The AI Agent Gold Rush: The Race to Monetization

Introduction

In the past six to eight months, AI agents have flooded the market, evolving from simple task automation to sophisticated entities with deep reasoning and contextual awareness. This rapid transformation is now reshaping the economics of AI, with companies moving beyond democratization to aggressive monetization.

The newest frontier sees AI agents marketed as elite knowledge workers. OpenAI, for instance, is gearing up to launch domain-specific agents at high price points—$2,000 per month for knowledge work, $10,000 for software development, and $20,000 for intensive academic research. These staggering price points hint at a larger play: offsetting OpenAI’s mounting financial losses, which reportedly hit nearly $5 billion last year. And where OpenAI leads, others will follow, potentially setting a new pricing benchmark for premium AI agents.

This monetization wave is forcing a strategic reset across industries. Traditional enterprise software firms, once defined by CRM, ERP, or ITSM solutions, are now racing to rebrand as AI-first companies. Salesforce declared at Dreamforce 2024 that it was no longer a CRM firm but an AI company. Databricks, once synonymous with data management, has pivoted to large language model (LLM) development and orchestration. Meanwhile, ServiceNow recently acquired an agentic AI company, signaling its ambition to dominate AI agent deployment.

As AI agents transition from an emerging technology to a multibillion-dollar industry, the question is no longer whether they will redefine enterprise software but how quickly and at what cost. Will businesses embrace these high-ticket AI assistants, or will market forces drive a price war?

The AI Agent Gold Rush: Who Is Cashing In?

Figure 1: AI agent ecosystem

As AI agents move from experimental tools to revenue-generating products, the market is coalescing around three distinct players, each with a unique stake in the monetization race:

1. 1. The IT Giants: Using AI Agents as a Hidden Hand in IT Services Megadeals
      IT services giants, such as Cognizant and LTIMindtree, are integrating AI agents into their managed services contracts, not as standalone products but as value multipliers. With smaller deals ($1M–$50M) on the rise, their real bet is on securing high-value transformation contracts ($100M–$500M). To win these, they are bundling AI agent solutions into larger engagements, proving measurable productivity gains. At the same time, they are building AI agent marketplaces, enabling enterprises to scale AI adoption with seamless, plug-and-play deployment.

• • •  In January 2025, Accenture launched a suite of 12 industry-specific AI agents spanning financial services, healthcare, and manufacturing under its AI Refinery for Industry service. It aims to scale this initiative to over 100 agents by the end of 2025, covering additional verticals and operational use cases.
    •  During its January 2025 earnings call, Infosys announced the ongoing development of over 100 AI agents targeting banking, financial services, IT operations, and cybersecurity. The company confirmed plans for phased rollouts throughout the year, with integrations into its existing enterprise automation offerings.

1. 2. Big Tech’s AI Agent Play: Embedding Agents into Every Enterprise Workflow
      Companies such as Salesforce, ServiceNow, and Oracle are not just selling AI agents but weaving them into the enterprise workflow fabric. From HR and sales to customer service and IT management, these tech giants are ensuring AI adoption is not optional but inevitable. Their pricing model is not just about usage; it is tethered to existing application licenses, creating a seamless (and inescapable) AI upsell. The real goal? Locking in long-term revenue streams from Fortune 500 and creating a low-cost entry point to attract small and medium businesses.

• • •  Over the past six months, Salesforce has expanded Agentforce, its low-code AI agent development platform, with over 10,000 AI agents operational across sales, HR, customer service, and IT. The latest iteration enables AI agents to run in the background and autonomously trigger based on events or alerts without user input. Salesforce employs a freemium model, with standard pricing at $2 per user per conversation and enterprise pricing customized to usage and integration needs.
    •  Microsoft has rolled out prebuilt AI agents across domains such as sales and IT and industries such as retail, manufacturing, and finance, along with a dedicated Azure AI agentic service to build, customize, and deploy AI agents within Microsoft Copilot Studio or the Microsoft 365 Copilot platform. It offers these capabilities as a pay-as-you-go service, with prices starting at one cent per message. The service can also be availed through prepaid message packs of $200 for 25,000 messages. For reference, each interaction involves a question and answer; a successful interaction requires 32 messages.

1. 3. The Startup Playbook: Building AI Agents for Every Niche
      A new wave of AI-first startups is carving out its own space in the AI agent economy, focusing on hyperspecialized solutions from legal research assistants to AI-driven supply chain optimizers. Agility, interoperability, and deep customization provide them a competitive edge. Unlike Big Tech offerings confined within proprietary ecosystems, these startups are developing purpose-built AI agents that seamlessly integrate across an enterprise’s entire data landscape. Coupled with low-code platforms, they empower businesses to fine-tune AI agents for specific workflows, often at a fraction of the cost. As enterprises seek more control over AI deployment, these startups are positioning themselves as the go-to choice for industry-specific implementations.

• • • Cognition AI has launched Devin, an AI agent that can automate the full software engineering life cycle, covering ideation, storyboarding, code generation, debugging, deployment, documentation, and optimization. It offers these agents at a starting price of $500 per month and provides custom pricing for enterprises based on their requirements.
    • Healthcare-focused companies, such as Suki and Hyro, have introduced AI agents designed for physicians. These agents automate tasks such as appointment scheduling, patient information retrieval, and treatment recommendations. For instance, the Suki agent pricing starts at $200 per user per month and scales up to $400 based on feature complexity and integration depth.

The Acquisition Wars Intensify: The Race to Secure Agentic AI Capabilities

With Big Tech shaping pricing norms, service providers scaling adoption, and startups carving out specialized niches, the AI agent economy is rapidly taking shape. The question now is: who will own the most lucrative piece of the pie?

Service providers are doubling down on AI agent capabilities, leveraging their deep industry expertise and developer talent to build proprietary repositories of AI agents and even their own LLMs, which is apparent in moves by Infosys and Tech Mahindra. Some, like Accenture, are making strategic investments in agentic AI startups through venture funds. These providers are simultaneously leveraging their partnerships with Big Tech and startups to integrate AI agents with enterprise data and industry-specific needs while retaining the option to deploy their in-house agent stacks where applicable.

Meanwhile, Big Tech is on an acquisition spree, scrambling to buy agentic AI capabilities. The same trend exists among industry-specific AI startups predating the ChatGPT era. They are now racing to acquire agentic AI expertise to stay competitive. The competition for AI dominance is intensifying, and those without native capabilities are investing significantly to stay in the game.

Strategic Imperative for CXOs in AI Agent Implementation

1. 1. Build modular AI architecture: They must build a modular architecture with API-based model integration, containerized deployment, and swappable components to enable quick adoption of new models without vendor lock-in. A modular framework simplifies integration with governance tools and adaptive workflows, ensuring AI agents can scale and evolve with minimal disruption.
   2. Invest in cross-platform orchestration: AI agents from enterprise application vendors are often confined to their native ecosystems, creating silos that limit coordination across the broader enterprise data stack. To overcome this, organizations should leverage service providers’ expertise to build a centralized orchestration layer that enables seamless agent interaction across disparate environments. This approach breaks down communication barriers between vendor-specific solutions, enhances real-time decision-making, reduces latency, and ensures AI agents work cohesively, even as models evolve and expand.
   3. Establish robust governance: Effective AI agent deployment requires strong governance to manage process complexity and business alignment. Businesses must establish clear guidelines for data access, model version control, and AI agent performance tracking to prevent inconsistencies in outputs and deviations from business objectives. They should adopt agent-specific governance practices such as feedback loops for real-time adjustments and game-theoretic conflict resolution to align agent outputs. A structured change management process should cover model updates, workflow adjustments, and operational changes to ensure smooth transitions and minimal disruption.

By Chandrika Dutt, Research Director, Avasant, and Abhisekh Satapathy, Principal Analyst, Avasant