Something strange is happening in go-to-market teams. The mechanics of how AI products generate value have changed, but the frameworks used to position, price, and sell them haven’t. Traditional GTM strategies—rigid pricing tiers, static feature packaging, and months-long campaign cycles—were built for a SaaS era in which value was gated by access to software.
But AI isn’t just another SaaS layer. It doesn’t play by those rules. And companies still clinging to them are finding themselves outpaced by the economic realities of AI-driven value creation.
AI Is Reshaping the Unit of Value
In the AI economy, value isn’t tied to access; it’s tied to outcomes. What was once sold in licenses or user seats is now sold in completions, predictions, actions, or decisions. It’s dynamic, contextual, and deeply tied to usage.
Take AI infrastructure company Lambda Labs, for example. Instead of traditional software licenses, they’ve introduced a consumption-based model that charges customers based on a real-time usage index. This allows them to align pricing with the actual cost of compute-intensive AI workloads, something that fluctuates depending on how and when services are used.
Similarly, Salesforce’s AI support products now charge per resolved ticket handled by an AI agent, not per seat. That’s a clear example of value-based pricing in action, where payment is tied to a successful outcome, not access.
The Problem with Legacy Pricing and Packaging
Legacy pricing models (per seat, per month) break down quickly in AI-first businesses. They were built for predictable usage and flat infrastructure costs. AI introduces variability on both fronts.
Gorgias illustrates this transition well. Through its AI Agent platform, it now offers outcome-based pricing for autonomous support. Instead of paying only for support agents, companies are charged based on how many tickets are resolved by AI, without human intervention. That pricing model better aligns with the value the customer receives—and the cost incurred to deliver it.
These hybrid models—part SaaS, part AI—are becoming more common. But many early-stage AI companies still fall into the trap of forcing AI features into outdated packaging, often resulting in eroded margins and buyer confusion.
Marketing’s Blind Spot: Misalignment with Finance and Product
A big part of the problem is that marketing often isn’t looped into how AI products really generate revenue or how expensive it is to deliver that value.
That disconnect is most visible in how companies explain their pricing to customers. Consider a company offering AI-based competitive intelligence or automated messaging. The product may be delivering immense value, but if marketers are focused on top-of-funnel ebook downloads while revenue is coming from triggered workflows and usage-based expansion, the message won’t land.
The result? Misaligned expectations internally, and buyer hesitation externally. It’s a tough spot to be in, and an unnecessary one.
A New Mandate: Marketers as Strategic Translators
This is where the AI-era CMO must evolve. Today’s marketing leaders must be strategic translators between product, finance, and the market, or the “connective tissue” that drives the company forward. That means understanding how usage maps to value, how value maps to pricing, and how pricing supports both growth and margin.
For example, a company using AI to personalize web experiences isn’t selling software; they’re selling higher conversion rates. If the AI increases conversion by 30% and revenue by 5%, pricing by user seat doesn’t make much sense. Communicating that value, and tying it back to usage patterns, is what modern CMOs must now master.
Collaboration as a Growth Strategy
None of this works without cross-functional alignment. Product wants to know how customers use features. Finance wants predictable revenue. GTM teams want clarity and simplicity. Without a shared understanding of how AI value is created and captured, these groups talk past each other, and the customer pays the price.
Brightback, a company I founded, learned this the hard way. We launched usage-based pricing but managed billing manually via spreadsheets. Can you imagine? That quickly became unsustainable. Once we adopted usage-aware billing and aligned systems across departments, we could finally scale, and justify pricing.
The lesson? AI demands operational agility, not just product innovation.
The New Go-To-Market Flywheel and Rethinking Customer Success
GTM in the AI era isn’t about the perfect launch, it’s about continuous loops. Launch, test, learn, refine. Marketers must operate like product teams, iterating on messaging and monetization in near real time.
AI models improve with usage, and so must the strategies used to bring them to market. That includes pricing experimentation, onboarding flows, and sales enablement.
You don’t just ship pricing and forget it anymore. You treat it like a product.
This iterative mindset extends to customer success, which takes on new complexity in the world of AI. Outputs vary by user. ROI depends on adoption. And trust must be earned.
Companies like Salesforce now embed usage dashboards and ROI calculators to help customers see their value clearly. The best teams don’t just teach customers how to use the product; they help them feel the value. That means clearer benchmarks, tighter feedback loops, and storytelling grounded in results.
Case studies, analogies, and proof points aren’t “nice to have”—they’re how you close deals and build long-term loyalty in this new economy.
A Call for a New Kind of Marketer
The marketers who will thrive in the AI era won’t just be storytellers. They’ll be operators. Economists. Systems thinkers.
They’ll bridge the gap between evolving usage, emergent value, and dynamic pricing. They’ll align cross-functional teams and equip customers with clarity. Most of all, they’ll recognize that when value is dynamic, marketing must be too.
Because in the AI economy, the story of how something works is only half the battle. The real challenge is explaining what it’s worth, and making sure that’s a story everyone can understand.
