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

The pricing model that built the SaaS industry is being replaced in real time. Is your finance team ready for what it does to your core metrics?

In episode #374, Ben Murray breaks down the four SaaS P&L metrics that break when per-seat pricing dies. Public tech leaders are already shifting fast. ServiceNow now drives 50% of net new business from non-seat-based pricing, Workday is reporting hundreds of millions in AI ARR, and GitHub is moving Copilot to usage-based billing. If you are a SaaS CFO or finance leader still modeling on a single blended gross margin, your benchmarks are about to stop working.

• Why the AI product gross margin sits around 52% and how a 30% revenue mix shift can compress your blended margin by 10 to 15 points
• How AI COGS scale directly with product usage, breaking the near-zero incremental cost assumption traditional SaaS finance was built on
• Why one blended LTV no longer works once you have heavy, medium, and light AI usage cohorts, and how to rebuild LTV to CAC by cohort
• How CAC payback period shifts when gross margin is no longer a single number across the customer base
• The new frameworks finance teams need to model hybrid subscription plus usage and outcome-based pricing before the board notices the margin compression

Tune in to get ahead of the pricing shift before your next forecast and board deck go out.

• Ben's blog post on the SaaS pricing revolution: https://www.thesaascfo.com/saas-per-seat-pricing/
• Ben's AI course for SaaS finance leaders: https://www.thesaasacademy.com/ai-finance-metrics-saas

Is per-seat pricing dying a slow death, and is your SaaS expense structure ready for its replacement?

In episode #373, Ben Murray breaks down the shift from per-seat subscriptions to usage and outcome-based pricing, and what it means for your finance org. Bloomberg projects subscription pricing falling from 60% to 30% of SaaS models over the next decade, while outcome-based pricing climbs from 10% to 60%. This is no longer a thesis on a slide. GitHub, Salesforce, Zendesk, Intercom, Figma, HubSpot, and others are already repricing, and public companies are reporting AI ARR in the hundreds of millions. If you cannot answer what your AI margins are when the board asks, you are already behind.

• See exactly how legacy SaaS leaders are repricing, from Zendesk charging per automated resolution to Salesforce billing per AI conversation plus flex credits, and what GitHub's June 1 move to token-based billing signals for the rest of the market.
• Understand why a single bucket of cloud hosting that blends traditional infrastructure with inference spend leaves you blind, and what instrumentation to put in place before budget season.
• Learn the questions your board will ask about AI margins, and how to answer whether low-usage customers are quietly subsidizing your heaviest users.
• Get the case for reconvening your pricing committee now to align product roadmap, AI features, and the expense framework that tracks them.
• Know which AI unit economics to track by revenue stream and by usage bucket so you can defend margin as your pricing model changes in real time.

Listen now and put the tracking framework in place before the AI margin questions land on your desk.

• Ben's blog post: https://www.thesaascfo.com/saas-per-seat-pricing/
• New course on AI unit economics and metrics: https://www.thesaasacademy.com/ai-finance-metrics-saas

Are you a legacy SaaS company quietly hoping for a recovery that isn't coming?

In episode #372, Ben Murray breaks down two slides from Jason Lemkin's State of SaaS keynote at SaaStr Annual that every SaaS operator and CFO needs to confront. The four categories Lemkin laid out will tell you exactly where your company sits in the AI transition, and whether your ARR growth is real or borrowed time. If you're building, leading, or financing a SaaS business right now, this is the reality check that should reshape how you frame your strategy for the next board meeting.

• Understand the four SaaS+AI categories Jason Lemkin used to map every software company, and which one quietly signals the end of the road
• Learn why AI driving expansion revenue versus net new customer acquisition matters more than top-line ARR growth right now
• See which public SaaS companies are pulling off the AI-powered rocket ship growth and what they share
• Hear the "tired versus wired" narratives that separate operators stuck in 2024 talking points from those building what's next
• Get a clear lens for whether your AI features are real revenue drivers or just a story you're telling investors

Tune in to find out where your company actually sits before the next board meeting forces the question.

• SaaStr Annual / Jason Lemkin: https://saastrannual.com/
• Ben's new AI metrics course: https://www.thesaasacademy.com/ai-finance-metrics-saas

If you're shipping AI product lines, are you measuring the two metrics that actually tell you whether your AI is making money — or burning it?

In episode #371, Ben Murray covers two AI unit economics metrics every SaaS CFO and founder should be tracking today: the Inference Expense Ratio and the Work-to-Inference Ratio. Traditional SaaS metrics aren't enough anymore — and a year from now, when your board, investors, and potential acquirers start asking for AI margin and efficiency data, the companies that built the chart-of-accounts structure now will have clean answers. Everyone else will be scrambling.

• The Inference Expense Ratio (AI revenue ÷ inference cost) — and why you can start calculating this from your GL today if your chart of accounts is set up properly
• The healthy benchmarks: 10:1 for AI-infused products, 5:1 for AI-native, and why 3:1 is the warning zone where inference is silently eating your gross margin
• Why this metric only works if your chart of accounts cleanly separates AI revenue from non-AI revenue — and the SKU tagging that makes it possible
• The Work-to-Inference Ratio — how Salesforce's "agentic work units" concept lets you measure whether your AI is getting more efficient over time
• Why every AI product needs its own definition of a "work unit" — record updated, report generated, MCP called — and how the wrong definition will distort your margin trends
• The chart-of-accounts evolution every SaaS company needs right now: from SaaS-only structure to SaaS + AI, with new GL accounts for inference cost in DevOps COGS
• How the Inference Expense Ratio connects to Ben's ROSE metric — measuring revenue produced per dollar of employee, contractor, and agentic AI spend

Tune in to get the AI unit economics framework in place — before your board and investors start asking the questions you can't answer.

• Ben's new AI course: https://www.thesaasacademy.com/ai-finance-metrics-saas
• ROSE metric: https://www.thesaascfo.com/saas-rose-metric/

Are AI inference costs already eating into your gross margin — and you can't even see them on your P&L?

In episode #370, Ben Murray breaks down exactly what belongs in AI COGS for SaaS companies offering an AI-first or AI-infused product line. Inference bills are stacking up fast, infrastructure-layer spend is the surprise line item nobody priced in, and most finance teams haven't built the GL account structure to capture any of it cleanly. If you don't get the framework in place now, you'll be reporting AI gross margin you can't actually defend by next quarter — and your board will notice.

• The 5 cost categories every AI COGS framework needs — inference, model hosting/GPU infrastructure, the AI infrastructure layer, monitoring and observability, and AI-specific support
• Why AI inference costs deserve their own GL account — and shouldn't be buried inside your cloud hosting bill where they disappear
• The surprise cost line one industry report flagged as the #1 unexpected AI expense — hiding in data platform usage, networking, and egress
• How to structure your COGS cost centers so you can deliver clean margins by AI product line, not just lumped together at the company level
• Why token tracking by customer cohort (heavy / medium / light users) is now table stakes for any AI product sold as a subscription
• The deployed-engineer question: should AI support tickets sit with tech support or a specialized team — and how that decision rewires your margin model

Tune in to get the AI COGS framework in place before your gross margin lands on a board slide you can't defend.

• Ben's new AI course: https://www.thesaasacademy.com/ai-finance-metrics-saas
• Ben's blog post: What Should Be Included in AI COGS: https://www.thesaascfo.com/what-should-be-included-in-ai-cogs/
• SaaS Metrics Foundation course: https://www.thesaasacademy.com/the-saas-metrics-foundation