How AI is Reshaping Software Pricing

For years, software pricing models followed a familiar structure: companies paid per user, per month. This approach was predictable and widely accepted. However, with artificial intelligence redefining the capabilities of software, traditional pricing structures are becoming increasingly obsolete. AI is not merely an add-on feature—it is replacing human-driven tasks and fundamentally altering how software is valued.

The Shift: From Per-Seat to Outcome-Based Pricing

Consider customer support software like Zendesk. Historically, organizations paid approximately $115 per agent per month. However, AI-powered support bots now manage a significant share of customer interactions, reducing the need for human agents. In this context, charging per seat no longer aligns with the actual value delivered.

This shift has led to the rise of outcome-based pricing. Instead of paying for access, businesses are now charged based on measurable results, such as:

• The number of issues resolved by AI

• The efficiency of automated responses

• The overall reduction in human intervention

This model ensures that pricing reflects the effectiveness of the software rather than the number of users interacting with it.

The Economics of AI: Why Usage-Based Pricing Matters

Another driver of this transformation is the cost structure of AI itself. Unlike traditional software, where adding a new user incurs minimal additional cost, AI usage generates tangible expenses. Running sophisticated models, making API calls, and processing extensive datasets all contribute to operational costs. As a result, many companies are adopting usage-based pricing, where customers pay based on AI consumption rather than a fixed subscription fee.

For instance, companies leveraging AI from providers like OpenAI or Anthropic must account for the computational cost behind every AI-driven interaction. This has led to a "pay-as-you-go" approach, where businesses are charged based on:

• The number of AI-generated responses

• The volume of data processed

• The complexity of automated actions executed

A Hybrid Approach: Balancing Usage and Outcomes

Many companies are blending different pricing strategies to optimize value capture. Decagon, for example, employs a hybrid model that incorporates both per-conversation (usage-based) and per-resolution (outcome-based) pricing. This dual approach ensures that customers pay for:

• The level of engagement with AI (measured by interactions)

• The tangible results AI delivers (such as resolved issues)

Adoption Trends: AI-Native Startups vs. Established Enterprises

The pace of adoption varies across the industry. AI-native startups—companies built from the ground up with AI as a core function—are more agile in adopting outcome-driven and usage-based pricing models. Companies such as Decagon, Cursor, and ElevenLabs exemplify this trend, leveraging AI’s capabilities to develop pricing structures that directly align with performance and efficiency.

Conversely, legacy software providers such as Zendesk, Notion, and Canva are integrating AI into their platforms but remain more conservative in their pricing evolution. Many continue to rely on traditional seat-based or bundled pricing, reflecting the complexities of transitioning long-standing customer bases and revenue models to entirely new structures.

The Broader Implications: AI as a Catalyst for Business Transformation

AI-driven pricing shifts extend beyond software. The technology is restructuring business operations, from customer support and sales to marketing and internal workflows. Companies are reevaluating how they quantify value—whether by access, task completion, or results achieved. As AI capabilities expand, pricing models will continue to evolve alongside them.

Some of the key transformations include:

• The redefinition of labor and automation in customer service

• AI-driven decision-making in sales and marketing

• A shift towards data-centric business models

Looking Ahead: The Future of Software Monetization

AI-driven pricing models remain in flux, with companies testing and refining strategies to align with cost structures and customer expectations. The landscape is set for continued experimentation as firms seek an equilibrium between operational sustainability and delivering value.

As AI reshapes software pricing, similar transformations may extend to other industries. The central question moving forward is not just how we price software but how we define value in an increasingly AI-driven world.