{"id":166916,"date":"2026-03-05T09:34:50","date_gmt":"2026-03-05T09:34:50","guid":{"rendered":"https:\/\/www.intelligentcio.com\/eu\/?p=166916"},"modified":"2026-03-05T09:34:50","modified_gmt":"2026-03-05T09:34:50","slug":"manufacturing-organisations-double-ai-investment-but-only-37-ready-to-operationalise-ai-riverbed-study-finds","status":"publish","type":"post","link":"https:\/\/www.intelligentcio.com\/eu\/2026\/03\/05\/manufacturing-organisations-double-ai-investment-but-only-37-ready-to-operationalise-ai-riverbed-study-finds\/","title":{"rendered":"Manufacturing organisations double AI investment but only 37% ready to operationalise AI, Riverbed study finds"},"content":{"rendered":"\n

Riverbed research shows strong AI momentum across manufacturing but highlights major gaps in readiness, data quality and enterprise-scale implementation.<\/em><\/p>\n\n\n\n

Riverbed has released the manufacturing industry results from its Global Survey on The Future of IT Operations in the AI Era <\/em>– revealing both strong momentum and significant readiness gaps in AI adoption.<\/p>\n\n\n\n

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 operationalise AI at scale.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

As organisations in the manufacturing sector aim to advance their AI journey, there are several significant barriers hindering wide-scale adoption.<\/p>\n\n\n\n

While more than half (57%) 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.<\/p>\n\n\n\n

Almost half (47%) lack confidence in the accuracy and completeness of their organisation\u2019s 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.<\/p>\n\n\n\n

\u201cThe 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,\u201d said Richard Tworek, Chief Technology Officer, Riverbed.<\/p>\n\n\n\n

\u201cHowever, 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\u2019re 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 organisations to scale AI across the enterprise.\u201d<\/p>\n\n\n\n

Tool consolidation a top IT priority for manufacturers<\/strong><\/p>\n\n\n\n

Amid changing processes and varying priorities, manufacturers have pursued an array of IT tools to support shifting goals.<\/p>\n\n\n\n

The research found that, on average, organisations 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 optimise efficiencies across IT operations.<\/p>\n\n\n\n

Vendors will be well-served to continue exploring their tools\u2019 capabilities, with 91% of manufacturing organisations considering new tools as they look to consolidate.<\/p>\n\n\n\n

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%).<\/p>\n\n\n\n

Unified communication in need of reform<\/strong><\/p>\n\n\n\n

With AI and remote work set to transform manufacturing organisations worldwide, the survey found enthusiasm for unified communication tools and their integration into operations.<\/p>\n\n\n\n

\u2022 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.<\/p>\n\n\n\n

\u2022 Despite growing adoption, these tools still have significant room for improvement. Less than half (45%) are satisfied with UC tools\u2019 performance and 42% of manufacturers report experiencing issues with video calls, messaging platforms and more.<\/p>\n\n\n\n

\u2022 The top three challenges organisations face with UC tools include limited visibility (51%), dropped calls (42%) and integration challenges with other enterprise systems (38%).<\/p>\n\n\n\n

Adoption of OpenTelemetry across manufacturing<\/strong><\/p>\n\n\n\n

Manufacturing leaders surveyed also report their views on OpenTelemetry (OTel) and its place within their organisation.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

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 organisation, indicating a substantial interest in this technology.<\/p>\n\n\n\n

AI data movement and network performance<\/strong><\/p>\n\n\n\n

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 organisation\u2019s overall AI strategy, with 31% stating it\u2019s critical and foundational to how they design and executive AI.<\/p>\n\n\n\n

To further support AI initiatives, 75% of manufacturing respondents plan to establish an AI data repository strategy by 2028.<\/p>\n\n\n\n

Respondents also confirmed their top three considerations when enabling their organisation to move and scale data effectively were:<\/p>\n\n\n\n

\u2022 Network performance and ability (96%)
\u2022 Cost of data movement and storage (94%)
\u2022 AI model proximity to data and interoperability between environments (both 93%)<\/p>\n\n\n\n

Additionally, as manufacturing organisations 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.<\/p>\n\n\n\n

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.<\/p>\n","protected":false},"excerpt":{"rendered":"

Riverbed research shows strong AI momentum across manufacturing but highlights major gaps in readiness, data quality and enterprise-scale implementation. Riverbed has released the manufacturing industry results from its Global Survey on The Future of IT Operations in the AI Era – revealing both strong momentum and significant readiness gaps in AI adoption. While 87% of […]<\/p>\n","protected":false},"author":58,"featured_media":166917,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[21064,649,93],"tags":[24687,24688,19032,11129,22869,22723,24689,20667,16761,9399],"class_list":["post-166916","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","category-manufacturing","category-top-stories","tag-ai-data-strategy","tag-ai-in-manufacturing","tag-ai-readiness","tag-aiops","tag-enterprise-ai-adoption","tag-it-operations","tag-manufacturing-it","tag-observability","tag-opentelemetry","tag-riverbed"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/www.intelligentcio.com\/eu\/wp-json\/wp\/v2\/posts\/166916","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.intelligentcio.com\/eu\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.intelligentcio.com\/eu\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.intelligentcio.com\/eu\/wp-json\/wp\/v2\/users\/58"}],"replies":[{"embeddable":true,"href":"https:\/\/www.intelligentcio.com\/eu\/wp-json\/wp\/v2\/comments?post=166916"}],"version-history":[{"count":1,"href":"https:\/\/www.intelligentcio.com\/eu\/wp-json\/wp\/v2\/posts\/166916\/revisions"}],"predecessor-version":[{"id":166918,"href":"https:\/\/www.intelligentcio.com\/eu\/wp-json\/wp\/v2\/posts\/166916\/revisions\/166918"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.intelligentcio.com\/eu\/wp-json\/wp\/v2\/media\/166917"}],"wp:attachment":[{"href":"https:\/\/www.intelligentcio.com\/eu\/wp-json\/wp\/v2\/media?parent=166916"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.intelligentcio.com\/eu\/wp-json\/wp\/v2\/categories?post=166916"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.intelligentcio.com\/eu\/wp-json\/wp\/v2\/tags?post=166916"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}