Webinar Recap: Validating AI Features – Succeed at AI Pricing with KANO & Credits

Anyone looking to integrate AI features into B2B software or HR tech quickly runs into a fundamental hurdle: Traditional flat rates per user seat (seat-based pricing) no longer work once AI agents take on tasks independently and incur variable token costs. Many product and pricing teams therefore immediately ask themselves: What price tag should we put on our AI features?

But this question doesn’t go far enough. Setting prices without first validating the true value and demand wastes development resources. In an exclusive deep-dive webinar, ,Dr. Sebastian Voigt, and Nazira Balogun (hy Consulting), together with Louise Leitsch (CPROat Appinio), demonstrated what a structured pricing process looks like—from feature validation and packaging all the way to a marketable hybrid and credit model.

First validate what is being built—then what is required

A successful monetization process does not begin with price surveys, but with the product’s value proposition. In practice, teams tend to develop every conceivable AI feature and subject them to complex conjoint tests. An efficient approach, however, is divided into two distinct phases:

Phase 1: Feature Validation (Kano & TURF)

Before price points are tested, it must be determined which features actually provide real added value and where they should be positioned within the product:

• Kano Analysis: Classifies features into clear categories. Must-haves (e.g., simple AI text summaries) are expected as standard and should be included in the core product at no extra charge. Performance features increase in value linearly with usage and are perfectly suited for credit-based models. Delighters (features that delight customers, such as specialized sourcing or compliance agents) create differentiation and justify premium packages or add-ons.
• TURF Analysis (Total Unduplicated Reach and Frequency): Identifies the leanest feature set that reaches the maximum number of customers. It prevents development time from being wasted on features that merely target the same audience twice.

Phase 2: Pricing Validation (Conjoint Analysis & Pricing)

Only once the optimal bundle has been determined does pricing research follow. Using conjoint analysis, bundle types, agent scopes, free credit allowances, and price points can be tested simultaneously. For isolated standalone modules, agile methods such as Gabor-Granger or Van Westendorp are suitable.

The AI Packaging Framework in the HR Tech Example

Using the example of HR software—ranging from employee management to recruiting—the classification based on frequency of use (adoption) and willingness to pay (value) can be clearly understood:

• Core Product (High Adoption, Low Value): Customers expect basic features, such as automated job posting templates, as standard. These remain part of the core model.
• Premium Tier (High Adoption, High Value): AI-powered workflow automations for onboarding or performance analytics significantly reduce the HR workload. They serve as the strongest driver for upgrades to higher package tiers.
• Add-ons (Low Adoption, High Value): Specialized features such as AI-powered workforce planning or automated screening agents appeal to specific enterprise segments and are priced as add-ons.

Hybrid Pricing: Why Credit Systems Set the Standard

The benchmark, which analyzed over 4,000 software companies, highlights a clear shift: Pure subscription models are losing ground, while **hybrid models (subscription + usage)** are forming the new foundation.

A structured credit system resolves the conflict-of-interest dilemma:

1. Predictability for customers: A fixed base fee includes a monthly credit allowance for regular usage.
2. Margin protection for providers: Intensive AI usage and fluctuating token costs are neatly monetized through flexible credit top-ups.
3. Scalability: Teams can flexibly use credits across various features.

Real-world data confirms the success: By switching to AI credits, a software provider was able to increase its gross margin by 2.5 percentage points to 85%—with a net dollar retention rate of 136%.

Modern Market Research in Practice: The Appinio Approach

Just as in software development, AI is also fundamentally transforming market research. Routine tasks such as data cleaning, quality control, and text analysis of open-ended responses are handled by AI systems in a matter of seconds. This allows insights teams to focus entirely on strategic study design and the interpretation of results.

To enable companies to test pricing, packaging, and target audience needs without relying on outdated processes, Appinio prioritizes maximum flexibility. Through a transparent credit model, valid price tests, concept validations, or representative studies can be implemented in no time. With innovative features like Appinio Conversations, teams can also engage in qualitative dialogue with AI-based personas in advance to test initial hypotheses before fieldwork begins.

Conclusion: The Three Guiding Principles for Your AI Pricing

1. Features Before Pricing: First, use Kano and TURF to validate which features pass the relevance test.
2. Hybrid beats flat-rate: Protect your margin from variable AI infrastructure costs with transparent credit allocations.
3. Consider Segmentation: SMB, mid-market, and enterprise customers differ drastically in their requirements for budget control, auditing, and packaging.

You can find the webinar slides here.

You can find the complete SaaS & AI Pricing Report 2027, as well as further analyses, directly on the hy website.

Would you like to validate your own pricing hypotheses or product features directly with the market? Try out our platform today or talk to our research experts. We’ll provide you with fast, well-founded insights to help you make your next strategic decisions!