The Per-Seat Problem in AI
As B2B software increasingly integrates artificial intelligence, a fundamental pricing challenge emerges: the traditional per-seat model often breaks down. This model, which charges based on the number of individual users, struggles to capture the true value generated by AI-driven features. AI doesn't just augment individual tasks; it can fundamentally transform workflows, automate entire processes, and deliver exponential efficiency gains. Charging linearly for each user fails to reflect this disproportionate value, leaving revenue on the table and potentially stifling adoption if the per-seat cost becomes prohibitive for large teams leveraging AI extensively.
This is precisely the juncture where Ulrik Lehrskov-Schmidt's pricing consultancy, Willingness to Pay, steps in. The firm is not an AI application itself, but rather a specialized service designed to tackle this complex pricing dilemma for B2B and AI-first companies. They are the go-to experts when businesses realize their current pricing strategy, often inherited from simpler SaaS models, is no longer fit for purpose in the age of intelligent software.
The core issue stems from the nature of AI value. Unlike a word processor or a CRM that a single user interacts with for specific tasks, AI can operate in the background, continuously optimizing, analyzing, or generating outputs that impact multiple users or entire business units simultaneously. A generative AI tool, for instance, might draft marketing copy for a whole department, analyze vast datasets for a research team, or automate customer support responses for a service desk. The value isn't confined to the direct user; it permeates the organization. Per-seat pricing, in this context, becomes a blunt instrument, ill-suited to measuring the nuanced, often exponential, value AI delivers.
Beyond Per-Seat: Value-Based Pricing for AI
Willingness to Pay focuses on helping companies transition from simplistic user-based metrics to sophisticated value-based pricing frameworks. This approach ties the price of the software directly to the economic value it delivers to the customer. For AI products, this means understanding how the AI component drives tangible outcomes such as increased revenue, reduced costs, improved efficiency, or enhanced customer satisfaction. The consultancy works with clients to identify these key value drivers and translate them into a pricing structure that aligns with customer perception and actual business impact.
This often involves a deep dive into the customer's business operations. Lehrskov-Schmidt and his team analyze how the AI product is used, who benefits from its outputs, and what quantifiable improvements it brings. This might mean measuring the reduction in hours spent on a task, the increase in conversion rates due to AI-powered insights, or the cost savings from automated processes. The goal is to move away from an input-based cost (number of users) to an output-based value measurement.
Consider a B2B AI platform that analyzes customer feedback to predict churn. Per-seat pricing might charge the customer success manager, the marketing analyst, and the product lead. However, the true value is in the proactive retention of high-value customers, which could save the company millions. Value-based pricing would seek to capture a portion of these savings, perhaps through tiered pricing based on the volume of customer data analyzed, the number of churn predictions generated, or a revenue-share model tied to successful retention campaigns. This ensures the software provider is compensated for the significant impact their AI is having, while the customer pays a price that reflects the substantial benefit they receive.
The Consulting Process
The engagement with Willingness to Pay typically begins with an assessment of the current pricing model and its shortcomings. They then conduct detailed market research, customer interviews, and internal data analysis to understand the perceived and realized value of the AI product. This diagnostic phase is critical for building a foundation of trust and understanding with the client.
Following this, the firm collaborates with the client to design new pricing models. These models might include:
- Tiered Pricing: Based on usage volume (e.g., API calls, data processed, insights generated) rather than user count.
- Feature-Based Pricing: Differentiating pricing based on access to specific AI capabilities or advanced features.
- Outcome-Based Pricing: Directly linking the price to a measurable business outcome (e.g., a percentage of cost savings or revenue uplift).
- Hybrid Models: Combining elements of different approaches to best suit the product and market.
The surprising detail here is not the complexity of AI itself, but how often established B2B companies, even those leading in AI innovation, default to outdated pricing structures. They are so focused on building and refining the AI that pricing strategy often becomes an afterthought, leading to missed opportunities and misaligned value propositions. Willingness to Pay acts as a crucial external catalyst, forcing a re-evaluation of how value is quantified and captured in the AI era.
Who Needs This Service?
Any B2B company developing or heavily integrating AI into its core product offering is a potential client. This includes SaaS companies offering AI-powered analytics, automation tools, generative AI solutions, predictive modeling, or any service where the AI component delivers significant, non-linear value. Companies experiencing slow adoption despite a strong product, customers complaining about high per-seat costs for features they don't fully utilize, or those struggling to articulate the ROI of their AI investments are prime candidates.
The firm's expertise is particularly valuable for emerging AI startups that need to establish a scalable and defensible pricing strategy from the outset, as well as for more mature companies looking to pivot their existing offerings to better leverage AI capabilities. By optimizing pricing, these companies can unlock new revenue streams, improve customer relationships through fairer value exchange, and ultimately build more sustainable and profitable businesses in the rapidly evolving AI landscape.
If you run a B2B AI company and find yourself questioning whether your current pricing accurately reflects the value your product delivers, it might be time to consider a shift. The conversation around pricing is no longer just about cost; it's about the fundamental economic transformation your AI enables.