Why 95% of AI Projects Fail and How to Succeed

🚀 Back to school: Generative AI: From Buzz to Business Impact AI is evolving at lightning speed… But how can businesses turn hype into real value? 📘 Our ebook explores how forward-thinking organizations are scaling AI to deliver measurable results, with insights drawn from real-world deployments in companies like Groupe BPCE , Laboratoires Pierre Fabre, and the Région Île-de-France. 🔍 Inside you’ll find: ✅ Strategic lessons from scaling AI projects ✅ Critical success factors: data, governance, sustainability, talent ✅ What’s next: AI agents, multimodal tech & regulation ✅ Use cases that move the needle 💡 Ready to turn AI promise into performance? 👉 https://bit.ly/3JLrt8d #AI #GenerativeAI

AI is with us for about 20 years in various forms - but successful projects in any case rely on clean, certified and well-managed data to turn the hype into real value. #AI #GenerativeAI

🚀 Back to school: Generative AI: From Buzz to Business Impact AI is evolving at lightning speed… But how can businesses turn hype into real value? 📘 Our ebook explores how forward-thinking organizations are scaling AI to deliver measurable results, with insights drawn from real-world deployments in companies like Groupe BPCE , Laboratoires Pierre Fabre, and the Région Île-de-France. 🔍 Inside you’ll find: ✅ Strategic lessons from scaling AI projects ✅ Critical success factors: data, governance, sustainability, talent ✅ What’s next: AI agents, multimodal tech & regulation ✅ Use cases that move the needle 💡 Ready to turn AI promise into performance? 👉 https://lnkd.in/d4CrNeRY #AI #GenerativeAI

🚀 Back to school: Generative AI: From Buzz to Business Impact AI is evolving at lightning speed… But how can businesses turn hype into real value? 📘 Our ebook explores how forward-thinking organizations are scaling AI to deliver measurable results, with insights drawn from real-world deployments in companies like Groupe BPCE , Laboratoires Pierre Fabre, and the Région Île-de-France. 🔍 Inside you’ll find: ✅ Strategic lessons from scaling AI projects ✅ Critical success factors: data, governance, sustainability, talent ✅ What’s next: AI agents, multimodal tech & regulation ✅ Use cases that move the needle 💡 Ready to turn AI promise into performance? 👉 https://lnkd.in/er7E6JgE #AI #GenerativeAI

🚀 Back to school: Generative AI: From Buzz to Business Impact AI is evolving at lightning speed… But how can businesses turn hype into real value? 📘 Our ebook explores how forward-thinking organizations are scaling AI to deliver measurable results, with insights drawn from real-world deployments in companies like Groupe BPCE , Laboratoires Pierre Fabre, and the Région Île-de-France. 🔍 Inside you’ll find: ✅ Strategic lessons from scaling AI projects ✅ Critical success factors: data, governance, sustainability, talent ✅ What’s next: AI agents, multimodal tech & regulation ✅ Use cases that move the needle 💡 Ready to turn AI promise into performance? 👉 https://lnkd.in/eHujHzkz #AI #GenerativeAI

🚀 The rise of foundation models is redefining the boundaries of what's possible with machine learning. As these models become more sophisticated, they're transforming industries by enabling rapid development of tailored AI solutions. However, their complexity and resource demands pose significant implementation challenges. Organizations must strategically invest in scalable infrastructure and talent to harness their full potential. At DataEngite, we empower businesses to navigate this landscape with customized AI deployment and robust infrastructure support. Embrace the future of machine learning with us. 🌟 #MachineLearning #AI #Innovation #DataEngite #FoundationModels #Strategy #DataEngite

🔴 95% of corporate AI pilots fail, according to a recent MIT Media Lab study (2025). And here’s the kicker: Not because the models don’t work. They fail because: 👉 Nobody thought about system integration 🔌 👉 Budgets went to the wrong places 💸 👉 Governance slowed everything down 🛑 The algorithm isn’t the problem. Execution is. 👉 If your org has an “AI strategy,” ask yourself: Is it really a strategy, or just a collection of pilots that will never scale? 📎 Source: MIT/NANDA report, covered by Fortune (Aug 2025). #innovation #AI #strategy #futureofwork

Most AI pilots aren’t delivering. A new MIT study shows only 5% of enterprise generative AI trials generate measurable results. The main issues: weak data foundations, misaligned use cases, and ballooning costs. On the flip side, organizations with strong infrastructure and clear goals are seeing wins. Success requires more than just adopting AI. Governance and alignment drive ROI. Infrastructure matters as much as models Is your team seeing results yet, or still in “pilot mode”? #EnterpriseTech #AI #DigitalStrategy

In machine learning, the difference between an average model and a high-performing one often lies in the quality of its features. Raw data, while valuable, is rarely ready for direct use. This is where feature engineering comes in, the art of refining, enhancing, and transforming data into meaningful inputs that help your model see patterns more clearly and make smarter predictions. When done right, feature engineering doesn’t just improve accuracy, it can completely transform business outcomes, empowering organizations to predict trends, identify risks, and make data-driven decisions with confidence. Remember, powerful insights don’t just come from having massive amounts of data, they come from optimizing the right data. #MachineLearning #FeatureEngineering #AI #DataDriven #Innovation #KulanaTech #TermoftheWeek

Many AI experiments fail because they repeat the same mistakes from the early digital transformation era: lots of hype, many pilots, little that scales. Check out “Beware the AI Experimentation Trap” where the authors warn that 95% of generative AI investments produce no measurable returns. They argue that much of current experimentation is diffuse, disconnected from what customers really need, and overly focused on flashy or peripheral tests instead of core capabilities. Key lesson: anchor AI experiments in solving real customer problems.  “The takeaway is not that AI experimentation is broken, but that it must be disciplined — focused on solving core customer problems; chosen with frameworks like intensity, frequency, and density; run at low cost to enable iteration; and designed with scaling in mind through empowered ‘ninja’ teams.” For product designers & PMs, that means: before building or approving yet another pilot, ask: • What customer-pain does this address, and how often? • How intense is the need vs how visible is the opportunity? • Can we test cheaply, learn fast, and scale if successful? It’s tempting to chase novelty with AI. But without customer-centric discipline, we risk repeating the digital transformation cycles where many firms experimented a lot—and few really delivered. If you want strategies to escape this trap, this article is well worth your time. 🔗 https://lnkd.in/e4BFZNGG #productdesign #AI #PM #experimentation #customersuccess Thanks Dimitri Samutin for sharing this article initially.