Most conversations about AI in pricing start with the technology. But perhaps we should start somewhere less interesting: whether the organization is in a position to use it at all. Readiness is not one thing. A company can be strong in some areas while still stalling in others that matter. 1. Data quality and accessibility Transaction history sits in the ERP, win-loss in the CRM, discounts in a spreadsheet somebody maintains by hand. Product codes differ across markets. Some

AI may be pricing's greatest catalyst — but not for the reason you might think. AI is everywhere. Companies are experimenting with it across functions, decisions, and processes. And with all the excitement around AI, it is only natural that people are tempted to ask it questions they have struggled with for years. Pricing is an obvious candidate. What should we do with our prices? Are we overpricing or underpricing this customer? What is the right price for this product? Wher

When competitor intelligence adds context, when it adds a true signal — and when it makes AI-driven pricing decisions worse. Competitor prices seem like an obvious input into AI-driven pricing decisions, especially Price Optimization. After all, how can you determine the right price without knowing what the market is charging? Or can you? A recent case A recent FutureUP case showed why the answer is not so simple. The competitor information was close to a best-case scenario:

The challenge A customer once asked us to identify optimal prices across multiple geographies. The challenge looked familiar: Demand varied significantly by location, purchasing power differed across markets, and a single pricing approach was clearly leaving money on the table. One of the customer's first hypotheses was straightforward: Competitor prices should be a strong predictor of the optimal price. After all, competitors operate in the same market. They target similar c

Pricing has an unusually broad impact on a business. It affects revenue, profitability, customer acquisition, retention, sales behavior, positioning, and even operational capacity. Yet pricing decisions are often made through fragmented processes, incomplete analyses, internal compromises, or assumptions that have not been tested against actual market behavior. Some mistakes are obvious, such as discounting too aggressively. Most pricing errors are often deeply embedded withi

A Free Quick Assessment Tool to Help You Choose the Right Pricing Approach Pricing is one of the most powerful levers for improving revenue and profit. But deciding how to approach pricing is often less obvious than it seems. Should the work stay with the internal team? Is it time to bring in external pricing expertise? Would a focused AI-based price uplift scan be enough to identify the opportunity? Or does the company need a more complete pricing software solution? These qu

In a previous article, we wrote about the Pricing Activation Framework — the journey from diagnostics to AI-powered pricing guidance and measurable business impact. The core idea was simple: Diagnostics explain what happened. Pricing intelligence helps identify what can happen next. Strategy and execution help achieve real business targets. From Pricing Diagnostics to Price Uplift Most companies already have pricing data, dashboards, reports, and customer information, but man

From Diagnostics to AI-Powered Guidance and True Business Impact Pricing has always been one of the most powerful levers for business performance. A small pricing improvement can have a disproportionate effect on profit, often more than equivalent improvements in volume or cost. Yet in many organizations, pricing remains underused, underanalyzed, or treated as a periodic commercial exercise rather than a strategic capability. The problem is rarely a lack of data. Most compani

A practical guide to turning complexity into competitive advantage Price optimization has always been challenging — but AI has raised both the ceiling and the stakes. Done right, it becomes a powerful growth engine. Done poorly, it amplifies bad decisions at scale. Today, most companies are still stuck in the “NOW” state — fragmented data, unclear signals, limited internal alignment, and reactive pricing decisions. The opportunity is to move toward the “AFTER” state — structu

Beyond Elasticity: Understanding Price Sensitivity in Real-World Systems Many teams believe they’ve solved pricing once they estimate price elasticity. The logic seems straightforward: “If I know how demand reacts to price changes, I can predict volume, simulate scenarios, and find the optimal price.” Clean. Quantitative. Convincing. In reality? It rarely works like that. 🤔 Let’s unpack why. Elasticity Is Not Stable — It’s a Moving Target Price elasticity is often treated as

A Simple Framework to Understand Revenue Changes - the key role of AI In a recent post , we introduced a simple framework to analyze how price, cost, and volume impact profit. In this post, we apply the same thinking to revenue — because before margins, costs, or efficiency, everything starts with how revenue moves. Revenue changes over time — but too often, we don’t fully understand why. 🤔 Was it: higher prices? more volume? or a different mix of products, channels, or cust

A Simple Framework to Find Out Profit changes year-over-year — but too often, we don’t fully understand why. 🤔 Was it a shift in costs? A change in volume? Or was it pricing — often considered the most powerful profit lever? Without a structured way to break this down, businesses risk reacting to symptoms rather than addressing the real cause. A Simple Way to Decompose Profit Impact To truly understand what’s driving profit movements, you can break the change into three impa

Despite its direct impact on profitability, pricing remains one of the most underdeveloped capabilities in modern organizations. The core issue is simple: pricing is still not treated as the strategic priority it should be. But why does this happen? Below are some of the most common root causes. Not all apply everywhere, but you’ll likely recognize at least a few behind most pricing failures or weak pricing management. Pricing Teams Are Too Small to Drive Impact Compared with

How to maximize revenue and profit Finding the optimal price for profit and /or revenue is the holy grail for most businesses. But it’s far less straightforward than it seems. Let’s break it down. Revenue is (relatively) simple We know the basics: If a product is inelastic (e > -1), increasing the price increases revenue If it’s elastic (e < -1), increasing the price decreases revenue So, in theory, if you know elasticity, you can estimate the price change that maximizes reve

Most Companies Track Price. Very Few Track Pricing Performance. Most companies know their: List price Discounts Margins But very few companies track pricing performance . Pricing performance answers a different question: How effectively does pricing drive revenue, profit, and market outcomes? To answer that question, companies need a system of pricing metrics , not just a few isolated KPIs. Below is a simple framework for thinking about pricing metrics. Price Level Metrics Th

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 to

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