“I had the pleasure of co-working with Lorenzo during this last year at Be Mng Consulting where we shared some significant experiences in the Consulting practice. I've been impressed by his capability to drive complex projects and his effectiveness in grasping the client's needs achieving strongest achievements. Outstanding on defining business strategy to develop sales opportunities in the market!”
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Vlad Larichev
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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Dr Imad Syed
Today in Rome - Join us for this critical conversation The Uncomfortable Truth About Digital Transformation After two decades of building data governance frameworks across 1000+ global implementations, I've witnessed a consistent pattern: The conversation always starts with dashboards. It should start with data integrity. The Industry's Blind Spot: 73% of asset management transformation initiatives fail before delivering ROI. Not from inadequate technology or budgets. From foundational data architecture failures that make intelligent operations mathematically impossible. What Global Deployments Have Taught Us: → Predictive maintenance without governed master data becomes sophisticated guesswork → Organizations achieving asset intelligence excellence started with governance frameworks, not visualization layers Today's Session Framework | Rome 16:05: ✦ Case study analysis: Global implementations that transformed operational intelligence ✦ The three critical master data mistakes costing organizations millions in hidden operational friction ✦ Why industry analysts (Gartner, Forrester) consistently recognize governance-first approaches The Strategic Inflection Point: We're past the era of "collect everything and figure it out later." Master data governance isn't the constraint preventing your digital transformation. It's the foundation enabling it. Let's connect at the conference or reach out if you're facing similar data governance challenges. Looking forward to the dialogue with asset management professionals addressing these head-on. SAP Asset & Service Management Conference by TAC Insights | Rome |16:05-16:35 #DataGovernance #AssetIntelligence #EnterpriseArchitecture #SAP
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Project Manager | Team Leader | Innovation Seeker
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