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Riverbed’s Global Survey on The Future of IT Operations in the AI Era highlights trends in the manufacturing sector’s adoption of AI technologies, showing both progress and gaps in scaling AI initiatives.
The survey found that 87% of manufacturing leaders report that their AIOps initiatives have met or exceeded expectations. However, only 37% feel prepared to operationalise AI across the enterprise, with 62% of AI projects still in pilot or development stages. Additionally, 90% of respondents indicated that improving data quality is critical to AI success.
Despite positive sentiment, several barriers remain. Nearly half of organisations expressed concerns over the accuracy and completeness of their data, and only 34% rated their data as excellent in terms of relevance and suitability. These results suggest a gap between leadership optimism and technical readiness.
Tool consolidation is a priority for IT operations. On average, manufacturers currently use 13 observability tools from nine vendors. To address sprawl and cost concerns, 95% of respondents are planning to consolidate tools. Key objectives include improving tool integration (48%), reducing vendor management overhead (47%), and increasing IT productivity (46%).
Unified communication (UC) tools are widely used, with 42% of employees employing them weekly. However, less than half of respondents reported satisfaction with these tools. Common challenges include limited visibility (51%), dropped calls (42%), and integration issues with enterprise systems (38%).
OpenTelemetry (OTel) adoption is increasing: 44% of organisations have fully implemented it, 42% are in the process of adoption, and 97% agree it is critical to observability strategies. OTel is viewed as foundational for initiatives such as AI-driven automation, with 37% of respondents reporting it as a strategic mandate.
Data movement is also a key factor for AI success. A total of 91% of respondents cited it as important, and 75% plan to establish an AI data repository strategy by 2028. Primary considerations for data enablement include network performance (96%), cost of data movement (94%), and AI model proximity to data (93%).
Manufacturing organisations also emphasised the importance of network efficiency and data security, with 79% indicating these elements are essential to their AI strategies.
