Agentic AI: Redefining enterprise software pricing and reshaping business models

Agentic AI is revolutionizing pricing models in Indian SaaS and enterprise software by replacing human users with AI-driven bots. This shift is moving companies away from traditional per-user billing towards more dynamic, consumption- and outcome-based models, including API and AI-unit pricing. While AI services now often come with premium pricing, there is growing demand for pricing predictability, leading to the rise of hybrid and bundled service offerings. Emerging trends like value-based subscriptions, shared value partnerships, tokenized services, predictive pricing, and industry-specific models offer greater flexibility, align costs with outcomes, and foster innovation, ultimately enhancing customer trust.

Agentic artificial intelligence is reshaping the business models of Indian SaaS and enterprise software companies, as firms increasingly replace human users with AI-driven bots. This shift is prompting a move from traditional per-user pricing to more dynamic models such as consumption- and outcome-based billing. 

In this evolution, some companies are charging a premium for advanced AI products, while others are adopting API-based billing, where customers pay based on actual usage. Bundled service offerings that integrate AI with other solutions are also gaining traction.

Mr. Girish Mathrubootham, founder and executive chairman of Freshworks, explained that businesses previously paid software fees based on the number of users—such as $50 per person for a 100-member team. Now, AI agents can autonomously handle tasks, reducing the need for large teams and lowering user-based licensing costs. 

The Lifecycle management firms have noted a shift in pricing models to focus on the number of agents and workflows instead of user counts. According to Shobhit Jain, managing director at Avendus Capital, nearly  40–50% of enterprise software operations have transitioned away from the per-user, per-transaction model. Companies are increasingly adopting output-based pricing, where fees depend on the outcomes delivered. 

Additionally, some firms are introducing distinct pricing models for GenAI services to capture the growing demand.

What is Agentic artificial intelligence?

Agentic artificial intelligence (AI)—autonomous systems capable of perceiving, reasoning, and acting without constant human oversight—is profoundly transforming the business models of Indian SaaS and enterprise software companies. This shift is not merely about integrating AI features; it’s about reimagining software delivery, customer engagement, and value creation.

Traditionally, Indian SaaS companies offered tools that required user intervention. Now, they’re evolving into platforms with embedded AI agents that can autonomously execute tasks. For instance, Gupshup has introduced a library of 15 customizable AI agents, enabling clients like Lenskart and Cars24 to automate customer interactions with sophisticated reasoning and decision-making capabilities. 

Similarly, Zoho’s Zia Agents and Zia Agent Studio empower businesses to create autonomous agents tailored to their workflows, enhancing operational efficiency. Zomato’s launch of Nugget, an AI-native customer support platform, exemplifies this trend, claiming to resolve up to 80% of queries autonomously .

The integration of agentic AI is prompting a shift from traditional subscription models to outcome-based pricing. Platforms like KOGO AI offer an ‘AI agent marketplace’ with over 100 agents, allowing businesses to pay based on usage or specific outcomes . This model provides flexibility and aligns costs with value delivered, although it introduces challenges in cost predictability.

Key Emerging Pricing Models

1. Outcome-Linked Pricing

Outcome-linked pricing aligns an AI vendor’s compensation with tangible, measurable results—such as cost savings, improved efficiency, or increased revenue. Rather than charging based on software usage or availability, vendors earn based on the actual value delivered.

How It Works: The customer and vendor agree upfront on clear performance metrics—like cost reductions, productivity gains, or revenue uplift. The vendor’s fee is then calculated as a percentage of the value generated or as a fixed amount tied to the outcomes achieved.

Example: If a procurement AI helps a company save $1 million by optimizing supplier contracts, the vendor might charge 10% of the savings—earning $100,000.

Best Suited For: 

• Cost-saving initiatives such as fraud detection, risk reduction, or supply chain optimization.
• Revenue-generating efforts including marketing performance, sales enablement, or personalized e-commerce experiences.

Key Challenges

• Attribution: Determining how much of the outcome is directly due to the AI solution versus other contributing factors.
• Variability: Market changes or external events can influence results, making consistent value delivery—and pricing—more complex.

2. Dynamic Performance Pricing

Dynamic performance pricing adjusts fees based on how actively the AI system is used and the volume of value-driving actions it performs. As AI activity increases, so does the vendor’s compensation.

How It Works: Pricing is linked to operational metrics such as the number of tasks executed, data processed, or AI-driven decisions made. Higher usage or impact results in higher fees.

Example: A supply chain AI may incur higher charges during peak periods, such as holidays, when it performs more real-time optimizations to manage demand.

Best Suited For: Industries with seasonal or variable demand—like e-commerce during holiday spikes or logistics in high-volume shipping windows.

Key Challenges: Cost Predictability: Customers may push back on rising costs during peak times, especially when budgets are already under pressure.

3. Hybrid Pricing Models: A Mix of Stability and Performance

Hybrid pricing combines a fixed subscription fee for basic services with variable fees based on performance or results.

How It Works: Customers pay a predictable base fee for access to the system, along with additional charges based on specific performance metrics or outcomes.

Example
An AI HR platform charges a monthly fee for its core features and an extra fee for each successful hire made through its recommendation engine.

Best Fit
This model suits businesses that want cost predictability while still capturing value from high-impact outcomes.

Challenges
Hybrid contracts can be complex, requiring careful negotiation to balance fixed and variable pricing components.

Future Trends in AI Pricing: The Road Ahead

As agentic AI evolves, pricing models are expected to shift to align more closely with delivered value, creating greater flexibility and trust. Key emerging trends include:

1. Value-Based Subscriptions: The AI Fitness Plan
   Future subscription models will integrate performance metrics, allowing fees to adjust based on outcomes. For instance, a CRM AI could charge based on the retention boost it delivers, offering rebates if targets aren’t met. This aligns vendor incentives with customer goals, building trust and reducing risk.
2. Shared Value Partnerships: The Venture Capital of AI
   In this model, vendors and customers share both risks and rewards. Vendors may waive upfront costs in exchange for a percentage of the value generated over time. For example, an AI vendor could install software for free and earn a share of the savings it generates. This is ideal for long-term projects where mutual trust is crucial.
3. Tokenized AI Services: AI as a Tradable Commodity
   Blockchain technology could enable tokenized pricing, allowing companies to buy tokens for AI services and redeem them as needed. This offers flexibility, enabling businesses to buy, sell, or trade tokens, much like cloud credits, making AI accessible to smaller companies with fluctuating needs.
4. Predictive Pricing: The Surge Model for AI
   AI systems may eventually set their own prices using predictive algorithms. For example, a diagnostic AI could adjust fees based on seasonal demand, similar to surge pricing in ride-hailing services. This ensures businesses only pay based on the value they receive, adapting to market dynamics.
5. Industry-Specific Pricing Playbooks
   Pricing models will become tailored to specific industries, with solutions designed to address sector-specific challenges. For example, AI in retail could charge based on inventory turnover, while healthcare AI could charge per successful diagnosis. This removes ambiguity and accelerates adoption by aligning with industry goals.

These trends indicate a shift towards more adaptable, outcome-based AI pricing models that promote innovation and collaboration. As AI systems advance, these pricing strategies are being trialed across industries and are likely to become the norm. By aligning pricing with outcomes and fostering flexibility and collaboration, vendors can build trust and nurture long-term customer relationships.