**Steven Forth** is CEO of Ibbaka. Connect on LinkedIn

Pricing AI agents is going to need new approaches. These agents are fundamentally different from the applications we are used to using. They are generally meant for specific purposes, they exchange information with each other, and they can be organized in sequences to support more complex processes. They are, in an odd way, complete in themselves and part of larger swarms.

Ibbaka defines AI agents as follows.

An agent is a piece of software that takes an action on behalf of a user.
An AI agent is an agent that leverages AI in order to decide what action to take and/or to execute the action.
An AI agent has the ability to observe its environment, take actions based on its observations, and then learn from the results of these actions

Download the Ibbaka report on Value Pricing and Billing for the Agent Economy

How are we going to price these agents and communicate their value?

One of the most powerful ways to innovate and find solutions is concept mapping. For a general treatment, see Some innovation patterns for concept blending.

How could we use concept blending and generative AI to develop approaches to pricing AI Agents?

In concept blending, one begins with a generic space and then uses this with two input spaces to generate a blending space. Generative AI is ideal for this sort of thing.

We begin with the generic space of pricing design, and then use the input spaces of Jobs to be Done and AI Agents to generate ideas for pricing AI agents.

Why Jobs to be Done? One of the most interesting new approaches to pricing is Gary Bailey’s Jobs-Led Pricing. He has shown that Clayton Christensen’s Jobs to be Done pricing is relevant to pricing and can be used to make sense of pricing AI. This works because agents are meant to perform specific jobs, and it is easy to connect the job to the value of the job. As value is the foundation of good pricing, this makes for a good match.

Some well-known agents and the jobs that they do are…

Aomni (Aomni): Automates prospect research and sales strategy generation for sales teams
Brightbot (BrightHive): Manages dataflows and integrations
FinAI (Intercom): Performs a customer support job and resolves customer tickets
Lindy AI (Lindy): Personal assistant for professionals, automating calendar management, email drafting, and travel coordination
Leena AI (Leena AI): Automates enterprise workflows and integrates with platforms like SAP and Salesforce to improve productivity

The basic framework for a concept blend has a generic space and two input spaces. In our case, the generic space is pricing design, and the input spaces are Jobs to be Done and B2B SaaS or AI Agents. We chose to generate blends on both B2B SaaS and AI agents so that we can compare B2B SaaS pricing with AI agent pricing.

These concept blends were generated using a proprietary prompt sequence that was run through Perplexity using a variety of models.

Concept Blend for Jobs to be Done and B2B SaaS in the Context of Pricing

Concept blend for Jobs to be Done Pricing for B2B SaaS generated April 2025.

The integration of Jobs to be Done (JTBD) with B2B SaaS creates a powerful new approach to pricing design that moves beyond traditional feature-based tiers to focus on the specific outcomes customers seek to achieve. This concept blend generates innovative pricing strategies that directly align with customer value.

Core Elements of the Blend

Job-Centric Pricing Tiers
Instead of conventional feature-based packages, pricing tiers are structured around the specific jobs customers need to accomplish. This approach recognizes that different customers "hire" the same software to perform different jobs, each with varying value propositions that justify different pricing levels.

Outcome Success Metrics
Pricing is directly tied to measurable outcomes that customers use to evaluate success in their core jobs. This creates clear connections between price and value, potentially enabling performance-based pricing components where customers pay based on achieved results rather than software access alone.

Contextual Value Adjustments
The pricing model incorporates contextual modifiers that adjust based on the specific circumstances in which jobs occur. This recognizes that the value of accomplishing the same job varies significantly based on company size, industry, or urgency, allowing for dynamic pricing that reflects true value in context.

Struggle-Based Segmentation
Customer segmentation shifts from traditional demographics to grouping customers based on common struggles preventing job completion. This approach identifies discrete segments with underserved outcomes, creating natural pricing tiers that address increasingly complex or critical struggles.

Value Communication Evolution
Communication about pricing transforms from feature lists to clear articulations of how the product helps customers make progress in their jobs. Pricing pages organize information around job outcomes rather than product capabilities, making value immediately apparent to potential customers.

See the complete blend here on Perplexity.

Concept Blend for Jobs to be Done and B2B SaaS in the Context of Pricing

Concept blend for Jobs to be Done Pricing for B2B AI Agents generated May 2025.

This concept blend creates AI systems that autonomously design, implement, and adapt pricing structures based on the specific "jobs" that pricing needs to accomplish for each stakeholder. Unlike traditional pricing approaches that focus on features or competitive positioning, these agents identify and respond to the causal mechanisms of value perception while operating with minimal human intervention.

Core Components:

Job-Oriented Pricing Intelligence: The AI agent identifies the specific "jobs" that pricing needs to accomplish for different stakeholders - what businesses need pricing to do (capture value, drive adoption, segment markets) and what customers need pricing to do (signal value, provide flexibility, create fairness).

Contextual Value-Capture Automation: The system autonomously adjusts pricing structures based on the specific context of customer jobs-to-be-done, dynamically aligning price with the value perceived in different usage scenarios.

Outcome-Driven Pricing Frameworks: Instead of static price lists, the agent establishes dynamic pricing frameworks that adapt based on measured outcomes achieved by customers, linking price directly to the successful completion of customer jobs.

Struggle-Resolution Pricing: The agent identifies specific struggles customers face in accomplishing their jobs and designs pricing structures that actively reduce those pain points, potentially charging more where it removes significant obstacles.

See the complete blend on Perplexity.

Comparing Jobs to be Done Pricing for B2B SaaS and B2B AI Agents

Are there important differences between Jobs to be Done Pricing for B2B SaaS and AI Agents? Yes.

B2B SaaS pricing has not made much use of the dynamic pricing solutions available from the big pricing optimization vendors like PROS, Pricefx, Zilliant, Vendavo, etc. Will that change with AI agents? One consequence of the move to an agent economy could be a convergence of the dynamic pricing optimization platforms (which have a lot of AI capabilities) and the billing systems (which are moving rapidly to support agent billing).

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