Everyone says seat-based pricing is dead, but do you actually have an outcome you can charge for?

In episode #377, Ben Murray breaks down the 12 steps to building an outcome-based pricing plan, drawn from analyzing real, live outcome-based pricing pages and the fine print buried in their terms and conditions. Outcome pricing is complex to design and even harder for customers to understand: when are they charged, and where is the failure point at which they aren't? For SaaS founders and CFOs weighing a move to outcome- or agentic-AI pricing, getting the unit, success criteria, and spend controls right is the difference between a model customers trust and one that creates budget anxiety and billing disputes.

• How to decide whether you even have a billable outcome, and why a completed customer result is not the same as an activity.
• How to define the outcome unit and write success criteria twice, with real examples from Intercom's Fin, Help Scout's AI Answers, and Zendesk's 72-hour resolution window.
• Why failure forgiveness is a conversion tool, not just billing logic, and how measurement windows protect you from outcomes that unravel later.
• How to choose your commercial structure, anchor price to labor savings, revenue, or risk avoidance, and plan for the training lag before charges begin.
• Why spend controls and auditable billing events are non-negotiable, and how to know when outcome pricing is the wrong model entirely.

Tune in for the full framework, then grab the deep-dive blog post before you design your next AI pricing plan.

• Ben's blog post: 12 Steps to Creating an Outcome-Based Pricing Plan: https://www.thesaascfo.com/how-to-build-outcome-based-pricing/

Is your 2027 software budget ready for the AI spend that's about to blow past every forecast you've built?

In episode #376, Ben Murray covers five takeaways for CFOs from the Pricing I/O AI Pricing Report, produced in partnership with Benchmarkit, which surveyed 296 software buyers in Q1 2026. With budget season around the corner and demand for tokens, agentic AI, and tools like ChatGPT and Claude climbing fast, the gap between what buyers want and where AI pricing is heading has never mattered more. If you own a software budget or sell AI software, these findings reshape how you should think about predictability, governance, and the guardrails buyers are actually asking for.

• Why buyers rank predictable total cost as a top-3 priority, far above low entry price, and why the seat-based pricing obituary may be premature for enterprise deals.
• What the 89% budget-overrun rate really signals: a forecasting problem on the buy side, not vendors changing the rules after signing.
• Why credit and token pricing is the single hardest model to evaluate, and what Salesforce's new agentic work units mean for your bill.
• The surprising finding that IT, not Finance, owns AI cost risk, and why department-level allocation of token spend is the fix.
• Why buyers want soft caps, alerts, and approval steps over hard cutoffs, and where hard caps get genuinely painful in outcome-based pricing.

Tune in to get the buyer-side data shaping AI pricing before you lock in your 2027 budget.

• Pricing I/O AI Pricing Report: https://www.benchmarkit.ai/widget/ai-pricing/cy-26?utm_source=TheSaaSCFO&utm_medium=Podcast&utm_campaign=TheSaaSCFO
• Ben's blog post: 12 Steps to Creating Your Outcome-Based Pricing: https://www.thesaascfo.com/how-to-build-outcome-based-pricing/

Do you actually know which of your AI customers are making you money and which are quietly destroying your gross margin?

In episode #375, Ben Murray breaks down the shape of AI usage and why the distribution curve of your customers determines whether your AI subscription product is profitable. This is why Anthropic and GitHub changed their pricing. Heavy users on a flat subscription can quietly turn a 40% gross margin into a negative one, and most finance teams are not tracking token usage by customer in enough detail to see it coming.

• The three AI usage distribution scenarios every SaaS CFO needs to model: normal, right skew, and left skew, and what each does to your gross margin
• Why a right-skewed distribution means your light users are subsidizing your heavy users, and how to spot when that subsidy stops working
• How a left-skewed distribution can leave 80% of customers unprofitable and drag overall gross margin into the negatives
• Why median, mean, and P90 token usage by customer are now core SaaS finance metrics, not just product analytics
• What finance needs from product and engineering — usage by customer, model mix, input and output token pricing — to run real pricing scenario analysis

Tune in before your next pricing review and find out where your AI margin is actually leaking.

• Ben's AI metrics course with the usage distribution template and free preview: https://www.thesaasacademy.com/ai-finance-metrics-saas
• AI readiness quiz: https://www.thesaasacademy.com/ai-finance-metrics-saas