Published on January 3rd, 2025
Introduction
As AI continues to revolutionize the enterprise software landscape, the way SaaS companies deliver value to customers is being fundamentally reshaped. In Part 1 of this series, we explored how AI agents impact SaaS pricing models. In this part, we will examine the more disruptive changes that AI agents are bringing to how SaaS products are packaged, sold, and delivered—and the implications for legacy enterprise application vendors like Oracle, Salesforce, and SAP.
The Rise of Systems of Agents: The New Paradigm of Enterprise Software
The emergence of Systems of Agents marks a profound shift in the enterprise software market. Traditionally, software vendors sell tools to help users perform tasks—whether that’s managing customer relationships, running marketing campaigns, or analyzing data. However, the rise of AI agents introduces a new approach where software vendors offer the complete outcome instead of just the tools to get there.
A System of Agents represents multiple autonomous agents that work collaboratively to achieve an outcome, essentially automating entire workflows. This means that instead of a company purchasing a software tool to assist human employees in their work, they might soon purchase the work itself. In this model, AI agents complete tasks and deliver results, reducing or eliminating the need for human workers to perform these roles.
AI-Driven Outcomes Over Tools: The Shift in SaaS Business Models
For SaaS vendors, this shift will require a complete rethinking of their business model. Instead of charging companies based on seat licenses (i.e., the number of users), vendors could charge based on the results or outcomes their software delivers. For example, a CRM vendor like Salesforce could move from charging a per-user fee to charging for the number of qualified opportunities, closed deals, or renewals their AI-powered system generates.
This change in pricing models represents a broader trend in the enterprise software world. Companies will no longer be paying for the privilege of using a tool; they will be paying for the value that the tool delivers, with payments tied directly to outcomes. This change is significant because it highlights the potential of AI to not only automate tasks but also take on more complex, results-driven roles, enabling companies to scale with less reliance on human labor.
Labor Replacement: AI Agents as Digital Workers
A core selling point for AI-powered systems of agents is the potential for labor replacement. Many functions that were traditionally performed by human workers are now being automated by AI, including administrative tasks, repetitive customer service queries, and even areas like sales, marketing, and HR. This trend is accelerating, with some venture capital firms projecting the potential for AI to displace up to $4.6 trillion worth of labor costs across industries.
However, there’s a key distinction between labor replacement and labor transformation. While AI is poised to take over repetitive and mundane tasks, human employees will still play a critical role in areas that require empathy, creativity, and strategic thinking. What’s more, AI will likely complement human workers in many situations, augmenting their capabilities and enabling them to focus on higher-value tasks.
For example, an AI agent could be responsible for conducting initial sales outreach, identifying qualified leads, and even scheduling meetings. However, it would still be a human employee’s responsibility to close the deal and nurture the client relationship. This hybrid approach offers companies the best of both worlds, using AI to handle repetitive tasks while allowing human employees to focus on areas where they can add unique value.
Vertical AI Agents: Focused Automation in Niche Markets
While some AI agents aim to automate broad, enterprise-wide processes, others are targeting more specialized, vertical markets. Vertical AI agents are purpose-built to address specific, industry-specific tasks—such as automated quality assurance for software testing, AI-driven recruitment, and automated customer support for specific sectors.
Startups like Momentic, Triplebyte, and Salient are examples of companies that are developing AI-powered solutions for specialized functions. For instance, Momentic automates quality assurance (QA) testing, an area that is usually labor-intensive and time-consuming. Triplebyte uses AI to automate the recruitment process for software engineers, saving companies time and resources in the hiring process.
The potential for vertical AI agents to disrupt SaaS vendors is immense, particularly in markets that have been underserved by traditional software tools. For example, industries like healthcare, legal, and logistics could see dramatic shifts in how they operate, as AI agents take on repetitive, administrative tasks that were once handled by humans.
The End of Monolithic Applications: Unbundling Software with AI
One of the most significant changes AI agents will bring to enterprise software is the collapse of monolithic applications. For decades, large enterprise vendors like Oracle, SAP, and Microsoft have built comprehensive, all-in-one software suites that cover a wide range of functions—CRM, HR management, financial accounting, and more. However, this approach often creates complex, bloated systems that are difficult to customize and adapt.
In contrast, Systems of Agents promote the idea of breaking down monolithic software into a more modular, composable architecture. Rather than relying on one all-encompassing software package, companies can purchase discrete AI-powered agents that handle specific tasks or processes, such as customer service, sales management, or data analytics. These agents would work together to deliver the desired outcome without the need for a massive, monolithic system.
This unbundling approach is already happening with the rise of microservices and composable enterprise solutions. For example, Salesforce is embracing this shift by introducing Agentforce 2.0, a digital labor platform that envisions an AI-driven solution for customer relationship management (CRM) that goes beyond simple automation. Instead of relying on human workers to use a CRM tool, customers would pay for the completed outcome, whether that’s a new customer acquisition or improved sales funnel performance.
While this shift to modular, outcome-focused solutions has the potential to drive tremendous efficiency, it also poses challenges for traditional enterprise vendors. Companies that have invested in legacy systems may find it difficult to transition to a more fragmented, AI-driven environment, and they will need to rethink their product offerings and value propositions to remain competitive.
The Roadblocks to Full Automation: Data Quality and Trust
While AI agents promise to streamline enterprise processes, there are significant hurdles to widespread adoption. One of the biggest challenges is ensuring that the data these agents rely on is clean, well-structured, and complete. Many organizations today rely on messy, unstructured data that’s not easy to interpret or use for automation.
AI agents depend on accurate data to make decisions and deliver results. If the data they’re working with is incomplete or poorly structured, the results could be flawed. Additionally, many organizations still lack robust data governance practices, which can make it difficult to ensure the quality and security of the data used by AI systems.
Moreover, building trust in AI-driven outcomes is another challenge. Many enterprises are still wary of AI’s ability to deliver results that align with their strategic goals, and there’s skepticism around whether AI agents can truly replace the nuanced decision-making and expertise of human workers. Overcoming this trust barrier will require transparency, accountability, and clear demonstrations of how AI agents add value.
Frictionless Enterprise: The Future of SaaS
Despite these challenges, the transition to a world of AI agents is well underway. Companies that have embraced modular architectures and digital ecosystems are well-positioned to benefit from this disruption. The concept of a Frictionless Enterprise, where digital tools, systems, and agents work together seamlessly across the entire organization, is already becoming a reality.
This vision involves unbundling and rebundling existing software and processes to create more agile, efficient, and AI-powered systems. By breaking down traditional silos and automating repetitive tasks, companies can free up their human employees to focus on strategic, high-value work. This shift will allow organizations to remain competitive in an increasingly digital and automated world.
Conclusion: Preparing for the AI-Driven Future
The rise of AI agents represents a major disruption for the SaaS and enterprise software industries. However, while the potential for transformation is immense, it will take time for AI agents to fully permeate the enterprise landscape. Over the next decade, we will witness the gradual unbundling of monolithic applications into modular, outcome-driven solutions, powered by AI agents.
As companies embrace the concept of Systems of Agents, they will need to navigate the challenges of data quality, trust, and integration with existing systems. The companies that succeed will be those that can adapt to the changing landscape and leverage AI to create more efficient, outcomes-focused solutions.
The future of SaaS will be defined by AI agents, but how quickly this transformation happens and what shape it takes will depend on how organizations adapt to these new technologies. For now, the road ahead is filled with experimentation, learning, and innovation, but the ultimate result will be a more agile, efficient, and AI-driven enterprise ecosystem.
