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.

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

Part 2: 𝗕𝗲𝘆𝗼𝗻𝗱 𝗣𝗼𝗿𝘁𝗲𝗿’𝘀 𝗙𝗶𝘃𝗲 𝗙𝗼𝗿𝗰𝗲𝘀: 𝗧𝘂𝗿𝗻𝗶𝗻𝗴 𝗖𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝗼𝗻 𝗶𝗻𝘁𝗼 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 (Part 1: see https://lnkd.in/eNP8ih5Y) (Part 3: see https://lnkd.in/eYAnkeVS) Michael Porter’s Five Forces framework has shaped how managers and academics analyze industries. It remains an elegant way to map the external environment at the industry level. Porter’s view of strategy, however, was forged in an era when industries were stable, boundaries were clear, and competitive advantage was largely internal. The external environment was portrayed as hostile: every force around the firm—suppliers, buyers, new entrants, rivals, and substitutes—was a potential threat to profitability. Strategy was about defending margins, erecting barriers, and capturing value. But today’s reality is far more fluid. Industries blend into one another, technologies converge, and value is co-created across networks. The same actors that once appeared only as adversaries have become indispensable partners for innovation, agility, and growth. Competitors may share platforms; suppliers co-develop technologies; customers co-create solutions; and substitutes may reveal entirely new markets. If we look at the business world through this new lens, Porter’s five “forces” can also be five “sources” of advantage. Collaboration doesn’t replace competition—it complements it. The real challenge for managers is to find the balance point along a continuum that runs from pure competition to deep collaboration. * Competitors remain rivals, but also potential partners in standard-setting, data sharing, or open-source development. * New entrants are disruptors, but also agile innovators with whom incumbents can partner, invest, or co-develop. * Suppliers can squeeze margins—but when engaged early in design, they become co-innovators. Toyota’s keiretsu model and Unilever’s annual innovation summits with strategic suppliers both show how collaboration can yield efficiency and renewal. * Customers may demand more, but their insights and data now drive innovation. Co-creation platforms—from LEGO Ideas to Tesla’s user forums—turn buyers into creative partners. * Substitutes, once seen only as threats, can signal new opportunities. Netflix, for instance, transformed from a DVD substitute to a platform that redefined how entertainment is consumed. The comparative table below contrasts Porter’s competitive interpretation of each force with a collaborative perspective—a framework better suited when success depends as much on connection as on protection. #Strategy #Innovation #Ecosystems #Collaboration #OpenInnovation #DigitalTransformation #Leadership #BusinessStrategy #MichaelPorter #BlueOceanStrategy #Coopetition #Agility #ValueCreation #Management

Stakeholder Engagement Map for Sustainability 🌎 Sustainability advances when companies move from speaking to stakeholders toward building solutions with them. Engagement becomes powerful when it shifts from information-sharing to participation and co-creation. Employees are not passive recipients of corporate policies. When positioned as innovators and ambassadors, they can drive cultural change that scales faster than top-down initiatives. Investors increasingly evaluate not only financial returns but also resilience and impact. Open dialogue and credible disclosures create the foundation for financing models that reward long-term value creation. Regulators and policymakers shape the boundaries of what is possible. Proactive collaboration ensures that emerging rules both protect society and enable business innovation. NGOs and civil society connect business with pressing social and environmental realities. Partnerships with them help translate global challenges into concrete, measurable corporate actions. Customers bring more than purchasing power. Through collaboration and product co-design, they accelerate the adoption of sustainable solutions and redefine what markets demand. Suppliers and partners extend responsibility beyond a single enterprise. Joint innovation in sourcing, standards, and technology transforms sustainability into a shared endeavor across the value chain. Communities ground sustainability in place. When businesses co-invest in local development, they secure trust and create ecosystems that benefit both society and the enterprise. Media and opinion leaders influence how actions are perceived. Transparent storytelling backed by evidence strengthens legitimacy and reinforces accountability. Academia and experts contribute the critical lens of science and independent validation. Engaging them ensures that strategies are rooted in knowledge, not convenience. Risk and resilience demand collective approaches. Working groups and cross-sector alliances elevate sustainability from individual commitments to systemic impact. True engagement means entering a space of shared design. It is in these interactions that sustainability moves from compliance to transformation, and from promises to outcomes. #sustainability #business #sustainable #esg

I’ve found that most HealthTech founders assume innovation is their differentiator. In practice, it rarely is. The UK doesn’t lack technical brilliance or world-class research. What it lacks is translation – the ability to move from promising R&D to meaningful, sustained adoption inside the NHS. The hardest problems aren’t technical. They’re organisational. Structural, financial, and cultural frictions shape the pace of progress far more than the quality of the technology itself. Procurement is the clearest example. Despite endless reform attempts, it still prizes unit cost over value. I’ve watched technologies capable of saving millions across a pathway fail an affordability test because their upfront cost exceeded a local trust’s limit. It’s no surprise that nearly a third of suppliers now avoid NHS tenders altogether – the commercial terms just don’t work. Funding models make it worse. More than 70% of NHS trust leaders cite financial constraints as the main barrier to digital transformation. Even when solutions clearly deliver long-term savings, capital accounting rules often prevent reinvestment of those gains into operational budgets. The result is predictable: effective innovations that never reach scale because the fiscal space to adopt them simply doesn’t exist. Then there’s the human system. Clinical adoption depends less on technical brilliance and more on how technology fits the rhythm of care. Too often it adds friction – extra logins, duplicate steps, more admin. Around one in three trust leaders still call poor IT infrastructure a critical barrier. And culture matters just as much. Clinicians’ scepticism toward opaque AI tools isn’t resistance. It’s accountability. Trust has to be earned through transparency, evidence, and co-development. The technologies that scale are the ones that integrate clinicians early, turning potential critics into advocates. Yes, there are positive shifts. NICE’s move to consider cost-effectiveness, not just cost-saving, is significant. Regulatory agility has improved. But the underlying system frictions remain. The UK is still a world-class testbed, not yet a world-class market. After two decades, my conclusion is simple: HealthTech success in the UK isn’t about innovation quality anymore. It’s about system mastery. The winners will be those who can navigate NHS economics, align incentives, build trust, and embed change deep within clinical practice. The frontier, as I see it now, isn’t technical. It’s organisational. P.S. If you’re a HealthTech founder, DM to explore how to navigate the system, not just build for it.

AI Adoption: Reality Bites After speaking with customers across various industries yesterday, one thing became crystal clear: there's a significant gap between AI hype and implementation reality. While pundits on X buzz about autonomous agents and sweeping automation, business leaders I spoke with are struggling with fundamentals: getting legal approval, navigating procurement processes, and addressing privacy, security, and governance concerns. What's more revealing is the counterintuitive truth emerging: organizations with the most robust digital transformation experience are often facing greater AI adoption friction. Their established governance structures—originally designed to protect—now create labyrinthine approval processes that nimbler competitors can sidestep. For product leaders, the opportunity lies not in selling technical capability, but in designing for organizational adoption pathways. Consider: - Prioritize modular implementations that can pass through governance checkpoints incrementally rather than requiring all-or-nothing approvals - Create "governance-as-code" frameworks that embed compliance requirements directly into product architecture - Develop value metrics that measure time-to-implementation, not just end-state ROI - Lean into understanability and transparency as part of your value prop - Build solutions that address the career risk stakeholders face when championing AI initiatives For business leaders, it's critical to internalize that the most successful AI implementations will come not from the organizations with the most advanced technology, but those who reinvent adoption processes themselves. Those who recognize AI requires governance innovation—not just technical innovation—will unlock sustainable value while others remain trapped in endless proof-of-concept cycles. What unexpected adoption hurdles are you encountering in your organization? I'd love to hear perspectives beyond the usual technical challenges.

Last year at Microsoft, I spent months untangling Microsoft Copilot’s global rollout. AI’s biggest roadblocks? They weren’t technical at all. Imagine a meeting room in Kauala Lumpur. Someone says, “We’ve always done it this way.” Another whispers, “AI will replace our jobs.” A third leans in: “Our data is too sensitive for AI.” Familiar script, right? Truth is, the toughest challenges weren’t coding or infrastructure, they were deep-seated habits and fears. The breakthrough? It always came from the believers. In every successful Copilot launch, we found our internal champions early like GAURAV JOSHI, Sergey Oreshin, the ones eager to explore, not argue. We trained them, armed them with quick wins, and let their teams see real ROI instead of vague promises. Progress snowballed from those first pockets of success. Here’s a three-step playbook I swear by: 1️⃣ Start with the believers: Map out your internal AI curiosity. 2️⃣ Equip and coach them: Focus on real teams, not abstract rollouts. 3️⃣ Let their results speak: Showcase ROI, then scale, fear melts before evidence. Every company talks about technical innovation, but it’s culture that makes or breaks AI adoption. So, what’s the single biggest cultural barrier you’ve seen hold back real innovation? Share your story below and let’s gather ideas that move the needle. (This is why I collect lessons weekly in Executive AI Essentials—check my profile if you want the next playbook.) PS: Pic made in wonderful Malaysia, but Nano Banana ironed my shirt :)

Are you in advocacy or influence and still using static spreadsheets as a stakeholder map? If so, you need to change course. Now. Why? Because your spreadsheet won’t properly navigate the SMH that is 2025: • Medicaid cuts in the “Big, Beautiful Bill” • AI disrupting everything • Budget deficits and stock market volatility • Wars in the Middle East, Ukraine, elsewhere • Trade wars, tariff escalations, job cuts. • Free speech fights, antisemitism, and extremism • Inflation, immigration crackdowns, data security concerns These aren’t normal times folks. And your advocacy strategy can’t be either. A real stakeholder map in 2025 should work like a live operating system: updating constantly, filtering by issue, engagement level, and digital footprint. You must constantly watering the proverbial 🌼 🌹 🌺 to win. Here’s what that looks like: Stakeholder Type: Media, Hill staff, trade orgs, agency heads, donors, advocacy groups, coalitions. The usual suspects. Still essential, but just one part of the bigger picture. By Issue: Map your landscape around what actually matters now. Different issues = different allies. Period. If you’re not tracking stakeholders across industry specific flashpoints like AI, Medicaid, trade, immigration, or DEI, you’re flying blind. By Position: Ally, neutral, detractor; on this issue, at this moment. Nobody is “always with you” anymore unless they’re on payroll. And even then. Get real about this. By Influence + Interest: High influence, low interest? Your job is to make them care. Low influence, high interest? They can still amplify or derail you. By Engagement Level: 1 = Active 2 = Warm 3 = Cold but still meaningful. Track across both allies and critics. Where’s your team spending time and why? By Relationship Owner: Who owns the relationship? What’s the origin? What’s your backup plan if they ghost? Redundancy matters more than ever. By Digital Footprint: Your map should surface stakeholders with domain authority in policy, media, and increasingly, AI platforms. If the names on your list aren’t being cited, surfaced, or scraped into training data, you’re not influencing the future conversation in the way that people search and advocate. Static stakeholder lists are a liability. They don’t flex. They don’t prioritize. They definitely don’t win. Build something smarter today, because you’re either at the table or you’re on the menu. 💪 📰 ❤️ 🏛️

#contractmanagement and #innovation are two of my personal interests. I see so many joint innovative projects and ideas fail abysmally. So how can a contractmanager support innovation? When innovating together with suppliers or clients, several critical aspects come into play. These are of particular importance when the outcome of the innovation project is highly uncertain. As a contract manager in such a scenario, you would need to adapt and consider the following key points: Clear communication and alignment: It is crucial to have clear communication and alignment with suppliers or clients regarding expectations, goals, and responsibilities. In uncertain innovation projects, all parties must be on the same page to navigate challenges and adapt to changing circumstances. Flexible contracting: Traditional contracts may not be suitable for highly uncertain innovation projects. Contractmanagers need to develop flexible and adaptive contract structures that allow for adjustments as the project progresses. This may involve incorporating clauses for scope changes, performance milestones, and risk-sharing mechanisms. Risk Management: Managing risks becomes even more critical in uncertain innovation projects. Contract managers must identify potential risks, assess their implications, and develop mitigation strategies collaboratively with suppliers or clients. Proactively addressing risks can help minimize disruptions and ensure smoother project execution. Innovation Governance: Establishing a robust innovation governance framework is essential for effective collaboration. Contractmanagers should define fast decision-making processes, allocate resources, and set up mechanisms for monitoring progress and addressing issues promptly. A clear governance structure helps maintain focus and direction throughout the innovation project. Results-Oriented focus: In uncertain innovation projects, focusing on outcomes and value creation is paramount. Contract managers should place emphasis on delivering tangible results that benefit all parties involved. This may require a shift from traditional performance metrics to more adaptive measures that reflect the dynamic nature of innovation. Collaborative Problem-Solving: When uncertainty is high, collaborative problem-solving becomes key. Contractmanagers must foster a culture of open communication, creativity, and cooperation to navigate challenges effectively. Encouraging shared learning and knowledge exchange can lead to innovative solutions and stronger partnerships. When innovating together with suppliers or clients in highly uncertain projects, contract managers need to prioritize clear communication, flexible contracting, risk management, innovation governance, results-oriented focus, and collaborative problem-solving. Adapting to uncertainty and actively addressing challenges while maintaining a focus on value creation are essential for successful innovation collaborations.

𝗣𝗿𝗼𝗰𝘂𝗿𝗲𝗺𝗲𝗻𝘁 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗮 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗰𝘆𝗰𝗹𝗲 and definitely not an occasional burst of inspiration or isolated ideas. It’s a continuous, structured process that drives sustainable business results and operational improvements. It transforms internal requirements and external market insights into enhanced outcomes through collaboration, experimentation and adaptation. As a framework it creates an infinite cycle focusing on: 1️⃣ 𝗡𝗲𝗲𝗱𝘀 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 & 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 to fully understand potential areas for innovation stakeholders care about and who could be aligned with market trends and opportunities. 🔹 Innovation needs a clear purpose and pain points or opportunities to solve. 2️⃣ 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝘃𝗲 𝗖𝗼-𝗖𝗿𝗲𝗮𝘁𝗶𝗼𝗻 & 𝗜𝗱𝗲𝗮𝘁𝗶𝗼𝗻 by engaging suppliers, startups, and cross-functional teams to propose solutions and develop prototypes. 🔹 Innovation is a result of teaming up across disciplines and partnering. 3️⃣ 𝗣𝗶𝗹𝗼𝘁 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 & 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻 to test and continuously refine solutions in a controlled environment based on product feedback and market changes. 🔹 Innovation lives from experimenting, understanding and fine-tuning. 4️⃣ 𝗦𝗰𝗮𝗹𝗲 & 𝗗𝗲𝗹𝗶𝘃𝗲𝗿 𝗺𝗲𝗮𝘀𝘂𝗿𝗮𝗯𝗹𝗲 𝗶𝗺𝗽𝗮𝗰𝘁 that leads to the desired results such as cost efficiencies, improved workflows or enhanced supplier performance. 🔹 Innovation creates value when results can be scaled and demonstrated. 5️⃣ 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 & 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 results and gather lessons learnt to inform further performance improvement or future innovation cycles. 🔹 Innovation must be measurable, both metrics and learning wise. 𝗣𝗿𝗼𝗰𝘂𝗿𝗲𝗺𝗲𝗻𝘁 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗮 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲. One that empowers Procurement teams to move beyond transactions and enables enterprises to harness a wealth of possible outcomes resulting from a collaboration of internal experts and an ecosystem of external partners. ❓How does your organisation approach Procurement innovation, if at all? ❓Are traditional mindsets, as many commented on an earlier post, and a risk-averse culture the biggest barriers to innovate? Looking forward to read your views in the comments.