Why most companies fail to extract value from AI

A few weeks ago, Andreessen Horowitz released a report showing where startups are spending their money on AI. Many of the tools focus on communication, either helping us connect or helping us avoid each other. Sales tools, email assistants, presentation generators, and chatbots are all designed to manage how we talk and listen. Hopefully these tools can create more opportunities for better communication and don't destroy the art of story telling. The best approach is to use AI as an editor and a coach, so we can communicate better and more often. https://lnkd.in/gpTmr-CJ

95% of companies spend billions on AI. They get nothing back. MIT tracked 300 projects to find out why the math doesn't work. The results are sobering. Enterprise spending on generative AI hits $30-40 billion annually. Yet 95% of AI initiatives deliver zero measurable business value. Zero. The MIT study reveals a harsh truth. It's not about model size or compute power. It's about execution. The "GenAI Divide" shows: • Only 5% of AI projects generate rapid revenue gains • Legacy enterprises consistently underperform startups • Organizational readiness matters more than technology specs • Most companies see productivity dips after AI adoption Startups are winning this race. They build AI-ready processes from scratch. They align technology with workflows. They focus on single, specific use cases. One startup went from zero to $20 million in revenue within a year. Not because they used bigger models. Because they executed better. Large enterprises throw money at compute-heavy solutions. They skip the foundation work. They ignore workflow integration. They wonder why AI fails to deliver. The research suggests a shift is coming. Algorithmic efficiency gains will soon outpace brute-force compute scaling. Smaller, targeted models will compete with massive ones. This isn't about anti-AI sentiment. It's about smart AI deployment. Success factors include: • Strategic governance frameworks • Workflow alignment • Organizational preparedness • Measurable outcome tracking The companies winning with AI aren't using the biggest models. They're using the right approach. What's your experience with AI implementation? Are you seeing real returns or just hype? #AI #ArtificialIntelligence #BusinessStrategy 𝐒𝐨𝐮𝐫𝐜𝐞: https://lnkd.in/em4KQ75Q

95% of companies spend billions on AI. They get nothing back. MIT tracked 300 projects to find out why the math doesn't work. The results are sobering. Enterprise spending on generative AI hits $30-40 billion annually. Yet 95% of AI initiatives deliver zero measurable business value. Zero. The MIT study reveals a harsh truth. It's not about model size or compute power. It's about execution. The "GenAI Divide" shows: • Only 5% of AI projects generate rapid revenue gains • Legacy enterprises consistently underperform startups • Organizational readiness matters more than technology specs • Most companies see productivity dips after AI adoption Startups are winning this race. They build AI-ready processes from scratch. They align technology with workflows. They focus on single, specific use cases. One startup went from zero to $20 million in revenue within a year. Not because they used bigger models. Because they executed better. Large enterprises throw money at compute-heavy solutions. They skip the foundation work. They ignore workflow integration. They wonder why AI fails to deliver. The research suggests a shift is coming. Algorithmic efficiency gains will soon outpace brute-force compute scaling. Smaller, targeted models will compete with massive ones. This isn't about anti-AI sentiment. It's about smart AI deployment. Success factors include: • Strategic governance frameworks • Workflow alignment • Organizational preparedness • Measurable outcome tracking The companies winning with AI aren't using the biggest models. They're using the right approach. What's your experience with AI implementation? Are you seeing real returns or just hype? #AI #ArtificialIntelligence #BusinessStrategy 𝐒𝐨𝐮𝐫𝐜𝐞: https://lnkd.in/d5N2HHNa

95% of companies spend billions on AI. They get nothing back. MIT tracked 300 projects to find out why the math doesn't work. The results are sobering. Enterprise spending on generative AI hits $30-40 billion annually. Yet 95% of AI initiatives deliver zero measurable business value. Zero. The MIT study reveals a harsh truth. It's not about model size or compute power. It's about execution. The "GenAI Divide" shows: • Only 5% of AI projects generate rapid revenue gains • Legacy enterprises consistently underperform startups • Organizational readiness matters more than technology specs • Most companies see productivity dips after AI adoption Startups are winning this race. They build AI-ready processes from scratch. They align technology with workflows. They focus on single, specific use cases. One startup went from zero to $20 million in revenue within a year. Not because they used bigger models. Because they executed better. Large enterprises throw money at compute-heavy solutions. They skip the foundation work. They ignore workflow integration. They wonder why AI fails to deliver. The research suggests a shift is coming. Algorithmic efficiency gains will soon outpace brute-force compute scaling. Smaller, targeted models will compete with massive ones. This isn't about anti-AI sentiment. It's about smart AI deployment. Success factors include: • Strategic governance frameworks • Workflow alignment • Organizational preparedness • Measurable outcome tracking The companies winning with AI aren't using the biggest models. They're using the right approach. What's your experience with AI implementation? Are you seeing real returns or just hype? #AI #ArtificialIntelligence #BusinessStrategy 𝐒𝐨𝐮𝐫𝐜𝐞: https://lnkd.in/dk-kGkbv

With AI software increasingly hogging the enterprise spotlight, companies and investors are spending like never before. In the first half of 2025, AI startups raised over $44 billion, more than all of 2024 combined. By the end of this year, a Goldman Sachs analysis estimates that total investments in AI will soar to almost $200 billion. But all that money is, to put it gently, a reckless gamble. In the US at least, investors have essentially bet the farm on the idea that AI will soon lead to gains in labor productivity — the amount of goods and services workers are able to produce in a given time — that have never been seen in the history of humankind. https://lnkd.in/gdZrM2dw

🚀 The Agentic AI Revolution is Here – And It's Transforming Every Industry 🚀 According to Gartner's latest projections, by 2028, one-third of all enterprise software applications will include agentic AI. This isn't just another tech trend – it's a fundamental shift in how we work, comparable to the advent of the internet itself. What Makes Agentic AI Different? Unlike traditional software that follows rigid, pre-defined steps, agentic AI systems can: ✅ Decompose complex objectives into actionable plans ✅ Self-reflect and iteratively improve their performance ✅ Take autonomous actions via APIs and collaborate with other specialized agents Real-World Impact Across Industries: The results speak for themselves: ⭐ Tech: Amazon saved 4,500 years of development time modernizing 10,000+ applications, realizing $260M in annual cost savings ⭐ Financial Services: Rocket Companies transformed customer experience by aggregating 10 petabytes of financial data for personalized mortgage guidance ⭐ Pharmaceutical: AstraZeneca accelerated clinical trial decisions, speeding life-saving therapies to market The Future is Multi-Industry: From automating supply chain operations to revolutionizing customer service, from accelerating R&D to transforming financial planning – agentic AI is reshaping: 📊 Manufacturing & Operations 🏥 Healthcare & Life Sciences 💰 Financial Services & FinTech 🛒 Retail & E-commerce ⚡ Energy & Utilities 🎓 Education & Training The Challenge? Preparing Your Organization Today. Success requires more than just technology – it demands new governance models, upskilling programs for "agentic literacy," and a cultural shift toward human-AI collaboration. ⚙️ This is where Neo-ji comes in. ⚙️ At Neo-ji, we're not just talking about the agentic future – we're building it. Our platform helps organizations seamlessly transition from experimental AI to production-grade agentic systems that deliver measurable business outcomes. Whether you're looking to boost workplace productivity, accelerate critical business workflows, or speed innovation and research, Neo-ji provides the foundations, tools, and expertise to make agentic AI work for your business. Ready to join the agentic revolution? The organizations seeing the greatest success aren't those with the most ambitious plans – they're the ones who started early and are learning fast. Don't wait for all the answers. Pick a business problem that matters and start building today. Let's shape the agentic future together. 📩 DM us to learn how Neo-ji can accelerate your agentic AI journey 🔗 Visit our website to explore our solutions 💬 Comment below with your thoughts on agentic AI's potential in your industry #AgenticAI #ArtificialIntelligence #DigitalTransformation #Innovation #FutureOfWork #EnterpriseAI #BusinessTransformation #Neoji

MIT just proved 95% of enterprise AI projects fail. Here's what kills me: Everyone's shocked. I've been watching this movie for 12 years. Big company forms AI committee. Six months of PowerPoints. Million-dollar budget. Zero results. Meanwhile, the plumber down the street just 3x'd his revenue with a $500/month AI agent. The report says enterprises fail because they can't build AI. Wrong diagnosis. They fail because they're trying to build AI at all. See the pattern? Enterprise: "We need a custom LLM trained on our proprietary data integrated with our legacy systems through a phased rollout pending committee approval." SMB: "I need my phones answered at 8 PM." One gets a 200-page strategy deck. The other gets customers. Here's what MIT's study missed: The companies crushing it with AI aren't building it. They're buying it from startups who actually understand the problem. My HVAC client doesn't care about transformer architecture. He cares that his AI agent just booked a $12,000 commercial job while he was at his kid's soccer game. The gap isn't technical. It's philosophical. Enterprises think AI is a project. Startups know it's a product. While Fortune 500s debate "AI readiness," startups are shipping AI that works. Today. For real businesses. Making real money. 95% failure rate? That's not an AI problem. That's an enterprise problem. The revolution isn't coming from companies with AI steering committees. It's coming from founders who turned on an AI agent and let it work. What's your take - should enterprises build or buy their AI?

AI startups are taking control of their data, investing in proprietary datasets to enhance model performance and gain a competitive edge. Link: https://lnkd.in/di2CpuPe #AI #Data #Startups #Innovation #Technology #Models #Performance #Edge #Proprietary #Investment #Growth #Development #Future #Digital #Intelligence #Analytics #Trends #Leadership #Strategy #Success

Why 86% of Companies Are Getting AI Implementation Wrong (And How to Get It Right)... By 2030, 86% of companies expect AI to significantly impact their operations. Yet most are focusing on the wrong question entirely. The question isn't "Where can we use AI?" but "Where will AI create genuine value?" MIT research reveals the companies succeeding with AI think differently from the start. Apollo Global Management assesses AI's impact across entire industries before investing, helping portfolio companies like Cengage cut content costs by 40% and Yahoo improve engineering productivity by 20%. Michelin identified over 200 AI use cases through proof-of-concept testing, now generating €50 million in annual ROI with 40% year-on-year growth. The game-changer? "Vibe analytics" - allowing business leaders to ask questions directly to their data and get insights in minutes rather than weeks. One Southeast Asian telecom uncovered more financially relevant insights in 90 minutes than they typically generate in 90 days. For smaller businesses, this means starting with proof-of-concept projects that demonstrate clear value before scaling. Focus on processes that directly impact your bottom line, measure results rigorously, and build from there. Bamboo AI can help to identify the key process to automate and integrate AI where it will make real, instant results. Get in touch to find out more. https://lnkd.in/dXiHTJxU #AIStrategy #BambooAI #BusinessInnovation #DataAnalytics #SmallBusiness #DigitalTransformation #BusinessValue #AIImplementation #GrowthStrategy

“42% of enterprise AI initiatives were discontinued in 2024." Why? The models are already good. The process isn’t. Most teams add AI as a feature without changing how the work gets done. AI forces a rethink of basic ops that are usually out of scope for a software trial: - team size and structure - how departments collaborate - how documents are created, named, and stored That's why we are seeing more tech companies own the service layer. Confident they can capture more of the upside and expand the addressable market. Good article on this: https://lnkd.in/e9ZTMEV7