If you followed last week in AI, one thing is clear: agents are getting everywhere, and every major tech player is rushing to get positioned to gain market shares in this developing market, driven by three scaling laws of AI, which are concurrently propelling the AI industry.

Just last week, from Microsoft Build to Google I/O and, finally, Antrhopic’s latest releases, they all pushed in a single direction: the augmentation of human capabilities via agentic AI.

While it’s buzzy in the short term, this trend is here to stay, whether you call it MCP, agents, or something else.

The point is we’re getting to a place where AI systems can work continuously for hours to achieve very complex outcomes.

This progress combines extended thinking, tool use, instruction following, and reward hacking.

And we’re only at the start of this consolidating trend.

How did we get here, and what’s next?

Also, this is a comprehensive navigational guide on the good and bad we can expect from outcome-based business models and how to deal with them.

Let me explain!

The weekly newsletter is in the spirit of what it means to be a Business Engineer:

We always want to ask three core questions:

* What’s the shape of the underlying technology that connects the value prop to its product?

* What’s the shape of the underlying business that connects the value prop to its distribution?

* How does the business survive in the short term while adhering to its long-term vision through transitional business modeling and market dynamics?

These non-linear analyses aim to isolate the short-term buzz and noise, identify the signal, and ensure that the short-term and the long-term can be reconciled.

The software industry has undergone a remarkable transformation over the past four decades, not just in terms of technology and capabilities but fundamentally in how software is priced, distributed, and consumed.

This evolution reflects broader shifts in business models, customer expectations, and the very nature of value creation in the digital economy.

The Box Software Era (1980s): High Stakes, High Rewards

The 1980s marked the dawn of commercial software, characterized by what we now call "box software" or "shrinkwrap software."

This model was born out of necessity; the internet as we know it didn't exist, and software distribution required physical media like floppy disks, CDs, and later DVDs.

The Economics of Scarcity

Software in this era commanded premium prices ranging from hundreds to thousands of dollars for a single license.

Microsoft Office, for instance, could cost $500-700, while specialized enterprise software packages reached $10,000 or more.

This pricing reflected several key factors:

* Development costs were front-loaded: Companies invested heavily in development without guaranteeing market success.

* Distribution was expensive: Physical manufacturing, retail partnerships, and inventory management added significant costs.

* Updates were infrequent: Major version releases happened every 2-3 years, requiring customers to purchase upgrades separately.

* Piracy concerns: High prices partly compensated for expected revenue loss due to unauthorized copying.

Customer Impact

For businesses and consumers, software purchases represented major capital expenditures.

IT departments must carefully evaluate software investments, often requiring lengthy purchase approval processes.

The perpetual license model meant customers owned the software indefinitely but bore the risk of technological obsolescence.

This model created a unique dynamic: software companies needed to justify high prices with comprehensive feature sets, leading to increasingly complex "Swiss Army knife" applications that tried to serve every possible use case.

The SaaS Revolution (2000s): Democratizing Access

The emergence of reliable internet infrastructure in the late 1990s and early 2000s enabled a fundamental shift toward Software-as-a-Service (SaaS).

Pioneered by companies like Salesforce, this model transformed software from a product into a service.

The Subscription Breakthrough

SaaS introduced several revolutionary concepts:

* Lower barrier to entry: Instead of $5,000 upfront, customers could access enterprise software for $50-100 per month.

* Continuous updates: Software evolved constantly, with new features and security patches delivered automatically.

* Scalable pricing: Companies could start small and expand usage as they grew.

* Reduced IT burden: No more server installations, backup management, or version compatibility issues.

Business Model Innovation

The subscription model fundamentally altered the relationship between software companies and customers.

Instead of one-time transactions, vendors needed to demonstrate value instead of one-time transactions to prevent continuous churn.

This led to:

* Customer success becoming paramount: Vendors invested heavily in onboarding, training, and support.

* Predictable revenue streams: Monthly or annual recurring revenue (MRR/ARR) became the key metric.

* Product development acceleration: Faster feedback loops enabled more responsive product evolution.

* Global accessibility: Small businesses in developing markets could access the same enterprise-grade tools as Fortune 500 companies.

Market Transformation

By the mid-2000s, SaaS had proven its viability across multiple categories, including CRM, HR, accounting, project management, and more.

The model's success attracted massive venture investment, fueling the growth of companies like Workday, ServiceNow, and HubSpot.

The Consumption Era (2010s): Precise Value Alignment

As cloud infrastructure matured with Amazon Web Services, Microsoft Azure, and Google Cloud Platform, a new pricing paradigm emerged: consumption-based or usage-based pricing.

This model aligned costs directly with actual usage, creating unprecedented transparency and flexibility.

The API Economy

The rise of APIs (Application Programming Interfaces) enabled granular measurement of software consumption:

* Pay-per-call: Services like Twilio charge per SMS sent or phone call made.

* Storage-based: Cloud storage providers charge per gigabyte for storage and transfer.

* Compute-based: Processing power could be purchased by the hour or minute.

* Transaction-based: Payment processors charged per transaction processed.

Benefits for All Stakeholders

This model created win-win scenarios:

For customers:

* Cost predictability: Expenses scaled directly with business growth.

* Elimination of waste: No paying for unused capacity or features.

* Lower financial risk: Could test services with minimal upfront investment.

* Better budget allocation: IT spending became an operational expense tied to revenue.

For vendors:

* Reduced sales friction: Customers could self-serve and start using products immediately.

* Natural expansion revenue: Growing customers automatically generated more revenue.

* Improved product insights: Usage data revealed which features delivered real value.

* Global scalability: Automated billing enables customers to be served worldwide.

The Platform Effect

Consumption-based pricing enabled the rise of platform businesses.

Companies like Stripe, Shopify, and AWS built ecosystems where success was aligned; when customers' businesses grew, the platform's revenue grew proportionally.

The Outcome Era (2020s): Paying for Results

The current decade is witnessing the emergence of outcome-based pricing, where customers pay not for software access or usage but for specific business results achieved.

This represents the ultimate alignment of vendor and customer interests.

Beyond Software to Solutions

Modern outcome-based models focus on business impact:

* Revenue generation: Sales tools that charge based on deals closed.

* Cost savings: Efficiency software that reduces a percentage of costs.

* Risk mitigation: Security services paid based on threats prevented.

* Performance improvement: Marketing tools compensated based on conversion rate increases.

Real-World Examples

Several companies are pioneering this approach:

* Clay: Charges based on successful sales outcomes generated through their platform.

* Some cybersecurity vendors: Price based on security incidents prevented or compliance goals achieved.

* Marketing platforms: Compensation tied to conversion improvements or customer acquisition cost reductions.

* HR technology: Payment linked to retention improvements or time-to-hire reductions.

The Trust Factor

Outcome-based pricing requires unprecedented trust and transparency between vendors and customers.

Vendors must be confident in their ability to deliver results, while customers must trust in accurate measurement and reporting of outcomes.

The Broader Economic Implications

This evolution reflects deeper changes in the global economy:

From Ownership to Access

The shift mirrors broader trends from ownership to access—similar to how Spotify replaced CD purchases, or how Uber transformed transportation. Customers increasingly prefer paying for outcomes rather than owning assets.

Risk Transfer and Sharing

Each evolution has progressively shifted risk from customers to vendors:

* Box software: Customer bears all risks after purchase.

* SaaS: Shared risk with vendors responsible for uptime and updates.

* Consumption: Vendors risk unused capacity, and customers risk only actual usage.

* Outcome-based: Vendors bear performance risk,and customers pay only for success.

Data and AI Integration

Advances in data analytics and artificial intelligence enable the progression toward outcome-based pricing.

Vendors can now:

* Predict which customers will achieve specific outcomes.

* Optimize their solutions for measurable business impact.

* Provide real-time performance dashboards and reporting.

* Adjust pricing based on value delivered.

What's Next?

As we go through the market discovery phase of AI agents, the question remains of how to price the technology as it matures and becomes embedded in the enterprise ecosystem.

It does sound good, in theory, to price based on “jobs done” or outcome, but what does that mean?

The issue is that pricing based purely on outcome isn't easy.

So, I dived into some pricing models developed by different enterprise players who are either AI natives (Sierra) or are converting to an AI-first pricing model.

As artificial intelligence evolves from simple tools to autonomous agents capable of making decisions and completing complex tasks independently, the traditional software pricing paradigms are being fundamentally challenged.

Agentic AI systems, autonomous agents that can perceive their environment, make decisions, and take actions to achieve specific goals, require entirely new approaches to pricing that align with their unique value proposition and capabilities.

Unlike traditional software that provides capabilities or access, agentic AI delivers outcomes.

This fundamental shift from "what it can do" to "what it accomplishes" drives a comprehensive transformation in how we structure, negotiate, and implement pricing models in the enterprise AI space.

The Seven Pillars of Agentic AI Pricing

Based on our extensive analysis of market trends and real-world implementations, seven distinct pricing models have emerged as the primary approaches for monetizing agentic AI systems.

Each model serves different use cases, risk tolerances, and organizational contexts.

1. Pay Per Outcome: The Pure Results Revolution

Model Overview: Payment occurs only when the AI successfully completes a defined business outcome, with no charges for failed attempts or partial completions.

How It Works: This represents the purest form of outcome-based pricing. Companies like Sierra AI have pioneered this approach by charging customers only when their AI agents successfully resolve customer service tickets without human intervention. If the AI escalates to a human agent, no charge is incurred.

Best Applications:

* Customer service and support automation

* Sales process automation with clear conversion metrics

* Problem resolution scenarios with binary success/failure outcomes

Real-World Implementation: Sierra AI's model charges clients only for successful customer service resolutions, while Zendesk offers a similar approach at $1.50 per successful AI resolution. This model has proven particularly effective in customer support environments where resolution quality can be clearly measured and validated.

Customer Risk Level: Low - Customers pay only for proven value delivery, making this the lowest-risk model from a buyer's perspective.

Strategic Considerations: This model requires vendors to have extremely confident AI systems and robust measurement frameworks. It's ideal for building trust with new customers but requires careful definition of what constitutes "success."

2. Hybrid Base + Outcome: Balancing Predictability with Performance

Model Overview: Combines a smaller base fee with variable outcome-based components, providing revenue predictability for vendors while maintaining outcome alignment.

How It Works: Companies establish a modest monthly or annual base fee that covers platform access and basic services, then add variable fees tied to successful outcomes. This approach reduces vendor risk while maintaining strong customer value alignment.

Best Applications:

* Complex, multi-step business processes

* Enterprise implementations requiring ongoing support

* Scenarios where setup and maintenance costs are significant

Real-World Implementation: C3.ai employs this model with a substantial upfront pilot fee ($250,000 for a 3-month production pilot) followed by usage-based pricing post-deployment. This ensures the vendor can cover initial implementation costs while transitioning to outcome-based pricing as the system proves its value.

Customer Risk Level: Low-Medium - Base fees provide some cost predictability while variable components align with value delivery.

Strategic Considerations: This model works well for complex enterprise deployments where significant upfront investment is required. It allows vendors to cover implementation costs while demonstrating commitment to customer outcomes.

3. Revenue Share: The Commission-Based Approach

Model Overview: Vendors receive a percentage of revenue directly generated or recovered by the AI system, creating perfect alignment between vendor success and customer financial outcomes.

How It Works: Rather than charging for activities or access, vendors participate directly in the financial success they create. This model is particularly powerful for applications where AI can directly impact revenue streams.

Best Applications:

* Sales automation and conversion optimization

* Payment recovery and financial recovery systems

* Chargeback management and dispute resolution

* Revenue optimization scenarios

Real-World Implementation: Chargeflow charges 25% of successfully recovered chargebacks with no upfront or monthly fees. FlyCode operates similarly, taking a percentage of payment recovery while guaranteeing significant ROI improvements (4-6X monthly recovery, 20X net new revenue).

Customer Risk Level: Low - Customers only pay when they make money, creating a "can't lose" proposition.

Strategic Considerations: This model requires sophisticated tracking and attribution systems. It's most effective when AI can be directly tied to measurable financial outcomes and when the vendor can confidently deliver consistent results.

4. Consumption-Based: Pay-As-You-Go for AI Interactions

Model Overview: Traditional usage-based pricing where customers pay per conversation, interaction, or AI agent activation, regardless of outcome quality.

How It Works: This model treats AI agents like traditional cloud services, charging based on the volume of usage rather than the quality of outcomes. It provides predictable per-unit pricing while scaling with customer adoption.

Best Applications:

* High-volume, standardized interactions

* Chatbots and initial customer contact scenarios

* Routing and triage applications

* Scenarios where outcome measurement is difficult

Real-World Implementation: Intercom charges $0.99 per conversation handled by their Fin AI agent, regardless of whether the conversation results in a successful resolution. This provides predictable pricing but doesn't align with outcome quality.

Customer Risk Level: Medium - Customers pay for usage regardless of value received, creating potential for high costs without proportional benefits.

Strategic Considerations: This model is easier to implement but may create customer satisfaction issues if AI performance is inconsistent. It works best for high-volume, standardized interactions where baseline performance can be reasonably assured.

5. Subscription + Usage: The Hybrid Predictability Model

Model Overview: This model combines fixed monthly or annual subscription fees with usage-based overage charges, providing baseline predictability with scaling flexibility.

How It Works: Customers pay a base subscription that includes a certain level of AI usage and then pay additional fees for usage beyond those thresholds. This model mimics traditional SaaS pricing while accommodating the variable nature of AI workloads.

Best Applications:

* Predictable workloads with occasional spikes

* Enterprise environments requiring budget predictability

* Organizations transitioning from traditional software to AI solutions

Real-World Implementation: Many traditional SaaS companies adding AI features (like Salesforce and HubSpot) use this model, charging base subscription fees plus additional charges for AI feature usage beyond included allowances.

Customer Risk Level: Medium-base fees provide cost predictability, but usage charges can create budget uncertainty.

Strategic Considerations: This model helps bridge the gap between traditional software pricing and AI-specific approaches. It's beneficial for established software companies to add AI capabilities to existing products.

6. Performance Tiers: Quality-Based Pricing

Model Overview: Pricing varies based on achieved performance levels, accuracy metrics, or SLA compliance, incentivizing higher-quality AI performance.

How It Works: Instead of flat pricing, rates adjust based on measurable performance criteria. Higher accuracy or better SLA compliance results in lower per-transaction costs, incentivizing vendor and customer to optimize performance.

Best Applications:

* Quality-sensitive applications where accuracy is paramount

* Scenarios with clear SLA requirements

* Industries with regulatory compliance needs

* Applications where performance variations significantly impact business value

Real-World Implementation: Healthcare AI coding systems often use this approach, where pricing decreases as accuracy increases. For example, $5 per coding task at 90% accuracy, $3 at 95% accuracy, and $2 at 99% accuracy.

Customer Risk Level: Low-Medium - Customers benefit from better performance through lower costs, while maintaining predictable pricing ranges.

Strategic Considerations: This model requires sophisticated performance measurement and reporting systems. It works best when performance metrics directly correlate with business value and when both parties can influence outcomes.

7. Guaranteed Savings: Risk-Free Value Creation

Model Overview: Vendors charge a percentage of documented cost savings achieved, often with money-back guarantees if minimum thresholds aren't met.

How It Works: This model shifts all performance risk to the vendor while creating shared upside. Customers pay only when measurable savings are achieved, often with contractual guarantees around minimum performance levels.

Best Applications:

* Process optimization and efficiency improvements

* Energy management and cost reduction initiatives

* Operational automation with measurable cost impacts

* Risk-averse organizations requiring guaranteed outcomes

Real-World Implementation: Siemens offers energy management AI with pricing based on percentage of energy cost reductions achieved, backed by performance guarantees. Customers pay a percentage of verified savings with no charges if targets aren't met.

Customer Risk Level: Very Low - Customers face essentially no risk as they only pay for achieved savings, often with guarantee protections.

Strategic Considerations: This model requires vendors to have exceptional confidence in their AI systems and robust measurement capabilities. It's ideal for building trust and penetrating risk-averse markets but demands careful cost modeling.

Strategic Implementation Framework

Choosing the Right Model

The selection of an appropriate pricing model depends on several critical factors:

* AI System Maturity: More mature, proven AI systems can support outcome-based models, while newer systems may require consumption-based approaches initially.

* Customer Risk Tolerance: Risk-averse customers prefer guaranteed savings or pay-per-outcome models, while growth-oriented customers may accept consumption-based pricing for faster implementation.

* Measurability of Outcomes: Clear, measurable outcomes enable sophisticated pricing models, while ambiguous value propositions require simpler approaches.

* Competitive Landscape: Differentiation opportunities exist in offering more customer-friendly pricing models than competitors.

* Vendor Financial Position: Outcome-based models require vendors to carry more risk and potentially longer cash conversion cycles.

Implementation Best Practices

* Start Conservative, Evolve Progressively: Many successful companies begin with consumption-based models to prove AI value, then migrate toward outcome-based approaches as trust and performance improve.

* Invest in Measurement Infrastructure: Sophisticated pricing models require robust analytics, attribution, and reporting systems. This infrastructure often becomes a competitive advantage.

* Align Internal Incentives: Sales compensation, customer success metrics, and product development priorities must align with chosen pricing models to ensure consistent execution.

* Design for Transparency: Customers need clear visibility into how charges are calculated and what drives pricing variations. Transparency builds trust and reduces friction.

* Plan for Edge Cases: Define clearly what happens when outcomes are partially achieved, when external factors influence results, or when system performance degrades.

Market Trends and Future Directions

The Outcome-Based Future

Market evidence strongly suggests that outcome-based pricing models will become increasingly dominant for agentic AI systems. Several factors drive this trend:

* Customer Demand: Enterprise buyers increasingly refuse to pay for AI capabilities without proven results, driving demand for pay-for-performance models.

* AI Capability Improvements: Vendors gain confidence to offer outcome guarantees as AI systems become more reliable and predictable.

* Competitive Differentiation: Early adopters of outcome-based pricing gain significant competitive advantages in sales cycles.

* Investor Pressure: AI companies face increasing pressure to demonstrate clear value delivery rather than just technology capabilities.

Industry-Specific Evolution

Different industries are adopting outcome-based pricing at varying rates:

* Customer Service: Leading the adoption with clear, measurable outcomes and high-volume interactions providing statistical reliability.

* Financial Services: Strong adoption driven by regulatory requirements and clear ROI measurement capabilities.

* Healthcare: Slower adoption due to regulatory complexity, but growing interest in performance-based contracts.

* Manufacturing: Increasing adoption for predictive maintenance and quality optimization applications.

Technology Enablers

Several technological developments are accelerating the adoption of sophisticated pricing models:

* Advanced Analytics: Better attribution and measurement capabilities enable more precise outcome tracking.

* Blockchain and Smart Contracts: Automated execution of outcome-based pricing agreements with transparent, immutable records.

* Real-Time Processing: Immediate outcome measurement and billing enable more responsive pricing models.

* AI Performance Monitoring: Sophisticated monitoring systems provide the reliability needed for outcome guarantees.

Outcome-Based Pricing: What Can Go Wrong?

While outcome-based pricing for agentic AI represents a compelling evolution in software monetization, the reality of implementation reveals significant challenges, risks, and potential pitfalls that vendors and customers must carefully navigate.

The seductive appeal of "pay only for results" can obscure complex underlying issues that, if not adequately addressed, can lead to failed implementations, damaged relationships, and financial losses for all parties involved.

1. The Attribution Problem: Who Gets Credit for Success?

The Challenge: In complex business environments, attributing specific outcomes to AI systems becomes extraordinarily difficult.

What Goes Wrong:

* Multiple Variables: Business outcomes are influenced by countless factors, including market conditions, human decisions, seasonal variations, competitive actions, and other technology systems. Isolating AI's contribution can be nearly impossible.

* Correlation vs. Causation: Just because an outcome improves after AI implementation doesn't mean the AI caused the improvement.

* Baseline Disputes: Establishing accurate baseline measurements becomes contentious when significant money depends on proving improvement.

Real-World Example: A sales AI system is implemented when the company also launches a new marketing campaign, hires experienced sales staff, and introduces a new product line. When sales increase by 30%, determining how much credit the AI deserves becomes a source of ongoing dispute.

Financial Impact: Legal costs, relationship deterioration, and potential contract termination can far exceed the value of the disputed outcomes.

2. The Measurement Nightmare: Defining Success

The Challenge: Creating clear, unambiguous definitions of successful outcomes that both parties agree upon.

What Goes Wrong:

* Subjective Interpretations: Different stakeholders can interpret terms like "customer satisfaction," "successful resolution," or "quality improvement" differently.

* Gaming the System: When specific metrics are targeted, both AI systems and human operators may optimize for the metric rather than the underlying business goal.

* Metric Evolution: Business priorities change, but outcome-based contracts may lock parties into outdated success criteria.

Real-World Example: An AI customer service system is measured on "successful resolutions," but the definition doesn't account for customers who claim satisfaction initially but return with the same issue days later. The vendor counts these as successes, while the customer considers them failures.

Financial Impact: Continuous disputes over metric definitions can consume management attention and lead to expensive contract renegotiations.

3. The External Factor Trap: When the World Changes

The Challenge: Outcome-based contracts often fail to account for external factors beyond either party's control.

What Goes Wrong:

* Market Disruptions: Economic downturns, industry disruptions, or competitor actions can impact AI effectiveness regardless of system quality.

* Regulatory Changes: New regulations can alter how success is measured or make certain outcomes impossible to achieve.

* Customer Behavior Changes: The AI may perform perfectly, but changing customer expectations or behaviors can impact measured outcomes.

Real-World Example: A recruitment AI system performs well until new privacy regulations drastically limit available candidate data, making previous performance levels impossible to maintain. The vendor is penalized for factors completely outside its control.

Financial Impact: Vendors may face significant revenue loss due to circumstances beyond their control, while customers may lose access to valuable AI capabilities.

Vendor-Specific Risks and Failures

4. The Cash Flow Crisis: Revenue Unpredictability

The Challenge: Outcome-based pricing creates highly unpredictable revenue streams that can destabilize vendor operations.

What Goes Wrong:

* Delayed Recognition: Revenue may not be recognized until outcomes are measured and verified, creating significant cash flow delays.

* Seasonal Variations: Vendor revenue becomes similarly volatile if customer outcomes vary seasonally.

* Customer Concentration Risk: Heavy dependence on outcome-based contracts with few large customers creates existential business risk.

Real-World Example: An AI vendor signs a major outcome-based contract expecting $2M in annual revenue, but customer business seasonality means $1.5M comes in Q4, creating nine months of cash flow challenges that nearly bankrupt the company.

Financial Impact: When revenue predictability is low, working capital requirements increase dramatically, and access to traditional financing becomes difficult.

5. The Performance Penalty: When AI Systems Degrade

The Challenge: AI systems can experience performance degradation over time due to data drift, model decay, or changing conditions.

What Goes Wrong:

* Model Drift: AI performance naturally degrades as real-world conditions diverge from training data.

* Data Quality Issues: Customer data quality changes can impact AI performance without vendor control.

* Feedback Loops: Poor outcomes can create negative feedback loops that further degrade performance.

Real-World Example: A fraud detection AI performs excellently initially but gradually becomes less effective as fraudsters adapt their techniques. Despite maintaining the same system, the vendor's revenue drops steadily as detection rates decline.

Financial Impact: Vendors face declining revenue while maintaining system operation costs, potentially creating unsustainable unit economics.

6. The Support Cost Explosion: Hidden Implementation Expenses

The Challenge: Outcome-based contracts often underestimate the ongoing support and optimization required to maintain performance.

What Goes Wrong:

* Continuous Tuning: Maintaining outcome performance requires constant system optimization and adjustment.

* Customer Training: Ensuring customer teams use AI systems optimally becomes an ongoing vendor responsibility.

* Integration Complexity: Deeper integration requirements emerge as outcome dependencies become clear.

Real-World Example: An AI scheduling system requires constant fine-tuning to maintain optimization outcomes, leading to support costs that are 300% higher than initially projected, turning a profitable contract into a loss-maker.

Financial Impact: Support costs can easily exceed contract value, especially for complex enterprise implementations requiring continuous optimization.

Customer-Specific Risks and Failures

7. The Vendor Dependency Trap: Loss of Control

The Challenge: Outcome-based contracts can create unhealthy dependencies that limit customer flexibility and control.

What Goes Wrong:

* Vendor Lock-in: Switching becomes prohibitively expensive when outcomes depend heavily on specific vendor systems.

* Loss of Internal Capability: Organizations may reduce internal expertise, becoming overly dependent on vendor performance.

* Strategic Inflexibility: Long-term outcome-based contracts can prevent organizations from adapting to changing strategic priorities.

Real-World Example: A company becomes so dependent on an AI vendor's outcome guarantees that it eliminates its internal analytics team. When the vendor relationship sours, the company lacks the internal capability to maintain operations.

Financial Impact: Organizations may pay premium costs to maintain vendor relationships they cannot afford to exit, reducing negotiating power significantly.

8. The Expectation Escalation: When Success Breeds Unrealistic Demands

The Challenge: Successful outcome-based implementations can create unrealistic expectations for future performance improvements.

What Goes Wrong:

* Moving the goalposts: Success in one area leads to demands for improvements in adjacent areas not covered by original contracts.

* Performance Plateaus: AI systems often reach performance plateaus, but customer expectations continue rising.

* Scope Creep: Success leads to requests for expanded scope without proportional increases in vendor compensation.

Real-World Example: An AI system successfully reduces customer service costs by 30%, leading management to expect similar improvements in sales, marketing, and operations—areas the system was never designed to address.

Financial Impact: Unrealistic expectations can lead to contract terminations and deterioration of vendor relationships, requiring expensive replacements or internal development.

9. The Outcome Myopia: Optimizing for the Wrong Things

The Challenge: Focus on specific, measurable outcomes can lead to the optimization of metrics rather than overall business value.

What Goes Wrong:

* Metric Fixation: Organizations become obsessed with contracted metrics while ignoring broader business impact.

* Unintended Consequences: AI optimization for specific outcomes may negatively impact unmeasured but important areas.

* Short-term Thinking: Pressure for immediate outcome achievement can undermine long-term strategic goals.

Real-World Example: An AI system optimizes customer service resolution speed, achieving contracted targets but significantly reducing customer satisfaction as issues are resolved quickly but superficially.

Financial Impact: Achieving contracted outcomes while damaging overall business performance can be far more expensive than the original problem the AI was meant to solve.

Systemic Market Risks

10. The Racing to the Bottom: Competitive Pressure and Unsustainable Models

The Challenge: Market competition in outcome-based pricing can lead to unsustainable vendor promises and customer expectations.

What Goes Wrong:

* Vendor Desperation: Competitive pressure makes vendors accept unfavorable outcome-based terms to win deals.

* Customer Unrealistic Expectations: Success stories create market expectations that are impossible to replicate across all implementations.

* Market Consolidation: Only the most prominent vendors can sustain outcome-based models, reducing competition and innovation.

Real-World Example: Smaller AI vendors accept outcome-based contracts with minimal base fees to compete with larger players, leading to bankruptcies when performance targets prove unachievable.

Financial Impact: Market consolidation reduces customer choice and can lead to higher long-term costs as competition decreases.

11. The Regulatory Backlash: When Outcome-Based Models Face Legal Challenges

The Challenge: Outcome-based pricing models may face regulatory scrutiny or legal challenges as they become more prevalent.

What Goes Wrong:

* Consumer Protection Issues: Regulators may view some outcome-based models as unfair to customers, especially when vendor incentives misalign customer interests.

* Antitrust Concerns: Outcome-based models might create market behaviors that attract regulatory attention.

* Liability Questions: When AI systems are responsible for outcomes, liability and insurance questions become complex.

Real-World Example: Healthcare AI vendors using outcome-based pricing face regulatory investigation when cost-cutting incentives appear to compromise patient care quality.

Financial Impact: Regulatory compliance costs and potential legal liabilities can make outcome-based models economically unviable in regulated industries.

The Implementation Failure Patterns

12. The Pilot-to-Production Problem: When Success Doesn't Scale

The Challenge: Successful pilot implementations often fail to translate into successful production deployments under outcome-based contracts.

What Goes Wrong:

* Scale Complexity: Factors that don't matter in pilots become critical at scale.

* Integration Challenges: Production environments reveal integration complexities that are not apparent in controlled pilots.

* Performance Degradation: AI systems working well with curated pilot data may struggle with the variety and quality of real-world data.

Real-World Example: An AI system achieves 95% accuracy in a controlled pilot with 1,000 transactions but drops to 70% when processing 100,000 diverse real-world transactions daily.

Financial Impact: Vendors may invest heavily in scaled deployments that fail to meet outcome targets, while customers face disruption from underperforming systems.

13. The Human Factor Failure: When People Don't Adapt

The Challenge: Outcome-based success often depends critically on human behavior changes that are difficult to predict and control.

What Goes Wrong:

* Resistance to Change: Employees may resist using AI systems effectively, undermining outcome achievement.

* Training Inadequacy: Insufficient training on AI system optimization prevents outcome realization.

* Cultural Misalignment: Organizational culture may not support the behavioral changes necessary for AI success.

Real-World Example: A sales AI system can technically improve conversion rates by 40%, but sales teams resist using AI recommendations, achieving only 10% improvement and missing contracted outcome targets.

Financial Impact: When technically sound AI systems fail due to human factors, both vendors and customers lose, often leading to expensive contract disputes and relationship deterioration.

Mitigation Strategies and Best Practices

While the risks of outcome-based pricing are significant, they are not insurmountable. Successful implementations require:

For Vendors:

* Robust Baseline Analysis: Invest heavily in understanding customer environments before committing to outcomes

* Gradual Transition Models: Start with hybrid pricing and evolve toward outcome-based as confidence builds

* Force Majeure Protections: Include comprehensive external factor protections in contracts

* Performance Monitoring: Implement real-time monitoring to detect and address performance degradation quickly

For Customers:

* Realistic Expectation Setting: Understand that AI systems have limitations and performance variability

* Internal Capability Maintenance: Retain sufficient internal expertise to avoid complete vendor dependency

* Comprehensive Success Metrics: Define success broadly to avoid metric optimization at the expense of business value

* Change Management Investment: Invest heavily in organizational change management to support AI adoption

For Both Parties:

* Collaborative Contract Design: Work together to create fair, realistic, and mutually beneficial agreement terms

* Regular Review Processes: Build in regular contract review and adjustment mechanisms

* Shared Risk Models: Design models where both parties have skin in the game for success

* Exit Strategy Planning: Plan for contract termination scenarios from the beginning

Proceed with Informed Caution

Outcome-based pricing for agentic AI represents a significant evolution in software monetization that powerfully aligns vendor and customer interests.

However, the complexity of implementation and the potential for significant negative consequences require careful consideration and expert execution.

The most successful outcome-based implementations will acknowledge these risks upfront, design comprehensive mitigation strategies, and maintain realistic expectations about what AI systems can and cannot achieve.

Organizations that rush into outcome-based contracts without adequate preparation and protection may be worse off than those that use traditional pricing models.

The future of AI pricing will likely include outcome-based elements, but the path forward requires learning from early failures, developing industry best practices, and creating legal and regulatory frameworks that protect all parties while enabling innovation.

As the market matures, we expect to see more sophisticated approaches that balance the benefits of outcome alignment with the practical realities of AI system limitations and business complexity.

The winners will be those who can navigate these challenges thoughtfully and systematically rather than those who chase the latest pricing trend without understanding its implications.

The Key Take

Agentic AI drives a fundamental shift in enterprise software pricing from paying for access or usage to paying for outcomes, reshaping how value is delivered, measured, and monetized.

As AI systems evolve into autonomous agents capable of achieving complex business results, traditional models like subscriptions and usage fees give way to outcome-based frameworks such as pay-per-result, revenue share, and guaranteed savings.

While this shift aligns vendor incentives with customer success and reduces buyer risk, it also introduces significant challenges around attribution, performance variability, financial unpredictability, and operational complexity.

Navigating this new landscape requires thoughtful model selection, robust measurement infrastructure, aligned incentives, and a shared commitment to value creation.

Recap: In This Issue!

The Rise of Agentic AI and the Pricing Paradigm Shift

* Agentic AI marks a shift from tools to autonomous agents that deliver results, not just capabilities.

* This requires rethinking software pricing models — from access- and usage-based to outcome-based frameworks.

Historical Context: Four Software Pricing Eras

* Box Software (1980s): High upfront pricing, one-time purchases, limited updates.

* SaaS (2000s): Subscription-based, continuous delivery, and customer success-driven.

* Consumption-Based (2010s): Pay-per-use via APIs, storage, or compute; granular billing aligned with actual use.

* Outcome-Based (2020s): Pricing tied to business results like cost savings, revenue generation, or risk mitigation.

The Seven Pillars of Agentic AI Pricing

These models reflect diverse enterprise use cases and risk tolerances:

* Pay Per Outcome – Pay only for successful task completion (e.g. Sierra AI).

* Risk: Low for customer

* Ideal for: Support resolution, binary success metrics

* Hybrid Base + Outcome – Base fee plus performance-linked variable.

* Risk: Low-medium

* Ideal for: Complex enterprise AI deployments

* Revenue Share – Vendor earns % of revenue generated (e.g. Chargeflow).

* Risk: Low for customer

* Ideal for: Sales, payments, monetization tools

* Consumption-Based – Pay per interaction or API call (e.g. Intercom).

* Risk: Medium

* Ideal for: High-volume AI workflows

* Subscription + Usage – Fixed fee with usage overage charges.

* Risk: Medium

* Ideal for: Traditional SaaS transitioning to AI

* Performance Tiers – Pricing varies by accuracy or SLA.

* Risk: Low-medium

* Ideal for: Regulated and precision-critical sectors

* Guaranteed Savings – Payment tied to verified cost savings.

* Risk: Very low for customer

* Ideal for: Energy, ops automation, efficiency tools

Strategic Framework for Selecting Pricing Models

* AI maturity: Newer systems favor consumption; proven systems can offer guarantees.

* Customer risk appetite: Risk-averse buyers prefer outcome guarantees.

* Outcome measurability: Clear KPIs enable performance-based models.

* Vendor cash flow: Outcome models can delay revenue recognition.

Market Trends Accelerating Outcome-Based Pricing

* Customer demand for ROI clarity

* AI reliability enabling confident performance promises

* Investor pressure for clear value delivery

* Industry-specific adoption patterns:

* High: Customer service, financial services

* Emerging: Manufacturing, healthcare

Enabling Technologies

* Advanced analytics and attribution

* Real-time billing infrastructure

* AI performance monitoring tools

* Smart contracts for automated outcome enforcement

What Can Go Wrong: 13 Risks of Outcome-Based Pricing

A comprehensive breakdown of failure patterns:

Attribution + Measurement Risks

* Outcomes are often influenced by multiple factors (e.g., sales boost from marketing and AI).

* Disputes arise over what counts as success, metrics gamification, and outdated KPIs.

External Disruption

* Economic shifts, regulatory changes, or user behavior can undermine AI effectiveness beyond vendor control.

Vendor Risks

* Revenue unpredictability strains finances (e.g., seasonal revenue impact).

* Model degradation due to drift or data issues hurts long-term outcomes.

* Support costs surge due to constant tuning and deeper integration.

Customer Risks

* Vendor dependency increases as internal capabilities erode.

* Expectation inflation leads to unsustainable demands beyond initial scope.

* Outcome myopia causes over-optimization of narrow metrics at the expense of broader goals.

Systemic Risks

* Race to the bottom as vendors underbid to compete, risking insolvency.

* Regulatory backlash in sensitive industries (e.g., healthcare AI with cost-cutting incentives).

* Pilot-to-production gap where early success doesn't scale due to complexity.

* Human behavior issues (resistance, poor adoption) undermine AI performance.

Mitigation Best Practices

For Vendors

* Start with hybrid models, evolve into performance-based.

* Invest in attribution, baselining, and real-time monitoring.

* Design contracts with force majeure clauses and defined exceptions.

For Customers

* Set realistic expectations and retain internal expertise.

* Broaden success metrics beyond short-term KPIs.

* Invest in change management to drive adoption.

For Both

* Co-develop success definitions and transparent pricing models.

* Plan for scale, edge cases, and contract reviews upfront.

The Future of AI Pricing

* Outcome alignment is inevitable but not one-size-fits-all.

* Success will come to those who view pricing as a strategic tool, not a transactional detail.

* Early adopters must embrace the opportunity and complexity of outcome-based models to build trust, deliver value, and drive sustainable growth.

With massive ♥️ Gennaro Cuofano, The Business Engineer

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