identified as a key factor to critical success in the implementation of AI initiatives, 91% of manufacturing respondents cited the movement and sharing of data as important to their organization's overall AI strategy, with 31% stating it's critical and foundational to how they design and executive AI. To further support AI initiatives, 75% of manufacturing respondents plan to establish an AI data repository strategy by 2028.

Respondents also confirmed their top three considerations when enabling their organization to move and scale data effectively were:

  • Network performance and ability (96%) 
  • Cost of data movement and storage (94%) 
  • AI model proximity to data, and interoperability between environments (both 93%)

Additionally, as manufacturing organizations strive to stay competitive, ensuring superior network efficiency and robust data security is a top priority, as 79% report that network performance and security are essential to their AI strategy.

Methodology: The survey polled 1,200 business decision-makers, IT leaders and technical specialists across seven countries and multiple industries, including the Manufacturing sector. The research was conducted by Coleman Parkes Research in July 2025.

Hot Topics

The Latest

When the Database Is the Problem, Is APM Enough?

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

From Cleanup to Prevention: The Future of Cloud-Native Cost Optimization

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

The Real Enterprise AI Risk Isn't Hallucinations

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

Beyond Buy vs. Build: The New Playbook for Data Pipelines

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

The Corporate Network Is Fading - Here's What Replaces It

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

IT Leaders Must Establish a Network Data Architecture Practice

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

Data Center Outages Are Decreasing but More Costly

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

Observability Has a Physical Layer Now: What AI Infrastructure Teaches IT Operations

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

TLS Certificate Expiration Is Becoming an Observability Problem

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprise Cloud Observability: Choosing the Right Starting Point

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...