Business Insights and Analysis

🔎 What Insurtech Insights USA 2026 Says About the Next Phase of Insurance AI After writing about ITC Europe 2026, Insurtech Insights USA 2026 looks like the execution layer of the same question. ITC Europe highlighted accountability, operating discipline, and the need to turn innovation into measurable business value. At Insurtech Insights USA, the main stage keynote “The AI-Defined Insurer: Rewriting the Rules of Risk, Data, and Competitive Advantage” captured the next step well. AI is no longer being discussed only as a technology theme. It is being connected directly to risk, data, decisions, and competitive advantage. 1. 🎤 Keynote signal: risk, data, and competitive advantage The keynote framing matters because it moves the AI discussion away from tools and closer to the foundations of insurance work. The question is not simply how insurers add AI to existing processes. It is how AI changes decision rights, roles, governance, and accountability across the business. 2. 🧾 Underwriting: from automation to judgment design The underwriting discussion is no longer just about processing submissions faster. The real question is how AI changes risk selection, triage, escalation, and underwriter accountability. If agentic AI enters the upstream workflow, insurers need to define where AI can act, where humans must review, and how exceptions are managed. 3. 📊 Claims: from speed to decision quality In claims, speed matters, but it is not enough. AI can reduce handling time, but poor data and unclear rules can also make bad decisions faster. The real issue is whether insurers can improve consistency without weakening human accountability. 4. 🤝 Distribution: being understood in the right decision context Distribution is no longer only about digitising existing channels. If customers use AI tools to compare and choose insurance, insurers need products, disclosures, and advice journeys that are easier for both humans and AI to interpret. The question is not only how insurers sell. It is how they are understood. 5. 🏗️ Takeaway: what defines the AI defined insurer The AI defined insurer will not be defined by AI tools. It will be defined by how risk, data, decisions, and accountability are redesigned around AI. That is the real shift. And this is where the hard reality begins. If data, rules, workflows, and accountability remain fragmented, AI may simply process unreliable information more efficiently. For many incumbents, the hardest challenge may not be the AI itself. It may be the legacy structures that make clear decisions, usable data, and accountable workflows difficult in the first place. #Insurtech #Insurance #AI #DigitalTransformation #Underwriting #Claims

What happens when risk managers become the risk shapers? Insurance / risk transfer is one of the most important enablers of the modern economy. Without it, infrastructure stalls, investment slows, and whole sectors struggle to operate. That is why the latest ShareAction article and Insuring Disaster 2026 report are worth reading carefully. The message is challenging, but also deeply constructive. Insurers have a pivotal role to play in the climate and nature transition; not just by withdrawing from high-risk activities, but by helping clients understand, price, reduce and adapt to risk in a world that is changing fast. The report points to some uncomfortable gaps: 🌡️ Climate and nature risks are still not consistently embedded into underwriting decisions 🌿 Nature-related risks remain underdeveloped, especially in catastrophe modelling 📉 Interim transition targets are uneven, particularly for underwriting portfolios 🍃 Adaptation and resilience remain major opportunities where insurers can lead This is not a story about failure - it's really about capability. Insurers understand risk better than anyone. They see physical impacts, asset exposure, system fragility and behavioural incentives with experienced clarity. That gives the sector a powerful role in shaping the transition, not just responding to it. At Tonkin + Taylor, we work closely with insurers to bring a scientific foundation to climate and nature risk assessment, drawing on deep technical expertise and an extensive track record across hazard, resilience, adaptation, biodiversity and infrastructure systems. In the end, credible transition planning cannot be built on aspiration alone. It needs: 🥇 evidence 🥈 scenarios grounded in physical reality 🥉 a clear view of where risks are emerging, where opportunities sit, and how underwriting, investment and client engagement can support long-term resilience. The implication for business strategy is clear: transition plans need to move beyond disclosure and into decision-making. For insurers, that means using climate and nature insight not only to protect the balance sheet, but to help clients build the kind of resilience a sustainable future requires. https://lnkd.in/eKSQrSDV #Insurance #ClimateRisk #NatureRisk #TransitionPlanning #Resilience #SustainabilityStrategy #ClimateAdaptation #GoodGrowth Jon Rix James Hughes James Russell Emma Coote Jessica Rodger Christian Barrington William McDonnell

Big Tech is still throwing punches - even with one hand tied behind its back. 👊 If you’re wondering why markets are up today, here’s the honest answer: no one ever truly knows. But here’s the next-best answer: Microsoft and Meta blew the roof off earnings last night. META ($1.5T) is up 4.5%, MSFT ($3.2T) is up 8.5% -and when companies of that size move, the whole market moves with them.  So what happened? Let’s start with Microsoft, the $3.2 trillion ✨Cloud-and-Copilot✨ empire: 📈 $70.1B in revenue (+13% YoY) and $25.8B in profit (+18%). Azure up 33%. 🏗️ After 10 straight quarters of pedal-to-the-metal AI capex, Microsoft tapped the brakes, just slightly. Capex declined QoQ from $22.6B to $21.4B. It’s not a slowdown; it’s a strategic shift. From land grab to landscaping. From “build it all” to “build where usage is real.” 🤖 Copilot usage is scaling (55% growth in enterprise customers QoQ, DAUs +2x). Azure AI workloads are contributing 16 points of its 33% growth. Infra spend is translating to customer pull-through. 🐶 Satya Nadella says 30% of Microsoft’s own code is now AI-generated. Kevin Scott predicts that number hits 95% within five years. Microsoft is dogfooding AI harder than anyone, and in doing so, reshaping how software is built and sold. ➡️ From silicon (Maia), to models (OpenAI), to infra (Asure AI), to end-user interfaces (Copilot), Microsoft is executing an Apple-style vertical strategy, only scaled for global enterprise. Now over to Meta, the $1.5 trillion ✨Ad-and-Algorithm✨ machine: 📈 Revenue: $42.3B (+16% YoY). Net income: $16.6B (+35%). $50B stock buyback announced (yes, billion with a B) 🏗️ Capex guide raised to $64–72B for the year. A big chunk is going to custom silicon and data centers optimized for AI. Meta has been relatively quiet about chips, but they’re building a full vertical AI pipeline as well. Think Microsoft, but for consumers. 📦 While everyone else is chasing monetization, Meta is chasing usage. They’re not trying to sell AI tools. They’re trying to make sure you use Meta platforms 10% more per day. So yes: Meta is spending aggressively. But they’re doing it with a consumer flywheel and ad engine that already converts attention into dollars. They don’t need to prove that AI has economic value. They just need to prove that AI can deepen engagement. The rest takes care of itself. ➡️ We’re used to thinking of Meta as an ads business wrapped in a social network. But increasingly, it’s an LLM company with a multi-billion-user training pipeline. Even with constraints - AI infra bottlenecks, regulatory landmines, macro uncertainty - Big Tech is playing from a structurally advantaged position. They have data, distribution, capital, user base, and the luxury of not needing to monetize AI today to justify the spend. These aren’t your grandma’s incumbents. They move like startups, but with trillion-dollar tailwinds. 🌪️

After decades of working with leaders at companies like Apple, Salesforce, and Cisco, we've identified 4 storytelling techniques that consistently work to deliver important messages in high-stakes settings: 1. Start with the unexpected Don’t begin your presentation with context. Instead, begin with the moment that makes people think, “Wait…what?” Instead of something like: “Here’s an update on our September campaign…” Try starting with the most interesting detail: “I broke our biggest marketing rule last month, and it worked.” Lead with the surprise. You can add context later. 2. Let people feel the tension After the surprise, don’t rewind to the beginning. Take your audience to the moment where things weren’t working. Flat numbers. Missed goals. Stalled progress. Instead of: “The campaign was underperforming, and our team went back to the drawing board.” Try:  "We were two weeks out from the end of the quarter. The campaign wasn’t producing results, and the team was out of ideas. That’s when I decided to take a risk...” You don’t need to explain the problem. You need to make people feel it. 3. Use real dialogue When your audience hears what was actually said, they stop listening to you and start visualizing the moment. This helps them connect emotionally with what you’re saying. Instead of: “The campaign manager said team morale was low and they were struggling to find a solution.” Try: “My campaign manager pulled me aside in the hallway and said, ‘We’ve tried everything. The team has been working overtime, and we don’t know what else to do.’” Dialogue brings listeners into the moment with you. It makes the story real. 4. Share the lesson Never assume people will infer the meaning you intended. End your story by answering: - What does this mean? - How should someone act differently now? Example: “Breaking our biggest marketing rule helped us turn this campaign around and hit our numbers. I strongly suggest we revisit our marketing guidelines. We could be leaving a ton of revenue on the table.” Without the lesson being clear, even a good story feels unfinished. These are the same techniques we teach to our clients at Duarte. Try them out during your next presentation and watch how people lean forward and tune in to your message. #ExecutivePresence #BusinessStorytelling #PresentationSkills

Our #EY half-year 2024 #IFRS17 and #IFRS9 benchmarking report is here! Dive into our latest insights as we explore insurers’ results reported in their half-year 2024 interim financial statements. We analyzed a panel of 46 international insurance groups, focusing on: ◾ Tailored #FinancialMetrics: discover key metrics used by us that offer valuable insights into the dynamics of the insurance market’s results ◾ #KeyPerformanceIndicators (KPIs): uncover how insurers are measuring their success and how reported their performance ◾ #Methodology changes: discover the significant changes in accounting policies and methodologies since year-end 2023 and their implications Join us in understanding the evolving landscape of #insurance #reporting and what it means for the #future! #insuranceaccounting #financialreporting #CFO

Markets aren't always rational, particularly in the short term, but market reactions to last week’s earnings announcements from some of the most scrutinized companies on earth — Meta, Google and Microsoft — caught my attention as an important signal. My interpretation is that while all three companies are pouring billions into AI-related capex, Wall Street is increasingly skeptical about whether consumer-facing AI (like Meta’s “personal superintelligence”) can justify the massive capex and deliver sufficient TAM. Meanwhile, Google and Microsoft are being given much more license to invest ahead of revenue and build capacity to meet existing and projected demand for enterprise applications, even if the ROI isn’t yet fully visible. What strikes me is how AI investment is mirroring to some extent the “growth at all costs” playbook — but with capacity spending. Meta's decline suggests to me that investor confidence is wearing thin for consumer-facing AI, while the market seems to be rewarding enterprise software that creates business value with AI. And that seems rational for the longer term given how enterprise software that incorporates AI can transform end-to-end business systems for customers.

If you’ve been following the Big Tech companies’ earnings reports, you know that they’re pouring more than ever into capital expenditure to pursue their AI futures. Amazon, Alphabet, Meta, and Microsoft all spent record sums last quarter on purchases of property and equipment — largely tied AI chips and data centers. And for the companies that offered forward-looking guidance, their capex plans for the year blew analysts’ already generous estimates out of the water. Amazon expects its 2026 capex to surge to $200 billion. Google is aiming for $175 billion to $185 billion. Meta estimates it will spend between $115 billion and $135 billion. All of those figures came in well above expectations and, for the most part, have weighed on their stocks. Microsoft didn’t give a formal 2026 capex outlook, but if its peers are any indication, spending will likely exceed the roughly $114 billion Wall Street expects for the calendar year. Of the Big Tech companies, just one stands apart this earnings season. Apple’s capital expenditure, already just a fraction of its peers, actually declined in the December quarter from a year earlier. For better or worse, Apple has struck its own path with AI. As we’ve argued before, it’s embracing AI but is not an AI company. Instead, it’s chosen a hybrid model, relying on both first- and third-party data centers — a move that keeps a significant amount of infrastructure spending off its balance sheet. And while Apple has said it expects capex to increase as it invests more heavily in AI, particularly to support its Private Cloud Compute, those outlays remain minimal compared with its peers. You can see that approach reflected in Apple’s decision to use Google’s Gemini, rather than an in-house model, to power the next generation of Siri and Apple Intelligence. The Google deal, reportedly worth about $1 billion a year, gives Apple access to a top-tier AI model for pennies on the dollar compared to what other Big Tech companies are spending to build their own. Of course, it also means Apple won’t fully own a technology that some see as powering the next industrial revolution. But if that revolution fails to materialize — or takes longer than expected — Apple won’t be left holding the most expensive bag in Silicon Valley history. https://lnkd.in/eDTFzE46

Technical leaders are failing in the boardroom. The critical gap 98% of C-suite execs want bridged. Most technical experts fail to impress. It’s a translation gap, not a talent gap. HBR and McKinsey agree: if you want to get heard in the C-suite, learn how to communicate in business language. If you want to stand out, influence decisions, and build trust at the highest level, here’s what to do differently: 1. Speak their language - not yours ↳ Skip jargon. Turn tech specs into business impact ↳ Filter every message through "What's in it for them? “We cut downtime by 40%, saving €2M this year.” 2. Story-stat-strategy - your power trio ↳ Data alone is easy to ignore. Link it to strategy ↳ Lead with a concrete user pain point “Support wait time dropped by 4 hours - customer satisfaction jumped by 20%.” 3. Frame concerns with 3Rs - risk, revenue, reputation ↳ Share potential risks, impact on revenue, or reputation - and always pair it with a solution ↳ “If we act now, we avoid a €500K risk to our revenue and keep our client trust intact.” 4. Use the 3 lenses - tech, team, topline ↳ Show leaders you see more than just the tech ↳ For every update, mention impact on team, tech and topline “We upgraded the system (tech), which cut team rework by 30% (team), and sped up our sales cycle (topline).” 5. Filter noise - boost signal ↳ Lead with the urgent issues on their radar ↳ “Fixing this speeds up our Q3 launch" 6. Tell a simple story - not just a status ↳ Facts fade, stories stick ↳ “Before, we had a 3-day delay. Now, we deliver in 1 day. Result? Happier clients.” 7. Show how your work drives the vision ↳ Tie today’s work to the company’s long-term future ↳ Add: “This move helps us hit our 3-year revenue target.” When you nail this language, you transform from "the tech person" into the strategic advisor who is indispensable for business growth. ♻ Repost this to empower more tech leaders. ➕ Follow me (Meera Remani) for C-suite communication blueprints. --- 🔔 In this week's newsletter, I’m sharing: ↳ The complete C-suite communication toolkit ↳ Mistakes to avoid, frameworks, examples ↳ Your 90-second CEO summary Join my inner circle - link below.

▪️ Strong 1Q 2025 earnings results and progress on trade negotiations have supported equity markets this week. Aggregate S&P 500 EPS grew 12% year/year in 1Q, beating the consensus expectation of 6% growth at the beginning of reporting season. The median S&P 500 stock grew earnings by 6%. ▪️ We maintain our forecast for 3% year/year S&P 500 EPS growth in 2025 to $253, which is below the top-down strategist consensus (+4%, $257) and bottom-up analyst consensus (+7%, $264). Strong operating results from 1Q present upside risk to our forecast, but our economists' forecast for de minimus GDP growth in the second half of this year lead us to leave our EPS forecast unchanged. ▪️ Amid heightened uncertainty, more companies than usual maintained forward guidance for full-year 2025. Many companies maintaining guidance did so while excluding the potential effects of tariffs. ▪️ 1Q results underscore the risk of a potential tariff-induced supply shock in the coming months. Although some managements have stated their intention to build up inventories, the inventory-to-sales ratio for the S&P 500 declined year/year. ▪️ Mega-cap tech re-emerged as the driver of earnings growth during 1Q earnings season. NVDA will report on May 28th. Excluding NVDA, 1Q earnings for the large tech stocks grew by 28% vs. 9% for the S&P 493. Consensus expects the difference between Magnificent 7 and S&P 493 EPS growth to converge from 10 pp in 2025 (15% vs. 5%) to just 2 pp in 2026 (15% vs. 13%), but 2025 EPS revisions have been more negative for the S&P 493 than the Magnificent 7 YTD (-4% vs. flat). ▪️ 1Q results also affirmed our view that the mega-cap tech stocks will drive S&P 500 capex this year. GOOGL reiterated its capex guide and META raised its capex range. However, capex revision breadth has declined, reflecting a weakening outlook for capex spending for the typical S&P 500 stock. ▪️ A recession is the main downside risk to our earnings forecast. S&P 500 EPS typically fall by 13% peak-to-trough during historical recessions. Our economists assign a 45% probability the US economy enters a recession in the next 12 months and the share of S&P 500 managements mentioning "recession" spiked on 1Q earnings calls. However, the mention of "layoffs" remains low, reinforcing the possibility of avoiding a major downturn. ▪️ We roll forward our 3-month S&P 500 return forecast to +0% and our 12-month return forecast to +9%. From the current S&P 500 price, these returns suggest levels of roughly 5700 and 6200, respectively. During the next few months, the combination of an already-tight equity risk premium and deteriorating growth data should make it hard for equities to rise substantially. Ultimately, however, the avoidance of an economic recession, increasing confidence in an earnings reacceleration in 2026, and insurance cuts by the Fed later this year should drive continued US equity gains during the next 12 months.