Ergonomic Pricing: The AI-Native Pricing Model
Plus: Win a free pricing engagement 👀
Welcome back to Good Better Best!
Earlier this week I saw a LinkedIn post by Christopher O’Donnell, CEO of Day.ai, announcing their first pricing model. I immediately knew I would write about it (you’ll soon find out why) and before I even had an angle — Christopher’s Co-founder, Michael Pici, reached out to see if I’d like to cover it.
The Day.ai team is building a fascinating model that straddles traditional SaaS licenses and outcome-based pricing, and I’m convinced it’s a strategy more companies should consider. Big thank you to Michael and Christopher for the access and insight 🙏🏼
PS. If you missed it last week, we’re launching a new service with Ulrik and the team at Willingness to Pay. As part of the launch, we’re giving away a free pricing engagement to one lucky SaaS company. Reply to this email or shoot me a DM on LinkedIn if you want to be added to the draw. More details to come next week!
On to today’s post.
Why AI is breaking seat-based pricing (and what to do about it)
AI doesn't just help people work — it works for them. When AI agents autonomously generate reports or process tickets, value stops being tied to user seats and starts being tied to outcomes.
In this blog post, get the inside scoop from HubSpot's Sam Lee, breaking down why hybrid pricing models are the future and how to transition without losing speed or simplicity.
Ergonomic pricing, explained
The SaaS pricing revolution is happening in slow motion. While the shift from seats to outcomes is real, most companies are getting stuck in the middle.
The problem isn't vision. It's execution.
Outcome-based pricing sounds great in theory: customers pay for results, not usage. But in practice? It’s insanely hard.
You need to do 3 things really well:
Clearly define outcomes
Ensure both vendor and customer agree on them
Build a product that consistently delivers those outcomes
Even if you check all 3 boxes, you have to navigate an ever-changing value equation. As AI gets more capable, the price you’re charging (or model you’re using) for that outcome may no longer make sense.
For all these reasons, true outcome-based pricing is a tall order. It’s why so many SaaS companies talk about outcome-based pricing but very few actually pull it off.
Enter Ergonomic Pricing — a framework that was recently introduced by Christopher O’Donnell, CEO of Day.ai. The Oxford English dictionary defines ergonomic as relating to or designed for efficiency and comfort in the working environment. Day.ai applies that same principle to pricing, defining it as pricing the way that most fits how customers want to buy and expand.
I’m convinced it’s a model more companies should be taking seriously.
How the model works in Practice
Before going deeper into their pricing model, some quick background on Day.ai:
Day.ai is building the CRM for AI Native companies, imagining a way for sales teams to work that mirrors how we work with other people already, rather than learning a piece of software that forces you to think like a computer.
Upon sign-up, Day.ai transforms your meetings, emails, Slack conversations, and web research into a structured layer with built-in reasoning and transparency. There’s no manual data entry necessary, and users get immediate value from the product for free. O’Donnell calls it “the best view-only seat of all time.”
Layered on top of that core functionality are Assistants, which transform what customers can do with this data and context. Instead of needing to speak CRM, customers can work with an assistant who already knows Day.ai inside and out. The idea is that users can simply describe what they want, and the assistant will make it happen.
And this is where the pricing model comes into play:
Day.ai doesn't charge for human users.
They don't charge for pure usage or outcomes.
Instead, they charge for "Assistants" — AI-powered entities that layer intelligence on top of your data, as flat-rate add ons.
It’s a modern version of the classic feature ladder: instead of upgrading to “Pro” or “Enterprise,” you layer in new assistants to expand the number of jobs the product can do for you. Those jobs include capabilities like:
Having natural conversations about your business
"Show me deals closing this month and flag any that look at risk and give reasoning"
"What happened in yesterday's call with Acme Corp?"
"Which prospects haven't we followed up with this week that we were supposed to?"
Setting up intelligent automation
"After every sales call, draft a follow-up email using my template"
"Send me a Slack notification when any deal goes dark for more than 5 days"
"Every Monday, send me a report of deals that need attention this week"
Delegating complex research and prep
"Research TechCo's recent funding round and create a one-pager for tomorrow's call"
"Find pricing conversations in the last month and summarize the key objections"
"Pull together everything we know about our champion at BigCo"
Creating and modifying anything instantly
"Create a view of all enterprise deals over $50k sorted by close date"
"Help me build a table with product feedback from all of our recent customer calls”
"Create action items to re-engage any sales opportunities that we haven’t spoken with in the last 7 days"
Importantly, each human user can have zero assistants, one assistant, or multiple assistants, with different tiers of Assistants offering varying combinations of models, tools, and automation capabilities.
An assistant that keeps your CRM up to date based on your conversations and helps you draft emails is $75 per user per month. As you add more sophisticated tasks, the price for additional agents increases accordingly.
What I love about this model is that it carves out a smart middle ground between per-seat licenses and the elusive dream of true outcome-based pricing. It’s a flexible, agent-inspired model that lets vendors charge for value without overpromising impact.
The Transparency Layer
Day.ai's pricing model includes another crucial element — radical pricing transparency.
Monthly pricing gets you list price.
Annual contracts get you 20% off.
Volume discounts deepen automatically as you scale.
All pricing is visible upfront with no negotiation required.
This transparency isn't just customer-friendly, it's smart strategy.
As O'Donnell notes: "We're not trying to maximize software margins while AI costs are still high." Instead, they're building trust and reducing friction in the sales process.
This approach recognizes a fundamental truth about AI pricing: we're still in the experimentation phase. Companies that optimize for learning and customer adoption today will be better positioned when the market matures.
Who should consider Ergonomic Pricing
As great as it sounds, the model isn't universally applicable. It works best when:
Your product blends software and services: Like AI assistants, agents, or other hybrid offerings where the value isn't purely in access to functionality.
You can create meaningful capability tiers: The model requires distinct levels of sophistication that customers can easily understand and value.
Your customers struggle with usage-based budgeting: Particularly relevant for AI products where consumption can vary wildly based on use case and user behavior.
You're in a market where attribution is genuinely difficult: When the gap between using your tool and generating business outcomes is too large to measure cleanly.
You want to optimize for adoption over short-term margins: You have to be comfortable giving away more value upfront to drive long-term customer success.
If you can check any of check those boxes, it’s likely worth considering.
Why This Matters
Ergonomic pricing represents a maturation of SaaS pricing strategy. Instead of forcing customers into models that work well for vendors, it starts with customer behavior and works backward to sustainable economics.
This model also solves three critical problems that plague modern SaaS pricing:
1. The Budgeting Problem Unlike consumption-based models, customers can predict their costs. You're not buying tokens or credits that fluctuate with usage, you're buying digital team members with predictable capabilities.
2. The Value Alignment Problem Unlike seat-based models, pricing scales with the value delivered. More sophisticated work requires more sophisticated assistants, creating natural upgrade paths.
3. The Attribution Problem Unlike outcome-based models, you don't need to prove a direct line between tool usage and business results. The value is in the Assistant's capabilities, not the outcomes they produce.
As O'Donnell puts it: "We landed somewhere that feels right: predictable for customers, sustainable for us, aligned with how people actually want to buy and use AI tools."
The early response suggests they're onto something. And if ergonomic pricing proves scalable, it could become the new playbook for AI-native SaaS companies looking to escape the false choice between licenses and outcomes.
How we can help when you’re ready
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PricingSaaS Index: Check out the PricingSaaS Index to track competitors, scroll pricing histories, and create a swipe file of pricing pages for inspiration.
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