Riverbed AIOps has revealed new insights into the manufacturing sector’s progress in artificial intelligence adoption, highlighting strong returns on investment but significant readiness gaps. The findings come from Riverbed’s Global Survey on “The Future of IT Operations in the AI Era.”

According to the survey, 87% of manufacturing leaders and technical specialists said the ROI from their AIOps initiatives has met or exceeded expectations. However, only 37% reported being fully prepared to operationalize AI at scale. At the same time, 62% of AI projects remain in pilot or development stages. Additionally, 90% of respondents agreed that improving data quality is critical to AI success.

The findings show that while manufacturers are eager to leverage AI to streamline operations, reduce costs, and manage increasingly complex global supply chains, many are still working to close the gap between ambition and enterprise-wide execution.

As manufacturing organizations advance their AI journey, several barriers continue to hinder large-scale adoption. Although 57% expressed confidence in their AI projects, persistent data quality challenges remain a central obstacle. Nearly half, or 47%, lack confidence in the accuracy and completeness of their organization’s data to deliver the right outcomes. Moreover, only 34% rated their data as excellent for relevance and suitability. These figures highlight a disconnect between leadership optimism and technical implementation realities.

Richard Tworek, Chief Technology Officer at Riverbed, said the manufacturing industry is investing heavily in AI to transform IT operations. He noted that nearly nine in ten companies are meeting or exceeding ROI expectations from their AIOps investments. However, he added that many still face readiness and preparedness gaps, as well as data quality issues that hinder progress. He emphasized that Riverbed is helping manufacturing customers close these gaps with safe, secure, and accurate AI built on high-quality real data, enabling organizations to scale AI across the enterprise.

Meanwhile, tool consolidation has emerged as a top IT priority for manufacturers. The research found that organizations in the sector use an average of 13 observability tools from nine different vendors. As a result, 95% are consolidating tools to reduce sprawl, cut costs, streamline operations, and optimize IT efficiencies. Furthermore, 91% are considering new tools as part of their consolidation strategy. The top drivers include enhancing tool integration and interoperability at 48%, reducing vendor management overhead at 47%, and improving IT productivity at 46%.

In addition, unified communication tools are gaining attention as AI and remote work reshape manufacturing operations. The survey found that 42% of employees use unified communications tools throughout their work week, while 66% of respondents consider them essential for effective weekly operations. However, satisfaction remains limited. Only 45% are satisfied with performance, and 42% report issues with video calls, messaging platforms, and related tools. The leading challenges include limited visibility at 51%, dropped calls at 42%, and integration challenges with other enterprise systems at 38%.

The survey also examined the adoption of OpenTelemetry. It found that 44% of manufacturing organizations have fully implemented OpenTelemetry, while another 42% are in the process of adopting it. Overall, 97% agree that cross-domain OpenTelemetry correlation is critical to their observability strategy. Additionally, 93% said OpenTelemetry forms a foundation for future initiatives such as AI-driven automation. Notably, 37% reported that OpenTelemetry is already mandated within their organization.

Data movement and network performance were also identified as critical components of AI success. A total of 91% of respondents cited the movement and sharing of data as important to their AI strategy, with 31% describing it as critical and foundational to AI design and execution. Looking ahead, 75% plan to establish an AI data repository strategy by 2028.

When enabling organizations to move and scale data effectively, the top considerations included network performance and ability at 96%, cost of data movement and storage at 94%, and AI model proximity to data as well as interoperability between environments at 93%. Furthermore, 79% reported that network performance and security are essential to their AI strategy.

Overall, the survey underscores that while ROI expectations are being met, manufacturers must address readiness gaps, data quality challenges, and infrastructure demands to fully realize the benefits of Riverbed AIOps at enterprise scale.

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