Conga and Copperberg push AI pricing

Pricing software is having a quiet moment in B2B. Not flashy consumer surge-pricing stuff — the real enterprise problem of what price a sales team should actually quote, right now, without giving away margin. That problem has lived in spreadsheets for years. But this spring, Conga and Copperberg both made the same basic point: pricing is moving closer to the center of the operating model, and AI is the tool they want companies to use to get there. ### What did Conga actually push? Conga’s April materials were about AI pricing as a live decision engine, not a reporting layer. The company framed pricing as one of the fastest, most controllable levers for protecting margin when costs, tariffs, demand, and buyer behavior keep moving. Its pitch is that AI should use real-time demand, cost, and competitive data to generate account-specific guidance, improve forecasting, and reduce end-of-period surprises. ### Why is that different from old pricing? Because old pricing in B2B was basically a mix of segmentation rules, tribal knowledge, and manual approvals. Conga’s own description of the problem is familiar — similar customers getting different prices, quotes slowing down, and margin leaking out because nobody has full visibility at the moment of the deal. The shift is subtle but important: from “?” ### What did Copperberg and Vendavo add? They pushed the execution angle harder. Copperberg’s new “Pricing Execution Gap Playbook,” built with Vendavo, says the issue is no longer just strategy. Companies may already have pricing tools, but the actual decision still happens manually when the quote is being built. The playbook is meant to close that gap with a diagnostic and a 90-day action plan, aimed squarely at manufacturers and distributors. ### What’s the most telling number here? It’s the spreadsheet number. Copperberg and Vendavo say 85% of companies still rely on spreadsheets for daily pricing decisions. That is the whole story in one stat — modern pricing software may exist, but the operating habit is still manual. At the same time, their 2025 report says 83% of dynamic-pricing users report improved accuracy and profitability, and mature CPQ adoption is uneven. ### Where does finance come in? This is the part people miss. AI can recommend a better price, but finance still has to explain what happened in revenue and gross margin after the quarter closes. That is where price-volume-mix analysis matters. PVM breaks performance into price effects, volume effects, and mix effects so FP&A can tell whether a margin gain came from actual pricing power, more units, or a richer product mix. One point from the finance side — mix is often the overlooked driver. ### Why pair AI pricing with PVM? Because otherwise teams fool themselves. An AI system might raise win rates and revenue, but if the gain came from discounting into higher volume, that is a very different story from holding price and improving mix. PVM is basically the audit trail for the AI narrative. It tells controllers and FP&A whether the machine improved economics or just changed the shape of sales. That makes pricing more governable — and more credible in front of a CFO. ### So what’s really changing? Pricing is turning from a sales support function into a cross-functional control system. Sales wants speed. Pricing wants consistency. Finance wants proof. Conga is selling the intelligence layer. Copperberg and Vendavo are selling the operating playbook. The common message is that AI pricing is no longer about finding the mathematically perfect number. It is about making better decisions fast enough to see whether those decisions actually worked.