Prysmianthe €20 billion global cable-solutions leader operating across more than 50 countries, rebuilt its entire ERP into an AI-ready cloud platform through RISE with SAP in four months, then scaled that foundation to more than 100 AI use cases. The documented outcomes: 70% automation of repetitive activities80% faster implementation of new solutions, and 50% quicker time-to-market for new products. Reported by SAP News in July 2026.
The foundation Prysmian inherited
Prysmian designs and manufactures cables for energy transmission and telecommunications across roughly 80 plants in more than 50 countries, generating around €20 billion in annual revenue. That global scale carried an equally large legacy. The group ran a sprawling ERP estate assembled over decades of growth and acquisition, where repetitive manual tasks absorbed engineering and operations hours, solution rollouts advanced slowly across regions, and each new product launch depended on data scattered through fragmented systems. Demand from the energy transition, grid upgrades, offshore wind, electrification, pushed the business toward faster delivery cycles and tighter coordination between plants. Leadership read the situation clearly: a clean, unified data layer stood as the precondition for any serious AI ambition. Layering models onto aging, fragmented infrastructure promised fragile and short-lived gains, while a purpose-built platform promised durable capability the whole organization could build on. For a manufacturer whose products underpin power grids and data networks, the cost of slow, error-prone processes compounded quarter after quarter, sharpening the case for a decisive rebuild rather than another round of incremental fixes.
The decision: platform first, use cases second
Prysmian chose to migrate its whole infrastructure to the cloud through RISE with SAPtreating AI-readiness as an infrastructure decision rather than a bolt-on feature. The migration reached an AI-ready state in four monthsexecuted with 48 hours of downtime across 80 plants, a tightly controlled cutover for a manufacturer running continuous, around-the-clock production. On that fresh foundation the company embedded SAP's Joule co-pilot and opened the path to more than 100 AI use cases, including intelligent order automation that routes and processes commercial orders with minimal human handling. Group CIO and Digital Officer Giovanni Cauteruccio led the program and framed embedded AI as "a key differentiator, enabling us to accelerate solution deployment and strengthen AI skills and culture across the organization." The order of operations mattered: the platform came first, the applications second, and the cultural investment ran alongside both. For a business that runs plants continuously, holding disruption to two days across the entire manufacturing network demonstrated planning maturity that later paid dividends in every downstream use case.
The result, with full context
The measured gains landed across three dimensions, each tied to the same platform. Repetitive activities reached 70% automationreleasing engineering and operations teams for higher-value design and problem-solving. Implementation time for new solutions fell by 80%compressing rollouts that had previously stretched across many months into a fraction of that span. Time-to-market for new products accelerated by 50%a direct commercial advantage in a sector where utilities and telecom operators reward speed and reliability. Beyond the headline percentages, the platform now hosts more than 100 live AI use cases, and the Joule co-pilot reshaped day-to-day interactions for users across the business. These figures come from SAP and Prysmian's own reporting following the four-month build, and Italian trade press covered the program as a highlight of the SAP Innovation Awards 2026. Readers weighing the numbers should treat them as vendor-and-customer sourced outcomes: the time horizon for the 100-plus use cases follows platform completion, and the 70% figure describes repetitive activities specifically, a precise scope worth keeping in view when comparing programs.
What other organizations can take from Prysmian
The transferable lesson cuts against the common corporate pattern of layering AI onto whatever systems already exist. Prysmian's sequence, clean the data foundation first, scale use cases second, converted AI from a scatter of isolated experiments into a repeatable, industrial capability. Three conditions made it work. Executive ownership at the CIO level gave the program authority and budget to move fast. A willingness to migrate wholesale, rather than patch incrementally, produced a coherent data layer instead of another fragmented estate. And deliberate investment in AI skills and culture, running in parallel with the technology, ensured the organization could absorb and extend what the platform offered. Organizations holding an aging ERP can draw a clear map from this case: fund AI-readiness as an infrastructure project, own it at board level, and expect one platform decision to unlock a hundred downstream applications. The old-economy cable manufacturer that rebuilt its data layer first now moves faster than many digital natives, a reminder that AI advantage begins with the plumbing.
Article by SAGASuccess Stories & Real Cases
SAGA covers enterprise AI implementations with verified outcomes. Every metric is sourced. Every company is named.