Something weird is happening in the AI world right now. Companies are investing heavily in AI tools, getting excited about the potential, but then most of their AI projects never make it past the pilot stage. A recent study found that over 90% of organizations increased their AI usage in 2024. Sounds great, right? Here's the catch: Only 8% consider their AI initiatives mature and successfully deployed at scale. So what's the problem? It's not the AI technology. The tools are getting better every month. More capable. More accessible. More affordable. The problem is data. Specifically, how organizations manage (or don't manage) their data. Here's what's actually happening: Most businesses have tons of data. Customer information. Sales records. Communication history. Product details. Process documentation. You name it. But that data is scattered across different systems, stored in inconsistent formats, riddled with duplicates and errors, and generally not set up in a way that AI can actually use effectively. The analogy that makes this clear: Imagine you hired a brilliant assistant to help analyze your business and find opportunities for improvement. But instead of giving them organized information, you hand them: Twenty-three different Excel spreadsheets with overlapping data Fifty PDFs with crucial information buried in paragraphs Hundreds of emails with decisions scattered throughout Sticky notes with important numbers but no context Documents saved with names like "Final_v3_REAL_USE_THIS.docx" Your assistant is smart and capable. But they'll spend 80% of their time just trying to make sense of the mess before they can do anything useful. That's what's happening with AI. What the research shows: When companies were asked about the biggest challenges preventing them from moving AI projects from pilot to production, data quality issues topped the list. Not lack of AI tools. Not technical complexity. Data. Specifically: 34% said availability of quality data 35% said data privacy concerns Many cited inconsistent data formats and poor data management infrastructure

My first AI me...haha

Anyone know about the AI Amplifier Masterclass? I’d send the link but not allowed use links…

“AI is a sparring partner,”? Let’s be honest: You only spar with equals. You don’t spar with someone who needs step-by-step instruction just to throw a competent jab. If the model needs five rounds of correction to stop producing: - generic filler - hallucinated logic - template content - tone mismatches then we’re not sparring we’re coaching an undertrained intern with infinite stamina. Iteration should refine good output. It shouldn’t have to salvage weak reasoning, missing context, and shallow pattern-matching disguised as intelligence. And blaming users like “you just need to stay in the conversation” is a convenient way to avoid admitting the obvious: If the first pass isn’t usable, that’s not a user error that’s a model limitation. People don’t expect AI to be magic. They expect it to be competent. If it can’t meet the baseline, no amount of “sparring” fixes that. You spar with equals. You babysit everything else.

We keep seeing the same pattern: someone tries AI, gets disappointed, and decides "it's not ready yet." But here's what's actually happening. They're treating AI like they'd treat a new hire, expecting it to just know what to do, read their mind, and deliver exactly what they wanted without any back-and-forth. That's not how AI works. And honestly, that's not even how people work. The mindset shift: The best analogy we've found is a sparring partner. Not someone who does the work for you. Not someone who reads your mind. But someone who pushes back, offers alternatives, and helps you think through the problem differently. When you spar with someone, you don't expect them to know exactly how hard to punch or which combinations to throw. You adjust in real time. You say "lighter" or "try this angle instead." The value comes from the interaction, not from them being perfect on the first try. AI is the same way. The magic happens in the conversation, not in crafting the one perfect prompt that generates the one perfect output. Here's what this looks like in practice: Version 1 (Treating AI like an employee): "Write me a blog post about productivity tips for entrepreneurs." AI gives you generic advice. You're disappointed and decide AI isn't useful. Version 2 (Treating AI like a sparring partner): "Write me a blog post about productivity tips for entrepreneurs." AI gives you generic advice. You respond: "This is too general. My audience is coaches who work from home with kids. Focus on strategies that work when you have 30-minute blocks of time max." AI adjusts. You respond: "Better. Now add a specific story or example for each tip so it feels real, not theoretical." AI refines again. Same starting point. Completely different outcome. The difference? You stayed in the conversation. Scenarios where sparring wins: Mark runs a consulting firm and was frustrated that AI-generated proposals felt flat. Then he realized he was dumping information and expecting polish. Now he treats it like a brainstorming session. First pass: rough ideas. Second pass: "Make this sound more conversational." Third pass: "Add specific metrics here." The proposals are better because he's coaching AI through his vision instead of expecting it to nail everything upfront.