Innovation Ecosystem Mapping

This week at Fortune Brainstorm Tech, I sat down with leaders actually responsible for implementing AI at scale - Deloitte, Blackstone, Amex, Nike, Salesforce, and more. The headlines on AI adoption are usually surveys or arm-wavy anecdotes. The reality is far messier, far more technical, and - if you dig into details - full of patterns worth stealing. A few that stood out: (1) Problem > Platform AI adoption stalls when it’s framed as “we need more AI.” It works when scoped to a bounded business problem with measurable P&L impact. Deloitte's CTO admitted their first wave fizzled until they reframed around ROI-tied use cases. ➡️ Anchor every AI proposal in the metric you’ll move - not the model you’ll use. (2) Fix the Plumbing Every failed rollout traced back to weak foundations. American Express launched a knowledge assistant that collapsed under messy data - forcing a rebuild of their data layer. Painful, but it created cover to invest in infrastructure that lacked a flashy ROI. Today, thousands of travel counselors across 19 markets use AI daily - possible only because of that reset. ➡️ Treat data foundations as first-class citizens. If you’re still deferring middleware spend, AI will expose that gap brutally. (3) Centralize Governance, Decentralize Application Nike’s journey is a case study: Phase 1: centralized team → clean infra, no traction. Phase 2: federated into business-line teams → every project tied to outcomes → traction unlocked. The pattern is consistent: centralize standards, infra, and security; decentralize use-case development. If you only push from the top, you have a fast start but shallow impact. Only bottom-up ownership gives depth. ➡️ You can’t scale AI from a lab. It has to live where the business pain lives. (4) Humans are harder than the Tech Leaders agreed: the “AI story” is really a people story. Fear of job loss slows adoption. ➡️ Frame AI as augmentation, not replacement. Culture change is the real rollout plan. (5) Board Buy-In: Blessing and Burden Boards are terrified of being left behind. Upside: funding and prioritization. Downside: unrealistic timelines and a “go faster” drumbeat. Leaders who navigated best used board energy to unlock investment in cross-functional data/security initiatives. ➡️ Harness board FOMO as cover to fund the unsexy essentials. Don’t let it push you into AI theater. (6) Success ≠ Moonshot, Failure ≠ Fatal. - Blackstone's biggest win: micro-apps that save investors 1–2 hours/day. Not glamorous, but high ROI. - Nike's biggest miss: an immersive AI Olympic shoe designer - fun demo, no scale. Incremental productivity gains compound. Moonshots inspire headlines, but rarely deliver durable value. ➡️ Bank small wins. They build credibility and capacity for bigger bets. In enterprise AI, the model is the easy part. The hard part - and the difference between demo and value - is framing the right problem, building the data plumbing, designing the org, and bringing people along.

I've watched 3 "revolutionary" healthcare technologies fail spectacularly. Each time, the technology was perfect. The implementation was disastrous. Google Health (shut down twice). Microsoft HealthVault (lasted 12 years, then folded). IBM Watson for Oncology (massively overpromised). Billions invested. Solid technology. Total failure. Not because the vision was wrong, but because healthcare adoption follows different rules than consumer tech. Here's what I learned building healthcare tech for 15 years: 1/ Healthcare moves at the speed of trust, not innovation ↳ Lives are at stake, so skepticism is protective ↳ Regulatory approval takes years usually for good reason ↳ Doctors need extensive validation before adoption ↳ Patients want proven solutions, not beta testing 2/ Integration trumps innovation every time ↳ The best tool that no one uses is worthless ↳ Workflow integration matters more than features ↳ EMR compatibility determines adoption rates ↳ Training time is always underestimated 3/ The "cool factor" doesn't predict success ↳ Flashy demos rarely translate to daily use ↳ Simple solutions often outperform complex ones ↳ User interface design beats artificial intelligence ↳ Reliability matters more than cutting-edge features 4/ Reimbursement determines everything ↳ No CPT code = no sustainable business model ↳ Insurance coverage drives provider adoption ↳ Value-based care is changing this slowly ↳ Free trials don't create lasting change 5/ Clinical champions make or break technology ↳ One enthusiastic doctor can drive adoption ↳ Early adopters must see immediate benefits ↳ Word-of-mouth beats marketing every time ↳ Resistance from key stakeholders kills innovations The pattern I've seen: companies build technology for the healthcare system they wish existed, not the one that actually exists. They optimize for TechCrunch headlines instead of clinic workflows. They design for Silicon Valley investors instead of 65-year-old physicians. A successful healthcare technology I've implemented? A simple visit summarization app that saved me time and let me focus on the patient. No fancy interface, very lightweight, integrated into my clinical workflow, effortless to use. Just solved an problem that users had. Healthcare doesn't need more revolutionary technology. It needs evolutionary technology that works within existing systems. ⁉️ What's the simplest technology that's made the biggest difference in your healthcare experience? Sometimes basic beats brilliant. ♻️ Repost if you believe implementation beats innovation in healthcare 👉 Follow me (Reza Hosseini Ghomi, MD, MSE) for realistic perspectives on healthcare technology

Stanford University's genies STORM & CO-STORM are revolutionizing interdisciplinary teamwork by facilitating the creation of Wikipedia-style articles and Roundtable Discussion conversions. 📚 In a world where experts seamlessly unite across disciplines, Stanford's STORM and CO-STORM employ Autonomous AI agents to delve into a myriad of online documents and research papers, fostering real-time collaboration for transformative breakthroughs. 🔆 STORM, or Synthesis of Topic Outlines through Retrieval and Multi-perspective Question Asking, pioneers an innovative framework enhancing interdisciplinary collaboration. By amalgamating diverse viewpoints and utilizing advanced retrieval techniques, STORM amplifies research exploration's clarity and depth. 💫 Building upon this foundation, CO-STORM introduces Collaborative STORMing sessions, fostering structured environments for brainstorming, solution refinement, and implementation to tackle contemporary challenges effectively into a conversational format of discussions amongst various experts. ✨ Insights gleaned from these genies highlight the enrichment of research depth and solution diversity through multi-perspective question asking, the productivity boost from enhanced retrieval systems, and the accelerated innovation driven by structured topic synthesis. 🌟 From revolutionizing healthcare to addressing global sustainability challenges, STORM and CO-STORM empower teams to unleash the collective information retrieval potential of the AI agents in research and development, shaping a brighter future. 💫 My experiments with these tools:- 🔆 I sought an article on one of my research topics "Collaboration amongst human experts, LLMs, and AI agents towards evaluations of AI systems" via STORM which appeared to be a good first draft. STORM used four different agents - A basic Fact Writer, a Software Engineer, a Data Ethicist, an AI Research Scientist to create an engaging and well-cited article. Check it out here - https://lnkd.in/d8_yi_rG 🔆 I also tried a conversation-style roundtable discussion on another topic of interest "Responsible Governance Framework for Generative AI Adoption for Small and Medium Businesses". Check it out here - https://lnkd.in/du8ap4dm ✨ Explore the research and platform:- 📜 Paper - https://lnkd.in/dDBWvqte 👩💻 Code - https://lnkd.in/dfq8HTxE 🌐 STORM/ CO-STORM - https://lnkd.in/dK7gj6SC 💫 How could these approaches redefine your field of interest? Please share your thoughts! #StanfordSTORM #CO-STORM #Collaboration #AIInnovation #AgenticAI #ResearchLeadership #InterdisciplinarySolutions #Innovation #Stanford

🌍 Broadening horizons: The key to innovation 🌱 In every profession, there’s a tendency to narrow our focus, to stay within the comfort zone of what we know and the boundaries of our specific field. But true innovation often lies in looking beyond those boundaries—exploring ideas, theories, and philosophies that may seem, at first glance, unrelated or even outdated. Take Malthusian economics as an example. Originally focused on the relationship between population growth and agricultural production, it’s a theory that some might consider obsolete in today’s context. Yet, its mathematical approach—juxtaposing exponential growth with linear or degrading resources—remains profoundly relevant. Imagine applying this lens to modern challenges like lithium availability versus the skyrocketing demand for batteries. Suddenly, a centuries-old theory sparks fresh insights into one of today’s most pressing issues. This is why expanding your intellectual toolkit beyond your immediate field is vital. Philosophy, economics, history, and even seemingly unrelated sciences can offer frameworks for understanding, questioning, and solving problems in innovative ways. The ability to connect dots across disciplines isn’t just a skill—it’s a superpower in a world that demands agility and creativity. So, whether you’re in procurement, technology, or any other field, don’t shy away from exploring ideas outside your domain. Even an “outdated” theory might be the spark that ignites your next breakthrough. #Innovation #InterdisciplinaryThinking #PhilosophyInBusiness #MalthusianEconomics #BroadeningHorizons

Disruption is fast. Adoption isn’t. In health care, truly disruptive tech rarely “goes viral.” Morris, Wooding & Grant (https://lnkd.in/ga6Xb2Pm) reviewed 23 studies that measured translational time lags. Results varied widely by method and stage, but the most repeated figure was ~17 years from research to routine clinical practice. Measuring from product launch, it is not uncommon for widespread commercial adoption to unfold over a decade. The first 2–3 years belong to pioneers running pilots; clinician "friends and family" are the primary adopters. This is the moment the management team realizes that all their launch forecasts are wildly optimistic and it's time to sheepishly inform the VCs that a Series D is in their near future. The cycle can only be broken by reimbursement. Only when payment arrives is sustainable growth possible.  Years 4–7 are mostly linear: training, workflow fit, financial clarity, an established revenue model, progressive product improvements, and early clinical evidence accumulate. These are the "hard slogging" years: one-on-one meetings educating and instructing care teams about an evolving value proposition. The product doesn't "sell itself" - the sales and clinical teams sell the product. This pattern isn’t just anecdotal. Foundational work in health-care diffusion shows that translating discoveries into routine care is slow and social, not purely technical. What you put in is what you get out. Years 8–10 are where the S-curve steepens, as guidelines catch up, KOLs normalize use, and publications coalesce into clinical consensus. The resulting run through the bell curve leads pundits to comment that widespread product uptake was "inevitable" - it wasn't. If you’re building or adopting disruptive tech: pilot early, publish relentlessly, design for workflow (not just efficacy), and make reimbursement & training first-class features. Two high profile examples: • Intuitive Surgical (da Vinci robotic surgery): FDA clearance arrived in 2000 for general laparoscopy. Adoption then compounded over the 2000s and 2010s, with robotic techniques ultimately capturing a dominant share in procedures like radical prostatectomy—illustrating a long, stepwise shift from early pilots to mainstream practice. • Dexcom (continuous glucose monitoring): First FDA-approved system in 2006 (STS). A major inflection came with Medicare coverage in 2017 for “therapeutic” CGM and continued guideline endorsement by the ADA—moving CGM from early adopters to the standard toolkit for insulin-treated patients. When it comes to disruptive product adoption in medicine, I try to remember Atul Gawande's wise words from “Slow Ideas” (The New Yorker): “We yearn for frictionless, technological solutions. But people talking to people is still the way that norms and standards change.”

The State of Energy Innovation: A Report from the IEA The IEA has released its State of Energy Innovation report, providing a comprehensive global assessment of the latest advancements, challenges, and opportunities in energy technology innovation. The report draws on over 150 innovation highlights, surveys of practitioners across 34 countries, and analyses of R&D spending, venture capital flows, and technology demonstrations. Key Takeaways: 1️⃣ The energy innovation landscape is highly dynamic, with advancements across a range of technologies and countries. 2️⃣ There's a growing focus on low-emission, modular, and mass-manufactured technologies, including batteries, electrolyzers, and solar PV. 3️⃣ The IEA has identified 18 "Races to Firsts" – key demonstration milestones for emerging technologies – to track and encourage progress. 4️⃣ These include the first carbon-free flight, the first repeatedly deployed small modular nuclear reactor, and the first low-energy intensity ammonia production. 5️⃣ Government support, market forces, finance, knowledge-sharing, and access to R&D facilities are essential components. 6️⃣ The global landscape of energy innovation is changing. China has overtaken the US and Japan as the largest single country for energy patenting, particularly in low-emissions technologies. 7️⃣ While public and corporate energy R&D spending has increased in recent years, venture capital (VC) funding for energy start-ups has declined recently. 8️⃣ The report includes focused chapters on three dynamic fields: diversification of battery mineral supplies, application of AI to energy innovation, and development of CDR technologies. Challenges: ✴️ Bringing innovative technologies to commercial scale remains a significant challenge, particularly for large-scale, first-of-a-kind projects. ✴️The recent decline in VC funding for energy start-ups could have long-term negative impacts on innovation. ✴️ The benefits of energy technology innovation are not evenly distributed globally, with emerging markets and developing economies facing significant challenges in accessing funding, infrastructure, and expertise. ✴️ Uncertainties surrounding policy commitments and regulatory frameworks create risks for investors and can slow down innovation. Opportunities: ✳️ The rapid growth and cost reductions in low-emissions energy technologies, such as solar PV, wind, batteries, and EVs. ✳️ Governments are developing new approaches to policy support, such as inducement prizes, open-access testing facilities, and collaborations on demonstration projects. ✳️ Innovation in battery minerals, AI for energy, and CDR presents significant opportunities to accelerate the clean energy transition. ✳️ International cooperation on R&D, demonstration projects, and policy can accelerate progress. #Energy #Innovation #RenewableEnergy #ClimateChange #Technology #Investment #IEA #Decarbonization

Two of the three 2024 Nobel Prize in Chemistry laureates worked in a corporate research lab. That narrative keeps coming up in the protein folding story. Yet the science of computational protein design and protein structure prediction was built on decades of discovery science, the majority of which happened in university labs. The breakthrough AI tools would not have been possible without five decades of funding from NSF to support building the Protein Data Bank or without the more than the 60,000 scientists who deposited their structural data into this open access archive. David Baker, the third laureate, was continuously funded by NSF since 1994 and it was since 1994 that an ongoing international competition brought scientists together to develop computational models for protein folding prediction. AlphaFold2 solved it in 2020. The discovery infrastructure built in academic settings, sustained by public investment, and maintained through a culture of open science is the long arc that would not have happened if we had to rely solely on market incentives. The corporate lab was critical in accelerating the solution that came after decades of discovery. It also would not have happened without the rich interdisciplinary engagement that took place within and across universities. The big breakthrough happened because of the convergence of structural biologists, chemists, evolutionary geneticists, computer scientists, and biophysicists, all working on the same challenge. Universities facilitate this kind of convergence by bringing disciplines together and enabling fundamental research with commercial application as a potential outcome rather than a prerequisite. Similarly, multiple-PI awards are essential structures for incentivizing interdisciplinarity, by bringing together researchers across departments, institutions, and disciplines to solve grand challenges. These grants support research groups as scientific units, rather than single PIs and they create a structure for transcending disciplinary boundaries to solve the world’s greatest challenges. They also provide the hands-on experiential education that trains the next generation of scientific leadership for the nation. The capacity that produced the protein folding breakthrough, among many of our top breakthroughs, was built over decades through sustained public investment in university-based discovery science and in mechanisms that support interdisciplinarity, open science, and fundamental research. Sustaining that capacity requires the same long-term commitment that built it.

The system worked. The transition failed. Cloud is live. Code is bug-free. Data migrated successfully. Project status: Complete. Six weeks later - teams are back in spreadsheets. Adoption rate: 15%. McKinsey 2024: 70% of digital transformations fail to meet objectives. In 85% of those failures, the technology worked perfectly. Here's what the radar chart reveals: Technical System Readiness: 98% Leadership Role-Modeling: 35% Shared Meaning & Buy-In: 27% Skills & Behavioral Mastery: 22% Incentive & KPI Alignment: 18% The budget imbalance mirrors this perfectly. 90% allocated to systems. 10% to people. Yet 70% of ROI depends on adoption. Four mechanisms guarantee failure: ❌ The Hypocrisy Gap ↳ Only 1 in 3 leaders change their habits ↳ CEO asks for the old spreadsheet once - transition dies ❌ The Training Fallacy ↳ Most users reach basic awareness, stop there ↳ Only 20% achieve mastery ↳ The rest build workarounds ❌ The Structural Sabotage ↳ New system launched ↳ Bonuses tied to old behaviors ↳ People choose the bonus every time ❌ The Engagement Exodus ↳ 70% of staff feel change is "done to them" ↳ Not "for them" or "with them" ↳ Resistance becomes their identity The 48-hour test predicts everything. If leadership modeling sits below 50%, teams revert to shadow processes within 48 hours of launch. Then the pattern completes: System gets labeled "broken." Transition gets ignored. Change lead gets fired. Document this before your next launch: ↳ Leadership modeling score (target: 70%+) ↳ Incentive alignment assessment (currently 18%) ↳ User engagement in design process ↳ Behavioral mastery milestones beyond training Your technology budget was never the problem. Your people budget was. -------- 🔔 Follow Justin R. for more Transformation insights ♻️ Share with someone launching a system next quarter

The Global Innovation Index 2024: Are We Really Innovating, or Just Riding the Same Old Waves? 🌍💡 Switzerland, Sweden, and the U.S. continue to reign supreme in the Global Innovation Index (#GII) 2024, showing that when you already have a solid foundation, staying at the top isn't all that difficult. 🏆 Yet, the clouds are gathering. 🌩️ R&D investments have slowed, publications are down, and venture capital is cautiously returning to pre-pandemic levels, raising a crucial question: Are we truly innovating, or are we merely managing the status quo? Emerging economies like China, India, and Vietnam are undeniably catching up, with impressive strides in R&D, high-tech exports, and creative industries. 📈 But let's not get too carried away—these gains are still fragile and require careful nurturing. Even as innovation spreads across Asia and Latin America, the real impact on social issues remains marginal. Social entrepreneurship, the GII's special theme this year, shows promise in addressing global challenges like poverty, sustainability, and injustice, yet it remains relegated to the sidelines of traditional innovation policies. 🌱🌍 In a world where digital and deep science are making strides—think genome sequencing, computing power, and electric batteries ⚡—we must ask ourselves: Are we focusing on the right innovations? The kind that drive tangible change for both economies and societies, or just the next flashy tech? As the global innovation landscape continues to evolve, the real question isn't just who leads the rankings, but whether the systems in place can sustain long-term, inclusive growth. Innovation shouldn't be about more for the few; it should be about better for the many. ⚖️ #Innovation #GII2024 #GlobalLeadership #TechProgress #Sustainability #SocialEntrepreneurship #EmergingMarkets #FutureOfWork #BusinessGrowth #DigitalTransformation #R&D #EconomicDevelopment #GlobalChallenges #InnovationEcosystems #TechForGood Source: World Intellectual Property Organization – WIPO & Advanced Study Institute of Asia

The UNESCO issued the India: Artificial Intelligence Readiness Assessment Report as a part of the India AI Impact Summit 2026, presenting the findings of India’s national assessment conducted under UNESCO’s Readiness Assessment Methodology to evaluate preparedness for implementing the Recommendation on the Ethics of Artificial Intelligence. The document examines India’s AI landscape across five dimensions: legal and regulatory, social and cultural, scientific and educational, economic, and technical and infrastructural. It maps existing laws, policy instruments, institutional mechanisms and sectoral initiatives that shape AI governance in the country. It also reviews India’s evolving approach, which relies on existing legal frameworks, sector-specific guidance, voluntary standards and multistakeholder consultations rather than a single overarching AI law. The report highlights India’s expanding AI ecosystem, supported by the IndiaAI Mission, digital public infrastructure, start-up growth, increasing research output and public-sector deployments across ministries. At the same time, it identifies challenges relating to regulatory coherence, data quality and accessibility, workforce preparedness, regional disparities in skilling, compute infrastructure gaps, and the need to operationalize ethical AI principles in practice. Based on secondary research and consultations with over 600 stakeholders, the report sets out eight policy recommendations. These include conducting a comprehensive AI risk and legal review, strengthening centre–state coordination, enhancing access to high-quality datasets, building public trust, preparing the workforce for AI transitions, integrating environmental sustainability into AI infrastructure planning, and promoting diversity within the AI ecosystem. A copy of the document is enclosed. #AI #ArtificialIntelligence #AIGovernance #ResponsibleAI #DigitalIndia #TechPolicy #DataGovernance #AIEthics #InnovationPolicy #IndiaAI P.S. This post is for academic discussion only.