I had the opportunity to discuss the transformative potential of AI in reshaping SaaS pricing models with Keerthi Sivakumar, a leader in monetization strategies at Freshworks. I introduced a framework on why SaaS apps have a gap in delivering value today, and how AI bridges this gap by adding intelligence, in various ways, into the mix.
Together, we explored how AI is driving a fundamental shift from traditional user or usage-based pricing to more sophisticated, outcome-driven models. Below is a detailed summary of the key points I made during the webinar, with Keerthi’s expert commentary adding valuable context.
The Current State of SaaS Pricing: User and Usage-Based Models
Traditional SaaS Pricing
SaaS pricing today is predominantly rooted in user or usage metrics. While this approach has served the industry well, it often fails to align with the ultimate value that customers seek. For instance, platforms like AWS charge based on API calls or storage consumption, while Salesforce prices according to the number of seats a customer utilizes.
The SaaS Value Gap
A significant challenge in the current model is what I call the “SaaS Value Gap.” This gap refers to the disconnect between what SaaS applications enable (user-level productivity and task efficiency) and what buyers actually want (tangible business outcomes like cost reductions or improved customer satisfaction).
This gap can lead to inefficiencies and dissatisfaction, as the pricing models do not always reflect the true value delivered to the customer.
AI’s Role in Bridging the Value Gap: Tying Intelligence into SaaS Pricing
AI as an Optimizer, short term
AI can be a powerful tool in optimizing existing SaaS pricing models. It allows for more personalized pricing strategies, enhances revenue management, and improves overall operational efficiency.
Personalized Pricing: AI enables dynamic adjustments to pricing based on user behavior, geographic location, or specific usage patterns. For example, Adobe’s Creative Cloud adjusts pricing based on the tools each user frequently utilizes.
Revenue Optimization: AI facilitates automatic adjustments in pricing and resource allocation, which ensures optimal revenue capture. A good example is Salesforce’s ability to automatically allocate seats based on actual usage patterns.
Efficiency Gains: AI allows for real-time monitoring of competitive pricing, enabling companies to stay competitive without the need for manual intervention.
“Customer service is seeing significant disruption due to AI capabilities and automation. While AI tools like Microsoft’s Copilot are increasing productivity rates, the adoption of such tools isn’t immediate. It requires a strategic integration into existing workflows, which means the benefits of AI optimization are only just beginning to be realized in practice.” Keerthi Sivakumar
AI as a Disruptor: Moving Toward Goal-Oriented Applications
Concept: The future of SaaS lies in AI-first applications that start with a business goal—such as reducing customer service costs—and then work backwards to determine the necessary workflows, user actions, and automation. This represents a fundamental shift from traditional user-centric models to outcome-centric models.
Example: In customer service, rather than focusing on the number of users or features, AI-first applications would prioritize outcomes like improving first-call resolution rates or reducing average handling time.
Emergence of Goal-Oriented AI Pricing Models
AI-First Value Ladder
Historically, SaaS value has been created from the bottom up—beginning with user productivity, moving to team collaboration, and eventually driving business outcomes.
AI-first applications invert this model. They start with the business outcome and then work backward to determine the minimal necessary workflow, user engagement, and automation required to achieve that goal.
This inversion represents a major change in how SaaS value is both delivered and monetized. Rather than charging for the number of users or features, SaaS companies will increasingly charge based on the outcomes their software helps achieve.
New Pricing Levers Introduced by AI
These are based directly on the new AI capabilities now possible with different AI algorithms, models and methods.
Predicting Events:
AI’s ability to forecast events (e.g., equipment failure) can be a basis for pricing.
Prescribing Actions:
AI that offers actionable recommendations (e.g., financial advice or prescription plan) can be priced based on the value of those actions.
Generating Insights:
AI-driven insights, such as customer behavior analytics, or summaries generated, can become new pricing metrics.
Controlling Processes:
AI-driven automation that manages processes (e.g., robotic process automation) could be priced based on efficiency gains.
Intelligent Interaction:
AI’s capability to engage with users through chatbots or virtual assistants can lead to pricing models based on interaction quality or resolution success.
“AI is quickly becoming a table stake, not just a differentiator. However, we need to recognize that integrating AI into existing business models and pricing strategies will take time. The shift to AI-driven, outcome-based pricing will require both sellers and buyers to evolve their understanding of value creation.” Keerthi Sivakumar
Challenges and Barriers to AI-Driven Pricing Models
Cost Factors and Input-Based Pricing
Many companies remain anchored to input-based pricing models, such as paying for the number of AI calls made, which doesn’t necessarily reflect the value delivered.
Overcoming the traditional cost-plus mindset is essential to transitioning to outcome-based pricing models that better align with customer value.
Predictability and Revenue Models
Transitioning to outcome-based pricing can introduce variability in revenue predictability, which is a concern for SaaS providers and customers who prefer stable, predictable costs.
Companies can implement pricing models with predefined bands or ranges to provide a level of predictability while still aligning with outcomes.
Customer and Organizational Readiness
Customers need to evolve their IT infrastructure and organizational processes to fully leverage AI-driven solutions. Pilots and gradual implementation are often necessary before full-scale adoption can occur.
Sales teams need to be equipped to sell these more complex, outcome-based solutions, and companies must educate customers on the value of AI-driven outcomes.
Measuring and Defining Outcomes
For outcome-based pricing to be effective, outcomes must be clearly defined, measurable, and agreed upon by both the vendor and the buyer. This requires precise definitions and robust tracking systems.
“Strategic planning is going to play a critical role in navigating the transition to AI-driven pricing. It’s no longer just about what capabilities your software has, but about how effectively those capabilities translate into measurable business outcomes for your customers.” Keerthi Sivakumar
Strategic Recommendations for SaaS Companies Embracing AI-Driven Pricing
Tailored Solutions
SaaS companies must move away from one-size-fits-all pricing models. Instead, they need to develop tailored solutions that align with the specific business goals and strategies of each customer.
Enhanced Strategic Planning
Effective planning is crucial for identifying the right use cases, defining pricing metrics, and ensuring the successful implementation of AI-driven solutions.
Focus on Outcome-Based Models
While challenging, transitioning to outcome-based pricing models will be essential for staying competitive in the AI-driven SaaS landscape. Companies that can effectively scale and deliver measurable outcomes will have a significant advantage.
“Outcome-based motions are the future, but they require careful planning and execution. The ability to scale and deliver high-performance outcomes will be a key differentiator for SaaS companies moving forward.” Keerthi Sivakumar
Conclusion: The Future of SaaS Pricing
The future of SaaS pricing is being reshaped by AI, leading to the rise of goal-oriented applications and outcome-based pricing models. While there are significant challenges to overcome, particularly around cost structures, predictability, and customer readiness, the potential benefits are enormous. Companies that can navigate this transition effectively will be well-positioned to capture new value and lead in the evolving SaaS market.
“AI is not just an optimization tool but a catalyst for transforming the entire SaaS business model. The shift from user-centric to outcome-centric pricing represents a paradigm change, one that will redefine how SaaS companies deliver value to their customers in the years to come.” Keerthi Sivakumar
Together, Keerthi and I provided a roadmap for how AI is poised to disrupt and redefine the SaaS pricing landscape. Our insights emphasize the importance of strategic planning, tailored solutions, and a focus on measurable outcomes as companies transition into this new era of AI-driven value creation.
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