AI's Impact on Business

Invisible UX is coming 🔥 And it’s going to change how we design products, forever. For decades, UX design has been about guiding users through an experience. We’ve done that with visible interfaces: Menus. Buttons. Cards. Sliders. We’ve obsessed over layouts, states, and transitions. But with AI, a new kind of interface is emerging: One that’s invisible. One that’s driven by intent, not interaction. Think about it: You used to: → Open Spotify → Scroll through genres → Click into “Focus” → Pick a playlist Now you just say: “Play deep focus music.” No menus. No tapping. No UI. Just intent → output. You used to: → Search on Airbnb → Pick dates, guests, filters → Scroll through 50+ listings Now we’re entering a world where you guide with words: “Find me a cabin near Oslo with a sauna, available next weekend.” So the best UX becomes barely visible. Why does this matter? Because traditional UX gives users options. AI-native UX gives users outcomes. Old UX: “Here are 12 ways to get what you want.” New UX: “Just tell me what you want & we’ll handle the rest.” And this goes way beyond voice or chat. It’s about reducing friction. Designing systems that understand intent. Respond instantly. And get out of the way. The UI isn’t disappearing. It’s mainly dissolving into the background. So what should designers do? Rethink your role. Going forward you’ll not just lay out screens. You’ll design interactions without interfaces. That means: → Understanding how people express goals → Guiding model behavior through prompt architecture → Creating invisible guardrails for trust, speed, and clarity You are basically designing for understanding. The future of UX won’t be seen. It will be felt. Welcome to the age of invisible UX. Ready for it?

I had lunch with a founder last week who pitched me on their "AI for operations" platform. I stopped them 3 slides in. General-purpose AI isn’t cutting it anymore. DeepSeek’s January breakthrough told us something important: efficiency & performance can coexist a lot earlier than most people thought. Startups are now excelling not by scale but by focus: they’re building vertical AI that deeply understands the messy, high-stakes workflows in sectors like healthcare, finance, and defense. Specialization is the new competitive advantage. 3 patterns I’m tracking across successful vertical AI startups: First, they pick massive but high-friction and high-value workflows. “AI for sales” or “AI for operations” is too broad. What’s effective is focusing on urgent, complex processes, like: ConverzAI streamlining high-volume recruiting for staffing agencies Tennr automating messy admin work Second, they build more than model wrappers. They create proprietary feedback loops and data assets that compound over time. This instrumentation is what turns a one-off tool into a durable, defensible product. Third, they expand from beachheads of earned trust. They wedge into multi-billion-dollar industries by solving problems in the hardest, least glamorous corners. From there they earn the right to expand and unlock bigger TAM over time. Choose one gnarly high-value workflow and go deep. Otherwise you might get stopped three slides in too.

The new Gartner Hype Cycle for AI is out, and it’s no surprise what’s landed in the trough of disillusionment… Generative AI. What felt like yesterday’s darling is now facing a reality check. Sky-high expectations around GenAI’s transformational capabilities, which for many companies, the actual business value has been underwhelming. Here’s why.… Without solid technical, data, and organizational foundations, guided by a focused enterprise-wide strategy, GenAI remains little more than an expensive content creation tool. This year’s Gartner report makes one thing clear... scaling AI isn’t about chasing the next AI model or breakthrough. It’s about building the right foundation first. ☑️ AI Governance and Risk Management: Covers Responsible AI and TRiSM, ensuring systems are ethical, transparent, secure, and compliant. It’s about building trust in AI, managing risks, and protecting sensitive data across the lifecycle. ☑️ AI-Ready Data: Structured, high-quality, context-rich data that AI systems can understand and use. This goes beyond “clean data”, we’re talking ontologies, knowledge graphs, etc. that enable understanding. “Most organizations lack the data, analytics and software foundations to move individual AI projects to production at scale.” – Gartner These aren’t nice-to-haves. They’re mandatory. Only then should organizations explore the technologies shaping the next wave: 🔷 AI Agents: Autonomous systems beyond simple chatbots. True autonomy remains a major hurdle for most organizations. 🔷 Multimodal AI: Systems that process text, image, audio, and video simultaneously, unlocking richer, contextual understanding. 🔷 TRiSM: Frameworks ensuring AI systems are secure, compliant, and trustworthy. Critical for enterprise adoption. These technologies are advancing rapidly, but they’re surrounded by hype (sound familiar?). The key is approaching them like an innovator...  start with specific, targeted use cases and a clear hypothesis, adjusting as you go. That’s how you turn speculative promise into practical value. So where should companies focus their energy today? Not on chasing trends, but on building the capacity to drive purposeful innovation at scale: 1️⃣ Enterprise-wide AI strategy: Align teams, tech, and priorities under a unified vision 2️⃣ Targeted strategic use cases: Focus on 2–3 high-impact processes where data is central and cross-functional collaboration is essential. 3️⃣ Supportive ecosystems: Build not just the tech stack, but the enablement layer, training, tooling, and community, to scale use cases horizontally. 4️⃣ Continuous innovation: Stay curious. Experiment with emerging trends and identify paths of least resistance to adoption. AI adoption wasn’t simple before ChatGPT, and its launch didn’t change that. The fundamentals still matter. The hype cycle just reminds us where to look. Gartner Report:  https://lnkd.in/g7vKc9Vr #AI #Gartner #HypeCycle #Innovation

Last week, a customer said something that stopped me in my tracks: “Our data is what makes us unique. If we share it with an AI model, it may play against us.” This customer recognizes the transformative power of AI. They understand that their data holds the key to unlocking that potential. But they also see risks alongside the opportunities—and those risks can’t be ignored. The truth is, technology is advancing faster than many businesses feel ready to adopt it. Bridging that gap between innovation and trust will be critical for unlocking AI’s full potential. So, how do we do that? It comes down understanding, acknowledging and addressing the barriers to AI adoption facing SMBs today: 1. Inflated expectations Companies are promised that AI will revolutionize their business. But when they adopt new AI tools, the reality falls short. Many use cases feel novel, not necessary. And that leads to low repeat usage and high skepticism. For scaling companies with limited resources and big ambitions, AI needs to deliver real value – not just hype. 2. Complex setups Many AI solutions are too complex, requiring armies of consultants to build and train custom tools. That might be ok if you’re a large enterprise. But for everyone else it’s a barrier to getting started, let alone driving adoption. SMBs need AI that works out of the box and integrates seamlessly into the flow of work – from the start. 3. Data privacy concerns Remember the quote I shared earlier? SMBs worry their proprietary data could be exposed and even used against them by competitors. Sharing data with AI tools feels too risky (especially tools that rely on third-party platforms). And that’s a barrier to usage. AI adoption starts with trust, and SMBs need absolute confidence that their data is secure – no exceptions. If 2024 was the year when SMBs saw AI’s potential from afar, 2025 will be the year when they unlock that potential for themselves. That starts by tackling barriers to AI adoption with products that provide immediate value, not inflated hype. Products that offer simplicity, not complexity (or consultants!). Products with security that’s rigorous, not risky. That’s what we’re building at HubSpot, and I’m excited to see what scaling companies do with the full potential of AI at their fingertips this year!

𝗕𝗹𝗮𝗰𝗸𝗥𝗼𝗰𝗸 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵𝗲𝗿𝘀 𝗗𝗲𝘃𝗲𝗹𝗼𝗽 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺 𝗳𝗼𝗿 𝗦𝘁𝗼𝗰𝗸 𝗣𝗶𝗰𝗸𝘀 Instead of relying on one frontier model, BlackRock built three AI “agents” that mimic different analyst roles:  • Fundamental Agent — parses 10-Ks and earnings reports  • Sentiment Agent — reviews news and analyst ratings  • Valuation Agent — studies prices, volatility, and volumes Each agent analyzes a stock independently, then enters a round-robin debate. Disagreements are argued until the agents reach consensus on whether to BUY or SELL — a process designed to mimic an investment committee. The system runs on Microsoft's AutoGen framework using GPT-4o, with custom tools for each agent: document parsing for 10-Ks, news summarization, and volatility calculators. The agents' recommendations change based on risk tolerance settings. The same volatile stock might get a SELL from a risk-averse agent but a BUY from a risk-neutral one analyzing identical data. Tested on 15 tech stocks over four months in 2024, the system outperformed both single agents and the benchmark in risk-neutral portfolios on a risk-adjusted basis (Sharpe ratios). In risk-averse portfolios, all approaches lagged the benchmark — since volatile tech names were excluded — but the multi-agent showed smaller drawdowns than single agents. The authors argue this setup improves analytical rigor and helps mitigate behavioral biases like overconfidence. While limited in scope and not a full portfolio optimizer, the study suggests specialized, debating agents may prove more reliable than general models for quantitative finance. Takeaway: LLMs can be a Swiss Army knife in daily life, but for mathematical analysis, specialized agents may be the sharper tool. Paper: 𝘈𝘭𝘱𝘩𝘢𝘈𝘨𝘦𝘯𝘵𝘴: 𝘓𝘢𝘳𝘨𝘦 𝘓𝘢𝘯𝘨𝘶𝘢𝘨𝘦 𝘔𝘰𝘥𝘦𝘭 𝘣𝘢𝘴𝘦𝘥 𝘔𝘶𝘭𝘵𝘪-𝘈𝘨𝘦𝘯𝘵𝘴 𝘧𝘰𝘳 𝘌𝘲𝘶𝘪𝘵𝘺 𝘗𝘰𝘳𝘵𝘧𝘰𝘭𝘪𝘰 𝘊𝘰𝘯𝘴𝘵𝘳𝘶𝘤𝘵𝘪𝘰𝘯𝘴 Links below for more info. Tianjiao (Tina) Z., Jingrao Lyu, Stokes Jones, Harrison Garber, Stefano Pasquali, Dhagash Mehta, Ph.D.

McKinsey & Company’s latest research tracks a shift we see across enterprise technology. The leaders in the next chapter of software will not be the companies that simply add AI to existing products. They will be the ones that build their businesses around intelligence and treat it as core to how they create, deliver and scale value. AI is moving from tools that assist people to systems that act on their behalf. This change raises the bar for product design, commercial models and customer experience. It also increases the need for clear priorities and disciplined execution. Most early investment has gone to chips, infrastructure and large models, but the next wave of value will be created at the application layer, where AI engages real enterprise data and real workflows. Companies with strong products, secure distribution and trusted customer relationships are best positioned to benefit. AI is not replacing software. It is expanding what software can do and accelerating outcomes for users. The companies that adapt first will capture more of the value created in this new agentic era and help shape how it unfolds. https://mck.co/4pdtg5I

Are We Overestimating What AI Can Understand—And Underestimating What It Changes? Here’s the paradox I see: On one side, skeptics say AI doesn’t “get it.” It doesn’t understand meaning, context, or strategy. True enough. On the other side, look around—our workflows, decisions, even the skills we value are already shifting because of AI. The 2025 AI Hype Cycle proves the point: GenAI is tumbling into the Trough of Disillusionment. Billions spent, CEOs still unimpressed. But foundational shifts—ModelOps, AI-ready data, AI engineering—are quietly setting the stage for real transformation. Meanwhile, AI agents sit at the Peak of Inflated Expectations: powerful in theory, messy in practice. So what matters more? → The fact that AI doesn’t truly “understand”? → Or the fact that, with or without understanding, it’s already reorganizing business models, leadership, and jobs? The solution? If you’re a leader today, stop asking whether AI “understands” and start asking: ✅ Where am I building the foundations (data, ModelOps, engineering) that will make AI productive? ✅ Which workflows should I redesign now—before competitors do it first? ✅ How do I prepare my teams for collaboration with AI, not just replacement by AI? In my opinion, Amara’s Law nails it: we inflate the short-term and miss the long-term. By the time we stop debating “understanding,” the rules of leadership, jobs, and advantage will already have shifted. 👉 So the real question is: are you preparing for understanding, or for change? #AI #FutureOfWork #AmarasLaw #Leadership #Innovation #HypeCycle

You're a #CTO. Your board asks: "What's our ROI on AI coding tools?" Your answer: "40% of our code is AI-generated!" They respond: "So what? Are we shipping faster? Are customers happier?" Most CTOs are measuring AI impact completely wrong. Here's what some are tracking: - Percentage of AI-generated code - Developer hours saved per week - Lines of code produced - AI tool adoption rates These metrics are like measuring how fast your assembly line workers attach parts while ignoring whether your cars actually start. Here's what you SHOULD measure instead: 1. Delivered business value 2. Customer cycle time 3. Development throughput 4. Quality and reliability 5. Total cost of delivery (not just development) 6. Team satisfaction Software development isn't a typing competition—it's a complex system. If AI makes your developers 30% faster but your deployment takes 2 weeks and QA adds another week, your customer delivery improves by maybe 7%. You've speed up the wrong part. The solution: A/B test your teams. Give half your teams AI tools, measure business outcomes over 2-3 release cycles. Track what customers actually experience, not how much developers produce. Companies that measure business impact from AI will pull ahead. Those measuring vanity metrics will wonder why their expensive tools aren't moving the needle. Stop measuring how much code AI generates. Start measuring how much faster you deliver value to customers. What are you actually measuring? And is it moving your business forward? -> Follow me for more about building great tech organizations at scale. More insights in my book "All Hands on Tech"

The latest study from the Council of Economic Advisers, The White House states that ~10% of jobs are vulnerable to AI disruption. That may seem alarming, but let’s take a step back. In 2018, 60% of the jobs Americans held didn't even exist in 1940—created by technologies that emerged over the years (David Autor). Here’s the real concern: Many AI-vulnerable jobs haven’t evolved to match their increasing complexity. Workers in these roles are more exposed to disruption because they haven’t been given the chance to upskill. But this isn't new. Economic evolution is the hallmark of a dynamic economy. Just like we’ve adapted to past technologies, workers and industries will adapt to AI. The key lies in how we approach it. Why businesses should care: Organizations that proactively identify and support employees vulnerable to AI disruption aren’t just doing good—they’re making smart financial decisions. 💡 Investing in upskilling and mobility for these workers could unlock millions in retention and productivity. Mass layoffs due to AI aren’t likely. The real shift? Slower hiring and reduced demand for certain roles. We’re already seeing fewer job postings for writers, coders, and even artists. So, what activities are at risk? Roles involved in processing information, analyzing data, scheduling, and administrative tasks are prime targets. Industries to watch? Architecture, engineering, legal, computer science, and mathematics. Surprising jobs at risk of AI disruption: Airline Pilots, Copilots, and Flight Engineers Nuclear Power Reactor Operators Private Detectives and Investigators Commercial and Industrial Designers These highly specialized roles, which traditionally require significant human judgment, are surprisingly vulnerable to AI-driven changes. Business leaders, what barriers are preventing you from launching upskilling initiatives to future-proof your workforce? The future of work is evolving, but we can shape how it unfolds. #FutureOfWork #AIandJobs #Upskilling #WorkforceTransformation #AI

𝗧𝗵𝗲 𝗙𝗼𝗿𝗴𝗼𝘁𝘁𝗲𝗻 𝗥𝗼𝗹𝗲 𝗼𝗳 𝗘𝗻𝘁𝗿𝘆-𝗟𝗲𝘃𝗲𝗹 𝗝𝗼𝗯𝘀 𝗶𝗻 𝗮𝗻 𝗔𝗜 𝗘𝗰𝗼𝗻𝗼𝗺𝘆 AI promises massive productivity gains. But it may also be quietly eroding how expertise is built. As AI enables senior employees to do more on their own, many entry-level roles—the primary source of learning by doing—are disappearing. This matters because the most valuable workplace skills are often 𝘁𝗮𝗰𝗶𝘁: absorbed through experience, not taught in classrooms or manuals. According to a recent study, today’s rush to automate early-career work may be socially excessive. While automation boosts short-term productivity, it also disrupts the intergenerational transfer of tacit knowledge. The result is a trade-off: higher output now, but weaker skills in the next generation—ultimately slowing long-term economic growth and, in some cases, reducing overall welfare. The implications are not trivial. Even modest levels of AI-driven automation at the entry level could lower long-run U.S. per-capita growth by an estimated 𝟬.𝟬𝟱 𝘁𝗼 𝟬.𝟯𝟱 𝗽𝗲𝗿𝗰𝗲𝗻𝘁𝗮𝗴𝗲 𝗽𝗼𝗶𝗻𝘁𝘀 𝗮𝗻𝗻𝘂𝗮𝗹𝗹𝘆. Over time, that compounds into a meaningful economic drag. AI co-pilots offer a partial remedy. They can help workers who missed early learning opportunities catch up later in their careers. But they also introduce a new tension: if AI makes skill gaps easier to mask, it may reduce incentives for juniors to develop those skills in the first place. 𝗧𝗵𝗲 𝗺𝗲𝘀𝘀𝗮𝗴𝗲 𝗶𝘀 𝗹𝗼𝘂𝗱 𝗮𝗻𝗱 𝗰𝗹𝗲𝗮𝗿: 𝗔𝗜 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗵𝗼𝘄 𝘄𝗼𝗿𝗸 𝗶𝘀 𝗱𝗼𝗻𝗲, 𝗯𝘂𝘁 𝗵𝗼𝘄 𝗲𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 𝗶𝘀 𝗳𝗼𝗿𝗺𝗲𝗱. 𝗔𝗻𝗱 𝗴𝗿𝗼𝘄𝘁𝗵 𝗱𝗲𝗽𝗲𝗻𝗱𝘀 𝗼𝗻 𝗯𝗼𝘁𝗵. To capture AI’s full potential, policy, firms, and universities must protect and expand early-career learning—through mentorships, apprenticeships, practical education, and AI systems that complement junior roles rather than erase them. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝘀𝗸𝗶𝗹𝗹 𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗻𝗼𝘁 𝗽𝗿𝗼𝗴𝗿𝗲𝘀𝘀—𝗶𝘁’𝘀 𝗯𝗼𝗿𝗿𝗼𝘄𝗲𝗱 𝗴𝗿𝗼𝘄𝘁𝗵. 𝗥𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲: Ide, Enrique. (2025). Automation, AI, and the Intergenerational Transmission of Knowledge. 10.48550/arXiv.2507.16078.