How to Increase Generative AI Adoption in Organizations

🚨 MIT Study: 95% of GenAI pilots are failing. MIT just confirmed what’s been building under the surface: most GenAI projects inside companies are stalling. Only 5% are driving revenue. The reason? It’s not the models. It’s not the tech. It’s leadership. Too many executives push GenAI to “keep up.” They delegate it to innovation labs, pilot teams, or external vendors without understanding what it takes to deliver real value. Let’s be clear: GenAI can transform your business. But only if leaders stop treating it like a feature and start leading like operators. Here's my recommendation: 𝟭. 𝗚𝗲𝘁 𝗰𝗹𝗼𝘀𝗲𝗿 𝘁𝗼 𝘁𝗵𝗲 𝘁𝗲𝗰𝗵. You don’t need to code, but you do need to understand the basics. Learn enough to ask the right questions and build the strategy 𝟮. 𝗧𝗶𝗲 𝗚𝗲𝗻𝗔𝗜 𝘁𝗼 𝗣&𝗟. If your AI pilot isn’t aligned to a core metric like cost reduction, revenue growth, time-to-value... then it’s a science project. Kill it or redirect it. 𝟯. 𝗦𝘁𝗮𝗿𝘁 𝘀𝗺𝗮𝗹𝗹, 𝗯𝘂𝘁 𝗯𝘂𝗶𝗹𝗱 𝗲𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱. A chatbot demo is not a deployment. Pick one real workflow, build it fully, measure impact, then scale. 𝟰. 𝗗𝗲𝘀𝗶𝗴𝗻 𝗳𝗼𝗿 𝗵𝘂𝗺𝗮𝗻𝘀. Most failed projects ignore how people actually work. Don’t just build for the workflow but also build for user adoption. Change management is half the game. Not every problem needs AI. But the ones that do, need tooling, observability, governance, and iteration cycles; just like any platform. We’re past the “try it and see” phase. Business leaders need to lead AI like they lead any critical transformation: with accountability, literacy, and focus. Link to news: https://lnkd.in/gJ-Yk5sv ♻️ Repost to share these insights! ➕ Follow Armand Ruiz for more

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

Billions poured into GenAI. Countless pilots launched. But this new MIT Massachusetts Institute of Technology report cuts through the noise — and the picture isn’t pretty. Here’s what stood out to me: ✅ Adoption is high: 80% of organizations have piloted tools like ChatGPT or Copilot, nearly 40% deployed them in some form. ✅ 5% of pilots are working & delivering millions in value, in customer support, software engineering, & back-office automation. ✅ Shadow AI is real: 90% of employees use personal tools like ChatGPT or Claude for work, even when their company hasn’t approved them. ✅ Winners customize deeply: the best results come from vendors& buyers who integrate into workflows, retain memory, & adapt over time. But here’s the hard truth: ❌ 95% of organizations are seeing zero ROI. Pilots stall, production deployments fail, & never deliver measurable impact. ❌ Enterprises lead in pilots, lag in outcomes. Big firms run the most experiments but struggle to scale, while mid-market players move from pilot to production in 90 days. ❌ Budgets are flowing to the wrong places. 70% of GenAI spend is going to sales & marketing because it’s easy to measure, but the highest ROI is in back-office automation. ❌ Tools don’t learn. They don’t retain feedback, adapt to context, or evolve with workflows — that is the single biggest reason adoption fails. The report calls this the GenAI Divide: 5% of companies crossing into real transformation, 95% stuck in expensive pilots. Are we chasing the wrong GenAI metrics? So how do we fix it? 🔹 Stop buying static tools. If a system can’t learn from feedback and retain context, it won’t cross the divide. 🔹 Prioritize workflow fit. Tools must integrate with the apps teams already use, not force them into brittle new systems. 🔹 Shift investment from shiny front-office pilots to back-office automation. That’s where the untapped ROI lives. 🔹 Empower line managers & power users. Bottom-up adoption outpaces top-down labs every time. Because if this report is right, the real test of GenAI in business isn’t adoption. It’s whether systems learn, integrate, and deliver lasting value. And the question remains, who’s going to make it across? And who’s going to be left behind? That’s why I’m not just talking about this divide — I’m building on the other side of it. At YOUnifiedAI, we’ve taken the lessons from this report to heart: ✅ No static tools. Our system learns and adapts. ✅ No brittle integrations. We connect to the tools founders already use. ✅ No chasing hype. We’re going where ROI is real — back office, finance, CRM, operations. We’ve spent my career building markets, scaling startups, and challenging industry assumptions. Now we're applying those lessons to create an AI-native system that actually works at the speed of business.