They collapse quietly, and long before you notice! 😲 Many industries are under serious margin pressure. The reasons are familiar: ⚠️ Intense competition ⚠️ Price pressure ⚠️ Limited customer budgets ⚠️ Rising energy and raw-material costs ⚠️ Macro uncertainty Even highly differentiated markets like AI are feeling it. For example, in a recent post, Gary Bailey  highlighted that OpenAI may be operating at margins as low as -70%. So here’s the real question: 👉 How easy is it t

AI is no longer about demos and hype — it’s about who can turn usage into profit! Almost a year ago, I wrote about the coming AI monetization war ! Since then, two shifts are impossible to ignore: 1️⃣ AI buyers now care more about business outcomes than the technology 2️⃣ AI vendors face pressure to monetize amid exploding compute costs 👉 OpenAI’s pricing experiments made this visible: The $200 Pro plan looked expensive. Yet even that tier struggles with profitability due

Most companies are locked inside it. And most don’t even know it. 👀 Pricing failures rarely look dramatic. They feel normal. Routine. Safe. That’s the problem! The four pricing traps   Here are 4 invisible pricing defaults holding companies back:   🔒 Set & forget pricing   Pricing is treated as a one-time decision. Product improves. Marketing evolves. But pricing stays frozen.   The catch:   Costs change. Inflation moves. Competitors adjust. Customers adapt.   Profit leaks

🤓 Everyone loves quoting McKinsey’s rule : 1% price increase → 10% profit increase! 🚀 It’s quoted everywhere. And sounds universal. It isn't. 🤔 👉 There is no 1→10 rule. The real relationship is simple: % profit change = % price change ÷ % profit margin That’s it. No magic. No mystery.   When the McKinsey rule actually works: Margins around 10% Typical for manufacturing and industrial firms Now look at tech 👇 SaaS companies: Contribution margin: ~80% 1% price increase → ~

Price cuts rarely help you hit revenue goals — and they almost never protect profit. 😲 In a previous post  (Part 1), we showed how the volume needed to keep profit steady is often unrealistic. Here’s Part 2 — and it reveals the next mistake: 👉 Skipping the elasticity check. Watch the video above to see the elasticity needed to hit profit targets for: 🔸 Margins from 5% → 80% 🔸 Discounts from 1% → 20% 🔸 Profit goals from –10% → +10% Here’s what the data makes clear: 1️⃣ N

The answer may surprise you👇 At the recent Professional Pricing Society  profitABLE 2025 conference in Barcelona, renowned pricing expert Augustin Manchon delivered a powerful keynote on AI Monetization and Trust!   He shared a thought-provoking scenario: You launch a new AI initiative. First results show +6% revenue 🚀 …but +30% customer complaints ⚠️ So, what do you do? Scale anyway — financial gains come first Delay — fix issues Pilot — learn more Kill it — trust is too

Price cuts don’t fail by chance — they fail by math. Here’s the checkpoint almost everyone forgets. 😯 Two checkpoints In a previous post (Part 1), we looked at the first checkpoint: ➡️ Required volume growth: 15%+ is challenging, 25%+ extremely hard  But there is also a second checkpoint: ➡️ Price elasticity A simple guide 🟩 Low elasticity (1.0–1.5) Buyers care less about price Stronger differentiation e.g., branded clothing, specialty coffee, toiletries 🟨 Medium elastic

Price cuts are easy to implement and welcomed by customers, but rarely deliver the results we expect. In a previous post , we examined why price cuts typically fail to generate the required revenue growth. Now, let’s extend the same logic to profit  — and here’s where things become even more challenging. When it comes to profit, one additional parameter changes the entire story: 👉 Your current margin. How Margin Shapes profit growth when discounting A single formula links m

Most discounts demand scary volume jumps. Read on to see what to check before you enter the discount zone👇 Discounting strains margins and cash flow 💸 And “cut price = sell more” is often weaker than it looks. Why? It’s just math. 🧮 One formula links revenue, price, and volume changes: r = p + q + p*q (r,p,q: % change of revenue, price, quantity) You can use this formula to estimate the real volume uplift needed to hit revenue targets at a given discount. In most cases, yo

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Forecasting the Future of Everything! FutureUP's Founder & CEO, George Boretos , hosts the latest episode of the prestigious Forecasting Impact ( #1 podcast in Predictive Analytics ), a monthly podcast by the International Institute of Forecasters about Forecasting and AI! In this episode, we speak with Dr. Theodore Modis , acclaimed forecasting expert, strategist, and founder of Growth Dynamics , about the science behind forecasting and the natural laws that govern technolo

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