{"id":46930,"date":"2026-04-16T12:41:33","date_gmt":"2026-04-16T12:41:33","guid":{"rendered":"https:\/\/www.gsma.com\/solutions-and-impact\/industries\/connected-manufacturing\/?post_type=gsma_theme_resources&p=46930"},"modified":"2026-04-16T12:42:15","modified_gmt":"2026-04-16T12:42:15","slug":"industrial-transformation-in-manufacturing-now-depends-on-integration-not-experimentation","status":"publish","type":"gsma_theme_resources","link":"https:\/\/www.gsma.com\/solutions-and-impact\/industries\/connected-manufacturing\/gsma_resources\/industrial-transformation-in-manufacturing-now-depends-on-integration-not-experimentation\/","title":{"rendered":"Industrial transformation in manufacturing now depends on integration, not experimentation\u00a0"},"content":{"rendered":"\n

“The future doesn\u2019t belong to companies that have the fastest network or the best technology. It belongs to partnerships that speak the same language. The language of measurable value.” Those words from Priya Kurien, Research Director at the IBM Institute for Business Value<\/a>, at the Manufacturing and Production Summit<\/a> at MWC26 Barcelona underscored a central point in the debate about how to realise industrial transformation at scale: manufacturers are not looking for isolated technologies, but for partners that can solve measurable operational problems. This is because manufacturing has moved into a more demanding phase. Now, the critical issue is whether technologies such as AI, private networks, automation and digital twins can be combined to solve operational problems at scale. Across the summit, the most serious contributions pointed in the same direction. Manufacturing does not need more siloed pilots. It needs integrated systems that improve resilience, productivity and control. <\/p>\n\n\n\n

Modernisation is critical because pressure on manufacturers is growing from several directions at once. Supply chains remain fragile. Labour shortages are becoming structural. Product cycles are shortening. Cyber risk is widening as more operational environments become connected. AI is also raising expectations that industrial systems should become more responsive, more predictive and more autonomous. Yet that promise only becomes useful when it is tied to the real constraints of production. As Priya Kurien put it, \u201cWhat manufacturing buys is not connectivity. They buy outcomes.\u201d <\/p>\n\n\n\n

Networks, edge computing and AI are not meaningful to manufacturers as abstract capabilities. They matter when they reduce downtime, improve quality, help plants respond faster, or make scarce expertise go further. Too often, suppliers still present infrastructure as the product, when manufacturers are looking for business outcomes. <\/p>\n\n\n\n

The pilot phase is ending, but scale is still difficult<\/strong>\u00a0<\/h3>\n\n\n\n

As Tunc Yorulmaz, Global Head of Networks at Accenture<\/a> explained, the current juncture in industrial transformation is the point where \u201cwe pass through POC cycle\u201d and where \u201ceveryone is asking what is real\u201d. The immediate question now is whether a system can be shown \u201cin a production system\u201d and in \u201ca real implementation.\u201d This is a reflection that the market is becoming less interested in demonstration and more interested in deployment.\u00a0<\/p>\n\n\n\n

Industrial transformation needs joined-up systems, not siloed offers<\/strong>\u00a0<\/h3>\n\n\n\n

One of the recurring snags facing the industry is that the provider ecosystem still mirrors the internal fragmentation of large manufacturers. Strategy, IT, operational technology and plant operations often sit in separate silos. Many solution providers operate this way. That makes transformation harder to scale, because no single offer addresses the whole operating environment. <\/p>\n\n\n\n

John Robinson, Founder and Creator of the Quorum Principle<\/a> argued that \u201cthere\u2019s a systemic problem in global manufacturing\u201d and that \u201cthe solution provider ecosystem is an exact mirror image\u201d of the same fragmented areas of expertise inside manufacturers. His point was not that specialist vendors lack value. It\u2019s that manufacturers are often forced to assemble a patchwork of disconnected solutions and then make them work together themselves. That is one reason so many pilots fail to expand into broader transformation. <\/p>\n\n\n\n

This challenge also explains why partnerships are a recurring subject of debate surrounding industrial transformation. Yet partnerships and collaborative efforts depend on alignment. As Priya Kurien argued, \u201cpartnerships\u00a0don\u2019t\u00a0fail because of technology. Partnerships fail when incentives, ownership, and success metrics are not aligned to the same outcome.\u201d That is a much more practical test.\u00a0Integration will remain partial unless the network provider, software company,\u00a0integrator\u00a0and manufacturer all work to the same business result.<\/p>\n\n\n\n

This is why the commercial model matters as much as the technical one. Tunc Yorulmaz warned that enterprises increasingly want faster engagement, new commercial structures and \u201can\u00a0outcome-based\u00a0approach\u201d rather than traditional charging logic. That fits what manufacturers are asking for. They are trying to improve throughput, reduce downtime, shorten lead times and cope with labour shortages. They are not trying to buy 5G as an isolated\u00a0product.<\/p>\n\n\n\n

AI is becoming useful, but trust and control still decide adoption<\/strong>\u00a0<\/h3>\n\n\n\n

AI deployments have\u00a0led to\u00a0gains in visual inspection, anomaly detection, field\u00a0service\u00a0and robotics support. Diego\u00a0Romeres, Team Leader\u00a0and\u00a0Senior Principal Research Scientist\u00a0at\u00a0Mitsubishi Electric Research Laboratories<\/a>,\u00a0said AI is already beyond hype when it improves \u201cthe KPI that\u2019s trying to optimise\u201d and cited \u201chigh speed visual inspection\u201d,\u00a0anomaly detection and machine status support on the shop floor as examples of real value.<\/p>\n\n\n\n

Yet industry still distinguishes between useful AI and fully trusted autonomy. When AI supports decisions, organisations can adopt it more quickly. When it starts acting inside physical control loops, questions of stability, verification and safety become much harder. As Romeres explained, trust in this context means predictable behaviour, verifiable performance and the ability to fail safely. <\/p>\n\n\n\n

This caution underscored the reality that factories are not about to hand control to autonomous systems wholesale. It means narrow, high-value applications are becoming credible, but their expansion depends on stronger governance, better connectivity and more disciplined validation.  <\/p>\n\n\n\n

A clear example of how trust is reinforced came from the complementarity between\u00a0Dassault Syst\u00e8me<\/a>s and\u00a0Archetype AI<\/a>. Dassault showed how physics-grounded virtual twins help engineers test and\u00a0validate\u00a0designs with confidence in the virtual world.\u00a0On the other hand,\u00a0Brandon Barbello of Archetype AI\u00a0applied\u00a0that same logic into the factory.\u00a0He argued\u00a0that \u201cthe physical world speaks in a different language.\u00a0It\u2019s\u00a0a language of vibration and temperatures and pressures and visual spatial data that we\u00a0don\u2019t\u00a0have on the web.\u201d Together, these approaches suggest that manufacturers will trust AI more when its outputs can be tested,\u00a0explained\u00a0and checked against operational reality.<\/p>\n\n\n\n

Security\u00a0and resilience are critical to\u00a0industrial transformation<\/strong><\/h3>\n\n\n\n

Once industrial environments become more connected, cybersecurity can no longer sit outside the transformation story. It becomes part of the operating model itself. Digital risk can come from several angles: expanded attack surfaces, legacy OT environments, supply chain exposure and data sovereignty. <\/p>\n\n\n\n

Amir Stephenson, Director of Networked Electromagnetic Systems for Lockheed Martin<\/a>, broadened the discussion beyond cyber risk alone. He argued that secure industrial transformation depends on maintaining \u201cpersistent, secure information flow across a hybrid network\u201d through the deliberate pairing of commercial and non-commercial technologies. In other words, resilience comes not just from blocking threats, but from designing networks that can operate securely across complex, mixed environments. <\/p>\n\n\n\n

David O’Byrne, VP Business Development at\u00a0Druid Software<\/a>\u00a0described the\u00a0security situation\u00a0starkly: industrial transformation has \u201ccreated the largest attack surface in the history of attack surfaces\u201d.\u00a0Yet Steve Shaw, 5G Solutions Marketing Lead from\u00a0Palo Alto Networks<\/a>\u00a0argued that security teams must stop acting as \u201cthe group of\u00a0no\u201d and instead help shape projects from the start. Those remarks matter because they shift security away from compliance language and into operational language. In manufacturing, a cyber incident is not just an IT event. It can mean disruption to production, exposure of intellectual property, or loss of control over essential systems.<\/p>\n\n\n\n

Sovereignty\u00a0is\u00a0likely a\u00a0long-term issue<\/strong><\/h3>\n\n\n\n

That leads to a broader point the industry has to contend with about sovereignty. The debate here is not reductive digital nationalism. It’s whether some industrial workloads, especially those involving sensitive engineering data, simulation or strategic infrastructure, require stronger control over where data sits and who governs access to it. In that sense, resilience now means more than redundancy. It includes trust in infrastructure, in data handling and in the continuity of operations. <\/p>\n\n\n\n

These issues\u00a0demonstrate\u00a0how\u00a0manufacturing\u00a0is evolving\u00a0and point to a more disciplined stage of digital change. The necessary technologies involved are becoming more sophisticated to meet long-term commercial demand. But progress now depends less on showcasing innovation and more on integrating it. The next gains will come from connecting AI, networks,\u00a0automation\u00a0and cybersecurity into one operational model that can stand up in production. That is a harder task than running pilots. It is also the one that now matters most.<\/p>\n\n\n\n

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

“The future doesn\u2019t belong to companies that have the fastest network or the best technology. It belongs to partnerships that speak the same language. The language of measurable value.” Those words from Priya Kurien, Research Director at the IBM Institute for Business Value, at the Manufacturing and Production Summit at MWC26 Barcelona underscored a central point in the debate about how […]<\/p>\n","protected":false},"author":14,"featured_media":46931,"template":"","meta":{"image":null,"json":{"gsma_resources_type":"0","gsma_resources_thumb":"null","gsma_resources_file":"null","gsma_resources_multi":null,"gsma_resources_wgr":null,"gsma_resources_video":"","gsma_resources_image":"null","gsma_resources_url":"","gsma_resources_date":"","gsma_resources_button":""}},"tags":[],"resource_categories":[55,64,61,81,78],"algolia_discover_type":[151],"class_list":["post-46930","gsma_theme_resources","type-gsma_theme_resources","status-publish","has-post-thumbnail","hentry","applications_services-artificial-intelligence-ai","applications_services-manufacturing","applications_services-network-apis","applications_services-robotics","usecase_general_cats-enterprise-sector","resource_categories-5g-auto","resource_categories-industry-engagement","resource_categories-manufacturing","resource_categories-manufacturing-technology","resource_categories-manufacturing-main","algolia_discover_type-article"],"yoast_head":"\nIndustrial transformation in manufacturing now depends on integration, not experimentation\u00a0 - Connected Manufacturing<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.gsma.com\/solutions-and-impact\/industries\/connected-manufacturing\/gsma_resources\/industrial-transformation-in-manufacturing-now-depends-on-integration-not-experimentation\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Industrial transformation in manufacturing now depends on integration, not experimentation\u00a0\" \/>\n<meta property=\"og:description\" content=\"“The future doesn\u2019t belong to companies that have the fastest network or the best technology. 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