People get so wrapped up in their technology that they forget outcomes are the most important piece of any solution deployed in a company.

I’ve been building an AI SaaS that helps people create social content through interviews. While developing the pricing model, I realized most AI companies make the same fundamental mistake.

They price their features instead of their results.

The Enterprise Reality Check

Freemium works for individuals. It doesn’t work for large enterprises.

A large enterprise will never start with a freemium model. They go directly to a salesperson. They need consultative sales, custom implementation, and security assurances that don’t align with low-barrier entry points.

This creates a critical pricing decision point. Individual users and enterprise clients require completely different value propositions.

For individuals, you can lead with accessibility and let them discover value over time. For enterprises, you need to demonstrate proven outcomes upfront.

The Hormozi Value Framework Applied

Alex Hormozi’s value equation reveals why outcome-based pricing works better than feature-based pricing.

Value equals dream outcome times perceived likelihood of achievement, divided by time delay and effort required.

Most AI companies focus on the wrong variables. They highlight technical capabilities (effort) instead of business results (dream outcome).

The most effective approach involves customer testimonials from companies similar to your prospects. Same industry vertical. Same size or revenue level.

This builds perceived likelihood of achievement, which dramatically increases your pricing power.

Practical Implementation Strategy

I’m currently developing what I call an “eternal plan” pricing model. Clients can choose month-to-month flexibility or pay higher upfront costs for lower ongoing fees.

The pricing structure reflects conviction levels. If you’re hesitant about the product’s future, you choose monthly. If you believe in long-term value, you upgrade to the eternal plan for lower total cost of ownership.

The key outcome I highlight is consistency in social media visibility, time saved creating content, increased posting frequency. Growth in engagement metrics.

The Referral Acceleration Method

Referrals remain the best way to get business, especially for AI solutions where trust barriers run high.

You speak generally about outcomes, then rely on individuals within the target account to translate value to their specific business context.

This approach aligns with current market shifts. AI is driving a shift toward outcome-based pricing where software becomes labor, forcing companies to rethink traditional per-seat models.

When you’ve done the sale properly, you don’t need complex performance guarantees. A simple cancel-anytime policy reduces friction while demonstrating confidence in your solution.

The Measurement Challenge

Better to focus on what you can measure directly through concrete, quantifiable metrics that provide clear insight into your content strategy’s effectiveness. Content creation time reduction represents a tangible efficiency gain where you can track the hours saved per piece of content, the streamlined workflows that eliminate redundant steps, and the automated processes that free up creative resources for higher-value activities. Publishing consistency improvements demonstrate your ability to maintain regular, predictable content delivery schedules that build audience expectations and trust, measured through on-time publication rates, reduced content gaps, and sustained posting frequency across all channels. Engagement rate increases encompass the measurable interactions that indicate genuine audience connection, including likes, comments, shares, click-through rates, time spent on content, and conversion metrics that directly correlate with business outcomes.

These metrics create a foundation for the bigger transformation without overpromising results you can’t control by establishing realistic benchmarks that acknowledge external factors beyond your influence while focusing on the operational improvements within your direct sphere of impact. This approach builds credibility through achievable goals, demonstrates progressive value delivery to stakeholders, and creates sustainable growth patterns that compound over time rather than relying on unpredictable variables like algorithm changes, market fluctuations, or competitor actions.

Building Your Value-Based Model

Start with the outcomes your AI actually delivers, not the technology that delivers them.

Create pricing tiers that reflect different conviction levels and usage patterns. Give customers choice between flexibility and commitment.

Use client examples from similar companies to build credibility. Focus on measurable interim results rather than ultimate business outcomes.

Most importantly, price based on the transformation you enable, not the features you provide.

Your AI’s genius lies in what it accomplishes for people, not in how cleverly it accomplishes it.