Vivek Asija

GTM Singularity... 🤔 Borrowed from the idea of AI singularity. The point where intelligence compounds. And the old way of operating quietly stops working. Applied to go-to-market, it describes a simple shift. AI stops assisting. And starts executing. I first heard this idea from the founder of FlashIntel.ai It stuck with me. Mostly because it put words to something I see everywhere. Growth doesn’t slow down because the strategy is wrong. It slows down because execution eats the entire day. (And somehow… the entire calendar.) In how it’s presented, FlashIntel's SuperAgent feels less like a GTM tool. And more like an intelligence layer across the stack. Most teams spend their time on invisible work. The kind no one puts in a deck — but everyone feels.. – Researching accounts – Preparing meetings – Pulling context from five different tools – Deciding what to do next – Chasing follow-ups SuperAgent is designed to take that layer on. • Meeting context prepared automatically • Signals connected across CRM, calendar, and data • Priorities surfaced without guessing • Workflows running end-to-end This is where the GTM Singularity idea becomes practical. When intelligence compounds: – Execution becomes repeatable – Decision fatigue drops – Growth stops depending on heroic effort Not because teams work harder. But because the system does more of the work. The future of GTM isn’t more tools. It’s intelligent agents doing the work humans shouldn’t be doing. 🤖 Tools → Assistants → Agents → Execution systems SuperAgent is a clear example of that shift. 👉 Check it out yourself: www.flashintel.ai Exploring AI so we can all work a little less. Follow along 🔔

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The best CMOs are efficient and bold. “If we drive efficiency in the attributable, we earn the right to experiment in the un-attributable.” SCREAM IT FROM THE BACK. I had the opportunity to listen in on a panel discussion between Ray Rike, 🌱 Allison Munro, and Jon Miller, who discussed research from 323 companies around budgeting and spend at Pavilion's CMO Summit. And if you are in a room with Allison and Jon, you listen. And the data was loud and clear: Marketing budgets are up. Not down. Not flat but up by 1% of revenue on average and almost 10% in absolute dollars. Yes, even in this “do more with less” era. Growth = Budget >>> The faster you grow, the more budget you get. Companies growing over 30%? Median marketing budget is 15% of revenue. (That’s double the average.) People vs Programs vs AI >>> Budgets are shifting from people to programs. And AI is doubling its share of spend, from 4% to 8% YOY. AI isn’t a line item. It’s a headcount replacement. Events are making a comeback. As ACV increases, event spend also rises. Demand gen spend goes down. Why? Because no one attributes brand until it's lost. Want to protect brand? Get headcount? Increase programs? Then model your budget, benchmark it, and tell the story before the CFO asks.

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🚨 Introducing: AI x GTM Summit, by The Signal 🚨 Over the last year, I've spent hundreds of hours learning how the best GTM teams are using AI. I'm bringing together the smartest people who are building at the bleeding edge of AI, automation, and modern technologies. To share 6 tactical ways to get ahead in 2026. The alpha from 2025 is being shared at the AI x GTM Summit. Real, tangible ways that AI-native GTM teams are working right now. We’re all learning together, and I believe in net-sum games (not zero-sum). So, the goal is to share 6 AI use cases that you can integrate into your company. AI is here to stay. But not everyone has 20 hours a week to experiment with AI to find the best use cases for it (because you’re busy with 99 other things in your day job). Yet, every revenue team is being told they have to hit a bigger number next year, with the same-sized team. GTM leaders need to know how to use AI as a lever, from trusted, agnostic operators who are on the front lines integrating AI into workflows, teams, and systems. This summit is tool-agnostic. Just world-class operators sharing tactical examples (not frameworks) of how they’ve implemented AI into GTM motions that you can implement this month—not next quarter. I know these are real because I’ve seen these use cases this year. These 20-minute "mini-sessions" will be the highest density of information per minute possible (while still having fun, of course—can’t forget that in B2B!). 📅 Agenda • 9:05 – 9:25 am PST: Sorting the world into your CRM (GTM gold is stuck in your top seller’s brain: How to extract+operationalize it) • 9:30 – 9:50 am: Infrastructure for agentic GTM: data stack, orchestration, and activation (PLG-focus) • 9:55 – 10:15 am: Getting agents deployed into your GTM systems: pitfalls, opportunities, and best practices • 10:20 – 10:40 am: Nuanced viewpoint on AI-generated outbound email copy • 10:45 – 11:05 am: Automating signals that work in your specific market • 11:10 – 11:30 am: Learn, Build, Scale: A Revenue Leader’s AI Transformation Roadmap Plus, two bonus sessions: • 11:35 – 11:55 am: Fireside chat with Rox’s CEO, Ishan Mukherjee: Revenue Agents • 12:00 – 12:30 pm: Q&A / Networking If you’ve found my content valuable, please consider signing up for this event. We will be offering exclusive giveaways and surprises to live attendees! 👀 📌 Details • Wednesday, December 17, 2025 • 9 am – 12:30 pm PST • Cost: free! (Thanks to our awesome sponsors, like Rox—go check them out!) Every session will be recorded and emailed to everyone who registers. So, if you can’t stay for the entire event, register to receive the recordings afterwards. Secure your spot now: https://luma.com/hh1aaf21 PS: Please forward this post or tag any GTM leaders who would find this event valuable. I’d be incredibly grateful. Stoked to see you on December 17th!

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The news story about McKinsey & Company leaning into AI with their internal tool, Lilli, is a perfect case study for the "Lean AI" mindset. For years, I've preached that AI's greatest value isn't replacing people, but freeing them from the "grunt work" to focus on high-value tasks. Lilli, which automates the creation of PowerPoint presentations, is doing exactly that. It's a clear signal that even the most prestigious consulting firms recognize the need to automate repetitive, low-leverage activities that have long been a rite of passage for junior talent. This is a step in the right direction. It allows brilliant minds to engage in the strategic thinking, client relationships, and complex problem-solving that are at the core of human value. However, the question remains: if AI tools take on the work of junior employees, what is the new training ground for future leaders? The "messy middle" is here. We must be intentional about how we use AI to develop the next generation, not just make the current one more efficient. This isn't about simply replacing jobs; it's about redefining them. The companies that will thrive are those that invest in their human capital and use AI to augment, not just automate. What's your take? How do we ensure that while we embrace the efficiency of tools like Lilli, we also nurture the talent pipeline that will drive our future? #AI #McKinsey #FutureOfWork #Leadership #DigitalTransformation #Jobs #Careers Rachel Kleinman Chip Cutter The Wall Street Journal

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How are you measuring the AI to ROI for GTM investments? As part of the State of AI in GTM research we conducted in partnership with Scale Venture Partners - a few key insights were uncovered including: Phase 1: 85% of AI in GTM adopters are seeing productivity gains from tasks such as: - drafting messaging- drafting emails - creating images - conducting account research) Phase 2: Where pipeline grows, revenue per GTM resource grows & GTM efficiency happens: Quantity and quality of work benefits evolves to measurable pipeline & win-rate improvement. Companies that are using AI systematically and regularly are reporting: 👉 3× higher pipeline lift in marketing 👉3× more SQOs in SDR 👉 3× higher win rate improvements in Sales 📏 But how to measure the impact of investing in AI in GTM 👇 The research findings and discussions with the Scale team (Craig Rosenberg, Kristina McMillan and Mark Gustaferro) came up with the two below metrics that a CRO and CFO can both love to justify AI investments: 1️⃣ GTM Efficiency = Net New ARR / GTM Expenses - this is a similar metric to David Spitz's "GTM Efficiency" - but flips the numerator and denominator in the Scale Venture Partners version - it is also similar to Blended CAC Ratio - which is calculated by dividing Sales & Marketing Expenses/New+Expansion ARR (no churn/downselll) GTM efficiency is LESS about the actual formula and more about HOW you are measuring and proving the increased GTM efficiency as measured by comparing "Expenses" and "Revenue Growth" 2️⃣ GTM Productivity = Net New ARR / # GTM employees - After the growth at any cost era, companies materially increased their focus on the ultimate human capital metric - ARR per FTE - As a result ARR per FTE increased ~ 20% across the majority of company revenue size ranges GTM productivity is very aligned to the ARR per FTE metric, but increase the focus on the financial outcome (Net New ARR) to the financial input (GTM HC) - It's important to start the baseline measurement today - to justify and prove the ROI for AI investments, this encapsulates the benefits of "doing more" = efficiency" and/or "doing better = effectiveness" and translates it into a financial metric that any CFO, CEO and investors will love 🙋‍♂️ We are thinking about doing a webinar on the findings, the strategies and the measurements to justify increasing the AI investment - interested? 📷 Brief snapshot of the key pages in the SVP report below that support the above post - they can be read in detail in Scale's State of GTM AI report

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Nobody is telling the truth about how disruptive this is going to be. Maybe since I’m ‘unaffiliated’ for now, I can. 👀 AI isn���t just coming for workflows. It’s coming for the foundations of knowledge work — and for the identities built on it. For CMOs and marketing leaders, this hits hard. For years, we’ve found meaning in our playbooks and how we execute. Now, much of that is being automated, flattened, or redefined in real time. And here’s the part we’re not talking about enough: This isn’t just a professional disruption. It’s a personal one. To survive it — let alone lead through it and flourish — we can’t keep pushing at the same velocity, hoping to outrun the change. We need to slow down. Reground. Rebuild. That means investing differently: Mind – Slowing down to build emotional resilience in a time of accelerated everything. Learning to lead from clarity, not reaction. Body – Remembering we’re not brains on a stick. Stewarding our embodied experience through sleep, movement, and nourishment — so we can actually sustain the work. Soul – Recognizing that our sense of identity is often wrapped up in our work — and that AI will inevitably challenge how we define purpose and value. This season demands we examine those foundations, and do so in community, not isolation. This is the real work of leadership in the Intelligence Age. Not just learning to prompt better. But learning to stay human — when everything else is speeding up.

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We skim. LLMs parse. That’s the gap killing AEO visibility right now. Clever intros and chunky paragraphs? They don’t get cited. ChatGPT, Perplexity, and Google AI Overviews ALL prioritize structure, clarity, and signal-to-noise ratio way more than style points. And teams that get it are seeing 40% higher inclusion rates in AI Overviews and featured snippets, according to our latest research. So what’s the difference between brands that get cited and those that don’t? From very high to medium impact, it’s: 1️⃣ Freshness Signals → “Last updated” schema = 1.8x more citations → NinjaOne + ActiveCampaign refresh 70% of content quarterly Tip: Add last updated schema to key pages. Set a 90-day refresh cycle for top URLs. 2️⃣ Readability & Structure → Full Article, FAQ, or HowTo schema = 78% lift → Clear heading hierarchy (H1 > H2 > H3) = +63% citations → Smartsheet, Grammarly, and ActiveCampaign all score 80+ Tip: Clean up your H-tags, use full schema, and keep sentences <25 words. 3️⃣ Snippet Extractability → Paragraphs more than 3 sentences? –59% snippet odds → Lists + definition blocks? +74% liftability → Ramp and Sage are built for AI summaries and it shows. Tip: Lead with answers. Use bullets, short blocks, and strong subheads. 4️⃣ Evidence Attribution → 2+ outbound links per 500 words = 2.4x more citations → Expert bylines are rare (24%), but HubSpot includes them every time. → Authoritative links = +40% citation rate Tip: Cite early, and include expert bios to boost trust + credibility. 5️⃣ Brand Alignment → Consistent tone, product names, and CTAs = up to 41% more citations → Asana, Vercel, and monday.com lead with aligned content across the board. Tip: Audit for tone drift and naming mismatches—especially on FAQs, landing pages, and help docs. Bottom line: signal and structure your content the way LLMs parse, and you won’t miss the mark like the rest. Our 2025 AEO Scorecard breaks down all five levers and the brands leading the pack. Link in the comments 👇

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The AI Bubble is Bursting—Why Neuromorphic Matters By Dana LeVan, Empowering the Future of Neuromorphic Computing & Intelligent Systems AI's investment boom was fueled by sky-high valuations and the belief that bigger models meant bigger profits. But as investors like Chamath Palihapitiya and Sequoia Capital warn about unsustainable AI economics, the cracks in traditional AI are showing. On a recent episode of the All-In Podcast, Palihapitiya, David Sacks, Jason Calacanis, and David Friedberg emphasized a critical shift: investors are no longer funding AI companies based on capabilities alone—they're demanding clear paths to profitability and cost efficiency. 🚨 The warning signs: - AI cloud computing costs grew 35% in 2024, doubling resource consumption year-over-year - McKinsey: 68% of enterprises struggle to prove ROI on large-scale AI - Nvidia’s 17% stock drop (Feb 2025) signals concerns over AI hardware sustainability The AI Reality Check Has Arrived Classic AI was built in an era of cheap capital. Now, profitability trumps hype: - Runaway costs: Training a top LLM now exceeds $20M, with inference at $700K/month for 1M users - Enterprise slowdown: Only 23% of planned AI projects reach production (Gartner) - Regulatory heat: The EU AI Act and U.S. rules demand model explainability—many black-box systems can’t comply As Sacks noted on the podcast, "AI startups that raised billions are struggling to generate real revenue." The hype cycle is over—AI must now prove its economics. The Neuromorphic Shift: A Smarter AI Strategy Investors are pivoting to neuromorphic AI, where energy efficiency and ROI are built into the architecture. Neuromorphic Software: Immediate ROI (12-18 months) 👉 Natural Intelligence Systems, Numenta, and Voicify optimize existing hardware, boosting efficiency 40-65% 👉 Lower costs: Works with today’s cloud/edge systems—no expensive hardware upgrades 👉 Proven impact: A financial firm cut fraud false positives 27% while reducing compute costs 38% Neuromorphic Hardware: The Long-Term Play (3-5 years) 👉 Next-gen chips (Intel Loihi 2, BrainChip Akida) show 175x energy efficiency gains 👉 Real-world traction: Mercedes-Benz & BrainChip are moving from R&D to commercial deployment 👉 Scalability: Neuromorphic chips make previously cost-prohibitive AI applications viable "Classic AI’s inefficiencies are built into its DNA," said the CEO of Natural Intelligence.AI. "Neuromorphic is rewriting the playbook—delivering intelligence without the energy drain." How Smart Investors Are Allocating Capital 1️⃣ Now: Invest in neuromorphic software for immediate efficiency gains 2️⃣ 18-36 months: Position in neuromorphic hardware with proven commercial adoption 3️⃣ 3-5 years: Target industries primed for transformation—edge computing, IoT, autonomous systems AI’s next winners aren’t in the hype cycle. They’re in neuromorphic. #NeuromorphicAI #AIRevolution #SaaS #FutureOfAI #CyberSecurity Chamath Palihapitiya Paul Dlugosch

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It's happening... AI is eating up agencies, but not how you’d expect. We're entering a new age of consulting - One where creative and professional services hinge on judgment, relationships, and vision and no longer win on execution and efficiency alone. Here’s the shift: David Maister defined 3 types of service work in 'Managing the Professional Service Firm' (a bible for anyone getting into the service firm game): - Brains: Complex, ambiguous problems needing creativity and strategy. Value proposition: “Hire us because we’re smart.” - Grey Hair: Familiar challenges solved by experience. Value proposition: “Hire us because we’ve done this before.” - Procedure: Repeatable tasks built for efficiency. Value proposition: “Hire us because we know how to do this efficiently.” AI’s tearing through them all - the last two more existentially than the first. 1. Procedure work? Toast. Scale AI powers data annotation for the likes of OpenAI and Tesla, turning what used to take teams of juniors several weeks, into a task for algorithms in hours. Then there’s UiPath, the robotic process automation king—think invoice processing or customer data entry, once the bread-and-butter of outsourcing firms, now handled by bots with near-zero error rates. Efficiency’s no longer a human selling point, and it's only the beginning. 2. Grey Hair? On the ropes. Harvey, a legal AI startup with $300M from its 2025 Series D, is automating contract reviews and due diligence - tasks that veteran lawyers billed big for. It’s trained on decades of legal patterns, spitting out answers faster than any grey-haired partner. Or take Anthropic, behind Claude, now tackling recurring ops challenges with custom AI that's pretty close to mirroring the instincts of seasoned execs. Experience is getting productized fast. Brains? Sweet spot. AI can optimize and replicate, but it stumbles in uncharted territory—crafting bold strategies or earning trust for high-stakes leaps. That’s where humans shine, critical thinkers reign, and cultural wisdom with impeccable taste can help companies change from a path to Blockbuster towards a path to Netflix. At LCA, we're a Brains firm with experience building and shipping products that retain customers, and efficiency driven by AI and operational excellence. Most firms are a blend, but they need clarity on which one they ladder up to in order to win. We partner with leaders to solve new problems across product and design. We help them find focus, align teams and organizations, and define what's next. We're: - Using AI to amplify creativity and ability, not replace it. - Betting on trust, storytelling, and taste in a world of perfect efficiency - Guiding clients to *what matters* - ideas, strategy, resonance, humanity... The future’s not less human, it’s more. Tools do tasks while people shape outcomes. Don't sleep on this shift and let your firm get AI'd away to a suite of tools and prompts.

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I give CEOs 3 new ways to measure AI success and none of them are traditional ROI. Not because ROI doesn't matter. Because by the time you see it, you've either already given up or your board has lost patience. Here's the pattern I see: Leadership teams bring me in after their AI strategy is developed. The roadmap looks perfect. The business case projects impressive returns. But the expectations? Those need recalibrating. AI deployment isn't just about design and deployment. It's about operationalization, critical mass adoption, and getting people to actually change how they work. And that takes time. A year, maybe two before you see the numbers everyone promised. So when I walk into these conversations, I do something different. I give them 3 new ways to measure success, what I call the “Three P's of AI Value” (© 2025 Sol Rashidi. All rights reserved): 🔹𝗣𝗿𝗲𝘃𝗲𝗻𝘁𝗶𝗼𝗻: What are we stopping from happening now that we couldn't prevent last year? 🔹𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲: What are we executing better with AI this year that we struggled with 12 months ago? 🔹𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻: What can we forecast with more accuracy now than 12 months ago? These metrics might feel softer than a hard ROI calculation. But here's why they matter: If you invest $2.5 million and expect $5 million back in year one, you're setting yourself up for disappointment. - You'll see incremental lifts. - Small improvements. - Numbers that are genuinely hard to quantify financially. But when you reframe success around Prevention, Performance, and Prediction, something shifts. Your ticker symbols start moving up. Your team sees progress. Your board understands the journey. And most importantly, you set realistic expectations while staying optimistic about what's possible. I'm positive about AI's potential. I'm just realistic about the timeline. How are you measuring AI success in your organization? Traditional ROI or something else?

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OpenAI just dropped a 33-page framework to help businesses identify and prioritize AI use cases across their operations (and it's worth your time) 👇 The guide walks you through how to: ↳ Spot opportunities across functions like marketing, product, and ops ↳ Map those opportunities to what they calls “capability blocks” (like summarization, classification, generation, etc.) ↳ Evaluate and rank use cases using clear frameworks and impact lenses ...And perhaps most interestingly, how to chain use cases together to rethink your workflows entirely While consultants are busy pitching to automate one-off tasks, your teams can be deconstructing the way you currently work and reassembling it more intentionally with AI as a core collaborator. Imagine content creation → SEO optimization → publishing → performance feedback loops, all integrated. Or... data collection → synthesis → decision support, all handled by connected workflows. If you’re SERIOUS about #aitransformation, this is the kind of resource that can jumpstart real change. ✅ Save this post and share it with your team.

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Want to get the most out of AI implementation? It's all about people. BCG and Columbia Business School just released latest insights from survey of leaders and employees. Read here: https://lnkd.in/eb-beUm6 The takeaways: 1. Understand employees' real sentiment around AI. The research shows executives consistently overestimate enthusiasm and understanding. Before pursuing more AI-driven productivity, don’t just measure how employees are feeling—find ways to have real conversations about how they’re feeling and what they’re seeing. 2. Stop talking about efficiency and start talking about goals. Let's move from focusing on initiatives to outcomes. If you can't measure what good AI adoption looks like in business results, you can't manage the transformation. 3. Practice intentional design in AI adoption. Any significant change requires an environment where people feel safe enough to experiment, empowered enough to solve problems, and engaged enough to continuously learn.

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How is 1:1 personalization like crafting a great cocktail? 🍹 Personalization is not a new idea. Phil Fernandez and were selling Epiphany Interaction Advisor (a reinforcement learning, multi-channel recommendation engine that picked the best decision in real-time from a library of always-on options) more than 20 years ago. And Marketo Engagement Programs, combined with triggered workflows and dynamic content, were capable of delivering intelligent streams of personalized emails (at least for users with enough growth hacking skills). Yet true 1:1 personalization mostly remains a promise, not a reality, for B2B. And after decades of inflated promises, any vendor talking about "personalized journeys" today is viewed with healthy and justified skepticism. WHY PERSONALIZATION HASN'T WORKED IN B2B 1️⃣ B2B is complicated, with non-linear journeys and complex buying committees. This makes the problem inherently hard. 2️⃣ Rules-based personalization schemes quickly devolve into spaghetti diagrams that are impossible to maintain. Theoretically possible but practically unmanageable. 3️⃣ Limited B2B data sets aren’t well suited to traditional machine learning. And many ML techniques “flatten” the most interesting behavioral data, such as which specific campaigns and messages someone would respond to. 4️⃣ Next-best-action is too simple for long, complex B2B journeys; we need to think more than just one move ahead. WHAT’S DIFFERENT NOW The ideas behind personalization were right, but the technology wasn't ready. Now it is. (Note: successful personalization isn't about superficial elements like mentioning someone's LinkedIn post in an automated email. And as Tejas Manohar recently pointed out, it’s not even about creating unique content for each buyer.) Real personalization is figuring out what to send, when to send it, and how to deliver it for every individual customer and account. Like a bartender mixing the perfect cocktail, you don’t need new ingredients for each person — just the right action at the right time. And as Phil points out, when you mix and match from pre-approved content libraries, you can easily create millions of unique combinations.  Humans and rules can’t manage that complexity, but modern AI can. Unlike traditional ML approaches, new techniques like transformers and reasoning LLMs can work with the smaller, more complex B2B data sets to mix up the right sequence of actions for each person. The same technology powering Instagram's eerily accurate recommendations can now be applied to B2B. In this new model, segment marketers focus on creating quality content, while AI handles orchestration. In other words, marketing teams focus on crafting exceptional experiences, while AI handles the complexity of delivering the right cocktail of actions. #MarketingAutomation #AIinMarketing #B2BMarketing #Personalization #MarketingOnTheRocks

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Fast Company recently explored three myths that quietly derail AI transformation. Becoming aware of these counterproductive beliefs, and how to defeat them, is essential for becoming an effective Frontier Firm.    🔹 Myth #1: Innovation units will save us.  Reality: 𝘋𝘦𝘥𝘪𝘤𝘢𝘵𝘦𝘥 𝘪𝘯𝘯𝘰𝘷𝘢𝘵𝘪𝘰𝘯 𝘵𝘦𝘢𝘮𝘴 𝘥𝘰 𝘴𝘱𝘢𝘳𝘬 𝘪𝘥𝘦𝘢𝘴, 𝘣𝘶𝘵 𝘵𝘩𝘦𝘺 𝘳𝘢𝘳𝘦𝘭𝘺 𝘴𝘩𝘪𝘧𝘵 𝘤𝘶𝘭𝘵𝘶𝘳𝘦 𝘰𝘯 𝘵𝘩𝘦𝘪𝘳 𝘰𝘸𝘯. Fix: Run parallel transformations—pair innovation efforts with broader organizational change. Quick wins build belief, but culture makes it stick.    🔹 Myth #2: Our people just need training.  Reality: 𝘛𝘳𝘢𝘪𝘯𝘪𝘯𝘨 𝘢𝘭𝘰𝘯𝘦 𝘥𝘰𝘦𝘴𝘯’𝘵 𝘤𝘩𝘢𝘯𝘨𝘦 𝘣𝘦𝘩𝘢𝘷𝘪𝘰𝘳. 𝘗𝘦𝘰𝘱𝘭𝘦 𝘯𝘦𝘦𝘥 𝘯𝘦𝘸 𝘪𝘯𝘤𝘦𝘯𝘵𝘪𝘷𝘦𝘴, 𝘯𝘦𝘸 𝘦𝘹𝘱𝘦𝘤𝘵𝘢𝘵𝘪𝘰𝘯𝘴, 𝘢𝘯𝘥 𝘯𝘦𝘸 𝘸𝘢𝘺𝘴 𝘵𝘰 𝘭𝘦𝘢𝘳𝘯. Fix: Focus on transforming the middle layer. Empower managers with budget, permission, and cover to experiment. Team managers are the gatekeepers of change.    🔹 Myth #3: AI makes it easy to slim down the workforce.  Reality: 𝘈𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 𝘳𝘦𝘪𝘯𝘷𝘦𝘯𝘵𝘪𝘰𝘯 𝘴𝘩𝘳𝘪𝘯𝘬𝘴 𝘤𝘢𝘱𝘢𝘤𝘪𝘵𝘺 𝘢𝘯𝘥 𝘰𝘱𝘱𝘰𝘳𝘵𝘶𝘯𝘪𝘵𝘺. Fix: Build alternative learning paths that develop human skills. Creative rotation programs, days dedicated to working without AI, and other opportunities to build and exercise your team's judgement faculties will help defeat AI-driven "deskilling" and the replacement mentality.    At Microsoft, we’ve seen that successful AI adoption isn’t about new tools and quick fixes. It’s about habits, incentives, and expectations built deliberately over time. Organizations that build cultures that expect change, instead of resisting it, will produce teams that drive and thrive in transformation.    Read the full article: https://lnkd.in/gtxpbpSi

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