AI agents are transforming the way SaaS companies operate, but with this shift comes a new set of pricing and packaging challenges. In this webinar, we’ll dive into the unique pricing strategies that are emerging as AI agents, co-pilots, and generative workflows become a core part of SaaS offerings.
Join Steven Forth, an expert in SaaS pricing strategy and Co-Founder of Ibbaka and valueIQ, as he shares practical insights into how SaaS companies can effectively monetize AI agents. We’ll discuss emerging trends, real-world examples, and proven approaches for pricing in this rapidly evolving space. This is an essential session for any SaaS leader looking to stay ahead of the curve.
What You’ll Learn:
The rise of AI agents creates new opportunities, but also new complexities, for SaaS pricing. In this session, we’ll help you navigate these challenges and develop strategies that work.
- Real-world examples of AI-first pricing models from top SaaS companies.
- The new landscape of AI-driven workflows and the implications for pricing.
- Four emerging AI packaging models: Agents, Co-pilots, Generators, and Service as Software.
- Outcome-based pricing strategies that reflect the true value of AI agents for customers.
- How to price for value in a world where AI is delivering more personalized and complex services.
Why Attend?
As AI agents reshape SaaS, it’s essential to stay ahead of pricing and packaging trends. This session is designed for SaaS leaders who want to:
- Utilize generative AI to rapidly develop and validate value models.
- Implement outcome-based pricing to better capture customer value.
- Design adaptive pricing models that align with AI-driven value delivery.
Who Should Attend?
This session is perfect for SaaS executives and pricing strategists who are looking to:
- Enhance revenue through intelligent pricing strategies.
- Stay ahead of market shifts driven by AI advancements.
- Ensure their pricing strategies reflect the true value delivered to customers.
About the Speaker
Steven Forth
Co-Founder & CEO at Ibbaka
Steven is a recognized authority on SaaS pricing, with over two decades of experience leading pricing transformations for companies ranging from startups to Fortune 500 firms. As Co-Founder of Ibbaka, he has helped companies rethink their pricing strategies to capture more value and drive growth in an increasingly AI-driven world.
Webinar Summary
1. What is an AI agent, and how does it fit into the future of software?
An AI agent, in my experience, is an autonomous software tool designed to execute tasks, make decisions, and achieve business goals on behalf of users. The degree of autonomy varies, with many agents still requiring some human input. The future of software lies in embedding agents and AI tools into everyday business platforms, such as CRM or billing systems. For example, in my work, we’ve integrated AI agents to automatically generate invoices, reconcile accounts, and even identify billing anomalies, significantly improving operational efficiency. The key takeaway is that AI agents will reduce manual work while enhancing productivity across various business functions.
2. Why is AI agent adoption happening now?
The adoption of AI agents is happening now because the technology has matured. The infrastructure, particularly in cloud computing, is robust enough to support AI agents in real-world business environments. In my previous company, we integrated AI to automate contract management, and this drastically reduced the time spent on contract reviews and invoicing. This transition is part of the broader shift to an agent economy, where specialized agents handle different tasks autonomously. As AI becomes more accessible and effective, it's increasingly adopted to automate the billing system and repetitive processes, which were previously time-consuming for finance teams.
3. How can legacy SaaS vendors adapt to the rise of AI agents?
For legacy SaaS vendors, adapting to the rise of AI agents involves embedding these agents into existing workflows. For instance, in a project I managed, we replaced outdated manual billing processes with AI-driven automation, cutting down billing cycles by 40%. Companies can take three approaches: create independent agents that offer new features, replace outdated functions with AI, or introduce complementary agents that enhance existing offerings. Each approach comes with different pricing implications, so companies must carefully assess the impact of these changes on their existing revenue models.
4. How does AI agent pricing differ from traditional SaaS pricing models?
Pricing AI agents can be more complex compared to traditional SaaS billing models. In my experience, pricing for AI agents is based on factors such as per-user, per-agent, or per-action. For example, a chatbot AI agent might be priced per customer interaction, whereas a more complex predictive agent may be priced based on usage volume. At a company I worked with, we implemented a credit-based pricing system for our AI tool, where customers bought credits for actions. This pricing model allowed us to offer flexible scalability, ensuring that the pricing aligned with the value provided.
5. How does credit-based pricing impact revenue recognition and forecasting?
Credit-based pricing introduces new challenges in revenue recognition. When credits are bought but not yet used, revenue isn’t recognized until the credits are consumed. I saw this firsthand when we transitioned to a credit-based model in my previous company. The system had to track credit consumption accurately and ensure that revenue was only recognized as credits were used. For forecasting, it was important to predict how quickly credits would be consumed, which required a more dynamic forecasting model. This shift led us to build a more agile billing system that incorporated usage billing and helped us avoid manual reconciliation.
6. What are the challenges in pricing AI agents based on their value?
The challenge in pricing AI agents based on value is understanding how they impact business outcomes. In one of my previous roles, we had to determine the value generated by an AI agent that automated data entry. After calculating the time savings and error reductions, we saw that the AI agent contributed to a 20% reduction in manual labor costs. To price the agent accordingly, we mapped its actions to specific value drivers like time saved, increased efficiency, and reduced errors. This approach allowed us to set a value-based price that reflected its impact on operations, but it took careful analysis to ensure we didn’t overestimate its value.
7. What role does autonomy play in AI agents, and how should it influence pricing decisions?
Autonomy in AI agents directly impacts pricing. In my experience, more autonomous agents, like AI-powered invoicing tools that handle billing end-to-end without human oversight, justify higher pricing. However, semi-autonomous agents that still require some input or supervision tend to be priced lower. For instance, when we implemented an AI tool for financial forecasting, the agent’s ability to update forecasts in real-time without manual input made it a premium offering. As technology advances, companies will need to adapt their pricing models to reflect the increasing autonomy of these agents.
8. How does the agent economy impact traditional billing systems?
As AI agents take over more tasks, traditional billing systems must evolve to accommodate flexible, usage-based pricing models. I’ve worked with companies where billing shifted from a fixed per-user model to a more dynamic approach, based on how often AI agents were used. This transition requires a sophisticated billing system capable of handling per-task, per-agent, or credit-based pricing, something traditional subscription models cannot easily accommodate. Systems like Zenskar’s, which automate billing and revenue recognition, are key in managing this complexity.
9. How can companies ensure a smooth transition to AI agent pricing?
To ensure a smooth transition to AI agent pricing, companies need to align their internal teams around clear pricing objectives. In my experience, we used frameworks like the Strategic Choice Cascade to define clear goals for our AI agents and then identified the value each agent would deliver. We also ensured that the finance, product, and engineering teams collaborated early on to understand the pricing implications. The transition is much smoother when pricing models are flexible and can evolve alongside the technology.
10. How does AI impact pricing transparency?
AI has made pricing transparency more critical, as customers expect to understand how they are being charged, especially when it comes to usage-based or agent-driven models. When we rolled out a new AI-driven tool, I made sure our pricing page clearly explained how we charged for each action an AI agent performed. For instance, if a user requested an AI-driven forecast, we broke down the cost per forecast based on the complexity of the data. I encourage companies to embrace transparency by clearly outlining the pricing metrics used for AI services, as this builds trust with customers.
11. What is the role of a prediction engine in AI agent pricing?
A prediction engine is essential for AI agent pricing, particularly when it comes to forecasting credit usage or task completion. In one of my companies, we implemented a prediction engine that analyzed past usage data to forecast future demand for our AI-driven services. This allowed us to fine-tune our pricing model, offering volume-based discounts and anticipating periods of higher demand. A robust prediction engine helps companies predict customer behavior, optimize pricing, and manage revenue more effectively.
12. How can AI agents create value for companies in diverse sectors?
AI agents can create value across sectors by automating tasks that were previously manual and time-consuming. I’ve seen AI agents used in companies like Box and Snowflake to manage expense categorization and customer inquiries. By offering flexible pricing models, these companies aligned their services with customer usage, ensuring scalability while maintaining pricing transparency. The key takeaway is that AI agents can scale operations across industries, making them more efficient while providing businesses with valuable insights and real-time data.
13. What are the key steps for companies to take as they adopt AI agents?
When adopting AI agents, I recommend focusing on three main areas: defining clear agent strategies, understanding the value generated by each agent, and building flexible pricing softwares. In my past roles, I worked closely with product teams to identify which tasks could be automated first, ensuring a smooth integration. It’s also crucial to build a solid data infrastructure to support AI agents, as accurate data is the foundation for success. Continuous monitoring and adjustments based on performance metrics are necessary to refine the adoption strategy.
14. How does AI adoption affect customer experience and pricing strategy?
AI agents significantly enhance the customer experience by providing personalized, timely, and efficient services. I’ve seen AI agents reduce customer wait times for billing inquiries and improve overall satisfaction. However, companies must adjust their pricing strategy to reflect the increased value customers receive. For example, an AI-driven invoicing system that resolves customer queries faster may justify higher prices for premium customers, offering them faster service in exchange for a higher fee. Pricing must be aligned with the value customers perceive.
15. What’s the future of AI in pricing strategy?
The future of AI in pricing strategy is rooted in value-based models that reflect the outcomes generated by AI agents. I foresee a shift towards pricing based on the impact an agent has on customer success rather than just usage volume. In my previous companies, we used AI to track customer behavior, which helped us refine pricing and ensure customers were paying for the value they received. AI will continue to evolve, making it possible for companies to offer more personalized and dynamic pricing models that directly tie to customer outcomes.
.webp)
