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

Data is everywhere. It’s pouring in from every corner of your business - sales figures, customer behaviour, market trends, pricing fluctuations. But here’s the kicker: data alone is useless unless you can turn it into something that drives real decisions. That’s where data-driven insights come in. They’re the secret sauce that transforms raw numbers into strategic gold. Let’s dive into how you can harness the power of data to not just survive but thrive in today’s competitiv

🤓 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 latest Gen4I Summit in Athens made one thing clear: AI is reshaping businesses right now! Here are the top insights driving the new AI reality: 1️⃣ AI is Everywhere Use cases are growing fast across operations, customer experience, product, and strategy. 2️⃣ Proof Over Promises Real deployments now lead the way. They deliver clear, measurable wins. 3️⃣ AI Must Impact the P&L The question has shifted from “Can AI do this?” to “Does AI improve revenue, margin, or speed?” 4

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 people think yes — reality says otherwise 👇 Many teams believe they need perfect data before using AI in pricing (or anywhere else). More data helps — but waiting for perfection delays results. You can start now and still create impact. Here’s where AI fails due to data — and where it shines. 👇 ❌ Areas where AI is unlikely to work ➡️ Limited accessibility:  Data is scattered across systems (ERP, CRM, etc.) with restricted or no access to key information. ➡️ Corrupted d

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

Too many companies rush to “plug in” AI — and end up optimizing for the wrong goals. ✔️ Yes, there are great examples — Amazon, Uber, and airlines that master dynamic pricing daily. ❌ But there are painful lessons — from Wendy’s surge-pricing flop to the Oasis concert ticket fiasco in the UK. So… how do you make sure your AI pricing initiative lands on the success side of history? Based on experience, check these boxes before you launch 👇 1️⃣ Clarity Define what success mea

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