.png)
Frameworks, core principles and top case studies for SaaS pricing, learnt and refined over 28+ years of SaaS-monetization experience.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
In the rapidly evolving landscape of procurement automation, organizations are increasingly turning to agentic AI solutions to streamline operations, reduce costs, and enhance efficiency. However, a critical question emerges for both providers and users of these systems: What's the most effective pricing model for AI-powered procurement agents? Should customers pay for the underlying tool usage or only for successful procurement outcomes?
This question isn't merely academic—it strikes at the heart of value alignment, risk distribution, and adoption incentives in the emerging AI agents ecosystem. Let's explore the different approaches, their implications, and how organizations can make the right choice for their specific needs.
The Rise of AI Agents in Procurement
Procurement automation powered by specialized AI agents represents one of the most promising applications of artificial intelligence in business operations. These systems can:
- Continuously scan supplier markets for optimal pricing
- Negotiate terms based on organizational parameters
- Manage approval workflows autonomously
- Identify cost-saving opportunities across departments
- Ensure compliance with organizational policies and regulations
As these capabilities mature, stakeholders must determine not just what these systems can do, but how their value should be measured and priced.
Understanding Pricing Models for Procurement AI
Usage-Based Pricing: Paying for the Process
Under a usage-based pricing model, customers pay for the resources consumed by the AI agent during operation. This typically includes:
- API calls to large language models (LLMs)
- Computational resources utilized
- Number of procurement requests processed
- Time spent on tasks or negotiations
This approach mirrors traditional SaaS subscription models where customers pay for access to technology rather than guaranteed outcomes.
Outcome-Based Pricing: Paying for Results
Conversely, outcome-based pricing ties costs directly to successful procurement results:
- Cost savings achieved compared to benchmark prices
- Successfully completed procurement cycles
- Negotiation improvements over human baselines
- Time saved in the procurement process
This model aligns the vendor's incentives directly with customer success—if the AI agent doesn't deliver value, the provider doesn't get paid.
Hybrid Approaches: The Middle Ground
Many organizations are finding that credit-based pricing offers a balanced approach:
- Customers purchase "credits" that are consumed based on usage
- Successful outcomes may require fewer credits
- Failed attempts might consume credits at a reduced rate
- Volume discounts apply as usage scales
This model allows companies to control costs while still maintaining some alignment with outcomes.
The Case for Usage-Based Pricing
Proponents of usage-based pricing for procurement agents highlight several compelling advantages:
Predictable costs: Finance teams appreciate the ability to forecast AI expenses with greater certainty.
Fair technology valuation: According to research from Forrester, the technology itself provides value through process improvement and time savings, independent of outcomes.
Lower vendor risk: Usage-based models don't place the entire burden of success on the technology provider, recognizing that procurement outcomes depend on many factors beyond the AI's control.
Early-stage practicality: For emerging AI agent technologies, measuring "success" can be subjective and difficult to quantify reliably.
As one procurement director at a Fortune 500 company noted in a recent industry forum: "We're paying for the technology to do things we couldn't do efficiently before. Whether each interaction results in savings is less important than the aggregate transformation of our procurement function."
The Case for Outcome-Based Pricing
Advocates for outcome-based pricing offer equally compelling arguments:
Perfect alignment: The vendor only profits when the customer realizes value, creating a true partnership dynamic.
Reduced adoption friction: Organizations can implement procurement automation with minimal downside risk.
Focus on results: Outcome pricing forces vendors to continuously improve their AI agents' capabilities rather than simply driving usage.
ROI clarity: Decision-makers can easily justify investments in procurement automation when costs directly tie to measurable business outcomes.
A 2023 Gartner report suggests that by 2026, over 40% of AI agent implementations will incorporate some form of outcome-based pricing, up from less than 15% today.
Critical Considerations for Choosing a Pricing Model
When evaluating pricing models for procurement agents, organizations should consider:
1. Implementation Maturity
Early in the adoption cycle, usage-based pricing may make more sense as both parties learn what's possible. As the system matures and outcomes become more predictable, shifting toward outcome-based models becomes more feasible.
2. Guardrails and Governance
Robust guardrails and orchestration frameworks are essential regardless of pricing model. These systems prevent AI agents from making costly mistakes or operating outside acceptable parameters—particularly important for outcome-based models where vendors shoulder more risk.
3. Measurement Methodology
For outcome-based pricing to work, both parties must agree on clear, objective measurement methodologies. Questions to address include:
- What constitutes a "successful" procurement?
- How are baseline prices established?
- What factors might justify exemptions?
- How frequently will performance be assessed?
4. LLM Ops Integration
The underlying LLM operations infrastructure must provide transparency, allowing both parties to understand:
- Which tools the agent is using
- How resources are being consumed
- Where bottlenecks or inefficiencies exist
- How improved models might change the value equation
Industry Trends and Future Directions
The procurement automation landscape continues to evolve rapidly. Recent developments suggest:
Vertical specialization: AI agents are becoming increasingly specialized by industry, with pricing models adapting to sector-specific needs and value metrics.
Performance benchmarking: Third-party verification of procurement AI performance is emerging, providing objective standards for outcome measurement.
Risk-sharing agreements: Advanced contracts that share both upside and downside risk between vendors and customers are gaining popularity.
According to a recent McKinsey analysis, organizations implementing AI-powered procurement with appropriate pricing models are achieving 15-30% greater cost savings compared to those using traditional approaches.
Making the Right Choice for Your Organization
The optimal pricing strategy for procurement agents depends on your organization's specific circumstances:
For organizations new to AI agents: Consider starting with a usage-based or credit-based model with clear caps to limit risk while building familiarity.
For procurement transformation initiatives: An outcome-based model may better align with strategic objectives and provide clearer ROI justification.
For hybrid approaches: Consider negotiating pricing that includes both base usage fees and performance incentives, giving both parties skin in the game.
Whatever model you choose, ensure your agreement includes:
- Clear escape clauses if performance doesn't meet expectations
- Transparency requirements for resource utilization
- Regular review periods to reassess the pricing structure
- Data ownership and portability provisions
Conclusion: Aligning Incentives in the Age of AI Agents
The question of whether to bill for tool usage or successful outcomes in procurement automation isn't one with a universal answer. As agentic AI becomes increasingly sophisticated, we're likely to see continued innovation in pricing models that fairly distribute risk and reward.
The most successful implementations will focus not on which pricing model is theoretically superior, but on which approach best aligns vendor and customer incentives in their specific context. By thoughtfully structuring these relationships, organizations can accelerate their procurement transformation while ensuring sustainable value for all stakeholders.
As procurement automation continues to mature, the conversation will likely shift from "usage versus outcomes" to more nuanced discussions about how to measure and share the unprecedented value that truly intelligent procurement agents can deliver.