Innovation Challenges in Tech

Hospitals don’t buy impact. HealthTech founders learn this too late. Every healthcare founder eventually makes the same claim. We save lives. It sounds unassailable doesn't it? The ultimate value proposition. Yet in practice, this framing rarely converts into adoption. Clinicians, procurement teams, and health systems make decisions based on operational utility, not moral gravity. Many innovations fail because they overestimate how impact is perceived. In theory, clinicians prioritise patient outcomes. In reality, they operate in environments shaped by throughput, staffing constraints, and regulatory pressure. When evaluating technology, they ask. Will this fit into my workflow? Will it reduce documentation time? Will it integrate with the EHR? A device that improves workflow efficiency by 20 percent can have more adoption potential than one that marginally improves survival rates but adds complexity. Functional value translates directly to reduced fatigue, higher patient throughput, and measurable ROI. When founders lead with “saving lives,” they position themselves in an ethical rather than operational frame. It feels noble but vague. Hospitals cannot quantify moral claims. Clinicians are sceptical of moral claims because every vendor makes them. Functional gains, by contrast, show that the founder understands the clinical environment. They suggest empathy not through virtue but through precision. Three facts underline the point: A significant majority of clinicians identify workflow disruption as a primary barrier to adopting new healthcare technologies, according to Deloitte (2023). Time savings and improvements in operational efficiency tend to correlate more strongly with technology adoption than purely clinical benefits, as reported by NEJM Catalyst (2022). HealthTech products that emphasize operational metrics are notably more likely to secure pilot funding, based on insights from Rock Health (2024). Moral impact earns admiration. Operational impact earns adoption. Founders in healthcare should design, measure, and communicate their value in functional terms first. Saving lives may be the outcome, but it is rarely the proposition that moves the market.

The web succeeded because it solved a coordination problem: millions of independent actors needed to link documents without central control. Open standards - HTTP, URIs, HTML - made this possible by providing a shared protocol layer that no single party owned. AI agents face an analogous coordination problem. In multi-agent systems, agents built by different parties must exchange not just data but meaning: what a "customer", "approved", or "delivery date" actually denotes. Natural language alone cannot solve this. LLMs can interpret natural language flexibly, but flexibility is precisely the problem when agents must act reliably on shared information. Ambiguity that agents can resolve through context becomes a source of failure when machines transact autonomously at speed and scale. Sharing meaning unambiguously requires two things: a formal system of semantics capable of precise entailment, and globally unique identifiers that can be resolved to authoritative definitions. Without formal semantics, agents cannot reason reliably about what follows from what. Without resolvable identifiers, "customer" in System A and "customer" in System B remain dangerously ambiguous - they might align, or they might not. These are not novel requirements. They are the foundational principles of the semantic web: RDF for formal semantics, URIs for identification, and HTTP for resolution. Anyone building agent interoperability from scratch will either fail to meet these requirements, or meet them and arrive at substantially the same architecture. The real question is whether to adopt these principles in open or proprietary form. Proprietary approaches face a structural problem: interoperability requires shared definitions, but shared definitions only become valuable when widely adopted, and wide adoption requires openness. This is the same network-effect logic that made the web's openness essential. A proprietary web would have remained a collection of walled gardens. The trajectory is therefore clear: as agentic systems mature and the cost of failed interoperability mounts, the pressure towards truly open semantic standards will intensify. It is inevitable. ⭕ Semantic Bow Tie: https://lnkd.in/e6z3hFVn ⭕ The "O" Word: https://lnkd.in/e7v4AjXZ 🔗 Build Your Own Semantics: https://lnkd.in/ezHU2amU

Lloyd's of London just killed a 7-year project. Blueprint Two was supposed to digitize the entire specialty insurance market by Q2 2024. Then 2028. Now it's dead. The team was disbanded. Leadership is reportedly looking to "draw a line" under the project and the "toxicity" associated with it. Here's what went wrong: They treated digitization of the market like a "Big Bang" event that required the entire industry to move at the exact same time to succeed. Carriers had to integrate their systems. Brokers had to change workflows. Everyone had to play along. Spoiler: Not everybody wanted to play. Before building Vantel, I worked at a carrier. I know how this goes. Carriers won't go through the headache of changing systems until the incentives make an overwhelming amount of sense. My take: If your product depends on insurers adopting technology, you probably made a bad bet. In fact, two prospects told me last week how hesitant they would be to use new products that relied on carriers adopting any sort of integration. The business still happens over email. Messy, manual, painful email. 7 years and hundreds of millions later, Lloyd's gave up. The consensus is becoming clear; the market will digitize. But we're clearly not there yet.

The Quiet Gatekeeper Technological change is not a tsunami that arrives from nowhere. It is shaped by institutions, regulation, market power, and human choice. Sarah O’Connor made that argument in the Financial Times this weekend, with a striking example: there are more radiologists working today than in 2016, the year Geoffrey Hinton declared we should stop training them, certain AI would replace them within five to ten years. Hinton has since admitted he was wrong about the timing. The familiar explanations all hold: AI augments expertise rather than replacing it, demand keeps growing, human judgement still matters. But one sentence in O’Connor’s piece stopped me: radiologists have not been replaced in part because insurers are cautious about backing fully autonomous diagnostic systems that, if something goes wrong, can fail “at a pace and volume that a radiologist having a bad day never could.” That is one of the least visible yet most consequential forces shaping how AI reaches commercial reality. And our industry does it, quietly, every day. In energy, the questions AI raises in power grids and process plants are being worked through. In cyber, the price and availability of cover is one signal organisations weigh as they design the systems they will have to insure. In marine and aviation, how fast autonomous shipping and drones scale is tied to how confidently the risks can be priced. The public narrative is about the technology: the data centres, the chips. The real story, whether it actually scales, runs through underwriting committees, reinsurance treaties, the terms on which capacity is offered, and the quiet calculations of risk professionals. Insurance is, at its core, a pricing mechanism for the cost of failure. When that cost is understood, capacity flows and the market lets the technology move. When it is genuinely unknowable, capacity becomes scarce, pricing climbs, appetite narrows and the technology stalls, however brilliant the demonstration. This is not conservatism for its own sake. It is insurance doing its core civilising job: forcing the cost of a new risk to become visible, traceable and priced before it moves from the laboratory into the lives of millions. Almost every novel, hard-to-price risk finds its way to the London Market. The judgements made here, thousands of times a week, help shape which AI applications scale, which slow, and which never leave the pilot. It is fashionable to cast our industry as a follower: slow, behind the curve. The opposite is closer to the truth. We are one of the institutions society has quietly entrusted to price the unknown and transfer risk. In an age when the AI conversation lives at the extremes, either utopian salvation or existential doom, this is a calmer, more grown-up role than either narrative allows. And it is one our market has played quietly, generation after generation, since long before anyone had heard the words “artificial intelligence.”  

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

Here’s a truth about healthcare innovation we don’t talk about enough: (Great ideas often fail—not because they lack potential, but because they face resistance.) We often hear things like: “Technology will revolutionize medicine.” “Innovation is the key to better patient outcomes.” “New tools make healthcare more efficient.” But here’s the reality: Innovation in healthcare isn’t just about having great ideas; it’s about overcoming the barriers to adoption. Here’s why promising innovations often struggle: → Risk Aversion: Healthcare professionals prioritize safety and stick to proven methods unless there's undeniable evidence. → Disrupted Workflows: New tools can feel like complications, threatening established routines and patient interactions. → Time Pressures: Clinicians, already stretched thin, often lack time to learn and adapt to new systems. → Organizational Culture: Traditional mindsets can stifle innovation, favoring profit-driven solutions over simpler, impactful ideas. The takeaway? For innovation to thrive, healthcare must address these barriers with change management, engagement, and clear demonstrations of value. What’s your take? How can we foster innovation in such a risk-averse industry? Let’s discuss below! 👇

Yesterday, I watched a vendor on the street complete four UPI payments in under 20 seconds. No forms. No documents. No “please wait while we verify.” Just instant identity + instant confirmation. Now contrast that with buying insurance. KYC takes days. Medical checks take weeks. Issuance feels like a mini-project. And customers wonder, “If UPI can verify me in seconds, why does insurance take a month?” That’s the gap. Not product. Not price. 𝐒𝐩𝐞𝐞𝐝. Lesson insurers can steal from UPI’s playbook are: 𝟏. 𝐔𝐏𝐈-𝐥𝐢𝐤𝐞 𝐢𝐧𝐬𝐭𝐚𝐧𝐭 𝐊𝐘𝐂 Real-time PAN/Aadhaar verification → no re-uploading → no endless follow-ups. 𝟐. 𝐈𝐧𝐬𝐭𝐚𝐧𝐭 𝐢𝐬𝐬𝐮𝐚𝐧𝐜𝐞 Low-ticket products shouldn’t need medicals and underwriting loops. If risk is low, issue instantly upgrade later. 𝟑. 𝐈𝐧𝐬𝐭𝐚𝐧𝐭 𝐭𝐫𝐮𝐬𝐭 UPI works because customers know it will work. Imagine insurance journeys where: You click → you’re verified → you’re covered in one flow. India didn’t adopt UPI because it was “fintech innovation.” India adopted it because it respected people’s time. Insurance can drive the same revolution. But only if onboarding stops behaving like 2005. #InsurTech #Fintech #DigitalTransformation #CustomerExperience #KYC

Bad Customer Experiences Invite Costly Regulation: The Biden Administration's 'Time is Money' Initiative   In recent years, we've witnessed a growing trend of government intervention in customer service practices. The latest example is the 'Time is Money' initiative, a multi-agency effort aimed at addressing corporate practices that waste consumers' time and create unnecessary obstacles. While this specific initiative comes from the current U.S. administration, it's indicative of a broader issue: when businesses fail to prioritize customer experience (CX), they invite regulation that can impact entire industries.   Many consumers have experienced frustrations with poor service. These pain points often include difficult cancellation processes, convoluted refund procedures, limited or ineffective access to customer support, and misuse of chatbots and AI tools that prioritize company interests over customer needs. When these issues become widespread, they create an environment where some see government intervention as necessary to protect consumer interests. The objectives of the Time is Money initiative include making the process of opting out of a service as straightforward as signing up for it, improving customer support to prevent "service doom loops," ensuring responsible AI implementation, and enforcing transparent policies for refunds and service changes.   While regulation aims to protect consumers, it comes with significant costs that affect both businesses and society at large. Government agencies must allocate substantial resources to develop, implement, and monitor new requirements. This process involves extensive research, drafting of rules, public comment periods, and ongoing enforcement efforts. On the business side, organizations face the burden of understanding new (and potentially vague or complex) regulations, implementing compliance measures, and reporting on their adherence.   The costs don't stop there – companies must also defend against potential infractions, which can lead to legal expenses and negative publicity for perceived or real violations. These financial and reputational damages can be severe, even for minor oversights. Moreover, the fear of non-compliance can stifle innovation as businesses become risk-averse.   Perhaps most frustratingly, these costs are often unnecessary burdens that could be avoided if organizations simply prioritized their customers' best interests from the outset. By focusing on delivering excellent customer experiences, businesses can not only sidestep these regulatory costs but also build stronger, more loyal customer bases.   The companies that thrive will be those that view excellent CX not as a burden or a reaction to regulatory pressure, but as a fundamental aspect of their business model and a key driver of long-term success. The choice is clear: invest in customer experience now, or face potentially costly consequences later.

AI BOOM – OR BUST? Big Tech’s hundred-billion-dollar AI infrastructure gamble is running headfirst into the Iron Law of Megaprojects. Right now, tech giants and governments are racing to build massive data centers, custom energy grids, and enterprise AI rollouts at a scale never seen before. It is the new frontier of megaprojects. The justification from leadership is uniformly the same: "AI is unprecedented. This technology is unique. Normal rules don't apply." But our database of 20,000+ projects tells a different story. When you think "this time is different," you're most at risk. We already see the classic symptoms play out in real time across the AI landscape: The Uniqueness Trap: Treating massive infrastructure builds as bespoke, "never-done-before" experiments rather than standardizing the delivery. Strategic Misrepresentation: Overpromising immediate, massive business benefits to justify eye-watering capital expenditure to Wall Street and boards. Optimism Bias: Underestimating the constraints of local power grids, supply chains, and regulatory approvals. The result? The Iron Law takes strikes again: Over budget, over time, under benefits. Over and over. If you want your major digital or physical infrastructure projects to survive, you have to stop building monuments. You need to build like Lego. The winners of the AI race won't be the ones who spend the most. It will be those who spend the smartest, by repeating small, fast, manageable blocks that deliver value iteratively – whether AI models, chips, compute, or electrons – rather than waiting for one massive "Big Bang" deployment. What about you? Are you seeing the AI rollouts or infrastructure builds in your industry falling into the Uniqueness Trap, or are they successfully breaking the Iron Law? Let’s discuss in the comments below. 👇 #ProjectManagement #Megaprojects #AI #Infrastructure #Leadership #BusinessStrategy