Reasons Generative AI Projects Stall

🚨 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

How's it really going with Generative AI project success? 🤔 Different sources provide varying figures on project outcomes, indicating the complexity of implementation - High pilot failure rate:  An August 2025 MIT study, "The GenAI Divide: State of AI in Business 2025," found that 95% of enterprise generative AI pilot projects fail to deliver measurable business value. Low production rate:  A Gartner survey found that only 48% of AI projects, on average, make it from a prototype to production. Similarly, one survey found that 88% of AI pilots never reach a production stage. Mixed ROI:  While some sources report that most AI adopters see a positive return on investment (ROI), others state that between 70% and 85% of projects fail to meet their desired ROI. The Key differences? In-house vs. External solutions. The success rate can depend on the approach an organization takes to development - External solutions see higher success:  The MIT study found that enterprises that partnered with specialized AI vendors for their solutions had a 67% success rate. In contrast, those that attempted to build their projects entirely in-house succeeded only 33% of the time. Specialized solutions beat general tools:  While general-purpose tools like ChatGPT are good for individual use, they often fail in enterprise environments because they lack deep integration and adaptability to specific workflows. Specialized, vendor-built solutions generally have better integration frameworks.   Reasons for generative AI project failures? 🤯 The problem is typically not the technology itself but the implementation strategy. Common reasons for failure include -  Unclear objectives:  Many companies implement AI without a specific, measurable business problem to solve, confusing technology with strategy. Poor integration:  Generic tools often do not connect well with existing enterprise systems like Customer Relationship Management (CRM) or Enterprise Resource Planning (ERP), forcing manual workarounds that negate efficiency gains. Ignoring back-office opportunities:  The MIT study found that most generative AI budgets go to sales and marketing, while the most significant ROI is often found in less flashy back-office automation. Lack of skilled talent:  A shortage of skilled personnel in-house to manage, integrate, and maintain AI solutions is a common barrier to success. Poor data quality:  Generative AI models are highly dependent on the quality and diversity of their training data. Biased, inconsistent, or low-quality data can lead to inaccurate outputs and project failure. Overall, it's important to do some Strategic Planning & Change Management for AI & IT change projects, to minimize the failure rates mentioned above! 🙌 #changemanagement #generativeai #strategicplanning

Why 95% of Generative AI Pilots Are Failing — And How to Fix It Recently, an MIT report grabbed headlines: 95% of enterprise generative AI pilots fail to deliver measurable business impact. Boardrooms are rushing into AI, budgets are swelling, yet results are lagging far behind expectations. Should this surprise us? Not at all. This isn’t an AI-specific problem. It’s a mindset and value problem. Here’s what every executive needs to know: 1. Put Business First, Not Technology Too many organizations chase AI because it’s trendy — not because they’ve clearly identified where it will create value. Success doesn’t come from applying AI tools for technology’s sake. It comes from starting with a business problem: • Where is value leaking today? • What pain points, if resolved, translate into measurable financial or customer benefits? • How can AI complement execution, not replace it? AI is a capability embedded within a business strategy, not a hammer searching for nails. 2. Build Strong, Connected Data Foundations AI’s power is only as good as the data it learns from. Without quality data governance, breaking down silos, and scalable platforms, AI risks amplifying noise — not insight. The age-old “garbage in, garbage out” rule has never been truer. 3. Invest in People and Change Management AI cannot live in isolated labs. The real ROI comes when frontline teams are empowered, leadership clarifies AI’s role as an enabler, and upskilling and trust-building are prioritized. Change management isn’t optional—it’s the critical lever to scale pilots into profit. 4. Embrace Failure as Part of the Journey A 95% failure rate is not a red flag to stop; it’s a call to learn and iterate deliberately. Responsible experimentation with a value-first mindset builds the organizational muscle to win at AI. Failure uncovers blind spots, sharpens focus, and creates the breakthroughs that ultimately stick. My Takeaway Generative AI isn’t failing business — businesses are failing AI by chasing shiny tools without discipline. The 5% early wins will expand rapidly — but only if we shift the conversation away from tools and hype, and toward clear, tangible business value. Let’s stop trying to make AI succeed for AI’s sake. Let’s make AI succeed because it moves the needle — for customers, for revenue, and for sustainable competitive advantage. If you want to lead AI in your organization — start with the value, build on data, empower your people, and accept failure as the path to real success. https://lnkd.in/g6sk49DA

𝟵𝟱% 𝙤𝙛 𝙂𝘼𝙄 𝙞𝙣𝙞𝙩𝙞𝙖𝙩𝙞𝙫𝙚𝙨 𝙖𝙧𝙚 𝙉𝙊𝙏 𝙛𝙖𝙞𝙡𝙞𝙣𝙜. Don't fall prey to the headlines. There are limitations with MIT's study, and people are the primary root causes of GAI pilot failure - not the AI. How? Let me explain... The main reasons why the GAI pilots failed include: 1) Poor solution fit. 2) Poor workflow integration. 3) Flawed implementation strategy. 4) Lack of investment in change management. 5) Misalignment with user needs. 6) Lack of leadership and/or team buy-in. 7) Unwillingness to adopt new tools. 8) Lack of executive sponsorship. These are all people-related issues. To be sure, there were tech-related issues with pilots, involving platform mismatches with legacy systems, startups overpromising & underdelivering, unexpected costs and token overages, and memory/learning challenges. However, these add up to maybe 20% of the reasons why the pilots failed vs. the 80% that were people related. With regard to the study's limitations: 1) Based on interviews with 52 companies and 153 survey responses 2) No details on company sizes, executive roles, or industry distribution 3) Success defined solely by 𝙥𝙪𝙗𝙡𝙞𝙘𝙡𝙮 𝙖𝙣𝙣𝙤𝙪𝙣𝙘𝙚𝙙 productivity gains 4) Findings that contradict other credible research (like claiming 50% of GAI budgets go to sales/marketing) While it's fair for the MIT researchers to say that they found a 95% failure rate of GAI pilots in their sample, it's a limited sample and they also relied on publicly disclosed AI initiatives. Speaking of which - let's talk about a very successful GAI pilot that hasn't been publicly disclosed, and I suspect there are many of these. A senior leader at a global telecom company told me about a pilot they recently ran using GAI to predict customer churn and to develop and deliver personalized offers for customer retention. Their pilot exceeded 125K customers retained vs. a 36K goal, and they generated $100M in increased margin within 1 year, forecasted potential of $200-$300M annually. Yes, 9 figure profit impact. The most important takeaway from this successful pilot? This pilot was not looked at as an AI project - but a business project for customer retention. AI just happened to be a way for them to help retain customers. Customer Lifetime Value (CLV) served as their primary metric. So, if your GAI pilots are failing - look in the mirror before blaming the AI. The infrastructure, processes, proper problem/solution fit, cultural readiness, and change capability for AI transformation require just as much investment as the technology itself - and likely a multiple! #AI #DigitalTransformation #Leadership #FutureOfWork