Tech Ecosystem Collaboration

Turning Europe into a quantum industrial powerhouse Europe has been the cradle of quantum mechanics, the revolutionary science born from the genius of Max Planck, Albert Einstein, Niels Bohr, Erwin Schrödinger, and other visionaries who rewrote the rules of physical reality. On 2 July 2025, in the year marking a centenary since the initial development of quantum mechanics, the Commission has adopted an ambitious European Quantum Strategy, integrating Europe's unique scientific heritage with its vibrant quantum ecosystem of startups, SMEs, large industries, research and technology organisations, academia and research institutes. The mission is clear: turn Europe into a quantum industrial powerhouse that transforms breakthrough science into market-ready applications, while maintaining its scientific leadership. We are imagining a Union where medical scans can detect illnesses at the earliest stages, accelerating from weeks of uncertainty to mere seconds of precise diagnosis; where sensors are able to warn about volcanic activity or water shortages before they happen; and where unprecedented computational power will be available to solve complex problems in logistics, finance and climate modelling. A safer Europe, where our personal data, critical infrastructure, and businesses will always remain private and well-protected; where transport systems are optimised to reduce congestion and prevent accidents; and air travel is guided by quantum-enhanced precision navigation, pinpointing objects' locations down to the centimetre. A greener Europe, where sustainable energy grids can flawlessly manage millions of electric vehicles charging simultaneously overnight. These tangible, transformative technologies are within reach through support from the EU Quantum Strategy. The quantum community has clearly outlined what's needed to achieve this future: · Combine Europe's scientific excellence to bring quantum breakthroughs rapidly to market · Develop advanced quantum supercomputers like the ones we are supporting under the Quantum Flagship and are acquiring under the EuroHPC Joint Undertaking to operate as accelerators next to our leading network of supercomputers · Deploy secure communication networks such as those under EuroQCI, our secure quantum communication infrastructure that will be spanning the whole EU, composed of a terrestrial segment relying on fibre communications networks linking strategic sites at national and cross-border level, and a space segment based on satellites · Support quantum startups and SMEs, enhancing supply chain resilience, and foster supranational innovation clusters · Integrate quantum advancements into strategic capabilities for security and defence, protecting citizens and infrastructure · Educate Europe's workforce through specialised initiatives like the European Quantum Skills Academy Quantum is not one more technology to add to the list; is a high tide that will deeply transform our society and economy.

🗞️ A must-read for anyone interested in European AI governance right now: this study, drafted for the Committee on Industry, Research and Energy (ITRE) of the European Parliament by the Policy Department for Transformation, Innovation & Health 👉🏼Analyses how the AI Act adopted mid-2024 is articulated with other key EU digital regulations 🔎 Examines interactions with: • GDPR • Data Act (DA) • Data Governance Act (DGA) • Digital Services Act (DSA) • Digital Markets Act (DMA) • Cyber Resilience Act (CRA) • NIS2 Directive, the New Legislative Framework (NLF) and product-safety / digital-elements rules 📖 A timely document as the #EU faces the demanding task of building digital rules that the world still lacks, balancing innovation, transparency and fundamental rights. ➡️ creating a broad legal ecosystem connecting data, algorithms and human values. 🎯 3 goals • Ensure trustworthy #AI in Europe — safe, transparent, respectful of rights and EU values. • Foster innovation and competitiveness • Provide legal certainty through a proportionate, risk-based approach. 🗺️ The study maps the interplay among current acts: 🔹with GDPR – Encourage joint guidance between data-protection and AI authorities to simplify impact assessments and ensure consistent supervision across Member States. 🔹with Data Act -Streamline obligations on data quality and access so that compliance supports, rather than slows, AI innovation. -Coordinate governance to prevent duplication and promote data flows for trustworthy AI. 🔹with Data Governance Act -Build bridges between data-sharing frameworks & AI requirements through interoperable standards and clear responsibilities for data use. 🔹with DSA / DMA -Use platform transparency & risk-assessment mechanisms to reinforce, not duplicate, AI Act duties -promote a coherent, innovation-friendly environment for general-purpose models 🔹with CRA / NIS2 / NLF -Align product-safety, cybersecurity & AI conformity processes to create 1 coherent certification pathway for digital products. 👉🏼an #AI Act as integrated regulatory ecosystem covering data, algorithms, products, platforms and rights = smart coordination turning compliance into trust and competitiveness. Future model proposed : • Principle-based horizontal rules with sectoral modules • Clear layering — data → algorithms → systems → services • Aligned definitions & conformity regimes • Simplified compliance for SMEs, rigorous oversight for high-risk systems 🧭 Practical steps forward ▶️Short term: joint guidelines (AI Act / GDPR), shared sandboxes, harmonised templates. ⏩️Medium term: clarify mandates, connect conformity procedures. ⏭️Long term: build a unified digital framework linking data, AI and platform rules, strengthen international standardisation& partnerships. ➡️ AI for good, trustworthy by design, aligned with rights and values. 🙏🏻 Authors Hans Graux Krzysztof G. Nayana Murali Jonathan Cave Maarten Botterman

Thrilled to share our new research, co-authored with Annabeth Aagaard and Oliver Gassmann, on how industrial digital platforms are transforming value creation in B2B ecosystems. In many manufacturing settings, platform governance has long been framed as a one-way street: the platform orchestrates, complementors adapt. Our study shows a very different reality. Across five platform providers and five leading manufacturers, we uncover dual orchestration — a dynamic, iterative form of co-governance where both sides continuously adapt roles as digital business models evolve. The paper offers: • A Platform DBM Process Model explaining how value is co-created and co-captured across initiation, proposition design, digital transformation, and revenue sharing. • A Dual Orchestration Governance Framework detailing how transparency, reciprocity, commitment, proximity, and coopetition enable stable collaboration in highly interdependent industrial settings. • Rich case evidence from global platform providers and industrial firms navigating interoperability, data rights, servitization, and emerging AI-driven business models. If you are working on digital transformation, industrial platforms, ecosystem strategy, or B2B business model innovation, I hope you will find the insights useful. Read the open-access article here: Dancing titans: Dual orchestration and governance in industrial digital platforms for B2B value co-creation (Technovation, 2026): https://lnkd.in/dK4_UZpz Happy to discuss the findings or explore collaboration around this line of research. #DigitalPlatforms #IndustrialPlatforms #DualOrchestration #PlatformGovernance #B2BInnovation #EcosystemStrategy #DigitalBusinessModels #Servitization #ValueCoCreation #ValueCoCapture #ManufacturingInnovation #DigitalTransformation #IIoT #PlatformEconomy #EcosystemGovernance #CollaborationDynamics #OpenInnovation #DataDrivenInnovation #Technovation #ResearchPublication

FY2026 Signals Joint Defense Tech The Pentagon isn’t looking for more tech. It’s looking for tech that fits the fight. What wins? interoperable, multi-domain, coalition-ready tech that aligns with how the U.S. and its allies will fight. Hear me out… 1. Integration Is the Mission PE 0604826J is the COG for CJADC2. It funds interoperability pilots with NATO, secure data sharing across services, and cross-domain C2 experiments like Bold Quest. Your tech needs to plug into this joint ecosystem. 2. Multi-Domain C2 Is Non-Negotiable The budget holds firm on digital datalinks, secure comms, and allied data exchange. Your tech must talk across domains and allies, don’t expect traction. 3. Rapid Prototyping Isn’t Dead—It’s Evolving RDER may be gone, but its intent lives on. The budget still backs prototypes that can shape joint force design. Demo utility in a joint context and watch your TRL skyrocket. 4. Congress ‘All In on Joint Tech’ is a buying signal. • $400M → Joint Fires Network • $400M → Joint battle management tools • $1B → Accelerated tech fielding • $2B → DIU scaling commercial tech 5. AI/ML, Autonomy, C5ISR—Joint prioritization isn’t just lip service. Budget lines explicitly call out: • Multi-service unmanned systems • Maritime robotics • Coalition-ready EW and ISR

AI governance is, of course, not one size fits all. Not even inside the one organization. Establishing and implementing adaptive AI governance is emerging as a critical competence for every company, selecting the processes to fit the use case, context, and situation. This is vital to enable the systems - and company - to scale. An article in MIT Sloan Management Review provides a useful framework and valuable insights, very close to my work. Two questions determine appropriate governance for the situation: 0 ➡️ Is AI static or adaptive ➡️ Is AI's scope narrow or broad. This yields four governance modes rather than a generic checklist. ⭐ Narrow static systems need rules-based controls. Make decision boundaries explicit, reviewable, and contestable. Model cards, validation testing, and bias checks before anything goes live. Keep human judgment in the loop even here. ⭐ "Ex post alignment" is needed for adaptive systems. "Ex post alignment controls... reveal not how a decision was made but whether its outcomes remain legitimate."  When models grow too complex to explain, stop asking how a decision was made and start asking whether the outcome is still legitimate. Audit after deployment, test against defined standards, and accept that some misalignment is inevitable. ⭐ AI governance needs to be applied to the ecosystem, not just the enterprise. This maps neatly onto my five levels of Humans + AI in the organization framework. The point is that errors travel laterally through APIs, data flows, and autonomous agents, and agentic AI makes them spread faster and reach further with less human oversight. Risk must be ecosystem-aware. Map interdependencies, monitor shared infrastructure, and build anticipatory oversight before failures cascade. ⭐ Human judgment must remain central and decisive. Treat AI outputs as informed signals, not truth. Teach people when to rely on the system and when to challenge it. Redesign the workflow around clear ownership of who decides, who escalates, and who acts, because most failures come from broken handoffs rather than bad predictions. Great framing from Gianvito Lanzolla Margherita PAGANI Christopher Tucci

One of the first reports I published when I joined Delta-EE in 2021 aimed to answer the question "What is a Virtual Power Plant?" At the time, you could ask 6 people what they meant by Virtual Power Plant (VPP) and get 7 definitions. People were using VPP to describe everything from aggregation alone to the full stack required to monetise distributed energy resources (DERs), and everything in between. It was impossible to compare one VPP company to another. We needed a way to talk about VPPs as a commercial service, not just a vague concept. While working on local market platforms for DERs at Electron, I'd been thinking about the steps from customer to cash, and the framework I called the VPP Value Chain was born: 1️⃣ Customer Acquisition - often forgotten by many tech-led platforms   2️⃣ Asset Connection & Control - though gateways and APIs 3️⃣ Aggregation - by asset, brand, or home 4️⃣ Optimisation - deciding which markets deliver most value 5️⃣ Market Interface - robust market communications for bidding and dispatch 6️⃣ Settlement & Billing - managing the money flow back to the customer Fast forward to today, and I am now seeing the framework used by the biggest names in the energy transition to map their ecosystems and analyse commercial strategies: • gridX uses it to define the modularity of their XENON platform. • EDF Pulse has adopted it to clarify the complex journey of residential flexibility. • SET Ventures use it to classify 70 energy-tech innovators. Have you seen it anywhere else? Even 70 companies is barely scratching the surface. I can see over 1,000 companies in Europe delivering at least one element of this value chain for DERs. More companies are specialising in one or two elements, and partnering with others to complete the value chain. I still see many of these companies involved in the value chain proudly announcing that they will revolutionise the energy industry. I hate to rain on their parade, but the revolution has already started. But the industry doesn't stay still for long. With announcements like the recent investments in Fuse Energy and tem, or PowerHive's partnership with Distro Energy, are matching platforms coming back into play? Five years on, how do you see the value chain evolving?

Two years ago, I moved to Austria with curiosity in my heart and a question in my mind: What if Austrian and Indian universities could do more together? This week, that “what if” felt more real than ever. TU Austria (a network of TU Wien, TU Graz, and Montanuniversität Leoben) this week has launched a powerful initiative with India, supported by €5 million in funding from the Austrian Federal Ministry of Science, Research and Economy. This isn't just another exchange program. It’s a strategic bridge — rooted in academic diplomacy, built on trust and excellence, and aimed at addressing the global challenges of our time: 📌 Circular economy 📌 Climate-neutral technologies 📌 Sustainable infrastructure 📌 AI and digitalization As someone who has worked for a decade at the intersection of academia, innovation, and international collaboration — from Indian universities to top US institutions, and now actively engaging with Austrian universities — I see this as a defining moment. A moment to: 🎯 Co-create joint research labs 🎯 Launch PhD exchange programs with dual supervision 🎯 Design transdisciplinary academic-industry clusters between Austria and India 🎯 Develop curriculum and mobility models for the future of STEM education This is where I’d love to contribute — as a consultant and bridge-builder for universities in both countries. Helping map opportunities, facilitate strategic dialogues, and enable world-class programs that serve both nations — and the planet. 📢 To university leaders, deans, researchers, and innovation offices — let’s talk. Because when two strong academic cultures come together, we don’t just exchange students. We exchange futures. Read more about the initiative: https://lnkd.in/dfvprh4U (English summary also available via TU Austria) #AustriaIndia #AcademicDiplomacy #JointResearch #HigherEducation #CircularEconomy #GlobalCollaboration #TUAustria #InnovationBridges

📄 New paper: Orchestrating and Designing Data Collaboratives: What Governance Model is Fit for Purpose? I get asked this a lot: 👉 What’s the difference between a data trust, a data union, a data commons…? 👉 And more importantly—when should you use which? Too often, these models are treated as competing “solutions.” But that framing misses the point. In reality, they reflect different governance logics—and each is designed to solve a specific coordination, agency, or collective action problem in data ecosystems. For instance: Data intermediaries → reduce transaction costs Data unions → rebalance power Data trusts → address legitimacy deficits Data commons → enable collective governance Data cooperatives → redistribute ownership and agency Data sandboxes → manage uncertainty Data spaces → enable scaling and interoperability So the real question is not: ❌ Which model is best? But rather: ✅ Which model is fit for purpose—given the problem you are trying to solve? That’s why I wrote this short paper. It proposes a purpose-driven typology and argues for moving beyond “institutional choice” toward institutional orchestration—where multiple models coexist and evolve within the same ecosystem. 👉 Because in practice, mature data ecosystems don’t rely on a single model—they layer and sequence governance arrangements over time. (And that’s where strategic data stewardship becomes essential.) 📖 Read the paper here: https://lnkd.in/eyT9e4gV 🤔 Curious how others are navigating this: What governance model have you seen work—and why? #data #datagovernance #governance #dataspaces #intermediaries

Is the Evolution of Functionally Aggregated DHTs essentially an Ecosystem Challenge? The authors observe a phenomenon of "aggregated intended purposes" of digital health technologies (DHTs), or "device-aggregates," increasingly being applied in groups of clinical tasks and sub-tasks, from the perspective of regulatory approval. At the highest level, 'super device' aggregates or device suites may be 1) coupled to form loosely defined parts of digitally integrated care pathways, such as hospital-at-home, or 2) cascaded serially. Other pathways are participatory care and patient navigation pathways, and AI-powered anticipatory care pathways are important. This two-article analysis is significant because it highlights the gaps and key issues of regulatory, HTA and reimbursement aspects of data-coupled collaborative innovation. 🔷 Regulatory: Authors note the evolution from passive to active groupings. From cascaded effects to networked, interconnected devices with dynamic dependencies and combined effects that need to be regulated as such. The emergent "super devices" reduce human intervention, necessitating airtight regulation, especially considering the inclusion of non-MDs which are deregulated. Interpreting EU regulations, the “lead” manufacturers of super-MDs (SMD) would be responsible to obtain approval for all components, which could be impractical given their non-manufacturer status for some. 🔷 Reimbursement: Gathering cost-effectiveness evidence introduces new complexities. These include the absence of comparators and the complex estimation of initial investments. Ongoing performance monitoring might solve part of the problem but in the absence of evidence ecosystem standards this will be highly impractical. 🔷 Inclusive evidence: In addition to regulating emergent system properties that arise in interactions, building, testing and evaluating super-MDs in primary care and public health settings and pathways is a limitation. Part two observes the following modalities: 1️⃣ Single manufacturer develops and seeks approval for SMD/components to perform a specific function.   2️⃣ Multiple manufacturers develop approved components brought together and placed on the market by a single commercial entity. 3️⃣ Multiple manufacturers develop approved components brought together and placed on the market as a service provided by a single commercial entity. 4️⃣ Multiple entities brought together flexibly and dynamically and possibly also automatically. As (4) points to a collaborative innovation ecosystem, an overarching challenge emerges: the requirement for regulatory and HTA pathways built on evidence sandboxes and regulated evidence ecosystems, leveraging data frameworks for data governance such as IEEE’s P3493.1™.   PART-1 https://lnkd.in/dv78qpnK PART-2: https://lnkd.in/dVrCN24w #HealthcareInnovation #DigitalHealth #InnovationEcosystem #MDR #SaMD #RegulatoryPolicy #HTA

#India - #Japan Joint Vision for the Next Decade . 1. A New Era for #Startups & #Innovation • Startup Support Initiative: Japan is launching the Japan-India Startup Support Initiative (JISSI) to expand business, link innovation ecosystems, and enable startups in both countries. This is a massive win for emerging businesses! • Funding & Investment: Get ready for increased capital flow! The partnership aims to mobilize funds for start-up companies, especially those in the AI field, through the dedicated "India-Japan fund of funds". • #SME Collaboration: There's a strong focus on Small and Medium Enterprises (SMEs), encouraging Japanese SMEs to visit India and expanding downstream industries, with the launch of an India-Japan SME Forum. • Deepening Research & Tech: The vision aims to tap into scientific and technological capabilities to promote frontier research in foundational sciences and foster cross-sector collaboration on commercializing new technologies. This includes collaboration in quantum technologies and high-performance computing. 2. Accelerating the Green Revolution: #EVs & #Battery Tech • Robust Battery Supply Chain: A significant highlight is the commitment to promote India-Japan battery supply chain cooperation to foster a healthy battery market and ecosystem. This is crucial for the burgeoning EV market! • Sustainable Mobility & Manufacturing: The partnership plans for the manufacturing of automobiles, including those utilizing sustainable fuel and environment-friendly fuel storage. The "Next Generation Mobility Partnership" will leverage advanced technologies and Indian talent to address challenges in infrastructure, logistics, and mobility, focusing on sustainable and environment-friendly solutions. • Clean Energy Transition: Expect strengthened energy cooperation under the India-Japan Clean Energy Partnership through the India-Japan Energy Dialogue, and efforts to promote a circular economy through waste-to-energy technologies and recycling. 3. Frontier Technologies: #Semiconductors & #AI at the Forefront • Semiconductor Supply Chain Resilience: The partnership is actively identifying and implementing concrete projects in semiconductors and critical minerals. There will be promotion of cooperation on resilient supply chains and market diversification, including through the Memorandum of Cooperation on Semiconductor Supply Chain Partnership. • AI Cooperation Initiative (JAI): This is huge! The Japan-India AI Cooperation Initiative (JAI) will promote bilateral and multilateral cooperation on AI, fostering an innovative and trusted AI ecosystem. • AI for Impact: Beyond general AI development, there's a specific mention of joint research in food technology and agricultural sciences, including innovations for Empowering Next Generation Agriculture (AI-ENGAGE). This comprehensive vision truly underscores a shared commitment to a free, open, peaceful, prosperous, and coercive-free Indo-Pacific region.