Hey — it’s Nico.

This is Behind Tactics 🧠, the Failory newsletter where I share the strategies behind the best startups.

This issue takes 7 mins to read. If you only have one, here’s what you need to know:

• Intercom’s Fin AI uses solution-based pricing, charging $0.99 per resolved ticket.

• Solution-based pricing aligns company incentives with customer success, allowing customers to pay only for tangible results without long-term commitments.

• This model is gaining traction as AI tools like Fin AI can independently handle tasks, making outcome-based pricing not only possible but practical.

• By comparing costs directly with traditional support agents, Fin AI’s model highlights potential savings, making it easy for businesses to see the value of outcome-based pricing.

Let’s get into it.

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The Strategy

Priced by Results

Intercom recently released Fin AI version 2, an AI-driven support agent that autonomously handles customer queries. The technology itself is impressive and could very well transform the customer support industry. But today, I want to focus on something else that caught my attention about Fin: its pricing model.

Rather than the typical monthly subscription or a flat service fee, Fin AI uses what’s called “solution-based pricing.” For every ticket it resolves, it charges $0.99. That’s it—no ongoing fees, no add-ons, just a simple “pay as you benefit” approach.

This model is getting a lot of attention because it’s refreshingly straightforward and shifts costs to reflect real, measurable outcomes.

For entrepreneurs and small business owners, solution-based pricing holds a lot of appeal. It takes the guesswork out of budgeting for support costs and aligns expenses with actual results. Instead of paying for potential use, you pay only when the AI gets the job done. This approach isn’t just new; it’s part of a larger trend in the AI and SaaS worlds.

The Power of Solution-Based Pricing

So, why are companies like Intercom moving toward solution-based pricing?

Solution-based pricing has a straightforward appeal: customers pay only when they benefit. It breaks away from the “pay whether you use it or not” model, aiming instead to align company incentives with customer outcomes. 

For many companies, especially those leaning on AI for support, this pricing approach also makes it easier to demonstrate direct value—every resolved query is a tangible outcome with a defined cost. And for businesses like Intercom, where Fin AI is pitched as a supplement (or even a replacement) for human support staff, $0.99 per resolution can be an attractive alternative to hiring and training staff.

This model is also flexible, especially for businesses that don’t have consistent support needs. For companies that experience spikes in customer demand or handle seasonal fluctuations, a “pay-as-you-benefit” approach means support costs only scale with use.

Why Is It Happening Now?

The move toward solution-based pricing is happening now because AI has changed the game. Traditionally, software was just a tool that helped humans get tasks done, so companies charged for access, whether or not it was used to its full potential. But AI is different—it’s designed to work independently, taking over tasks and delivering measurable outcomes on its own. This shift from a support role to a problem-solver role is what makes solution-based pricing possible.

As AI continues to evolve, solution-based pricing could become more widespread, especially in industries where outcomes are easy to measure and align with customer goals. While it may not be a fit for every service, we’re likely to see more companies experimenting with outcome-focused pricing as AI tools gain more capabilities. The shift suggests a future where companies pay primarily for what a tool accomplishes, rather than for the access to it.

Should I?

Why This Works

• Aligns Value with Customer Success: With solution-based pricing, companies only pay when they see results. This alignment makes customers feel like they’re getting a fair deal, knowing their costs reflect actual outcomes rather than potential usage. It’s a simple way to create trust and reinforce the tool’s value every time it’s used.

• Lower Risk, Higher Accessibility: For many businesses, the high upfront costs or long-term subscriptions of traditional pricing can be a deterrent. Solution-based pricing lowers the entry barrier, allowing companies to test the tool without heavy commitments. This model appeals particularly to smaller businesses and startups that need flexibility in their spending.

• Clear Cost Comparison with Human Support: AI-driven customer support tools like Fin AI can be easily compared to the cost of hiring a human agent. With each resolution priced at $0.99, businesses can directly see how the AI’s cost stacks up against hourly or salaried support staff, making budgeting straightforward and the decision to switch to AI easier.

• Scales with Demand: Solution-based pricing works well for businesses with fluctuating support needs. Instead of locking into a fixed cost, companies pay only when support is required. This dynamic approach allows costs to ebb and flow with demand, keeping budgets lean during quieter periods and scaling up as needed.

How to Apply It

• Identify Clear, Measurable Outcomes: For solution-based pricing to work, you need to pinpoint specific outcomes your tool delivers. Whether it’s a resolved support ticket, a lead generated, or a task completed, make sure it’s something you can easily track and quantify. 

• Bundle Outcomes for Predictability: To address customer concerns around variable costs, consider offering bundles that give a fixed number of outcomes at a slight discount. For example, if you’re charging per resolution, you could offer a package of 50 resolutions for a set price, providing a middle ground between flexibility and predictability.

• Showcase Real-World Savings: Solution-based pricing allows easy cost comparisons. For instance, Fin AI could highlight how $0.99 per resolution stacks up against the fixed costs of hiring a support agent, helping customers see the clear savings this model offers.

Yes, But

• Unpredictable Costs Can Deter Businesses: Many companies prefer stable, predictable expenses, especially when planning budgets. Solution-based pricing introduces a variable cost structure that can make financial planning more challenging. For some businesses, the uncertainty of paying per resolution might feel risky compared to a traditional subscription model with fixed costs.

• It’s hard to know how much to charge: For example, daydream, the startup I work in, could charge per organic click generated to the client. But each click has a different value for each company. The same goes for resolved tickets—each resolution might carry a different level of importance depending on the industry or specific needs of the client.

• Risk of Misaligned Incentives: Solution-based pricing can create competing priorities between the company and its customers. Fin AI, for example, has an incentive to resolve tickets as quickly as possible, as it only gets paid when a ticket is closed. The fewer messages or interactions required to reach that resolution, the more efficient (and profitable) it becomes for Fin AI. This could incentivize Fin AI to lower the quality of the support.

Keep Learning

Others Playing It

• CallFast, an AI assistant that calls your leads and tries to book appointment for you charges $0.60 per minute of connected call. This pricing structure aligns directly with the value delivered: each charge represents a real conversation with a potential client, making it easy for businesses to see the impact.

• PhoneScreenAI is another example. This AI tool screens job candidates over the phone and charges $3 per completed screen. This model gives hiring teams flexibility, only charging them when candidates are successfully screened, and allowing for clearer budget planning as they expand their search.

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That’s all for this edition.

Cheers,

Nico