Questions for the “AI Monetization in 2025” Webinar with Mark Stiving and Michael Mansard
Steven Forth is CEO of Ibbaka. Connect on LinkedIn
Ibbaka’s AI Monetization in 2025 report has been getting a lot of attention as it gives deep insights into how AI is being monetized in B2B SaaS and for B2B AI.
On Thursday, January 23 at 11:00 AM Eastern (8:00 AM Pacific / 5:00 PM Paris) we will have a webinar with value and pricing experts Mark Stiving (host of the essential Impact Pricing Podcast) and Michael Mansard from Zuora. They will ask their questions about the report and draw out the trends that will shape value, packaging, and pricing in 2025.
Register for the webinar here.
More than 330 companies responded to the survey. When clustered using the Leiden algorithm we found three distinct groups: Sceptics, Sustainers, Disruptors. Each group has significantly different beliefs and strategies.
Table from the Ibbaka AI Monetization in 2025 report.
As part of our preparation for the webinar, we ran the following prompt through Perplexity (one of our most used generative AI tools) and compared the responses of two different Large Language Models. Each of the models has its own personality and gives different responses, sometimes subtly different, sometimes substantially.
Prompt
<role> You are an expert in pricing and packaging for B2B SaaS and B2B AI. You have been invited to a webinar to discuss and ask questions about the report AI Monetization in 2025. You are preparing for the webinar. </role>
<context>
Ibbaka is a customer value management and value based pricing platform company. It conducts primary research on areas relevant to value management, pricing, packaging, and innovation. Each year it publishes an annual report on Net Revenue Retention and on AI Monetization.
The audience for the webinar is senior executives in B2B SaaS and B2B AI companies as well as value and pricing experts and industry analysts.
</context>
<tasks>
1. Summarize the key insights and recommendations from the report.
2. Find other reports that support or contradict this report.
3. Prepare a list of questions for discussion in the webinar.
</tasks>
<guidance>
Provide a step-by-step explanation of your reasoning for how you answered each of these tasks.
</guidance>
Response from Perplexity Pro Search
Step 1: Summarize key insights and recommendations
Key Insights:
Customer success agents are emerging as the killer app for B2B AI, with over 50% of respondents adopting them.
Four main packaging and monetization patterns are coalescing: agents, co-pilots, generators, and service as software.
Both sustaining and disruptive AI applications are gaining traction, with sustaining innovations dominating and using conventional pricing methods.
Flexible pricing models, such as outcome-based pricing or transferable credits, are winning in the market.
Recommendations:
Be clear on your strategy: choose between sustaining innovation or disruptive innovation.
Use one of the core patterns: Agent, Co-pilot, Generator, or Service as a Software.
Pay attention to pricing
For sustaining innovation, use value-based pricing
For disruptive innovation, adopt generative pricing
Focus on value and use a value model:
For sustaining innovation, keep existing value drivers
For disruptive innovation, create new value drivers
Consider using open-source models and explore collaboration opportunities with open-source communities and local universities.
Step 2: Find supporting or contradicting reports
To find other reports that support or contradict this report, I would need to conduct a thorough search of recent industry publications, analyst reports, and academic studies. However, based on the information provided in the report, I can highlight a few points that could be compared with other sources:
The report mentions research by Andreessen Horowitz showing that computing costs for AI models have decreased rapidly. This could be cross-referenced with other industry reports on AI infrastructure costs.
The report's findings on the adoption of AI agents and co-pilots could be compared with other market research on enterprise AI adoption trends.
The pricing strategies and monetization patterns identified in the report could be contrasted with other pricing studies in the B2B SaaS and AI sectors.
The report's clustering of companies into Skeptics, Sustainers, and Disruptors could be compared with other market segmentation studies in the AI industry.
Step 3: Prepare questions for webinar discussion
How do you see the balance between sustaining and disruptive AI innovations evolving over the next few years?
The report identifies four main packaging patterns: agents, co-pilots, generators, and service as software. Are there any emerging patterns that you think might become significant in the near future?
Can you elaborate on the concept of "generative pricing" and how it differs from traditional value-based pricing approaches?
The report suggests that customer success agents are the killer app for B2B AI. What factors do you think are driving this trend?
How do you see the role of open-source models and community collaborations evolving in the B2B AI space?
The report mentions that compute costs for AI have declined faster than expected. How do you think this will impact pricing strategies and gross margins for B2B AI companies?
Can you discuss the challenges and opportunities of implementing outcome-based pricing for AI solutions?
The report identifies three clusters: Skeptics, Sustainers, and Disruptors. How do you see these groups evolving, and what strategies should companies in each cluster consider?
How do you see the balance between per-user pricing and usage-based pricing evolving for AI-powered B2B solutions?
The report mentions the concept of "reasoning tokens" in pricing. How do you see this impacting the economics of AI solutions, especially for more complex applications?
Reasoning:
I focused on extracting the most salient points from the executive summary and recommendations sections for the key insights and recommendations. For the questions, I aimed to cover a range of topics addressed in the report, including pricing strategies, packaging patterns, market trends, and technological developments. The questions are designed to encourage deeper discussion and exploration of the report's findings, as well as to prompt insights about future trends and strategies in the B2B AI space.
Response from GPT 4o
Summary of Key Insights and Recommendations from the Report
Key Insights
Emerging Monetization Patterns:
Four primary packaging patterns for AI monetization are emerging: Agents, Co-Pilots, Generators, and Service as Software.
Agents and Co-Pilots are gaining traction, with Co-Pilots being the most popular due to their alignment with sustaining innovation.
Generators remain foundational but are fading into the background as other models become more prominent.
Sustaining vs. Disruptive Innovation:
Sustaining innovations enhance existing products and rely on conventional pricing models (e.g., per user).
Disruptive innovations create new markets or radically change existing ones, often requiring novel pricing models like generative or outcome-based pricing.
Flexible Pricing Models:
Outcome-based pricing, transferable credits, and token-based models are becoming more common, especially for disruptive innovations.
Companies like Intercom and Zendesk have successfully implemented outcome-based pricing for customer support solutions.
AI Adoption Trends:
Adoption of AI is growing, with significant investments in R&D (median ~50% for Sustainers; >80% for Disruptors).
Generative AI applications are increasingly integrated into B2B SaaS, with reasoning models expected to play a critical role in 2025.
Challenges in AI Monetization:
High compute costs, particularly for reasoning models, create pressure on gross margins.
Companies must ensure that AI applications deliver value significantly exceeding costs to justify pricing.
Recommendations
Strategic Focus:
Decide whether to pursue sustaining or disruptive innovation based on market position, risk tolerance, and growth aspirations.
Align AI packaging patterns (Agents, Co-Pilots, Generators, Service as Software) with innovation strategy.
Value-Based Pricing:
For sustaining innovations, use value-based pricing tied to existing value drivers.
For disruptive innovations, adopt generative pricing tied to new value drivers or outcomes.
Collaboration Opportunities:
Explore partnerships with open-source communities and academic institutions to reduce reliance on major proprietary models like OpenAI or Anthropic.
Adapt Pricing Models:
Use flexible pricing metrics such as per agent, per task completed, or outcome-based pricing to align with customer-perceived value.
Continuously track usage patterns and customer feedback to refine pricing strategies.
Market Segmentation:
Use price fencing (e.g., tiered or usage-based models) to address diverse customer needs while maximizing revenue potential.
Supporting Reports and Contrasting Perspectives
Supporting Reports:
Research from Andreessen Horowitz highlights the decline to compute costs for generative AI models, which aligns with Ibbaka’s observation that gross margin pressure may be less severe than initially anticipated.
Case studies from companies like Canva and Salesforce reinforce the report’s findings on the effectiveness of tiered packaging and agent-centric models.
Contrasting Perspectives:
Some industry analysts argue that token-based pricing may remain dominant longer than predicted due to its simplicity and transparency.
Critics of outcome-based pricing highlight challenges in defining and attributing outcomes, which could limit its adoption outside specific use cases like customer support.
Questions for Webinar Discussion
Strategic Choices:
How should companies decide between sustaining and disruptive innovation when introducing AI capabilities?
What factors should influence the choice of packaging patterns (Agents vs. Co-Pilots vs. Generators)?
Pricing Models:
What are the best practices for implementing outcome-based pricing in industries where outcomes are harder to define or attribute?
How can companies balance flexibility in pricing with the need for predictable revenue streams?
Value Capture:
What methodologies can companies use to quantify the value delivered by AI applications effectively?
How can businesses ensure that their value capture ratio remains competitive while covering high compute costs?
Collaboration Opportunities:
What role can open-source communities play in reducing dependency on proprietary AI models?
How can academic partnerships be leveraged to accelerate AI innovation while managing costs?
Emerging Trends:
How will reasoning models impact AI monetization strategies in 2025 and beyond?
What new challenges might arise as companies transition from token-based to outcome- or value-based pricing?
Customer Perception:
How should companies address potential pushback from customers regarding significant price increases tied to new AI functionality (e.g., Canva’s experience)?
These questions aim to foster a robust discussion on the evolving landscape of AI monetization in B2B SaaS and B2B AI industries.
Check out our past webinars with Mark Stiving, Kyle Poyar, and Michael Mansard
How New AI Functionality is Getting Priced with Mark Stiving
Monetizing Generative AI with Michael Mansard and Kyle Poyar
