重塑人类+人工智能工程

Systemic AI at theroot of manufacturingperformance The new growth infrastructurefor manufacturers Reinventing for Human + AI Engineering Authors Roland MayrSenior Managing Director, Industryand Enterprise, Industrials, Jean CabanesSenior Managing Director, Industry andEnterprise, Industrials, Götz ErhardtSenior Managing Director, SupplyChain and Engineering, Jean Cabanes is a senior leader atAccenture, working with industrialclients to drive reinvention acrosscomplex products, systemsand process chains. He advisesexecutive teams on reinventionthrough digital technologies, data Götz Erhardt focuses on industrialstrategies, digital and AI-poweredtransformations, performanceimprovement and new businessmodels. He supports clients indesigning programs for valuechain reinvention by speeding up Roland Mayr leads Accenture’s globalIndustrials industry, advising executiveteams on reinvention with digital,AI and data across complex assetstructures and process chains. Withover three decades at Accenture, hebrings deep automotive experience LinkedIn Tobias GeißingerManaging Director, Supply Chain andEngineering, Industrials, EMEA Andreas EgetenmeyerManager, Accenture Research,Industrials, EMEA Jeff BrehmManaging Director, Supply Chain andEngineering, Industrials, Americas Jeff Brehm focuses on businesstransformation within the engineeringand product development space.He advises executive teamson reinvention through digitaltechnologies and an enterprise-wide Andreas Egetenmeyer is a marketresearch expert with more than 15years of experience. He focuses onprimary and secondary research tosupport clients and shape Accenture’sthinking on disruptive technology and Tobias Geißinger works with industrialclients to drive engineering-ledtransformation across complexproducts, systems and processchains. He empowers clients toreinvent through digital tools, AI and The enablers:Building a cloud-based digital core Five moves to reinvent engineering Page 9 Integrate engineering across Page 20 Engineering as a growth engine Page 22 Software-defined products, tighter regulation and relentless costpressures are changing what engineering departments must deliver.By 2030, adaptability and speed will be the defining measures Leading companies recognize that the compounding realities of this decadecannot be resolved with incremental process tweaks or another tool addedto existing systems. Instead, they are reinventing processes, data, tools andhow work gets done across the value chain to dramatically improve critical The findings and insights of this study draw on 136 interviews withengineering leaders and practitioners at leading aerospace and •36 in-depth expert interviews with engineering decisionmakers conducted in January and February 2026 •100 AI-moderated interviews with front-line engineersresponsible for day-to-day engineering work conducted Our new research clearly confirms this. Across our interviews with 100engineers and 36 engineering leaders, we found legacy systems nearingtheir limits. Products are becoming more complex as cycles compress, andengineers report spending roughly half their day on documentation, reportingand information search rather than core engineering work. According to By the end of this decade, “perfect but late” engineering will leave behindthose who still cling to this paradigm: Late launches repeatedly erodemargin, trust and morale. Meanwhile, as hardware performance converges,differentiation shifts to software, customer experience and data-drivenimprovement. Launch day is no longer the finish line. It’s the start of version Crucially, yesterday’s toolchain is blocking AI’s potential: broken data flows,disconnected systems and friction-filled handoffs force engineers to huntfor answers, validate consistency manually and chase approvals—exactly thework AI can eliminate. Finally, the scarcest asset is capability, not compute. The implication is unavoidable: These converging pressures leave little roomfor incremental fixes. Leaders who want software-speed innovation withoutsacrificing safety, quality, compliance or cost discipline must reinventengineering as a system spanning processes, tools, roles, decision rights and In this context, AI can move from a support tool to a digital assistant thatexecutes work in flow, under clear decision rights and controls. One earlymarket signal came at Hannover Messe 2026, where Siemens introduced theEigen Engineering Agent, a new class of industrial AI designed for automationengineering. Unlike copilots that offer suggestions, the Eigen Engineering Siemens reports performance gains of up to 5xfaster execution and 50% higher efficiency.2 So, what does that reinvention require? It starts with an operating backbonethat connects the product story end-to-end: a digital thread built on acloud-based digital core. This provides a single source of access that linksrequirements, designs, software changes, tests, approvals, quality records, With