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1. The Great Pricing Shift
We're witnessing something unprecedented in business history: a fundamental reimagining of how companies price their products and services. This isn't just another technological shift; it's a complete restructuring of value creation and capture, driven by Artificial Intelligence's (AI) unique characteristics that don't fit neatly into our traditional pricing playbooks.
1. 1 The Crack in the Foundation
Traditional pricing models were built for a different era. They emerged from manufacturing economies where value was tied to tangible inputs, physical goods, labour hours, and later, software licenses. You could count, touch, or clearly define these things. A car's price reflected its materials and labour costs. Software was priced per user per month based on development and computing capacity.
But AI agents play by a completely different set of rules. They learn continuously, improve autonomously, and often deliver value that's difficult to pin down. So, how do you put a price on something that gets smarter over time? How do you charge for outcomes that emerge from a complex dance between algorithms, data, and user behaviour?"
The answer is simple: the old playbook no longer applies.
Economically, pre-AI era software was a static, depreciating asset built on a rigid set of rules, whose value eroded over time due to fixed functionality and obsolescence. In contrast, post-AI software has evolved into a dynamic, compounding asset that grows exponentially in value as it continuously learns, adapts, and provides increasing returns to users over its lifespan.
1.2 The Triggers: Why Now?
Several forces have emerged and converged to make this pricing revolution not just possible, but inevitable:
- The Autonomy Factor: Unlike traditional software that needs human input, AI agents work on their own. They don't just complete tasks; they also optimise, learn, and adapt. This continuous process creates an ongoing value that doesn't fit with conventional per-seat or per-transaction payment models
- The Attribution Challenge: The AI systems often work in the background, influencing outcomes in ways that are measurable but indirect. A recommendation engine, for instance, might drive a 15% increase in sales, but proving direct causation is crucial for pricing these systems appropriately.
- The Compound Value Effect: Traditional products either depreciate or maintain value over time. In contrast, well-designed AI systems become more valuable as they process more data and interactions. This creates a pricing paradox: the longer a customer uses the system, the more valuable it gets, yet traditional models often charge a flat monthly fee regardless of all that accumulated learning.
- The Risk Redistribution: AI's ability to measure its business impact is fundamentally changing how risk is handled. Instead of customers paying upfront for uncertain benefits, providers can now tie their compensation directly to the outcomes they deliver. This approach has been tried before in management consulting, but it has rarely succeeded due to attribution difficulties. Now, AI's data-driven nature finally makes this once-unworkable model viable.
Autonomy aligns with the X-axis, representing an AI's inherent ability to independently create value; moving from traditional tools (low autonomy) to advanced AI agents (high autonomy). Attribution, on the Y-axis, is the challenge of measurably linking that AI-driven value to specific outcomes, shifting from indirect impacts (low attribution) to directly provable results (high attribution). like outcome-based, emerge as AI systems become more autonomous and their impact more attributable.
2. The New Pricing Models Taking Shape
2.1 Continuous Value Subscriptions
The subscription model isn't new, but AI transforms its fundamental logic. Traditional SaaS charges for access to functionality. AI subscriptions charge for ongoing intelligence that improves over time.
Consider customer support AI agents. Rather than charging per ticket handled (which incentivises volume over quality), companies are moving toward subscriptions that account for the agent's improving performance. As the AI learns from more interactions, it resolves issues faster and more accurately, creating a subscription that becomes more valuable without increasing costs.
Challenges:
- Justifying recurring fees with consistent value improvements
- Managing expectations as AI capabilities evolve
- Balancing feature updates with stable pricing
How to Implement:
- Offer tiered plans based on usage or features (e.g., basic, pro, enterprise).
- Provide transparent performance metrics (e.g., resolution times) to demonstrate value.
- Use feedback loops to refine AI and justify pricing.
Key Aspects Utilised:
- Autonomy is High: AI independently handles tasks like customer queries, minimising human intervention.
- Attribution is AI-centric: Customers pay for the agent’s improved output (e.g., faster resolutions), not human effort.
- Compound Value Effect is Central: AI’s learning increases value over time, enhancing ROI without raising fees.
- Risk Redistribution is Moderate: customers face less risk as value grows, but providers must ensure consistent AI improvements.
Explanation:
High Autonomy (90%) as AI resolves tickets independently. Attribution ties price to AI output ($500 fixed fee). Compound Value effect boosts tickets by 60% over 12 months. Moderate risk (60%) on the provider to ensure value exceeds fee.Workings of an Example
2.2 Outcome-Based Pricing: Where Value Meets Price
This isn't entirely new; management consultants and advertising agencies have long tried to tie fees to results. But AI's ability to generate detailed attribution data finally makes this model scalable and reliable.
Intercom's approach with Fin AI exemplifies this shift: charging $0.99 per resolved customer issue rather than per seat or per message. The pricing directly correlates with value delivered, eliminating the disconnect between cost and benefit that plagues traditional models.
The key difference is measurement. Previous outcome-based attempts often relied on approximate attribution or lengthy evaluation periods. AI systems generate real-time performance data, making outcome measurement more precise and immediate.
Challenges:
- Defining clear, agreed-upon KPIs.
- Tracking and verifying outcomes transparently.
- Managing risk if outcomes fall short.
How to Implement:
- Collaborate on success metrics upfront.
- Use analytics tools for transparent tracking.
- Offer performance guarantees to build confidence.
Key Aspects Utilised:
- Autonomy is Moderate: AI delivers results, but human oversight ensures KPI accuracy.
- Attribution is Outcome Focused: Pricing reflects results (e.g., issues resolved), not just AI or human effort.
- Compound Value Effect is Moderate: AI improves outcome efficiency over time, increasing value.
- Risk Redistribution is High: Risk shifts to providers, who are paid only for verified results.
Explanation:
Moderate Autonomy (80%) with human oversight for KPIs. Attribution links price to outcomes ($1754 for 1772 issues). Compound Value effect increases outcomes by 77%. High Risk (11%) on provider for outcome shortfalls.
2.3 Platform Ecosystems: The Network Effect Multiplier
Platforms have been around for a while, think of Apple's App Store. However, AI agents create ecosystems with different dynamics. Traditional app marketplaces host static tools. AI agent marketplaces host learning systems that improve through interaction not just with users, but potentially with each other.
This creates compound network effects. Each new agent doesn't just add functionality; it potentially improves the entire ecosystem's intelligence through data sharing and collaborative learning.
Challenges:
- Ensuring quality control for third-party agents.
- Balancing revenue splits to incentivise developers.
- Maintaining platform security and trust.
How to Implement:
- Create an open API for developer integration.
- Establish clear revenue-sharing agreements (e.g., 70/30 split).
- Promote top agents to drive ecosystem growth.
Key Aspects Utilised:
- Autonomy is High: Platform and agents operate independently, with minimal human intervention.
- Attribution is Shared: Pricing reflects the combined value from the platform and developers.
- Compound Value Effect is High: Ecosystem grows more valuable as more agents and users join.
- Risk Redistribution is Moderate: Platforms bear the risk of maintaining quality, while developers risk creating viable agents.
Explanation:
High Autonomy (95%) as agents operate independently. Attribution splits revenue ($79,890) between platform and developers. Compound value effect drives 432% user growth. (90%) on platform for quality assurance.
2.4 Data Monetisation - From Byproduct to Business Model
Data monetisation has been discussed for years, but it often remained theoretical or limited to advertising models. AI changes this by making data's value more concrete and measurable. Clean, relevant datasets directly improve AI performance in quantifiable ways.
Healthcare providers, for instance, are beginning to license anonymised patient data to AI developers building diagnostic tools. The value is clear: better training data leads to more accurate AI systems, which deliver measurable improvements in patient outcomes.
Challenges:
- Ensuring privacy and regulatory compliance (e.g., GDPR, HIPAA).
- Maintaining data quality and relevance.
- Competing with low-cost or free data sources.
How to Implement:
- Invest in data cleaning and anonymisation.
- Target high-value industries (e.g., healthcare, finance).
- Use secure licensing agreements.
Key Aspects Utilised:
- Autonomy is Low: Data is static, though AI can enhance its value through processing.
- Attribution is Direct: Revenue comes from the data itself, with clear value to buyers.
- Compound Value Effect is Moderate: The Data’s value grows as AI applications expand.
- Risk Redistribution is Low: Buyers bear the risk of data utility, while providers ensure compliance.
2.5 Hybrid Models: The Transition Strategy
Perhaps most importantly, we're seeing hybrid human-AI pricing models that serve as bridges during the transition period. These models recognise that full automation isn't always desirable or possible immediately.
A marketing agency might use AI for data analysis and initial content generation, but rely on human strategists for creative direction and client relationship management. Clients pay a premium for this combination, getting AI's efficiency with human judgment and accountability.
Challenges:
- Balancing human and AI roles.
- Justifying premium pricing as AI matures.
- Training humans to work with AI tools.
How to Implement:
- Define clear roles for AI (e.g., data processing) and humans (e.g., strategy).
- Offer transparent pricing reflecting the combined value.
- Gradually increase AI’s role as trust grows.
Key Aspects Utilised:
- Autonomy is Moderate: AI handles repetitive tasks, humans provide high-value input.
- Attribution is Blended: Pricing reflects AI efficiency and human expertise.
- Compound Value Effect is Moderate: AI’s learning enhances efficiency, increasing value over time.
- Risk Redistribution is Moderate: clients face less risk with human oversight, while providers balance AI-human integration.
3. The Implementation Reality
Despite these innovations, adoption isn't uniform or immediate. Different industries are moving at different speeds, constrained by various factors:
- Financial Services are moving quickly toward outcome-based models for fraud detection and trading algorithms, where success metrics are clear and quantifiable.
- Healthcare is more cautious, with hybrid models dominating as practitioners require human oversight for liability and ethical reasons.
- Marketing and Sales are leading in platform ecosystems, with companies like HubSpot and Salesforce rapidly expanding their AI agent marketplaces.
- Manufacturing is embracing subscription models for predictive maintenance AI, where continuous monitoring and improvement directly translate to cost savings.
4. The Timeline and Challenges Ahead
This transition isn't happening overnight. Early adopters are experimenting with these models now, but mainstream adoption faces several hurdles:
4.1 The Attribution Problem
While AI improves attribution, it doesn't solve it completely. Complex business environments with multiple variables make it challenging to isolate AI's specific impact. Companies are investing heavily in attribution infrastructure, advanced analytics platforms, controlled testing environments, and sophisticated ROI modelling tools.
4.2 The Trust Gap
Outcome-based pricing requires significant trust between providers and customers. Clients must believe in the provider's ability to deliver results, while providers must trust clients to accurately report outcomes. This trust builds gradually through smaller engagements and transparent reporting.
4.3 The Revenue Predictability Challenge
CFOs love predictable revenue streams. Outcome-based models introduce variability that makes financial planning more complex. Many companies are adopting hybrid approaches—combining fixed fees with performance bonuses—to balance predictability with alignment.
5. Looking Forward
Industry analysts suggest we're in the early stages of this transition. Gartner predicts that by 2027, over 40% of enterprise AI investments will include outcome-based pricing components, up from less than 10% today.
The companies succeeding in this transition share common characteristics: they invest heavily in measurement infrastructure, build transparent relationships with customers, and design their AI systems from the ground up with pricing in mind rather than treating it as an afterthought.
Perhaps most importantly, they recognise that this isn't just about changing how they charge, it's about fundamentally rethinking how they create and deliver value. The pricing model becomes the product strategy.
Gartner predicts that by 2027, over 40% of enterprise AI investments will include outcome-based pricing components, up from less than 10% today.
6. The Broader Implication
This pricing evolution reflects something deeper: we're moving from an economy based on possession to one based on outcomes. Instead of buying tools and hoping for results, customers are increasingly able to buy the results directly.
This shift has profound implications beyond pricing. It changes how companies design products, structure organisations, and build customer relationships. It rewards true value creation over clever positioning or feature inflation.
The companies that master this transition won't just have better pricing models; they will have fundamentally better businesses. They will be aligned with their customers' success, measured by real outcomes, and constantly improving through the feedback loop that AI makes possible.
The great pricing shift isn't just coming, it's here. The question isn't whether it will happen, but how quickly companies can adapt to thrive in this new landscape where value flows in entirely new directions.
Pricing Spectrun with Pre AI and Post AI Era
Note on How to Position AI-enabled IT Applications or Products
Pre AI IT Applications or Products
- Focus: Functionality.
- Pricing: License, per seat, per user.
- Relationship: Transactional, Vendor-Client.
- Value: Defined by features.
- Business Model: Selling a tool.
Post AI - New SaaS Centric Application or Products
New SaaS - Services, AI Agents and Sharing
- Focus: Outcomes.
- Pricing: Performance-based, revenue share, outcome-based.
- Relationship: Partnership, Co-Creation.
- Value: Defined by impact.
- Business Model: Sharing in success.
Why the Shift?
The traditional model of selling static software based on licenses and features is becoming outdated. AI's ability to automate complex processes, provide real-time insights, and continuously improve through data creates a new value proposition. This shift moves the focus from "what the software does" to "what outcomes the software delivers," aligning the vendor's success directly with the customer's. It's a move from a transactional relationship to a long-term partnership.
Customer Benefits
AI-enabled products offer significant benefits to customers by providing:
- Guaranteed Value: Customers pay for results, not just access to a tool. This reduces risk and ensures a clear return on investment.
- Continuous Improvement: AI systems learn and adapt over time, making the product more effective and valuable with each interaction.
- Increased Efficiency and Automation: AI agents can automate routine and complex tasks, freeing up human resources to focus on strategic work.
- Hyper-Personalisation: Applications can be tailored to individual needs and preferences, leading to a more intuitive and effective user experience.
Vendor Benefits
Vendors also stand to gain significantly from this shift:
- Predictable Revenue: Outcome-based pricing models can lead to more stable and predictable revenue streams, as they are tied to long-term customer success rather than one-time sales.
- Stronger Customer Relationships: The partnership model fosters deeper collaboration and loyalty, reducing churn.
- Increased Market Share: By delivering measurable outcomes, vendors can differentiate themselves in a competitive market and command a premium for their services.
- Rapid Innovation: The feedback loops inherent in AI systems allow for faster product development and continuous improvement.
Key Challenges
Despite the benefits, there are challenges to this transition:
- Defining Clear KPIs: It can be difficult to establish and agree upon clear, measurable key performance indicators (KPIs) with customers upfront.
- Data Privacy and Compliance: Handling sensitive customer data requires a strong commitment to privacy, security, and regulatory compliance (e.g., GDPR, HIPAA).
- Risk Management: Vendors bear a higher level of risk under an outcome-based model, as their revenue is directly tied to the success of the application in the customer's environment.
- Managing Expectations: It is crucial to manage customer expectations regarding the capabilities and limitations of AI and the timeframe for achieving specific outcomes.
The shift to AI-enabled applications is not just about adding new features; it's a fundamental change in how value is created and delivered. This transition requires a new way of thinking about product positioning, one that focuses on shared success and measurable outcomes.
Related Posts:
Sources: Aswath Damodaran's Blog, Seeking Alpha, Sapphire Ventures, HBR, FourweekMBA, Tesla, AWS Blog, Gartner, Scientist, Forrester, Gartner, ChatGPT, Claude, Gemini, Perplexity, AFR, Bloomberg, Forbes, Economist, Times, Wired, Palantir, CIO, Excerpts from my book on GenAI The New Reality - 2023
References:
- Ramanujam, M., & Hartman, E. (2016). Monetising Innovation: How Smart Companies Design the Product Around the Price. Wiley.
- Intercom’s Fin AI pricing model: https://www.intercom.com/fin
- General insights on AI pricing trends: Web searches and industry analyses (e.g., McKinsey, Gartner reports on AI monetisation).
- Amazon. (2025). AWS Pricing. Available at: https://aws.amazon.com/pricing/
- Brynjolfsson, E., & McAfee, A. (2017). The Second Machine Age. W.W. Norton & Company.
- Canva. (2025). Pricing. Available at: https://canva.com/pricing
- Chui, M., et al. (2018). Notes from the AI Frontier. McKinsey Global Institute.
- Gartner. (2025). AI Market Forecast. Available at: https://gartner.com
- Hagiu, A., & Wright, J. (2015). Multi-Sided Platforms. International Journal of Industrial Organisation, 43, 162–174.
- Intercom. (2025). Fin AI Pricing. Available at: https://intercom.com/pricing
- NHS. (2025). Data Sharing for AI Research. Available at: https://www.nhs.uk/data
- OpenAI. (2025). API Pricing. Available at: https://openai.com/api/pricing
- Rachitsky, L. (2025). Podcast: Pricing in the AI Era. Available at: https://lennyrachitsky.com
- Salesforce. (2025). AppExchange Overview. Available at: https://appexchange.salesforce.com
- Slack. (2025). Pricing. Available at: https://slack.com/pricing
- Zendesk. (2025). Pricing. Available at: https://zendesk.com/pricing
