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.

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AI Deepfakes: Rethinking Trust in the Workplace

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

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Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

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