The advent of agentic AI—autonomous agents powered by Artificial Intelligence—has profound implications for the Software as a Service (SaaS) industry and the broader market of enterprise application vendors. This technological innovation is not just another incremental improvement; it is a seismic shift that promises to disrupt traditional business models. This disruption affects everything from product packaging to service delivery, ultimately leading to a significant shift in the value proposition from tools to outcomes. The analysis draws from viewpoints shared by key industry voices, particularly venture capitalists who are keenly monitoring these market shifts. These experts are instrumental in highlighting the potential and challenges posed by these groundbreaking advancements in AI technology.
The Shift from Monolithic Applications to Systems of Agents
One of the most significant changes anticipated with the rise of AI agents is the dismantling of traditional, monolithic enterprise applications. Historically, vendors like Oracle, Salesforce, and SAP built their businesses around comprehensive, all-in-one software solutions designed to meet a wide range of enterprise needs. These monolithic applications were seen as the gold standard for business software solutions. However, the emergence of AI-driven systems of agents suggests a move towards a more fragmented yet integrated approach that could render these monolithic solutions obsolete.
Industry figures Ashu Garg and Jaya Gupta from Foundation Capital have been particularly vocal about this shift. They argue that AI agents will fundamentally alter the enterprise stack, creating a modular landscape where discrete agents handle specific tasks autonomously. This isn’t merely a new category of software; it’s a complete rethinking of how enterprise software operates at its core. The modular approach offers businesses greater flexibility and scalability, enabling them to tailor their software solutions to specific operational needs. This shift is poised to unlock unprecedented efficiencies and performance improvements, changing the game for enterprise software vendors and their clients alike.
The New Business Model: Outcome-Based Pricing
A key theme across discussions about agentic AI is the shift from traditional SaaS pricing models to outcome-based pricing. In today’s enterprise software landscape, companies typically charge their customers per user or per seat—a model increasingly seen as outdated in an AI-driven world. This traditional model fails to align the cost of software with the actual value and outcomes it delivers to businesses. As AI agents become more prevalent, the industry is moving towards a pricing model where vendors charge based on the tangible outcomes their software delivers, fundamentally shifting the economic dynamics of the SaaS sector.
For instance, in the Customer Relationship Management (CRM) market, rather than billing clients for each Sales Development Representative (SDR) or Account Executive (AE) using the platform, companies would charge for the number of qualified leads generated or the deals closed by their AI agents. This shift underscores a broader trend: the transformation of software from a tool to be used into a service that delivers specific business results. Outcome-based pricing aligns the interests of SaaS vendors and their clients, ensuring businesses receive direct value from their software investments. This new approach has the potential to revolutionize how companies budget for and evaluate their software expenditures.
Expansive Market Opportunities and Labor Replacement
The potential market for AI-enabled services is colossal, primarily because these services can reduce or eliminate the need for human labor in many business functions. According to Foundation Capital, the Total Addressable Market (TAM) for such solutions could amount to trillions of dollars, reshaping the landscape of global enterprise spending. This estimate considers the vast amounts of money global enterprises currently spend on salaries for functions like sales and marketing, software engineering, and customer support. The Foundation team provocatively pegs this opportunity at $4.6 trillion, spotlighting the transformative potential of AI agents to replace human labor across various sectors.
However, this prospect raises several contentious issues. On one side, there is a sense of excitement about the efficiencies and cost savings AI agents can bring to businesses. On the other side, there is the uncomfortable reality of job displacement, where roles traditionally filled by humans are taken over by AI. This duality is evident in the investment community’s outlook, with some investors, like Jake Saper from Emergence Capital, acknowledging a continued role for human workers in tandem with AI. Others, however, push a more absolutist vision of full automation, anticipating a future where human intervention is largely unnecessary. Balancing these perspectives will be essential as the industry navigates the ethical and practical implications of widespread AI adoption.
Emergence of Hybrid Companies
A fascinating development within this shift to agentic AI is the rise of hybrid companies that combine AI-enabled software with human labor. These companies, operating in sectors like cloud migration, application maintenance, and customer support, seek to leverage the best of both worlds—automation for repeatable, rule-based tasks and human expertise for nuanced or complex situations. This hybrid model allows businesses to maximize efficiency while retaining the critical thinking and problem-solving skills that only humans can provide.
The hybrid model does not have many precedents, raising several questions about operational efficacy, brand trust, and workforce composition. For example, the balance between AI and human labor in these companies will be crucial in determining their success and acceptance in the market. How these companies manage this balance will likely set the standard for future industry practices. The hybrid approach also offers a potential solution to the ethical concerns around job displacement, as it creates opportunities for humans and AI to work collaboratively rather than competitively.
Industry-Specific AI Solutions: Vertical Markets
The conversation shifts to vertical markets where AI agents are expected to make significant inroads. Firms like Y Combinator see immense potential for vertical AI agents, arguing that these solutions could surpass the scale and impact of traditional SaaS offerings. Vertical markets offer fertile ground for AI agents, as these industries often require highly specialized, repetitive tasks that are ripe for automation. By focusing on specific industry needs, AI agents can deliver targeted solutions that drive efficiency and value.
Specific examples include automated QA testing (Momentic), software engineer recruitment (Triplebyte), responding to government RFPs (Sweetspot), and debt collection (Salient). These targeted applications illustrate how AI agents can revolutionize industries by performing repetitive administrative tasks that humans find monotonous and time-consuming. The impact of these solutions on vertical markets could be profound, leading to increased efficiency, reduced operational costs, and greater overall job satisfaction as human employees are freed from mundane tasks to focus on more strategic work.
Salesforce’s Response
Salesforce, a stalwart in the CRM market, is adapting to this new reality with its Agentforce 2.0 platform. Billing itself as a Digital Labor platform, Salesforce projects its Total Addressable Market for such AI-enabled labor to be as high as $7 trillion. This illustrates a strategic pivot where even established players are embracing AI to remain competitive. Salesforce’s move into the realm of AI labor platforms represents a broader trend among legacy software giants who recognize that staying ahead of the curve means integrating cutting-edge AI capabilities into their offerings.
However, the initiative has not been without its critics. The article voices skepticism about the overestimation of the value realizable from labor replacement and the underestimation of challenges inherent in this shift. There are concerns that some projections may be overly optimistic, glossing over the complexities and obstacles involved in fully integrating AI agents into existing enterprise workflows. The balance between innovation and practical implementation will be critical for Salesforce and other established players as they navigate these transformative changes. The success of platforms like Agentforce 2.0 will depend on their ability to deliver tangible, measurable outcomes without compromising the quality and reliability that businesses have come to expect from traditional software solutions.
Challenges and Skepticism
One critical point of skepticism revolves around the kinds of labor that AI is poised to replace. Often, tasks mentioned for automation, like data entry and reading faxes, are already automated in many enterprises. Therefore, the promise of AI agents may be overhyped when applied to these rudimentary functions. Critics argue that the true value of AI agents lies in their ability to handle more complex, high-level tasks that require real-time decision-making and adaptability—a significant leap from current automation capabilities.
Moreover, automating existing but sub-optimal processes carries significant risks. There is a danger of embedding inefficiencies or missing crucial steps that human ingenuity currently manages. For AI to truly revolutionize enterprise software, it must go beyond speeding up existing processes and aim to reimagine them altogether. This means not only making processes more efficient but also fundamentally transforming how work is done. The nuances of human judgment, intuition, and experience cannot be easily replicated by AI, and ignoring these factors could lead to subpar outcomes.
Risks of Over-Automation
Over-automation can also lead to unforeseen complications. For instance, processes that seem straightforward on the surface may involve hidden complexities and dependencies that AI cannot easily handle. There is also the risk of technological failures or security vulnerabilities when too much reliance is placed on autonomous systems. This emphasizes the need for a balanced approach that incorporates both AI and human oversight. Companies must carefully evaluate which tasks are suitable for automation and which require human intervention to ensure operational integrity and business continuity.
Additionally, Foundation Capital’s optimistic projections hinge on several assumptions about the current state and readiness of enterprise data and processes. The reality is that much of the unstructured data enterprises hold is poorly secured and inadequately mapped. Existing processes are often ad-hoc and rely heavily on human judgment that isn’t documented. While generative AI can mask such deficiencies, it cannot eliminate underlying flaws, leading to potential failures. A comprehensive, well-documented approach to data management and process design is essential for the successful deployment of AI agents in the enterprise.
Long-Term Vision: Frictionless Enterprise
Peter Fingar’s concept of the Frictionless Enterprise dovetails neatly with this vision of AI-driven transformation. He describes ongoing trends within the enterprise software market towards decomposing monolithic applications into flexible, composable architectures. This shift has been accelerated by the rise of AI, which enables end-to-end data integration and functionality within the flow of work instead of siloed applications. This “Tierless Architecture” prepares enterprise IT landscapes to embrace autonomous agents effectively, paving the way for more streamlined, efficient, and adaptable business processes.
The concept of unbundling and rebundling becomes critical in understanding the future trajectory of enterprise applications. Instead of automating existing processes, AI agents should enable entirely new workflows and process paradigms. For instance, Coupa’s AI-enabled network for collaborative sourcing illustrates how traditional procurement processes can be completely reimagined. This forward-thinking approach promises a more efficient and innovative way of conducting business transactions, albeit requiring time to develop and gain acceptance across various industries.
Conclusions and Immediate Implications
The emergence of agentic AI is revolutionizing SaaS and enterprise software, mirroring significant transformations akin to past technological advancements like the Internet, cloud computing, and mobile technology. Large Language Models (LLMs) at the core of generative AI promise substantial changes, though these will unfold over the next decade. Early adopters will spark notable developments in the coming years, but the full potential of these innovations will take time and effort.
Several key trends illustrate the current AI trajectory in enterprise software:
- Outcome-Based Pricing: There’s a shift from conventional pricing to models that charge based on AI-delivered results. This approach aligns software costs with their value, driving more efficient business outcomes.
- Labor Replacement Concerns: While AI stands to replace human labor, leading to significant market opportunities, it also poses ethical and operational challenges. Addressing these will be crucial to balance automation benefits with fair employment practices.
- Hybrid Models: AI-enabled hybrid models, where humans and AI agents collaborate, will become prevalent, especially for complex tasks. These models leverage both AI and human strengths to achieve optimal results.
- Impact on Vertical Markets: AI solutions tailored to specific industries will disrupt traditional sectors, driving innovation and efficiency. This presents new growth and transformative opportunities.
- Incremental Adoption: Despite AI’s rapid advancements, transitioning fully to AI-driven enterprise software will take time due to the complexities of existing system integration. A gradual approach is necessary for effective management.
In summary, agentic AI is set to redefine enterprise software, bringing both opportunities and challenges. A more efficient, outcome-focused software ecosystem is on the horizon, but efforts are needed to address data integrity, process optimization, and labor ethics. Navigating this journey will shape the future of SaaS and enterprise software markets.
