How to Use AI for Pricing Decisions

At the start of my career, pricing was often treated as an afterthought. Decisions were made based on instinct, outdated models, or by simply matching competitors. I witnessed how this approach consistently led to underperformance, weak positioning, and lost revenue opportunities. That experience shaped my belief that pricing is one of the most overlooked drivers of business growth. To solve this, we built the Predictive Sales Engine an AI-powered tool that brings clarity to pricing strategy. It analyzes actual market behavior to forecast revenue and sales volume at different price points. More importantly, it segments data to reveal how different audiences respond to pricing, allowing companies to set prices with precision and confidence. After working with hundreds of companies, the pattern is clear. When pricing aligns with how customers perceive value, businesses grow faster and more profitably. In a competitive market, using AI to guide pricing decisions is no longer a luxury. It’s a requirement for those aiming to lead rather than follow. #PricingStrategy #ArtificialIntelligence #PredictiveAnalytics #RevenueGrowth #ProductMarketing

Your AI agent just closed a deal, processed 40 claims, and rewrote a policy doc. Are you still going to charge per login? SaaS pricing made sense when software was a tool. But in the services-as-software world, the AI is actually doing the work, so how you charge needs to reflect that. Many leading companies like Harvey and Clay are rethinking traditional seat-based pricing. They’re finding ways to tie pricing more closely to the value delivered and work accomplished. Here’s the spectrum we’re seeing most AI companies fall on: ▶ Seat-based: Clean and predictable, but often disconnected from the gains the product delivers. ▶ Usage-based: Charges for tokens, minutes, or queries - transparent, but puts the burden on buyers to connect usage to ROI. ▶ Workflow-based: Priced per job done - docs processed, tickets closed, reports generated. This links revenue to actual work accomplished. ▶ Outcome-based: Tied to results - deals closed, hours saved, revenue unlocked. In theory, the cleanest alignment with value but hard to standardize in practice. Most AI startups aren’t ready for pure outcome pricing, and that’s okay. But breakout companies are designing pricing around what their product does and what customers would lose if it went away. 🧠 Harvey charges law firms roughly $1k per lawyer each year, but renewal talks are all about hours saved. ⚙️ Clay sells GTM automation, but equips its sales team with practitioners who actually do the work so the value starts accruing even before the contract is signed. In the end, AI buyers want results. If you’re building a services-as-software company, you’re doing the work and your pricing should reflect that.

I built my previous company to $10m ARR. But if I were running an AI company's pricing today, I would fix 4 things immediately: 1. Align pricing with how AI delivers value. Flat subscriptions do not work for AI. The cost structure is too volatile and the value too variable. Pricing must be usage-based, outcome-based, or hybrid. It has to move with your costs and the value your customer receives. Anything else is misalignment that bleeds you dry at scale. 2. Enforce entitlements like survival depends on it. AI infrastructure cost is unpredictable and spiky. If customers access more than they paid for, one power user can erode your entire margin. 3. Make pricing changes take minutes, not months. If every price change or plan update requires a six-week engineering sprint, you are leaving money on the table daily. It has to be a two to three minute configuration update. We have passed the time when you can wait months to adjust pricing. 4. Surface revenue signals before it is too late. Most companies discover they lost ten customers when they pull a report at month end. By then the money is gone. You need to see churn before it happens and spot expansion before you miss it. All 4 of these things compound. You leak money from misalignment, lose it through entitlement gaps, miss it because you cannot move fast, and never see it because your signals are buried in lagging reports. Fix these and you stop the bleeding.

From Fixed Pricing to Adaptive Pricing: The AI Shift That Changes Everything For decades, pricing was static. One price. Set by humans. Reviewed monthly. Applied to everyone. Today, AI has changed that model entirely. Adaptive pricing systems now: • Analyze demand, behavior, competitors, time, and events in real time • Update prices in milliseconds • Tailor pricing to segments or users • Capture high-demand moments automatically • Scale across thousands of products This is not just about higher revenue. It is about intelligent commerce. The real shift is from: Manual pricing decisions → Autonomous pricing systems Historical data → Real-time signals Periodic updates → Continuous optimization In fintech, SaaS, e-commerce, and payments ecosystems, this capability is becoming core infrastructure. The question is no longer “Should we use dynamic pricing?” The question is: How intelligent is your pricing engine?

Don't blunder when pricing AI features. Here's what we've learned from our customers' approaches. As AI reshapes how we work, the old “per seat, per month” pricing model doesn’t always capture the value that AI adds. Companies are trying new approaches, but each comes with trade-offs: 1️⃣ Per-user add-ons Notion’s AI add-on costs an extra $10 per user per month. It’s straightforward. But if some users rely on AI features heavily while others barely use it, this can feel awkward fast. 2️⃣ All-in bundling Grain gives it to everyone. No extra fees and everyone gets the AI features by default. It’s super simple, but if only half your users actually need the features, you might be over-delivering. 3️⃣ Usage-based pricing Another route is to charge based on how much AI “work” gets done. Think OpenAI’s API charges developers based on tokens consumed. The costs scale with value, but bills become unpredictable. Customers might hesitate if they can’t forecast expenses. 4️⃣ Outcome-based models Or tie the price directly to results like closed deals or candidates sourced. You pay for what you get, but tracking outcomes can get messy and lead to debates about what’s driving success. There’s no one “right” way yet. You might mix approaches or experiment until you find what resonates. As AI matures, pricing becomes less about covering costs and more about reflecting real, measurable value.