While 87 percent of manufacturing leaders and technical specialists report that ROI from their AIOps initiatives has met or exceeded expectations, only 37 percent say they are fully prepared to operationalize AI at scale.
A new survey from Riverbed looking at AI in IT operations finds 62 percent of AI projects still in pilot or development stages, and 90 percent 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.
Several significant barriers are identified as hindering wide-scale adoption. While more than half (57 percent) of manufacturing organisations 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 percent) lack confidence in the accuracy and completeness of their organization’s data to be able to deliver the right outcomes, and only 34 percent rate their data as excellent for relevance and suitability.
“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 percent) are already meeting or exceeding ROI expectations from their AIOps investments,” says Richard Tworek, chief technology officer, 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 sprawl is an issue, on average, organizations in this industry currently use 13 observability tools from nine different vendors. In response, 95 percent of manufacturers are consolidating tools to cut down on sprawl in an effort to reduce costs, streamline operations, and optimize efficiencies across IT operations.
You can see more of the survey’s findings on the Riverbed site.
Image credit: Momius/depositphotos.com
![why-an-adaptive-learning-model-is-the-way-forward-in-aiops-[q&a]](https://thetechbriefs.com/wp-content/uploads/2025/08/49202-why-an-adaptive-learning-model-is-the-way-forward-in-aiops-qa.jpg)
![how-businesses-are-adapting-to-the-challenges-of-ai-[q&a]](https://thetechbriefs.com/wp-content/uploads/2025/03/7164-how-businesses-are-adapting-to-the-challenges-of-ai-qa.jpg)
