André Lindenberg

𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗧𝗵𝗿𝗲𝗮𝗱 𝗶𝗻 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: 𝗧𝗵𝗲 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗮𝗹 𝗚𝗲𝗻𝗔𝗜 Together with Dr. Dirk Alexander Molitor and colleagues at Accenture Industry X, we are developing a framework for applying GenAI and Agentic AI in engineering, building on their proven value in software development. In software engineering and SDLC, the success of LLMs is predictable: code is highly structured, follows standardized syntax, has clear semantics, and can be fully compiled and tested. In other words: 👉 Classical software problems are machine-readable and 𝒄𝒐𝒎𝒑𝒊𝒍𝒂𝒃𝒍𝒆. This makes LLMs efficient at generating, interpreting, and improving software code and iterations - with little to no preprocessing. ⚙️𝗕𝘂𝘁 𝘄𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝘄𝗵𝗲𝗻 𝘄𝗲 𝘁𝘂𝗿𝗻 𝘁𝗼 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗱𝗮𝘁𝗮? Unlike software, engineering data is 𝘀𝗰𝗮𝘁𝘁𝗲𝗿𝗲𝗱 𝗮𝗰𝗿𝗼𝘀𝘀 𝗱𝗶𝘀𝗰𝗶𝗽𝗹𝗶𝗻𝗲𝘀 𝗮𝗻𝗱 𝘁𝗼𝗼𝗹𝘀. It comes in a wide range of formats - from requirement documents and SysML diagrams to CAD models and high-resolution simulations. Much of it is semi-structured, unstructured, or only partly interpretable by machines. Semantics are often implicit, domain-specific, and context-dependent. Most engineering data is not compilable and can’t be validated without connecting it to related data. 💡 𝗧𝗵𝗮𝘁’𝘀 𝘄𝗵𝘆 𝗿𝗮𝘄 𝗶𝗻𝗽𝘂𝘁 𝗶𝗻𝘁𝗼 𝗟𝗟𝗠𝘀 𝗼𝗿 𝗥𝗔𝗚𝘀 𝗮𝗹𝗼𝗻𝗲 𝗶𝘀𝗻’𝘁 𝗲𝗻𝗼𝘂𝗴𝗵 𝗶𝗻 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴. They require structured inputs and 𝗰𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗮𝘄𝗮𝗿𝗲𝗻𝗲𝘀𝘀 to generate meaningful output. To make GenAI and Agentic AI truly effective in engineering, we need to transform the data, connect it across systems, and make it machine-readable. 👉 This includes structured data transformation, semantic enrichment, knowledge graph integration, feature extraction, and embedding into accessible engineering platforms - whether through APIs, agent communication protocols (MCP/A2A), or tool-level integrations. 𝗧𝗵𝗮𝘁’𝘀 𝘁𝗵𝗲 𝘃𝗶𝘀𝗶𝗼𝗻 𝘄𝗲’𝗿𝗲 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝘁𝗼𝘄𝗮𝗿𝗱: Once engineering data is machine-readable, interpretable, and connected, 𝘄𝗲 𝗰𝗮𝗻 𝗹𝗲𝘃𝗲𝗿𝗮𝗴𝗲 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 𝗮𝗰𝗿𝗼𝘀𝘀 𝘁𝗵𝗲 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝘁𝗵𝗿𝗲𝗮𝗱 𝗳𝗼𝗿 𝗽𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀 as is now possible in the software development life cycle. 𝗜𝗳 𝘆𝗼𝘂’𝗿𝗲 𝗶𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗲𝗱 𝗶𝗻 𝗺𝗼𝗿𝗲 𝗱𝗲𝘁𝗮𝗶𝗹𝘀 𝗼𝗿 𝘄𝗼𝘂𝗹𝗱 𝗹𝗶𝗸𝗲 𝘁𝗼 𝘀𝗵𝗮𝗽𝗲 𝘁𝗵𝗶𝘀 𝘁𝗼𝗽𝗶𝗰 𝘁𝗼𝗴𝗲𝘁𝗵𝗲𝗿, 𝗿𝗲𝗮𝗰𝗵 𝗼𝘂𝘁 𝘁𝗼 𝘂𝘀 - 𝗹𝗲𝘁’𝘀 𝘀𝗵𝗮𝗽𝗲 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗮𝗹 𝗔𝗜 𝘁𝗼𝗴𝗲𝘁𝗵𝗲𝗿. ---- Daniel Spiess | Dr. Matthias Ziegler | Liam Friel | Dr.-Ing. Tobias Guggenberger | Boris Scharinger

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𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗙𝗮𝗰𝘁𝗼𝗿𝘀 𝗳𝗼𝗿 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 Digital transformation is about fundamentally reshaping how your organization thinks, operates, and creates value in a fast-changing, digital-first world. A study of 97 organizations uncovered 25 Critical Success Factors (CSFs) across 6 key dimensions. These are the real levers behind successful transformation. 𝗪𝗵𝗮𝘁 𝗱𝗶𝗱 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝗿𝗲𝘃𝗲𝗮𝗹? 𝗖𝗼𝗿𝗽𝗼𝗿𝗮𝘁𝗲 𝗖𝘂𝗹𝘁𝘂𝗿𝗲 𝘁𝗼𝗽𝗽𝗲𝗱 𝘁𝗵𝗲 𝗹𝗶𝘀𝘁. It was the #1 most frequently cited factor, and scored highest across all importance rankings. Organizations with adaptive, collaborative cultures showed significantly higher success rates in their transformation efforts. 𝗧𝗼𝗽 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗦𝘂𝗽𝗽𝗼𝗿𝘁 𝘄𝗮𝘀 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹. More than 50% of respondents rated it as having a very high influence on digital project outcomes. Leadership alignment isn’t a nice-to-have—it’s a make-or-break factor. These factors are structured across 6 key dimensions in a unified model: 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝘁𝗿𝘂𝗹𝘆 𝗱𝗿𝗶𝘃𝗲𝘀 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝘀𝘂𝗰𝗰𝗲𝘀𝘀: 𝗖𝗼𝗿𝗽𝗼𝗿𝗮𝘁𝗲 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻 • Corporate Culture • Unified Digital Strategy & Vision • Top Management Support • Leadership • Change Management • Digital Mindset • Qualification & Digital Talent 𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: Transformation starts with people, not platforms. If your culture and leadership aren’t aligned, nothing else will scale. 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 • Data Security • Unified Database • Modern Software & Hardware • Big Data / Analytics 𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: Technology is the foundation, but only works when securely integrated and designed for the future. 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 • Agile Implementation (Short Sprints) • Resources • Open Systems & Strategic Partnerships 𝗩𝗮𝗹𝘂𝗲 𝗖𝗿𝗲𝗮𝘁𝗶𝗼𝗻 • Networking the Value Chain • Cross-functional Development Teams • Operational Excellence (Lean Thinking) • Redefined KPIs 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 • Customer-Centric Management Models • Omnichannel Experience 𝗩𝗮𝗹𝘂𝗲 𝗣𝗿𝗼𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻 • Servitization • Fast Prototyping • Scalable Solutions 𝗪𝗵𝗮𝘁 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗺𝗲𝗮𝗻 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻? • Digital transformation isn’t about adding tech—it’s about changing    how your organization thinks and responds. • it’s a long-term shift in leadership, culture, and capability. • It’s about aligning strategy, people, and technology into one    cohesive, adaptive system. Ref: Critical Success Factors for Digitalization Projects-Christian Leyh et all.

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If Germany and Europe want to get back on a growth trajectory, we need to be bolder – and reduce regulation that limits agility and innovation. We should set our sights on the great opportunities new technologies offer – rather than focusing on the risks. The way forward for our economy is cloud and AI. These technologies can help us unlock new levels of efficiency and innovation, and it was great to discuss this – and much more – at the IAA in Munich yesterday. 

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𝗧𝗵𝗶𝘀 𝗶𝘀 𝗮 𝗱𝗲𝗳𝗶𝗻𝗶𝗻𝗴 𝗺𝗼𝗺𝗲𝗻𝘁 𝗳𝗼𝗿 𝗔𝗜: 𝗮 𝘀𝗵𝗶𝗳𝘁 𝗳𝗿𝗼𝗺 𝗿𝗮𝘄 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝘁𝗼 𝘁𝗿𝘂𝗲 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗼𝗳 𝗵𝘂𝗺𝗮𝗻𝘀, 𝘁𝗵𝗲𝗶𝗿 𝗴𝗼𝗮𝗹𝘀, 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝗿𝘀, 𝗮𝗻𝗱 𝗰𝗼𝗻𝘁𝗲𝘅𝘁. Very proud to share our latest Point of View on 𝗛𝘂𝗺𝗮𝗻-𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴, where the Capgemini 𝗔𝗜 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀 𝗮𝗻𝗱 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲𝘀 𝗟𝗮𝗯, in collaboration with Cambridge Consultants, sets a new direction for how we design, deploy, and scale intelligent systems. This is not just another publication - it’s a 𝗺𝗶𝗹𝗲𝘀𝘁𝗼𝗻𝗲 for the future of #AI, #AIRobotics, and #PhysicalAI. This paper is central to our mission: making #embodiedAI not just powerful, but meaningful systems that sense, understand, and support people in real-world environments. It lays the foundation for the next wave of Digital and Physical Interfaces, where humans interact naturally with systems that adapt to them to drive measurable impact. We explore three areas where this shift will unlock value for business: 1️⃣ 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴 – supporting better, faster, more contextual decisions 2️⃣ 𝗧𝗲𝗮𝗺𝗶𝗻𝗴 𝗮𝗻𝗱 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝘆 – turning AI and robotics into trusted teammates 3️⃣ 𝗔𝗱𝗮𝗽𝘁𝗶𝘃𝗲 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲𝘀 – creating interactions that feel natural and human-centric This is more than technology: it’s a strategic and cultural evolution that will shape the way organizations innovate, operate, and engage in the coming years. 👉 Discover why this publication matters for the future of your business: https://bit.ly/467YlRv #HumanMachineUnderstanding #FutureOfAI Pascal Brier Franck Greverie Ali Shafti Kary Bheemaiah Tim Ensor Dheeren Vélu Antonio Jesús Jaramillo Mesa Xavi Navarro Muncunill Borja Tinao Perez-Miravete Marc Blanchon Nitin Dhemre Jean-Francois DELERUE Esther Buck Alex Bulat- van den Wildenberg

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I keep seeing exchanges wrestle with the same pattern: systems built up over decades that now move like concrete. Each patch fixes yesterday’s issue but deepens tomorrow’s fragility. When one change risks triggering ten more, innovation slows to a crawl. What strikes me is how often leadership underestimates the cumulative cost of this friction. For me, the real marker of resilience is an architecture that can keep evolving without adding new layers of complexity. You’ll find more of my perspective on this in my latest article on exchange integration https://lnkd.in/eduYeFa2

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How GenAI is Changing the Engineering Profession Generative AI marks a digital turning point for the engineering world. A recent study by the Association of German Engineers (VDI) highlights how AI is beginning to transform the everyday work of engineers. The key message is clear: generative AI is meant to support—not replace—engineering professionals. There is particular potential for relief in repetitive and time-consuming tasks such as documentation, handling technical inquiries, or performing standard calculations. More complex applications are also gaining traction. In the future, AI could assist in analyzing production data, supporting design and development processes, or contributing to conformity assessments. One central principle stands out: responsibility for technical decisions should remain with humans. The study emphasizes so-called "human-in-the-loop" approaches, where engineers take on supervisory roles—validating, correcting, and ultimately approving AI-generated suggestions. The VDI study outlines ten specific recommendations, with three main goals: * Use generative AI in a targeted and purposeful way * Define and regulate responsibility clearly * Build up skills and capabilities within engineering teams Read the full study here: https://lnkd.in/eTE_ZDUX #AI #GenAI #Cloud #CloudFirst

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