Ecosystem Strategy Development

After spending three decades in the aerospace industry, I’ve seen firsthand how crucial it is for different sectors to learn from each other. We no longer can afford to stay stuck in our own bubbles. Take the aerospace industry, for example. They’ve been looking at how car manufacturers automate their factories to improve their own processes. And those racing teams? Their ability to prototype quickly and develop at a breakneck pace is something we can all learn from to speed up our product development. It’s all about breaking down those silos and embracing new ideas from wherever we can find them. When I was leading the Scorpion Jet program, our rapid development – less than two years to develop a new aircraft – caught the attention of a company known for razors and electric shavers. They reached out to us, intrigued by our ability to iterate so quickly, telling me "you developed a new jet faster than we can develop new razors..." They wanted to learn how we managed to streamline our processes. It was quite an unexpected and fascinating experience that underscored the value of looking beyond one’s own industry can lead to significant improvements and efficiencies, even in fields as seemingly unrelated as aerospace and consumer electronics. In today’s fast-paced world, it’s more important than ever for industries to break out of their silos and look to other sectors for fresh ideas and processes. This kind of cross-industry learning not only fosters innovation but also helps stay competitive in a rapidly changing market. For instance, the aerospace industry has been taking cues from car manufacturers to improve factory automation. And the automotive companies are adopting aerospace processes for systems engineering. Meanwhile, both sectors are picking up tips from tech giants like Apple and Google to boost their electronics and software development. And at Siemens, we partner with racing teams. Why? Because their knack for rapid prototyping and fast-paced development is something we can all learn from to speed up our product development cycles. This cross-pollination of ideas is crucial as industries evolve and integrate more advanced technologies. By exploring best practices from other industries, companies can find innovative new ways to improve their processes and products. After all, how can someone think outside the box, if they are only looking in the box? If you are interested in learning more, I suggest checking out this article by my colleagues Todd Tuthill and Nand Kochhar where they take a closer look at how cross-industry learning are key to developing advanced air mobility solutions. https://lnkd.in/dK3U6pJf

𝐎𝐩𝐞𝐧 𝐢𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧 is one of the most impactful paradigms in management research — and also one of the most misunderstood. Since Chesbrough coined the term in 2003, the idea that firms should deliberately open their boundaries to external knowledge has reshaped how companies innovate, how universities engage with industry, and how governments design innovation policy. But how well do we actually know the foundational literature? I have put together a list of 25 𝐜𝐥𝐚𝐬𝐬𝐢𝐜 𝐚𝐫𝐭𝐢𝐜𝐥𝐞𝐬 𝐢𝐧 𝐎𝐩𝐞𝐧 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧— spanning founding theory, empirical evidence, literature reviews, ecosystems, SMEs, users and communities, and practice. Academic papers and practitioner-facing pieces. The articles that shaped the field and continue to shape it. Starting today, I will share one article per day — from #25 down to #1 — with a short explanation of what each paper is about and why it still matters, for researchers and practitioners alike. 📌 #25 — Lichtenthaler & Lichtenthaler (2009), "A capability-based framework for open innovation" — Journal of Management Studies What is it about? This article extends Cohen & Levinthal's absorptive capacity concept into an open innovation framework. It identifies six interconnected knowledge-related capabilities — inventive, absorptive, transformative, connective, innovative, and desorptive — that firms need to manage knowledge flows across boundaries. It bridges dynamic capabilities theory with OI practice in a rigorous and comprehensive way. Why does it matter? For academics, it provided one of the most cited theoretical bridges between dynamic capabilities and open innovation. For practitioners, it offers a diagnostic checklist to assess whether their firm has the organizational capabilities required to actually benefit from openness — not just the strategic intent. Connective capacity has been central in understanding the success of OI, and desorptive capacity is still underexplored in the OI literature, but has been picked up by Rita McGrath and others. 🔗 Read it here: https://lnkd.in/eHWsPJ9z #OpenInnovation #Innovation #InnovationManagement #ResearchMatters #KnowledgeManagement #AbsorptiveCapacity

Last week, I held a quantum computing chip in the palm of my hand. Minutes earlier, I stood in front of WEIZAC—a 1950s computer that filled an entire room to deliver a fraction of your phone's computing power. The physical contrast is striking, but the strategic lesson is more profound: we're at a similar inflection point with AI today. 🔬 The Ecosystem Advantage What caught my attention wasn't just the technology—it was the ecosystem. Leading research institutions spinning off commercial ventures, which then contribute talent, capital, and real-world problem sets back to academic labs. This flywheel effect is how breakthrough research becomes market-defining companies becomes next-generation research. ⚛️ The Quantum-AI Parallel Quantum isn't just another computing paradigm—it's a reminder that the AI systems we're deploying today will seem primitive compared to what's being developed in research labs right now. Just as classical computing evolved from WEIZAC to quantum chips, AI will evolve from today's large language models to architectures we're only beginning to imagine. 💡 What Should Businesses Do? Don't just track the market – Stay connected to research organizations pushing the boundaries Look beyond today's deployments – The trends reshaping your industry in 5-10 years are being discovered in labs right now Build ecosystem connections – The companies that maintain strong ties to innovation hubs see the future coming first   The future doesn't arrive uniformly. It emerges from these innovation ecosystems, and proximity matters. 

$44 trillion of economic value? As natural ecosystems decline, more than half of global GDP—$44 trillion—is at risk, and with it, the stability of industries worldwide. The good news? Forward-thinking companies can turn this challenge into a competitive edge by investing in climate action and resilience. Natural disasters, driven by climate change, have increased by 80% over the past 50 years, exposing supply chains to unprecedented risk and creating a $3 trillion vulnerability in global operations. But while the cost of inaction is steep, the rewards for action are immense. Opportunities by the Numbers: • $10 Trillion in New Value: Embracing nature-positive practices could create $10 trillion in business value and 395 million jobs by 2030, benefiting every sector from tech to agriculture. • 10x Returns on Resilience Investments: Every $1 invested in climate resilience could yield up to $10 in returns, protecting businesses from climate impacts and cutting operational costs. • $98 Trillion Boost from Renewable Energy: Shifting to renewables by 2050 could add $98 trillion to the economy, create 42 million jobs, and protect companies from energy volatility. Where to Start? - Integrate Sustainability at the Core to capture investor interest, with ESG-focused assets projected to reach $50 trillion by 2025. - Invest in Green Infrastructure to cut costs, reduce risks, and build brand value. - Strengthen Supply Chains through responsible sourcing and regenerative practices to guard against climate disruptions. Companies that act now won’t just mitigate risks; they’ll unlock growth, resilience, and a competitive advantage for the future. #decarbonisation #co2 #emissions #nature #natureloss #supplychain #esg

50+ business opportunities from climate change adaptation and resilience🌍 The economic map of climate action is shifting quickly. Adaptation is moving from a side topic to a full marketplace with its own technologies, services, and solution clusters. The driver is clear. Physical risk is shaping decisions in companies, cities, and financial institutions. And this is opening a wave of commercial activity across food systems, infrastructure, health, water, energy, and biodiversity. The scale is expanding fast. Remote sensing, climate forecasting, resilient materials, cold resistant crops, atmospheric water harvesting, advanced cooling, and restoration services are entering mainstream planning. Each solution responds to a direct vulnerability created by a hotter and more volatile climate. The signal is impossible to ignore. The world crossed 1.5°C for the first time in 2024 and climate related losses have already exceeded $3T over the past 15 years. Risk is now operational. This is why adaptation investment keeps rising. Cooling technologies are scaling. Resilient construction materials are growing. Data driven climate analytics are advancing at 25 percent to 30 percent in several markets. The trajectory now mirrors the early expansion of mitigation but with wider reach across every sector. Instead of a few anchor technologies, we now see 60 plus adaptation opportunities forming a connected ecosystem. Each connected to essential functions such as safety, reliability, continuity, and protection. For business leaders, this shift is material. Adaptation is becoming a strategic capability. And understanding this landscape is now part of long term competitiveness. This is where the next wave of climate related value creation is emerging. Source: Already a Multi-Trillion-Dollar Market: CEO Guide to Growth in the Green Economy by the World Economic Forum and BCG #sustainability #sustainable #esg #climatechange

Turning emerging technologies into Bioeconomy impact The World Economic Forum’s Top 10 Emerging Technologies of 2026 highlights that the next wave of innovation is moving into the physical systems that shape our lives - energy, materials, food, health, infrastructure and security. For New Zealand the future is not just about adopting technologies, it is about shaping them around the missions that matter. At the Bioeconomy Science Institute, our missions-led science framework is designed to focus national capability on systems-level challenges: economic growth, biosecurity, climate resilience, biodiversity loss and the transition to higher-value, sustainable bio-based industries. The alignment: ✅Everything-to-grid energy, passive radiative cooling materials and direct lithium extraction point to a world where resilience is engineered into the built environment, energy networks and critical mineral systems. For New Zealand, these sit naturally alongside a Climate Resilient Bioeconomy. ✅Precision fermentation and bio-based manufacturing are no longer speculative. Precision fermentation is a way to produce food ingredients, chemicals and medicines using programmed microbes, with far lower dependence on land, climate and supply chains. That connects to our missions in Bio-Based Exports and Bio-Based Industries, growing the value of New Zealand’s primary sectors. ✅PFAS destruction is a powerful signal. The ability to break down persistent “forever chemicals” is not just a technology story; it is a stewardship story. It aligns with Thriving Ecosystems and Biodiscovery, where science protects whenua, flora, fauna and water systems while unlocking discovery from our unique biological heritage. ✅The health technologies, exosome drug delivery, personalised mRNA cancer vaccines and quantum simulation for drug discovery, show how biology, computation and precision design are converging. For the bioeconomy, this convergence matters and the same underlying capabilities can accelerate new foods, bioactives, biomaterials, and environmental tools. ✅World models for AI and lattice-based cryptography - enabling technologies for trusted, predictive, secure science. World models can help us simulate complex biological, environmental and production systems; lattice-based cryptography points to the need for long-term digital security in an era of quantum computing. They align strongly with the Transformational Technologies mission. It is not about chasing every emerging technology, but to connect the right technologies to the right missions. ✅Which emerging technologies strengthen our export advantage? ✅Which help protect our biological borders and ecosystems? ✅Which enable climate resilience in landscapes, forests, farms, oceans and communities? ✅Which create new industries from renewable biological resources? ✅Which must be shaped with Māori, industry, government and communities? #Bioeconomy #Science #Technologies #ClimateResilience #BiobasedInnovation

📊 New Primer (1/2): 💬 What are the main challenges in re-using social or non-traditional data for health and well being research? 📄 Our new primer, “The Emergent Social Data Ecosystem: Challenges and Opportunities for Innovation,” provides insights from our Social Data for Health project (with the Wellcome Trust Discovery Research Programme). The analysis highlights a growing ecosystem of actors, tools, and initiatives working to unlock the value of social data for health and wellbeing research. Yet despite this momentum, significant technical, institutional, and governance barriers remain. Among the most pressing challenges: ⚙️ Limited and Fragmented Data Access Many researchers still rely on short-term, ad hoc data agreements with private platforms or intermediaries, limiting long-term reuse, replication, and scalability. 🌐 Uneven Infrastructure and Services We identified more than 80 social data services globally, yet most remain under-resourced and heavily concentrated in high-income countries. 🔗 Complex Data Linkage Pathways Combining social data with clinical, epidemiological, or administrative datasets remains technically difficult and legally complex. 🤝 Gaps in Trust and Legitimacy Public expectations for transparency, reciprocity, and accountability are rising, while skepticism persists about the policy relevance and ethical use of social data. 💸 Short-Term Funding and Structural Inequities Project-based funding cycles and unequal access to tools, infrastructure, and data disproportionately affect researchers in LMICs. 🧪 Yet innovation is emerging. Across the ecosystem, researchers and institutions are experimenting with new approaches — including federated analysis, synthetic data generation, participatory social-license models, and new data stewardship roles designed to responsibly unlock data for public interest research. 📄 Read the primer: https://lnkd.in/e2MxxEVu 📘 Explore the full Social Data for Health report: https://lnkd.in/e6PFfmk5 #SocialData #HealthData #PublicHealth #DataGovernance #DataForGood #Research #DigitalHealth

Enterprise AI win = Partnership led The enterprise technology industry is a microcosm of the global economy — interconnected, interdependent, and powered by collaboration. We call it the Partnership Economy. At its core, AI innovation originates from hyperscalers, start-ups, and now data & AI platform companies. But innovation alone isn’t enough — it needs to be made consumable. That’s where the ecosystem steps in: - System Integrators (SIs) and ISVs build solutions that make technology enterprise-ready. - Vertical specialists make horizontal innovation relevant to industries and functions. - Consulting firms drive early adoption by articulating business value and enabling seamless integration. - Marketplaces and regional SIs ensure access and scale. As enterprises, SMBs, and governments adopt these solutions, telemetry from their usage fuels the next wave of innovation — creating a virtuous cycle of supply and demand. This is how technology value shifts — from enterprise investment → to ecosystem co-creation. Every player in the value chain — from hyperscaler to government agency — contributes to and benefits from the momentum of the Partnership Economy. Sidhant Rastogi Rajat Kohli Atul Srivastava Anshuman Tripathy Zinnov

AI agents are slowly becoming the new interface for the internet. One of the biggest signals came recently from Swiggy. Swiggy launched “Builders Club”. A platform that opens its commerce infrastructure to external developers and AI agents through MCP servers and APIs. This changes the role of Swiggy completely. It is no longer just a food delivery app. It is becoming AI commerce infrastructure. Developers can now build AI agents that can: • Order food automatically • Buy groceries • Reserve restaurant tables • Track deliveries • Make decisions based on budget, nutrition, timing, or preferences The important shift is this: Earlier: User → App → Transaction Now: User → AI Agent → APIs → Transaction That changes everything. Apps may slowly become operational layers in the background. AI agents may become the new interface users interact with. This is similar to what happened with: • Stripe in payments • Twilio in communications • Amazon Web Services in infrastructure Swiggy is trying to become the commerce layer for AI-native applications. The tech stack is also interesting: • MCP (Model Context Protocol) • Bedrock AgentCore • OAuth 2.1 • Support for LangGraph, CrewAI, OpenAI Agents SDK and more This means they are preparing for a future where thousands of external AI agents transact on top of Swiggy’s ecosystem. One important idea stands out: “Build for agents first, not just humans.” The next wave of startups may not build apps first. They may build: • Agent workflows • Orchestration layers • Trust systems • Memory systems • AI-native commerce experiences Very important trend to watch.

India’s quick food delivery space is on fire By 2030, this market is expected to cross ₹2 lakh crore Growing at a steady 18% CAGR We now have five players defining five radically different paths: 1. Zepto Cafe - Went from 30k to 100k+ daily orders - 50% gross margin on snacks & drinks - Built for 10-minute delivery via dark stores • Snack-first = higher margins than meals • Urban density + micro-warehousing is its engine • Positioned as a full-stack alternative to Zomato/Swiggy But: - Operations were paused in 44 stores across North India - Delhi NCR, Agra, Meerut, Haridwar, Gorakhpur, Amritsar, and Ghaziabad were impacted - Supply + staffing crunch triggered shutdown • Target to resume Q2 FY26 •Highlights the fragility of scaling ops too fast •High dependency on hyper-local labor & logistics 2. Bistro by Zomato  Zomato tried a restaurant-led 10-minute model.  It failed. • Kitchens weren’t ready • Restaurant menus were too long • CX was inconsistent - So they pulled the plug—and went all in on Blinkit’s Bistro kitchens. - Now active across Delhi NCR, Mumbai, Bengaluru. - More than 100 kitchens. Zomato now controls the experience end-to-end. • Tighter kitchen prep timelines • Curated, limited menus  • Blinkit infrastructure as a moat 3. Swiggy Bolt Swiggy’s counterpunch? Bolt - Live in 500 cities - 10–15 min food delivery - Now over 10% of total Swiggy food orders Unlike Zomato’s earlier model, Swiggy took a smarter route: • Partnered with restaurants to create Bolt-only prep stations • Menus capped at 8–10 items for speed • Uses cloud kitchen expertise to streamline ops Bolt isn’t about being everywhere. It’s about owning the urban “hungry-now” moment - Ideal for metros - Great for high AOV use cases - Appeals to speed-first professionals 4. Swiggy Snacc Snacc is Swiggy’s most interesting—and riskiest—play - A standalone app - Built for snack-first consumers - Targets urban, health-conscious professionals Think cold brews. Protein bars. Shakes. Delivered in <10 minutes. Unlike Bolt or Bistro, Snacc is not about meals. It’s about intent-driven indulgence. Why a separate app? • To test a focused vertical • To learn from behavioural signals • To keep branding distinct from Swiggy’s mainline But:  - Low order frequency.  - Harder to builda habit.  - Limited scale outside major cities. 5. bigbasket enters the chat BigBasket just announced a national rollout of 10-minute food delivery. Starting with: - 40 dark stores by July - Snacks from Starbucks and Qmin (Tata-owned) - No third-party brands involved The twist? They’re bundling food with existing grocery orders. This means: • Lower delivery cost per order • Higher AOV per cart • Repeat use from a loyal base And they’re expanding dark stores from 700 → 1200 by end-2025. So what’s really going on here? Standalone apps. Snack-only menus. Bundled logistics. This isn’t just food delivery anymore. It’s micro-commerce. Optimized for time, mood, and moment.