Last month, I conducted a LinkedIn poll to gather insights from industry professionals on what research they believe is most critical for developing robust pricing models for Generative AI (GenAI). As companies race to incorporate AI solutions into their offerings, pricing strategies have become key to ensuring that these offerings can be monetized while delivering value to customers. The poll results provide a snapshot of current thinking.

Poll Results Overview

The poll asked: “What research do you believe is most vital for developing robust GenAI pricing models?” 

Respondents were asked to choose from the following options:

1. Value Perception Studies

2. Cost Structure Analysis

3. Willingness-to-Pay Benchmarks

4. Competitive Pricing Strategies

With 75 industry professionals participating, the results offer valuable insights into the priorities of industry professionals when it comes to pricing generative AI solutions.

Here’s how the responses broke down:

• Value Perception Studies: 42.7% of respondents

• Cost Structure Analysis: 13.3% of respondents

• Willingness-to-Pay Benchmarks: 24% of respondents

• Competitive Pricing Strategies: 20% of respondents

Key Insights from the Poll

1. Value Perception Studies

It's not surprising that Value Perception Studies emerged as the top priority, garnering 42.7% of the votes. This aligns perfectly with the core principles of customer value management (CVM) and value-based pricing. As GenAI represents a paradigm shift in capabilities, many customers are still grappling with how to quantify its benefits.

Understanding how customers perceive and derive value from GenAI solutions is crucial for several reasons:

1. It enables the development of compelling value stories

2. It informs value-based selling strategies

3. It provides the foundation for value-based pricing models

In Ibbaka’s work on generative pricing, we've seen that understanding customer value perception plays a key role in determining willingness to pay for AI-driven solutions. For example,  Horizontal applications, like chatbots, offer broad utility across industries but may not deliver as much perceived value as vertical applications, such as AI-powered medical diagnostics, which are tailored for specific industries. This distinction in perceived value can significantly impact pricing strategies and customer willingness to pay for GenAI solutions.

Future research should focus on identifying key value drivers, measuring productivity gains, and understanding how GenAI integrates with existing workflows to enhance value creation.

2. Willingness-to-Pay Benchmarks 

Willingness-to-Pay (WTP) benchmarks, selected by 24% of respondents, are particularly useful when introducing innovative products like GenAI tools into the market. WTP studies help companies gauge how much customers are willing to spend based on perceived value and budget constraints.

Key areas for WTP research in GenAI might include:

• Exploring WTP across different customer segments and use cases

• Analyzing price sensitivity for various GenAI features

• Determining acceptable price ranges for different deployment models (e.g., API access vs. full applications)

By understanding customer price sensitivity, companies can set prices that maximize revenue without alienating potential buyers. This aligns with the importance of aligning pricing with customer expectations and market trends—a key principle in any successful value-based pricing strategy.

3. Competitive Pricing Strategies

With 20% of the vote, competitive pricing analysis is also seen as vital. While always relevant, GenAI presents unique challenges that may require more innovative approaches than simply benchmarking against competitors.

GenAI products often require hybrid or usage-based models that reflect their unique cost structures and value propositions. This approach aligns well with Ibbaka’s emphasis on creating dynamic, adaptive pricing models that focus on both internal cost drivers and external market conditions—key aspects of effective customer value management.

Research in this area should focus on:

• Mapping the competitive landscape across different GenAI applications

• Analyzing pricing models used by key players (e.g., usage-based, tiered, outcome-based)

• Tracking how pricing evolves as the market matures

This intelligence will help companies position their offerings effectively and adapt to market dynamics, a key aspect of successful value-based marketing and selling.

4. Cost Structure Analysis

Although cost structure analysis received the lowest share of votes compared to other options at 13.3%, it remains as a foundational factor in developing robust and sustainable pricing models for GenAI. Understanding computing resource costs, data acquisition expenses, and how costs scale with usage is critical for ensuring profitability while delivering value.

The cost of operating GenAI solutions can escalate quickly, especially when dealing with complex models and large datasets. Companies need to account for these costs when setting prices to avoid margin erosion. A deep understanding of cost structures allows businesses to align their pricing with operational realities, ensuring profitability while delivering value realization.

What Does This Mean for GenAI Pricing?

The poll results highlight a clear trend: while traditional competitive pricing strategies still matter, internal factors like value perception and willingness-to-pay benchmarks are seen as more critical when developing robust GenAI pricing models.

This aligns with broader industry trends we’ve observed at Ibbaka, where companies are increasingly focusing on understanding their own cost drivers and how customers perceive value in order to develop more dynamic and adaptive pricing strategies—especially within the context of a comprehensive CVM platform.

How Can Companies Use These Insights?

1. Begin with Value
   Understanding how different customer segments perceive the value of your GenAI product is crucial for setting prices that reflect its true worth. To do this you need a value model and then you need to test and improve that model with a combination of qualitative research and quantitative analysis.

2. Understand Costs
   Model your costs and see how they scale under different usage assumptions. This is especially important for generative AI applications where compute costs can be significant.

3. Test Willingness-to-Pay
   Regularly update your WTP benchmarks to ensure your prices remain aligned with customer expectations and market conditions. One way to do this is through a Van Westendorp study that asks the following four questions:

   1. At what price would you consider the product to be so inexpensive that you would question its quality? (Too Cheap)

   2. At what price would you consider the product to be a bargain - a great buy for the money? (Cheap/Good Value)

   3. At what price would you say the product is starting to get expensive, but you still might consider it? (Expensive/High Side)

   4. At what price would you consider the product to be so expensive that you would not consider buying it? (Too Expensive)

4. Check Competitive Pricing
   Price should be based on the value you deliver to your customers (V2C), but value is relative to the next best competitive alternative, so keeping an eye on competitors’ pricing strategies can help ensure you remain competitive in a rapidly evolving market

Conclusion

The poll results highlight a clear trend: while traditional competitive pricing strategies still matter, value perception and willingness-to-pay are even more critical when developing robust GenAI pricing models.

As the generative AI landscape continues to evolve, continuous research across all facets of GenAI pricing will be crucial. Companies that invest in building a robust, data-driven understanding of GenAI’s value, pricing dynamics, and market trends will be best positioned to succeed in this AI-driven era. By focusing on customer value management, value-based selling, and value-based pricing, companies can develop innovative pricing models that capture fair value while driving adoption of their GenAI solutions.