Tobias Zwingmann's Linkedin Analytics

"Our AI solution saves $250k in cost every year." "Yeah but it also costs $300k to run!" "$300k to run?!" - Yeah, because… AI has inverse economics. Unlike traditional IT projects where costs typically drop after implementation - AI project costs keep going up. Two main effects: 1) Oscillating costs from model maintenance 2) Growing costs that scale with usage AI cost structure is really less like buying a computer, but more like hiring a digital worker who needs ongoing training, monitoring, and intervention. So before building anything, ask: - Can your business sustain these recurring costs? - Is there a path to increasing value faster than costs grow? Ideally: Are AI costs so low they become negligible given the value you receive? A "successful" AI project that saves $250k but costs $300k is still a failed business decision. If your AI project doesn't survive quick napkin math, it's probably not worth pursuing. You can't budget AI like office equipment. That's why I use my 10K approach for every new AI project. More details in the link in the comments. -- PS: Enjoyed this post? Sign up for my free newsletter for more insights like this: https://lnkd.in/eFzzQrMJ

One thing that I learned along the last years of AI consulting is that complete AI outsourcing is the fastest way to kill your AI roadmap. You can't buy your way to AI success. Why? "Just get the tools, hire the vendors, let them handle everything" Sounds efficient. Feels smart. But it's a trap. Every company's processes are very similar, yet strangely unique. And this often just unfolds when you’re really digging in. Recent example: Company picks a great out-of-the-box chatbot solution.  Works perfectly.  Does exactly what they need. Then they realize it has no logging or tracing features built in. So how do you keep track whether the chatbot is working as promised or just hallucinating to your customers at scale? You can't add that later on.  You'd have to rebuild the whole thing. You're stuck. The problem isn't outsourcing itself. The problem is outsourcing your strategic decisions. You can't let vendors define how AI fits into your business.  You need to front-load your requirements and processes upfront. Ask yourself: To what degree do you want to be able to add features and build stuff? In my opinion, you need to own a certain share of that in any case. Develop internal capabilities alongside external partnerships.  Understand what you must own versus what you can delegate.  Build on platforms you can control and extend when needed. Strategic ownership doesn't mean building everything yourself. It means owning the decisions and delegating the execution. Don't let convenience today create impossible constraints tomorrow.

We all want ROI from AI (ok, most of us). Problem though is that pinpointing the return on AI is nearly impossible. Too hard to measure. That's why I use a practical middleground: The $10K Threshold. Here's how it works: What's the $10K Threshold? It's the minimum RECURRING profit an AI solution must generate to be worth your time. Why $10K specifically? $10K roughly equates to human labor costs in many regions. At $10K/month, that's about $500 per business day, $60 per hour, or $1 per minute of work. $10K forces a recurring mindset. The biggest mistake companies make with AI is focusing on one-time benefits. Sure, an AI tool might save $100K once—but then what? Real impact comes from ongoing value. $10K helps you prioritize AI projects that actually matter. Instead of chasing every AI trend, you focus on solving business problems that truly move the needle. Your threshold can scale based on your ambition: - $10K/year → A minimal impact, but still net positive - $10K/quarter → Achievable for most AI process improvements - $10K/month → Makes a serious business case for adoption - $10K/week → Transformational with enterprise-scale impact The exact number matters less than the mindset: "If this AI solution could earn at least X recurring value, I'd start today." Set that as your filter. If it doesn't hit that bar? Don't build it (yet). I came to the realization: You don't need to calculate the exact ROI of every AI project. You just need a clear profit threshold. Without it, AI is just an expensive tech experiment.