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AI tools drastically reduce the time needed to complete complex tasks, breaking the traditional billable-hour model for consultants and agencies. The focus must shift to value-based pricing, where compensation is tied to the problem solved or the output created, not the hours worked.

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AI enables a fundamental shift in business models away from selling access (per seat) or usage (per token) towards selling results. For example, customer support AI will be priced per resolved ticket. This outcome-based model will become the standard as AI's capabilities for completing specific, measurable tasks improve.

As agencies adopt AI to increase efficiency, clients will rightfully question traditional pricing models based on billable hours. This creates an "arbitrage" problem, forcing agencies to redefine and justify their value based on strategic insight and outcomes, not just the labor involved.

In categories like customer support, where AI can handle the vast majority of queries, charging per human agent ('per seat') no longer makes sense. The business model is shifting to be outcome-based, where customers pay for the value delivered, such as per ticket resolved or per successful interaction.

AI is splitting software into two categories: "access products" and "work products." While access tools can stick with seat-based pricing, work products (e.g., AI that processes legal contracts) must adopt outcome-based pricing, as value is tied to output, not the number of users.

The dominant per-user-per-month SaaS business model is becoming obsolete for AI-native companies. The new standard is consumption or outcome-based pricing. Customers will pay for the specific task an AI completes or the value it generates, not for a seat license, fundamentally changing how software is sold.

AI tools aren't just making employees more efficient; they are replacing human labor. This allows software companies to move from cheap per-seat pricing to a new model based on outcomes, like charging per support ticket resolved, capturing a much larger share of the value.

AI is moving beyond enhancing worker productivity to completing entire projects, like drug discovery or engineering designs. This shift means software will be priced like a services business, based on the value of the outcome delivered, not the number of users with access.

The next major business model shift in software is from seat-based pricing to outcome-based pricing (e.g., paying per task completed). This favors AI-native newcomers, as incumbents will struggle to adapt their GTM and financial models.

In the age of AI, software is shifting from a tool that assists humans to an agent that completes tasks. The pricing model should reflect this. Instead of a subscription for access (a license), charge for the value created when the AI successfully achieves a business outcome.

AI tools drastically reduce time for tasks traditionally billed by the hour. Clients, aware of these efficiencies, now demand law firms use AI and question hourly billing. This is forcing a non-optional industry shift towards alternative models like flat fees, driven by client pressure rather than firm strategy.

AI's Efficiency Gains Make Value-Based Pricing Essential, Killing the Billable Hour | RiffOn