3 value frameworks: What we've learned from 120+ AI agent companies

Most of the people we speak to don’t know how to price their agents.

They don’t know how because there are no frameworks or playbooks.

After talking to over 120 AI agent companies and analyzing millions of agentic data points in just our first month at Paid, we've identified three frameworks that separate the companies thriving from those watching their margins get crushed.

Whether you're building warehouse robots like our recent prospect, AI SDRs bombarding LinkedIn and outbound, or document processors handling the boring stuff nobody wants to do... these frameworks apply across the board.

🔄 Framework 1: The Direct Human Replacement Value

"If a human was doing it, how much would it cost?"

This is the starting point for AI agent pricing, but too many companies stop here. It's pretty simple: figure out what it would cost to hire humans to do the same stuff your agent does.

It's not just about volume of actions.

If an AI calls 6793 people today and they all hang up on it, no value is created. But, if it makes only 4 calls and 2 of them open opportunities for the sales team worth $50k each, it just created $100k in value with a teeny tiny percentage of the output.

For example, if your AI sales agent opens 10 qualified opportunities monthly and a human SDR (costing you $60K/year) only opens 5, you're delivering 2x the output. At minimum, you've created $60K in annual value.

This is your pricing floor, not your ceiling.

This approach makes sense to a lot of our customers (they get the math), but it limits how much you can charge.

tl;dr: Start here, but don't stop here. This framework is just the beginning, and if you only use this one, you're leaving money on the table.

⚡️ Framework 2: The Enhanced Performance Delta

"If a human was doing it, and you're doing it better, what's that delta?"

This second framework recognizes that AI doesn't just replace humans, it often blows them out of the water in ways that would require additional specialized people.

Our CEO Manny recently explained to an agentic founder recently: "When a BPO makes a call, does a transaction, hangs up, and moves on... your system is doing the same thing, but it's gathering all this information in the background, correlating it to other information, creating clusters of information, giving you insight."

For you, your agent might:

• Analyze 1,000 conversations and spot patterns no human could see
• Generate insights that would normally need a data team
• Never miss a detail, never have a bad day

To get the same value, a company would need to hire analysts. And they're gonna sit in there for a week, and it's gonna cost you $20,000. And you just got that for free!

tl;dr: Don't just think about the task being automated. Think about the superpowers your AI brings that would otherwise require additional specialized teams.

💎 Framework 3: The Unlocked Value Potential

"What value does your agent unlock that wasn't possible before?"

This framework focuses on entirely new stuff your agent makes possible.

Think: By seeing all these things, can I take action on these other things? Because that has a lot of value.

Examples include:

• Spotting problems before they happen
• Finding revenue opportunities nobody would have seen
• Enabling entirely new business capabilities

While this framework has the highest potential value, it comes with a warning: it has an attribution problem that people get “wrapped around the axle”. Unless you know how to attribute, this could derail you from the conversation that you're trying to have.

tl;dr: This framework captures the really transformative value of AI but gets tricky when you try to directly attribute it in pricing conversations.

📊 Turning 3 frameworks into actual pricing

So how do you go from understanding value to setting prices?

Paid’s data from successful AI companies shows a consistent approach:

1. Always price below the full value you're generating, but above your costIf you really understand the full value you're generating through all three frameworks, you'll probably realize you're constantly underpricing your solution.
2. Show the value explicitly in every customer interactionOur platform makes this possible through value receipts that show:This is the fee, this is the number of activities that you did that you're charging for, etc. More importantly: This is the human equivalent value activity.
   ffThis turns invoices from painful reminders into value demonstrations. You can say to your customer “even though you're paying me this much, this is the amount of value you got."
3. Align pricing to outcomes, not inputsWe’ve heard from our prospects and beta customers: "The whole kind of value-based pricing is something that we need to tap more and more into."

🌎 The real-world impact

You can drive real results, just like these companies

• One AI SDR company doubled their revenue after shifting from platform pricing to charging for high-value activities
• A customer service AI provider boosted their margins by 42% after properly accounting for the analysis value their system provided
• A document processing company bumped renewal rates from 70% to 95% by showing the full spectrum of value they delivered

🔰 Getting started today

Try applying these frameworks today:

1. Map every activity your agent performs to its human equivalent cost
2. Identify the enhanced capabilities your system provides that would require specialized teams
3. Document any transformative outcomes your system enables
4. Create value receipts that make this visible to customers

One of our customers recently said this statement that we love:

“Education about value can't be your way. It has to be their way... You have to educate them in the words that they understand.”

The AI economy is moving fast.

We think who properly articulate and capture their true value will thrive, while those stuck in outdated pricing models risk leaving millions on the table (or worse, building businesses with negative agentic margins).

Are you getting paid what your agent is actually worth?