AI SDRs, AI lead sourcing, AI lead qualification, AI sales rools … AI in the sales process is getting a lot of love. But the real change will be driven by AI in the buying process. How is AI being used in the buying process? How will this impact pricing? Do companies need to generate value and pricing in forms that are easy for AIs to consume and process? AI in the buying process will have a bigger impact than AI in the sales process.

On October 16 Brandon Hickie from LinkedIn and Companyon Ventures gave a compelling webinar on credit based pricing, why it is emerging as a dominant pricing model for AI applications and agents, and emerging best practices for implementation. Longer term, credit based pricing may change the techstack for pricing and billing management. Get a glimpse into the future.

Value models are central to best practices in pricing. In the real world though, there have been many obstacles to practical applications. Value models are too expensive, they take too much time to develop and they are hard to maintain. The expertise to apply them to real world problems is scarce. Generative AI changes all that, making what was scarce abundant. This removes one layer of contrastaints and we we are entering a new era where value models are widely available and easy to apply. What new constraints will this reval?

API management is a core part of the infrastructure in the agent economy. Ibbaka recently studied value in the API management category and uncovered some important ways APIs are creating value and competing with each other. The report includes a value map for the API management category and a design structure matrix (DSM) that shows the key clusters of functionality in this category.

Credit-based pricing is a hot topic with major software vendors integrating credit-based pricing into their pricing and monetization models. There are also some important objections.

1. This is just disguised cost-based pricing

2. Predictability of cost and revenue

3. Familiarity

4. Transparency

5. Revenue recognition

A well designed credit-based pricing model can address all of these.

Adoption of AI agents in the CRM category and the emergence of headless CRMs. This is the fifth installment in Ibbaka’s research of the adoption of agents by different categories of B2B software and the impact on pricing. One of the most interesting things happening here is the emergence of headless CRMs. Headless applications are the backend of an application without the UI. Vibe coding and agents are used to manage the user experience which can be customized for each company. Salesforce, Hubspot and Microsoft are leading the agent revolution in CRM.

Ibbaka has put together a stellar line up for its October webinars.

October 1, Wednesday, joint pricing industry leaders Augustin Manchon, Mark Stiving and Marcos Rivera for a wild conversation on how AI is transforming pricing roles.

October 16, Thursday, Brandon Hickie from LinkedIn and Companyon Ventures and Steven Forth will go deep into the credit based pricing model that is dominating AI and agent applications.

AI is already transforming pricing work and how it is carried out. A recent LinkedIn poll with more than 100 responses found that 80% of people expect AI to transform pricing roles. How will the roles transform? How will this transformation roll out? Here are some initial thoughts. Steven Forth, Marcos Rivera and Mark Stiving will discuss in a webinar on October 1, 2025.

Four perspectives on credit based pricing that will help you to make a decision on whether to adopt this pricing model and how other companies are succeeding (and sometimes failing). Includes insights from the four most important reports plus case studies with practical lessons. This is in preparation for an important webinar with Brandon Hickie of LinkedIn on October 16.

Credit based pricing models are increasingly common, especially with agentic AI. When should you choose this pricing model? What are the design goals? What are the key design decisions to be made? This post reviews the key questions about credit based pricing models and then proposes a step-by step guide to their design.

API access is becoming a tension point between AI agents and applications, legacy enterprise applications and their customers. AI has made data more valuable and placed new demands on systems. Legacy vendors are pushing back by throttling or even removing access. Customers believe that this data is their data and that they should decide who gets to access what data for what purpose. Legacy vendors may start to charge AIs for access, a cost that will be passed on the users.

PeakSpan Capital and Ibbaka have launched our 3rd annual NRR survey. NRR (Net Revenue Retention) is one of the critical metrics that measures the health of a subscription business. This year (2025) finds the business software industry in a phase shift as the emergence of agents as a key packaging pattern threatens to disrupt established practices. Is this disruption real? Will it show up in NRR performance? This survey will help to answer these questions.

The fourth in Ibbaka’s research series on how agents are being introduced in different B2B software categories. This post focuses on the learning technology space (LMS, LCMS, LXP, microlearning). Most companies are using agents to complement their current platforms. Some our using agents to introduce net new functionality. Replacement is not yet a common strategy. Pricing models remain per user with little exploration of agent pricing.

The agent economy is transforming how functionality is packaged and priced. Different categories of B2B software are responding in different ways. In this post we look at revenue intelligence, companies like Clari, Grok and Salesloft. Four patterns are identified and the pricing implications drawn out. AI based agent first companies like Oliv.ai are threatening to disrupt this cateogry.

Billing and subscription management software is where pricing strategy and pricing model design are executed. It is critical functionality for the pricing and monetization of AI agents? But in general this category has been slow to adopt the agent paradigm itself. Stripe Billing has made important moves in enabling customers to build agents. Paid has a disruptive solution that is starting to get some traction. But the category lags on pricing transparency and the adoption of credit-based pricing.

Agents are popping up all over as we shift from a subscription economy to an agent first economy. One example is the pricing software sector (PROS, Pricefx, Vendavo, Zilliant) where PROS and Pricefx are leading the drive to agents. Let’s compare the agent strategies of these four companies, see where there are overlaps and differentiation, and where there are gaps that innovators could drive through. How are (will) these agents be priced?

There are two different visions emerging for the role agents will play in B2B software. One view is that agents will be add ons to larger standard enterprise applications. The other view is that agents will become the main point of interaction and value creation. Both have quite different implications for pricing. We polled our LinkedIn communities to get additional insights into this. Of course there are multiple possible futures here and all may emerge in different B2B software sectors.

There has been a lot of discussion of how to price AI agents. But the busines senvironment is changing and the question has changed. Business leaders are now asking how agents are changing pricing. The rapid emergence of an AI driven agent economy is changing how we think about pricing of all B2B software and is leaking into many other conversations. This post explores these critical questions.

Agents are popping up everywhere. Vibe coding and good tooling have made it easy to develop agents. Users and buyers are looking for simple solutions that leverage AI. Agents are the result. There are two broad trends: net new agents and agents built to access functionality and data in existing apps. How do pricing approaches differ in these two cases?

One emerging strategy for designing and pricing a family of agents is the team. Agents are being designed as complements or replacements for well established functional teams. An example of this in the data space is Brighthive. They have designed their agents to reflect well established roles on a data team.