Why AI Projects Fail: The Real Starting Point

AI isn’t just about building models anymore. The real challenge is making AI useful: ✅Understanding the data ✅Asking the right questions ✅Deploying models that actually solve a problem ✅Tracking and improving them over time It’s easy to get stuck chasing “what’s new” in AI. The real edge comes from knowing how to turn complexity into clarity and deliver results. Curious to know — what’s the most practical AI skill you’ve picked up this year? #AI #MachineLearning #DataScience #CareerGrowth

"We want to use AI." But what they really needed was simple math. A few weeks back, one of our clients approached us to build an AI-based solution. They were an educational institution. Their goal was to track inventory leak (amongst other things). Now, sure, we could’ve trained a model to detect anomalies. But the outcome could be achieved with basic math. A simple look at standard deviation across inventory consumption was enough to flag concerns. No AI required. No inflated budgets. No tech for the sake of tech. We scrapped the AI idea and built a clean, insight-driven dashboard instead. Fast. Simple. Useful. And the client loved it. Because good AI consulting is about saying no to unnecessary AI. It’s easy to sell the hype. But real value comes from solving the right problem with the right tool. Ever had to say no to a client for their own good? Tell me your story 👇 #AIforGood #AI #ArtificialIntelligence #BusinessCaseStudy

📊 Data is the compass… but AI shows the way. In a world full of information, the real value doesn’t lie in the amount of data, but in how we turn it into smart decisions. 🤖 Today, AI helps organizations: Predict the future rather than just analyze the past. Personalize services for every user. Improve efficiency and uncover new opportunities. 💡 My question to you: What’s one decision you wish AI could help you make more accurately and faster? #AI #DataAnalytics #MachineLearning #Innovation

🤖 Many people think AI is hard. The reality? It’s actually harder than we imagine. ✅ Using AI feels simple—we just type a query, and it instantly understands. But behind that simplicity lies complex math, algorithms, and years of innovation. ⚡ The real difference is this: Using AI → Easy, accessible, user-friendly. Building AI from scratch → Challenging, technical, and demanding deep expertise. That’s what makes AI fascinating—what looks effortless on the surface is powered by layers of complexity underneath. 🚀 Let’s continue learning and pushing boundaries, because what feels “hard” today might just be tomorrow’s “easy.” #ArtificialIntelligence #AI #Innovation #Tech #Learning

What if the most important part of AI isn't the model itself? 🤔 A powerful AI can explain complex topics but can't remember your name from one conversation to the next. Why? Because the knowledge is in its training data, but the continuity is something we have to engineer. The real work is in designing the systems that give AI context and a sense of history. That's the difference between a one-off interaction and a truly intelligent tool. #linkedin #AI

AI hype is real, but simplicity wins often. People nowadays are jumping the gun towards AI, without even taking a 5 mins to think about: ->Do I really need AI to solve this? Or is the solution simpler and cheaper? I have seen some examples that can be solved with a simple key-word matching, but someone will jump to the famous line: ->Let's use AI. If you want to add the cherry on top, okay - use a simple AI API call to craft you a nice message after finishing the task you are trying to solve. What are some examples you've seen where AI was overkill and a simpler solution would’ve worked better? #ai #data #business

Stepping a bit outside my usual territory with this one, but AI is touching every role now. I haven’t had time to read it fully yet, but this handbook looks like a gem on creating real business value with AI. And it’s not written by a self-proclaimed “AI guru,” but by some serious people actually building and scaling AI at IBM: Rob Thomas, Paul Zikopoulos, and Kate Soule. That already gives it more weight. The book focuses on helping companies to move from just using AI to actually creating value with it, a shift most companies still struggle to make. What I appreciate so far is that this isn’t just theory: it’s full of real-world stories, lessons learned, and practical steps to go from AI experiments to measurable business outcomes. Full version linked in the comments ↓ #AI #ArtificialIntelligence #businessstrategy #DigitalTransformation #Innovation 

😅 This meme perfectly captures the current landscape.We should be asking more specific questions: -> What business problem are we trying to solve? -> What data do we have, and is it ready for an AI model? -> How will we measure success? Let's shift from a generic demand to a strategic approach. It's not about having AI "right now" but about implementing it intelligently and effectively to drive real value. #AI #Innovation #BusinessStrategy #FutureofWork #TechTrends

🤖 Most LLMs sound smart. But are they really thinking—or just remembering? 🤔 The gap between memorization and reasoning is where AI’s true intelligence gets tested. ➡️ Memorization: spitting out facts and patterns it has seen before. ➡️ Reasoning: connecting ideas, solving new problems, and adapting on the fly. This distinction matters for everything from building reliable AI assistants to shaping the future of decision-making tools. If an AI can’t reason, it’s not much more than a clever search engine. 👉 Read the full blog here: https://lnkd.in/gv-ZZYSF #AI #MachineLearning #ArtificialIntelligence #LLMs #Reasoning #AIResearch #AITrends #TechInnovation

Where Does AI Meet Statistical Significance? The more advanced our tools become, the more important it is to stay anchored in what we can trust. We just launched an updated scoring framework at Veritonic — one that blends the speed and scale of modern AI and machine learning with the rigor of traditional statistical significance. ⚖️ It’s a balance we think about constantly. 🔍 If we lean too far toward innovation, we risk speed without certainty. 🔎 If we lean too far toward tradition, we risk certainty without progress. The future of measurement lives in the space between — where new technology amplifies what we know, not replaces it. That balance is where clarity lives. And that’s where we’re building. 📈 What do you think — where should the line be drawn between innovation and rigor? #AI #MachineLearning #StatisticalSignificance #Innovation #DataDriven #MarketingAnalytics #Measurement #CreativeIntelligence