Jordi Barguno, MSIE, MBA’s Post

Over the past months, I’ve dived deep into cutting-edge AI at MIT. One study stood out: 95% of GenAI pilots at companies fail. But here’s the truth I’ve seen play out time and time again over 20+ years in tech: the failure isn’t about the technology - it’s about the “why.” If you don’t know why you’re implementing AI (or building any product), you’re headed straight for that 95% failure zone. Instead, start with a pain point: 👉 In your company 👉 With your customers 👉 In society Don’t implement AI just because it’s hype. Don’t build #AI products just for the sake of telling investors you did. AI is a powerful tool but it’s not always the best answer to a real problem, nor the wisest use of resources. As product leaders, our job is to stay disciplined: problems first, tech second. That mindset is timeless, even in the #GenAI era. https://lnkd.in/eBTKqgVG #ArtificialIntelligence #GenerativeAI #TechTrends #DigitalTransformation #ProductManagement #ProductLeadership #TechStrategy #ChiefProductOfficer #Innovation #BusinessStrategy #AIProductManagement

95% of AI pilots fail. 💥 This is per a recent study done by MIT. 💥 Wow. How could such an amazing technology like modern AI, have such an alarming failure rate? 🤷♂️ Well if you’re looking for the magic pill that will cure your revenue, AI isn’t it 95% of the time... According to the study, only 5% of AI pilots achieved a "rapid revenue acceleration". This was based on interviews of 150 leaders, 350 employees, and analyzed 300 AI project deployments. Now, this wasn’t because of the capability of the AI itself, but how companies were using it. 🤦♂️ It’s a tale as old as time, company wants to use the newest hottest tool. Company doesn’t analyze how to use a tool well. Company pushes deployment regardless, and costs pile up while revenues are nowhere to be seen. The big issue seems to be not the capabilities of AI, but the use cases. The 5% that succeeded, did so because they focused on a singular, likely quantifiable problem, and execute well on just that. Additionally, most enterprise projects that failed did so because they tried to build their own tool, instead of just purchasing one already made. What does this mean in regards to the AI hype train? We’re an IT company, so we understand the excitement around AI. Is it capable? Yes. Is it exciting? Yes! Is it a little scary? Also yes. AI isn’t a cure all for every problem, and there are always privacy concerns when using it, especially for your business. But the BIG takeaway here should be, that AI is great, if it is carefully considered in its use case. You can’t just shoehorn an AI solution to any problem. It is not advanced enough to handle that, yet - and that’s not a good method to problem solve anyway. We’re excited for the future of tech. 🙌 And we want to help you get there. If you’ve got a question about all things IT, we’d be happy to answer it for you. DM us or visit us at unisontech.com. Here’s the Fortune article we referenced when writing this post - which was not generated with AI 😉 #MIT #AI #generativeai https://lnkd.in/eEiXCteC

AI is not failing. Companies are failing AI. MIT reports that 95% of enterprise GenAI pilots do not succeed. Everyone jumps to the easy conclusion: “See, AI is overhyped.” Wrong. The problem is not the technology. Startups without legacy systems grow from zero to $20M in one year. Large corporates, in contrast, spend millions on in-house systems that fail, allocate budgets to shiny sales tools instead of automation, and isolate AI in “innovation labs” far away from the real business. Question: Will corporates ever make AI work, or are they destined to be left behind by the 5% who already use it effectively? https://okt.to/xlWAI3 #AI #FutureOfWork

A striking insight from MIT’s latest research: while generative AI is making headlines, 95% of pilots aimed at accelerating revenue fail to deliver. This is even higher than the 2024 estimate of a 90% failure rate for AI pilots reaching operational deployment. But there’s a silver lining. The study suggests back-office efficiency applications are showing more promise. These use cases are not only more viable but are also significantly reducing demand for traditional BPO services—a shift that could reshape entire industries. To succeed where others fail enterprises must focus on readiness of their business operations to embrace AI augmentation and on how to best customize these powerful tools for their specific purposes not just pilot generic consumer AI products. #AI #GenAI #DigitalTransformation #MITResearch #OperationalEfficiency #BPO #Leadership #InnovationStrategy https://lnkd.in/eGmWAvGs

A recent MIT report, as covered by various sources including Fortune, has found that approximately 95% of generative AI pilot projects at companies are failing to deliver a measurable return on investment. But the study suggests that the failure is not due to the technology itself, but rather a "learning gap" in how companies are implementing it. When a company truly understands what AI can do, and utilizes it properly, the benefits are immense. Companies can streamline repetitive tasks, freeing up employees to focus on more complex, creative, and value-adding work. This leads to increased efficiency and higher employee satisfaction. Properly implemented and utilized AI can also provide deeper insights from vast amounts of data, helping businesses make more informed, strategic decisions. Ultimately, success with AI lies in its ability to augment human capabilities, not replace them. https://lnkd.in/eEiXCteC

🚨 MIT Report: Why 95% of Generative AI Pilots Fail 🚨 According to MIT’s latest research, the problem isn’t that generative AI doesn’t work—it’s how companies adopt it. 👉 Internal builds succeed only one-third of the time. 👉 Purchasing AI tools from specialized vendors and building partnerships succeed about 67% of the time. The takeaway is clear: internal projects alone are not enough. Without the right expertise, infrastructure, and integration, most pilots stall before reaching measurable ROI. 💡 Companies that collaborate with experienced consultants and technology partners dramatically increase their chances of success—turning AI from hype into real business impact. I’ve seen this first-hand: success comes when AI is aligned to business goals, embedded into processes, and scaled with the right support. 🔗 The message from MIT is simple: Don’t go it alone. Work with the right partners to make AI actually deliver. #AI #DigitalTransformation #Consulting #TeamWorkCanada #BusinessStrategy https://lnkd.in/gRZdAcBS

A report from MIT revealing that 95% of AI pilot projects are unsuccessful has unsettled investors. However, it's the underlying reasons for these failures that should truly concern executives. These insights highlight critical issues within AI integration that need addressing. Lack of clear use cases, poor integration with existing systems, insufficient data quality, or unrealistic expectations. Instead focus on measurable outcomes, collaboration across the enterprise and the emergence of Agentic Web . #AI #AIChallenges

Don't let the headlines fool you: AI isn't failing, your *strategy* might be. You've may have heard the stat: "95% of AI pilot programs are failing." (https://lnkd.in/eEiXCteC). However, this is a bit misleading - it's not the technology, it's HOW companies are approaching AI integration. The tools ARE powerful, but many companies fall into an "experimentation trap", where efforts are scattered without clear business value or a path to scale. You need a DISCIPLINED APPROACH. 1. Target a specific pain point. 2. Choose proven, accessible tools. 3. Focus on tangible value and a path to integration. Try to avoid unfocused experiments. Instead of trying out AI for the sake of it, target specific business problems and think about scalability from the start:  What happens if this all goes RIGHT? #AIforSmallBusiness #AIImplementation #AIROI #TimeToValue Ready to implement practical and pragmatic AI workflows that actually deliver value? Reach out for a FREE 15 minute call to discuss your specific problems and the ways that you can make AI work for YOU.

💡 I just read a very interesting article in Fortune about a new MIT report. It says that 𝟵𝟱% 𝗼𝗳 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘂𝘀𝗶𝗻𝗴 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗮𝗿𝗲 𝗳𝗮𝗶𝗹𝗶𝗻𝗴. The study shows: • Only 𝟱% 𝗼𝗳 𝗽𝗶𝗹𝗼𝘁𝘀 create real business impact. • The problem is not the AI itself, but 𝗵𝗼𝘄 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝘁𝗿𝘆 𝘁𝗼 𝘂𝘀𝗲 𝗶𝘁. • Many projects stay as “experiments” and never scale. • Companies invested 𝗯𝗶𝗹𝗹𝗶𝗼𝗻𝘀 𝗼𝗳 𝗱𝗼𝗹𝗹𝗮𝗿𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱, but with little return. 😕 Are we living an AI bubble? 🏢 What are the main obstacles—culture, workflows, or budgets? 🚀 How can we make sure AI pilots bring real results and not just “tests”? 👉 The big lesson is that 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗶𝘀 𝗻𝗼𝘁 𝗲𝗻𝗼𝘂𝗴𝗵. To succeed, we need strategy, people, and change in the way we work. 🔗 Full article here: MIT report: 95% of generative AI pilots at companies are failing (Fortune) https://lnkd.in/dbhStEQ9

Before investing in AI, think; what do I need it for? If the answer is to increase the company's bottomline, you're approaching it wrong. It should be, where can we leverage AI to create workflow efficiencies and free up workers, who'll produce great results, which will increase the bottomline. Ask it to fill in spreadsheets, so we can deliver effective strategies for growth. #ai #digital #leadership #strategy https://lnkd.in/euS89dCP