A new global study from Riverbed, “The Future of IT Operations in the AI Era,” reveals that while manufacturing organizations have doubled down on AI investments and are already seeing strong returns, most are still far from operationalizing AI at scale. The survey shows 87% of manufacturing leaders report ROI from AIOps initiatives has met or exceeded expectations, yet only 37% say they are fully prepared to deploy AI enterprise‑wide.
The research highlights a sector eager to use AI to streamline operations, manage rising supply chain complexity, and boost efficiency. However, 62% of AI initiatives remain in pilot or development phases, signaling a significant gap between strategic ambition and implementation readiness. Central to this challenge is data quality: 90% of manufacturing respondents agree that improving data quality is critical to AI success, but nearly half (47%) lack confidence in the completeness and accuracy of their data, and only 34% rate their data as “excellent.”
“Manufacturers are investing heavily in AI, but gaps in readiness and data quality remain major obstacles to scaling it successfully.”
— Richard Tworek, Chief Technology Officer, Riverbed
“The manufacturing industry is investing heavily in AI to transform IT operations,” said Richard Tworek, CTO at Riverbed. “But many still face major challenges, from readiness gaps to persistent data quality issues. We’re helping organizations close these gaps with secure, accurate AI built on high‑quality real data.”
The study also reveals that manufacturers are battling rising IT tool sprawl. On average, organizations use 13 observability tools from nine vendors, prompting 95% to pursue consolidation efforts to reduce costs and improve efficiency. Integration and interoperability ranked as top priorities for leaders evaluating new tools.
As remote work and intelligent automation reshape the industry, unified communication (UC) tools are also under scrutiny. While 66% say these tools are essential, fewer than half are satisfied with performance, citing visibility gaps, dropped calls, and poor integration with enterprise systems.
OpenTelemetry adoption is accelerating, with 44% fully implementing the standard and another 42% in progress. Nearly all respondents (97%) believe cross-domain OTel correlation is vital to observability and AI-driven automation.
With data movement becoming critical to AI strategy, 91% of respondents view seamless data sharing as essential, and 75% plan to establish AI data repository strategies by 2028. Network performance, cost of data movement, and AI model proximity to data ranked among the top considerations for scaling AI.
The full report underscores a clear message: manufacturers are ready to embrace AI’s potential but must strengthen data foundations, modernize infrastructure, and consolidate tools to operationalize AI at scale.
