Engineering Market Research Strategies

On the ongoing negotiations at World Intellectual Property Organization – WIPO towards a new international legal instrument on intellectual property, genetic resources and associated traditional knowledge: ❓ If the new instrument were to be agreed next year and implemented in national/regional laws, what would change? Based on the current draft, one change would be that a patent applicant whose invention is materially/directly based on a genetic resource would need to disclose the country of origin or source of the resource. 🌿 For example, let’s say there’s a patent application for a new cosmetic, the development of which depended upon use of an oil obtained from a plant. The country of origin or source of the plant would need to be disclosed in the patent application.   💭 The same would apply to any traditional knowledge associated with the genetic resource on which the invention is materially/directly based. If the invention depended upon that knowledge, the Indigenous People or local community that provided it, or other source of the knowledge, would need to be disclosed. Currently, in the absence of such a new requirement, patent applicants do not normally need to disclose this kind of information unless it’s relevant for patent examiners to determine whether the invention is novel and inventive/non-obvious. ⚠The above is just a short and informal summary! ✔Read the draft text for precise information: https://lnkd.in/eFsgdrJq 👍 And please let me know if you find these short explainers helpful. Big thanks Frederic Perron-Welch for your technical checking of this post #geneticresources #patentlaw #patents #intellectualproperty #multilateralism #indigenouspeoples #culturalheritage

Over the last decade, #engineering services firms have quietly reshaped themselves through #acquisitions worth tens of billions of dollars. These were not random transactions driven by scale alone. Each deal reflected a deeper anxiety — and ambition — about relevance in a world where engineering is no longer defined by effort, but by intelligence embedded into systems. From large industrial players acquiring digital product engineers, to consulting majors strengthening deep engineering muscle, to services firms buying platform-led capabilities, one pattern is becoming clear: the industry has been trying to buy time. Time to adapt. Time to learn. Time to stay meaningful as technology cycles compress. Now, with #AI changing how products are designed, built, tested, and improved, the logic behind these acquisitions deserves a closer look. Which of these deals actually created value? Which struggled after the headlines faded? And what changes when the target is not just an engineering firm, but one that carries AI-native thinking at its core? In this article, I unpack the most significant engineering services acquisitions till 2025 — why they happened, what worked, what didn’t, and what they reveal about the future of M&A in the AI age. The recent Coforge–Encora acquisition becomes a lens to examine what comes next. This is not a deal summary. It’s a reflection on how intelligence, not scale, is becoming the real asset. DC*

#TeachMeTuesday (last before my summer break) | Innovation assets and firm survival: empirical evidence on patent value from U.S. IPO firms Being World Intellectual Property Organization – WIPO, and sitting on a ton of IP and in particular patent data, we spend a lot of time looking at patents as indicators of innovation. At the same time, we are acutely aware that patent counts only tell us so much. One patent may open an entirely new market; another may have little commercial or technological impact. (in some academic classes, I show data that only 2-3 out of 10 patents has significant economic value; or that only very few patents have very large economic value - but this data is old and probably imperfect. The reason: the value of a patent is inherently difficult to pin down; unlike another asset, say a car, the value of a patent will also vary depending on who is the owner, and what other complementary assets - e.g. a strong brand, or complementary know-how - the owner may have). But to the paper. A paper - which admittedly is not new - by Xiaofeng Wang, Rui Sun, Heshan Zhang and Wei Xiong examines the role and value of patents. 📄 Innovation assets and firm survival: empirical evidence on patent value from U.S. IPO firms https://lnkd.in/ec8r_C3R The authors follow 2,574 U.S. firms that went public between 2001 and 2017 and examine whether their patents helped them remain listed. And I have not seen a similar paper before (but happy to be corrected in the comments). The answer is yes – patents help firms remain listed - but the type of value matters. Authors distinguish between: 💵 Economic value: whether the market sees commercial potential in a patent. 🔬 Scientific value: whether the patent contributes to subsequent technological progress. Both are associated with better survival when considered separately. But when the authors examine them together, economic value is the stronger signal. It suggests that markets reward innovation most clearly when they can see a path from invention to income. Scientific value still matters. It appears particularly important for high-tech firms and for companies going public during periods of crisis. But its contribution is more dependent on context. Seems evident, but the mix of scientific and economic value is interesting, and to have the overall intuition backed up by solid US evidence has great value. Bottom line: A patent is an asset. But its real value depends on what the firm can do with it. Seems evident again, but having this backed up by empirical findings is easier said then done. So kuddos to the authors. (and please put updates to the paper or similar papers in the comments). #Innovation #Patents #IntellectualProperty #IPO #InnovationMeasurement #WIPO Another paper with same findings: https://lnkd.in/etzmpbHM Iain Cockburn Stefan Wagner

Beyond the IT valuations brouhaha: the divergent trajectory of IT&E Intelligence Transformation and Engineering, which I shall call IT&E, can be considered to be the descendant of Digital Engineering. I predict an upcoming boom for IT&E services, decoupled from the fortunes of the larger IT services sector. To understand why, one must remember that more than half the spend on IT services goes into “run” services, such as managed services and infrastructure opex. Less than half the spend goes into “build” services, and even here the lion’s share is services for the implementation of standard products like ERPs and CRMs. Only a thin slice, perhaps 10-15%, is Digital Engineering, where Nagarro is focussed. The client only “runs” what was “built” in the past, so the annual volume of managed services and infrastructure is dependent on the volume of systems built over the past years that are still in use. The “run” volume has a lot of inertia; it appears resilient in a “build” slowdown but lethargic when “build” accelerates. We have seen both situations in this decade already. Digital Engineering work tends to have higher engineering intensity, greater uncertainty in scope, faster iteration cycles, and continuous enhancement rather than a defined end state. Digital transformation initiatives have relied heavily on Digital Engineering services to establish differentiation and core competitive advantage on top of standard systems. Very similar services are going to be required for AI transformation initiatives. That is why I say that Digital Engineering is evolving into Intelligence Transformation and Engineering, or IT&E. Now, here is the case for the divergence of fortunes between IT&E and the rest of the IT services sector: • Arguably, AI productivity gains in “run” are likely to be more disruptive than in the “build” of enterprise systems, which is more complex and involves many stakeholders and disciplines. • Even within “build”, CIOs are starting to consider replacing standard products with custom digital solutions. So high is the spend on standard systems that even a small move in this direction can turbocharge growth in IT&E. • And as I have explained before, IT&E work can accelerate sharply from one year to next, which will never happen for “run” work. • Once there is confidence in the economy, confidence in the technologies available, and a few lighthouse examples in each industry, IT&E is bound to grow as AI transformation takes off.   The timing of this boom is hard to call, as it hinges on confidence returning across the economy, the technology, and the early proof points. But the direction is not in doubt. When AI transformation does take off in earnest, IT&E will be where the growth lands.

Recommended report: World Intellectual Property Organization (2024). #Generative #Artificial #Intelligence. Patent Landscape Report. Geneva. 💡 This report provides an analysis of patenting activity and scientific publications in the field of Generative Artificial Intelligence (#GenAI). It aims to shed light on the current technology development, key players, and potential applications of GenAI technologies. 👍🏼 #Goals: - Examine the global development and trends in GenAI patenting and research - Analyze patent trends for different GenAI models, modes (data types), and application areas >> The number of GenAI patent families has grown from just 733 in 2014 to over 14,000 in 2023, an increase of over 800% since the introduction of the transformer architecture for large language models in 2017. >> The growth in scientific publications has been even more dramatic, increasing from only 116 publications in 2014 to more than 34,000 in 2023. >> Over 25% of all GenAI patents and over 45% of all GenAI scientific publications were published in 2023 alone. 🧠 The remarkable surge in GenAI patents and #publications, especially in recent years, underscores the disruptive potential of these technologies and the intense race among companies and research institutions to secure intellectual property rights and drive innovation in this rapidly evolving domain. 👁️ 5 Key Ideas: 1. GenAI patent families and scientific publications have increased significantly since 2017, driven by advancements in deep learning, availability of large datasets, and improved AI algorithms. 2. Tencent, Ping An Insurance Group, and Baidu are the top patent owners in GenAI, with Chinese organizations dominating the top rankings. 3. China, the United States, and South Korea are the leading locations for GenAI invention based on patent data. 4. Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and decoder-based Large Language Models (#LLMs) are the main GenAI models with the most patents. 5. Key application areas for GenAI patents include software, life sciences, document management, business solutions, industry and manufacturing, transportation, security, and telecommunications. 🎯 3 Conclusions: 1. The release of OpenAI's ChatGPT in 2022 has been a pivotal moment for GenAI, driving public enthusiasm and further research and development efforts. 2. While Chinese organizations lead in terms of GenAI #patenting, companies like Alphabet/Google, IBM, and Microsoft are also major players, particularly in scientific publications and impactful research. 3. GenAI is expected to have a significant impact across various industries, enabling applications such as drug development, content creation, customer service, product design, and autonomous driving. Source: https://lnkd.in/eAZy-Pvc

When we talk about Digital Twins, the first use case that usually comes to mind is simulation. A virtual product prototype. A design validation model. An engineering optimization environment. But the bigger shift may be happening elsewhere. Digital Twins are evolving from asset-level intelligence toward real-time enterprise alignment across the product lifecycle. At the 𝗨𝗻𝗶𝘁 𝗟𝗲𝘃𝗲𝗹, DTs improve equipment visibility, component behavior, and asset reliability. This is where most organizations currently operate. At the 𝗦𝘆𝘀𝘁𝗲𝗺 𝗟𝗲𝘃𝗲𝗹, multiple twins begin coordinating production lines, shop floors, factories, and complex products. The optimization target shifts from machines to operational flow. For example, a design change in engineering could instantly ripple into manufacturing constraints, supplier availability, production schedules, service impact, and maintenance planning. That represents a very different level of industrial intelligence. The biggest transformation, however, will emerge at the 𝗦𝘆𝘀𝘁𝗲𝗺-𝗼𝗳-𝗦𝘆𝘀𝘁𝗲𝗺𝘀 (𝗦𝗼𝗦) 𝗟𝗲𝘃𝗲𝗹. This is where Digital Twins begin connecting the 𝗲𝗻𝘁𝗶𝗿𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗹𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲: 𝗗𝗲𝘀𝗶𝗴𝗻 → 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 → 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴 → 𝗦𝘂𝗽𝗽𝗹𝘆 𝗖𝗵𝗮𝗶𝗻 → 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 → 𝗠𝗮𝗶𝗻𝘁𝗲𝗻𝗮𝗻𝗰𝗲 And this is where the Digital Thread becomes critical. Because a twin is only as effective as 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗰𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗱𝗮𝘁𝗮 connecting PLM, MES, supply chain, and operational systems together. At that point, DTs stop functioning as standalone simulations. They become 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗰𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗹𝗮𝘆𝗲𝗿𝘀 capable of: • Creating a continuously synchronized enterprise from engineering through service • Simulating enterprise-wide operational impact • Dynamically rebalancing operations in real time • Continuously optimizing lifecycle performance At the same time, DT maturity itself is advancing: • 𝗣𝗮𝗿𝘁𝗶𝗮𝗹 𝗗𝗧𝘀 provide selective operational visibility • 𝗖𝗹𝗼𝗻𝗲 𝗗𝗧𝘀 replicate operational and engineering behavior • 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗗𝗧𝘀 derive intelligence using analytics and AI Here’s the shift: individual asset twins reduce downtime. But 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝘀𝘆𝗻𝗰𝗵𝗿𝗼𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻 fundamentally changes how organizations respond to supply chain disruptions, demand volatility, engineering changes, and margin pressure. Organizations racing to build better individual twins may miss the larger opportunity: the real competitive advantage comes from 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗻𝗴 𝘁𝘄𝗶𝗻𝘀 𝗮𝗰𝗿𝗼𝘀𝘀 𝘁𝗵𝗲 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗶𝗻 𝗿𝗲𝗮𝗹 𝘁𝗶𝗺𝗲. Which level do you think most organizations are actually operating at today and which level do they think they are operating at?

While most of us are focused on the revolution in AI and digital trends, there’s an equally important shift happening in engineering. India’s data center boom. It’s often described in terms of servers and energy, but I see it as a story of how digital growth is reshaping industries that once stood at the margins. Reliable backup power is the heartbeat of every data center, and gensets play a critical role. These gensets are not simple machines; they rely on precision components that demand the highest standards. This requirement is fueling growth in engineering, machining, and forging, sectors that for decades were seen as serving majorly in traditional industries like automotive or heavy equipment. Today, they are becoming indispensable to the digital economy. The expectations are uncompromising, the tolerances tighter, and the reliability requirements higher. This shift is creating new opportunities for Indian manufacturers to step confidently into a global role. I believe this convergence of digital demand and industrial precision is a competitive advantage for India. Yes, the energy challenge is real. But I see it as a catalyst rather than a constraint. It pushes us to innovate, to build more efficient systems, and to strengthen our industrial base. The data center boom is not only about meeting today’s digital needs. It is about creating tomorrow’s industrial strengths. And in that transformation, India is building resilience and long‑term advantage. #DataCenter #DigitalEconomy #Gensets #PrecisioComponents

Digital Twins and Industrial AI Triggered by recent keynotes, one thing is clear: Digital Twins combined with Industrial AI have crossed a decisive threshold. They are no longer innovation theatre or isolated pilots. They are becoming a foundational capability for how industrial companies operate, compete, and transform. For manufacturing and automotive companies with complex global production networks, this shift is not optional. Digital Twins are emerging as core levers for cost reduction, resilience, and speed—directly impacting margins, competitiveness, and risk exposure. The real power of Digital Twins lies not in visualization, but in their combination with AI-driven simulation, prediction, and optimization. When products, production systems, and processes are digitally represented and continuously enriched with operational data, companies can test decisions before they hit the factory floor. Virtual commissioning, simulated layout and volume changes, and predictive maintenance reduce ramp-up time, downtime, inventory, and operational firefighting. In capital-intensive industries with tight margins, this is not incremental improvement it is structural cost reduction and risk avoidance.   Manufacturing combines extreme complexity with relentless efficiency pressure. Product variants grow, software content explodes, regulatory demands tighten, and supply chains remain fragile while customers expect flawless quality at competitive cost. Digital Twins and Industrial AI enable a closed feedback loop between engineering, production, and operations: the so-called Digital Thread. Decisions move from siloed optimization to a shared, continuously updated model of reality. Companies that master this gain speed without losing control.   Digital Twins are not another tool rollout; they are an enterprise capability spanning Engineering IT, Production IT, OT, and Data & AI. The main bottleneck is rarely technology it is data. Fragmented models, inconsistent semantics, and poor data quality across PLM, MES, ERP, and the shop floor limit value creation. Without a solid data foundation, even advanced AI remains theoretical. As Digital Twins increasingly represent intellectual property and operational know-how, architecture, governance, and security become critical.   Large-scale industrial transformation is not just a technology or talent race. It is about judgement, prioritization, and execution discipline. These initiatives touch the core of the business: assets, safety, quality, cost, and risk. They require leaders who can balance speed with stability and innovation with operational continuity. This is where experience becomes a competitive advantage.   Digital Twins and Industrial AI will shape industrial operations over the next decade. This is redefining IT from technology delivery to orchestrating industrial value creation across engineering, manufacturing, and operations, while managing cyber and operational risk.

𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗧𝘄𝗶𝗻𝘀—𝗙𝗿𝗼𝗺 𝗠𝗼𝗱𝗲𝗹𝘀 𝘁𝗼 𝗠𝗼𝗻𝗲𝘆: 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝗩𝗮𝗹𝘂𝗲 𝗖𝗿𝗲𝗮𝘁𝗶𝗼𝗻 𝘎𝘭𝘰𝘣𝘢𝘭 𝘋𝘪𝘨𝘪𝘵𝘢𝘭 𝘛𝘸𝘪𝘯 𝘴𝘱𝘦𝘯𝘥 𝘸𝘪𝘭𝘭 𝘤𝘭𝘪𝘮𝘣 𝘧𝘳𝘰𝘮 $𝟮𝟭 𝗯𝗶𝗹𝗹𝗶𝗼𝗻 𝘪𝘯 2025 𝘵𝘰 ~$𝟭𝟭𝟱 𝗯𝗶𝗹𝗹𝗶𝗼𝗻 𝘣𝘺 2030—­𝘢 𝟯𝟲 % 𝗖𝗔𝗚𝗥, 𝘰𝘶𝘵𝘱𝘢𝘤𝘪𝘯𝘨 𝘮𝘰𝘴𝘵 𝘐𝘯𝘥𝘶𝘴𝘵𝘳𝘺 4.0 𝘴𝘦𝘨𝘮𝘦𝘯𝘵𝘴. Boardrooms have been chasing digital transformation for a decade, yet a considerable number of capital projects continue to lose money during commissioning and struggle to scale. #DigitalTwins flip that script. 𝗧𝗵𝗲 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆? They weave design, controls, and live production data into a single source of operational truth—turning every engineering and operations decision into a measurable business lever for safety, efficiency, and profitability. 𝗦𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗮𝘀 𝗧𝗵𝗲 𝗦𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝗚𝗮𝘁𝗲 Early-phase/Design Digital Twins (DTs) de-risk projects by stress-testing process design, control logic, and operator interfaces. Teams iterate virtually and walk onto site with engineering that is already race‑ready—trimming weeks from commissioning schedules and protecting margins on both greenfield and brownfield projects. 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗖𝗼𝗺𝗺𝗶𝘀𝘀𝗶𝗼𝗻𝗶𝗻𝗴 𝗮𝘀 𝗗𝗧'𝘀 𝗙𝗶𝗿𝘀𝘁 𝗦𝗽𝗿𝗶𝗻𝘁 Once the model drives a robot, a piece of equipment, or a production line into virtuality, phased upgrades get surgical. Control engineers debug logic against a high‑fidelity replica while factories/plants keep running. The finished simulation becomes the real‑time foundation for an operational DT. 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗗𝗧𝘀 𝗮𝘀 𝗟𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲 𝗦𝗮𝗻𝗱𝗯𝗼𝘅𝗲𝘀 Forward-looking firms don't shelve the DT after startup—they take it to a cross-functional playground: process, automation, operations, quality, and maintenance teams co-design optimizations, run hazard analyses, and rehearse procedures inside the DT, then push proven changes to production with confidence. 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗠𝗮𝗸𝗲𝘀 𝗼𝗿 𝗕𝗿𝗲𝗮𝗸𝘀 𝗥𝗢𝗜 A DT without stewardship drifts into obsolescence. High performers assign ownership and KPIs from day one. State-of-the-art platforms show how contextualized data, edge connectivity, and hybrid AI keep models updated, federated, and business-ready—turning sandbox insights into sustained cash flow. 𝗗𝗧𝘀 𝗺𝗼𝘃𝗲 𝘃𝗮𝗹𝘂𝗲 𝗰𝗿𝗲𝗮𝘁𝗶𝗼𝗻 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝘀𝗵𝗼𝗽 𝗳𝗹𝗼𝗼𝗿 𝘁𝗼 𝘁𝗵𝗲 𝗣&𝗟, reducing CapEx, enabling continuous improvement, and promoting the governance required to scale across sites. 💡 𝘞𝘩𝘪𝘤𝘩 𝘣𝘢𝘳𝘳𝘪𝘦𝘳—𝘥𝘢𝘵𝘢 𝘳𝘦𝘢𝘥𝘪𝘯𝘦𝘴𝘴, 𝘵𝘢𝘭𝘦𝘯𝘵, 𝘰𝘳 𝘭𝘦𝘢𝘥𝘦𝘳𝘴𝘩𝘪𝘱 𝘣𝘶𝘺‑𝘪𝘯—𝘬𝘦𝘦𝘱𝘴 𝘺𝘰𝘶𝘳 𝘰𝘳𝘨𝘢𝘯𝘪𝘻𝘢𝘵𝘪𝘰𝘯 𝘧𝘳𝘰𝘮 𝘴𝘤𝘢𝘭𝘪𝘯𝘨 𝘋𝘛𝘴 𝘢𝘤𝘳𝘰𝘴𝘴 𝘵𝘩𝘦 𝘦𝘯𝘵𝘦𝘳𝘱𝘳𝘪𝘴𝘦?—𝗗𝗿𝗼𝗽 𝗮 𝗰𝗼𝗺𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝗹𝗲𝘁'𝘀 𝗰𝗼𝗻𝗻𝗲𝗰𝘁! ***** ▪ Enjoy? Save💾 ➞ Like👍 ➞ Comment💬 ➞ Share♻️ ▪ Follow me and ring the 🔔 to stay current on Industrial Automation & Software insights!