4 Challenges While Pricing Your AI (and How To Overcome Them)

AI has changed the rules for software pricing. Where traditional platforms sold feature access, your AI copilots and agents deliver transformative outcomes that were practically unimaginable a year back. Marketing teams generate entire content calendars in a few hours. Developers build in days instead of quarters. Support teams handle double the tickets without adding headcount. 

These productivity gains break the conventional pricing models and challenge the unit economics of the previous generation of software. When a three-person team using AI matches the output of a thirty-person team, how do you price that value? How do you plan for expansion?

Meanwhile, AI-native startups build faster, leverage multiple models, and enter markets with completely different cost structures than incumbents. They’re rewriting competitive dynamics across industries.

The real challenge goes beyond setting prices. It’s about aligning monetization with AI’s actual value, which shifts rapidly as technology, costs, and market expectations evolve.

The Nonlinear Value Curve: How AI Challenges Traditional Pricing Models.

Traditional SaaS pricing worked because products delivered consistent value that scaled predictably with user count or usage volume. Everyone knew the rules.

As AI capabilities spread through legal, finance, and other industries, customers care more about results than access. AI value works differently from traditional software in four key ways:

1. The same feature delivers wildly different value.

What one customer gets from your AI depends on multiple factors, such as:

• Your underlying model’s sophistication
• Their data quality and how frequently it’s updated
• The complexity of the problems being solved
• How they’ve integrated it with their existing workflows

For some customers, an AI feature drives 10x efficiency. For others, the same feature might improve things only marginally. This makes it hard to set pricing that accurately reflects the value each customer receives.

2. Value doesn’t (always) scale with seats.

AI amplifies individual productivity in ways that break seat-based pricing:

• One support agent handles what used to require a team
• Content creators produce 10x more with the same effort
• Small teams now generate enterprise-level revenue without enterprise-level headcount

When AI enables a single user (or agent!) to achieve what once took ten, seat-based pricing hinders revenue capture. Companies no longer scale licenses at historical rates, and automation-driven efficiency makes user count an unreliable measure of value. 

Companies facing this challenge are testing new approaches. In the help desk category, we see:

• Premium pricing for AI assistants that multiply human productivity
• Usage-based or outcome-based pricing for AI agents
• Complete value metric shifts, like when HelpScout moved from charging per seat to charging per contact served

3. Usage and costs create conflicting incentives.

More AI usage creates competing pressures:

• It generates data that improves your models (a competitive advantage)
• It increases your own costs for LLM calls (a financial challenge). The type of output (text, audio, video) further affects your margins.
• Limiting usage protects margins but sacrifices data advantages
• Free tiers drive adoption but risk unexpected infrastructure costs

Source: X 

4. Value perception changes constantly.

AI capabilities double approximately every seven months, three times faster than Moore’s Law predicted for traditional computing.

Source: Metr.org. Measuring AI Ability to Complete Long Tasks. Mar. 2025.

This breakneck pace creates pricing headaches:

• Your costs fluctuate with adoption and underlying model pricing
• Customer value increases month over month as your models only get better
• What you charged a premium for last quarter becomes table stakes now

This explains why AI monetization remains experimental. When your product capabilities, market expectations, and cost structures all evolve simultaneously, finding the right pricing approach becomes a moving target.

How to overcome AI pricing challenges.

There’s no silver bullet for AI pricing. But if there’s one principle that holds true, it’s this: treat your pricing model as a living system. Don’t aim for perfection upfront. Instead, build in the ability to adapt, because your AI’s cost structure, value delivery, and customer expectations will all shift faster than in traditional SaaS. Teams that succeed here treat pricing like a product: they run experiments, measure what works, and iterate without friction.

But none of that’s possible without the right foundation. You need infrastructure that can meter usage accurately, evolve pricing without code changes, and surface insights your teams can act on. That’s what turns pricing theory into monetization results.

Organizations that connect value creation, revenue outcomes, and operational flexibility won’t just survive the pricing challenges—they’ll turn them into advantages.

Download your copy of the 2025 State of Recurring Revenue and Monetization—A deep-dive report on the emerging pricing strategies, AI adoption challenges, and organizational best practices defining the future of B2B subscription success.