Will AI agents replace SaaS? Key insights for 2025

The software industry stands at an inflection point where autonomous AI agents promise to fundamentally reshape how enterprises operate. These intelligent systems can now handle complex workflows that previously required human operators to navigate multiple applications, potentially rendering traditional software interfaces obsolete.

Major technology leaders predict a seismic shift in the next 18 months as AI moves from co-pilot functionality to autonomous operator. Microsoft CEO Satya Nadella suggests that business applications as we know them will collapse in the agent era, noting that they are essentially 'CRUD databases with business logic' that will migrate into the AI tier and that the very 'notion that business applications exist' will collapse in the agentic AI era, with AI systems updating multiple databases while embedding all business logic in the AI tier rather than individual applications.

This transformation affects every enterprise function — from engineering teams automating code reviews to support departments resolving tickets without human intervention. The question facing IT leaders isn't whether this shift will happen, but how quickly their organizations must adapt to remain competitive in an AI-driven landscape.

What Are AI agents and how do they differ from traditional SaaS?

AI agents represent a fundamental departure from the software paradigm that has dominated enterprise computing for decades. Unlike traditional SaaS applications that require users to click through interfaces, fill out forms, and manually execute workflows, AI agents operate as autonomous systems capable of reasoning through problems, making decisions, and taking action without constant human oversight. They understand natural language commands like "analyze our Q2 performance" or "optimize our ad campaigns" — eliminating the need for users to learn complex navigation paths through multiple applications.

The architectural differences between traditional SaaS and AI agents run deep. Traditional SaaS platforms rely on CRUD operations (Create, Read, Update, Delete) wrapped in user interfaces designed for human interaction. These systems follow predetermined workflows: a support ticket moves through defined stages, an expense report follows approval chains, a sales opportunity progresses through pipeline stages. Each action requires human initiation and decision-making at every step.

The evolution from database-centric to intelligence-centric architecture

Traditional SaaS emerged from a database-centric model where the primary innovation was moving software to the cloud. These platforms excel at:

AI agents, by contrast, embed intelligence at their core. They interact directly with APIs and databases without requiring graphical interfaces, orchestrating actions across multiple systems simultaneously. Where a human might log into three different applications to complete a workflow, an agent accesses all three systems concurrently, extracting data, applying logic, and executing actions in seconds rather than minutes.

Key capabilities that set AI agents apart

The transformative power of AI agents lies in their ability to handle ambiguity and adapt to context. Consider these fundamental differences:

AI agents are increasingly integral to business functions, with widespread adoption across the enterprise landscape. As of early 2025, 78% of organizations have implemented AI in at least one area, with projections suggesting this will rise to 80% by 2026. However, only 19% of executives report that revenues have increased more than 5% from generative AI investments, despite 87% expecting AI to generate revenue growth within three years, revealing a significant gap between expectations and realized benefits. Leading sectors, including financial services, technology, and retail, spearhead this transformation by integrating advanced AI capabilities.

The current state of AI agent adoption in enterprises

Beyond automation, AI agents provide contextual insights that support decision-making and innovation. Yet research examining enterprise API usage patterns found that 77% of enterprise AI deployment focuses on automation rather than collaboration, indicating businesses view AI agents primarily as replacement technologies rather than collaborative tools. By delivering timely and relevant information, they enhance productivity and allow employees to concentrate on strategic goals. As these agents continue to advance, their influence on redefining enterprise environments becomes increasingly significant.

AI agents are increasingly integral to business functions, with widespread adoption across the enterprise landscape. As of early 2025, 78% of organizations have implemented AI in at least one area, with projections suggesting this will rise to 80% by 2026. McKinsey estimates that AI could add between $2.6 trillion and $4.4 trillion in annual economic value across 63 analyzed use cases, equivalent to increasing the total impact of all artificial intelligence by 15 to 40 percent. Leading sectors, including financial services, technology, and retail, spearhead this transformation by integrating advanced AI capabilities.

In these industries, AI agents undertake sophisticated tasks, showcasing their adaptability and efficiency. They handle tasks such as coding assistance, customer support automation, financial process streamlining, marketing content creation, and human resources management. Klarna's AI assistant handled 2.3 million customer conversations in its first month, performing equivalent work to 700 full-time human agents while achieving customer satisfaction parity and reducing resolution time from 11 minutes to 2 minutes. This range of capabilities enables companies to optimize operations and reduce reliance on manual processes.

Beyond automation, AI agents provide contextual insights that support decision-making and innovation. By delivering timely and relevant information, they enhance productivity and allow employees to concentrate on strategic goals. As these agents continue to advance, their influence on redefining enterprise environments becomes increasingly significant.

How AI agents impact existing SaaS tools

AI agents are reshaping the SaaS landscape through four distinct scenarios: enhancing, compressing, outshining, and cannibalizing existing tools. Each scenario presents unique challenges and opportunities for businesses navigating this transformation. Understanding these dynamics is crucial for companies aiming to leverage AI effectively.

Transformative scenarios

Integration and security challenges

Integrating AI agents with existing software requires careful planning to ensure compatibility and security. Key considerations include:

Security remains a priority as AI agents handle sensitive data. Implementing stringent access controls is vital to protect information and maintain trust.

Breaking down silos

AI agents facilitate a more interconnected enterprise environment, breaking down traditional software silos. They enable:

As AI agents advance, their influence on the SaaS ecosystem will encourage businesses to rethink their technological strategies. Embracing these changes will be key to unlocking new efficiencies and staying competitive in an increasingly automated landscape.

Benefits of AI agents over traditional SaaS

AI agents introduce a transformative approach to enterprise productivity. Unlike traditional SaaS, which often requires manual initiation, AI agents autonomously manage tasks, providing round-the-clock efficiency. Their ability to handle multiple processes simultaneously accelerates operations, enabling businesses to achieve more in less time.

Advanced learning and adaptation

A key advantage of AI agents is their capacity for continuous learning and adaptation. This ability minimizes the need for extensive onboarding, as agents refine their operations through ongoing interaction. As they adapt, error rates decrease, resulting in more reliable outcomes.

Cost efficiency and resource management

AI agents also present a compelling case for cost efficiency, particularly in licensing and support. Traditional SaaS often involves per-user fees, which can escalate for growing teams. AI agents bypass this model by offering flexible solutions that scale with organizational demands.

Scalability is another critical benefit. AI agents seamlessly extend their capabilities across various departments, ensuring consistent and efficient performance. This adaptability makes them ideal for enterprises seeking to maximize productivity without increasing costs.

Key challenges in the AI agent transition

Transitioning to AI agents involves overcoming several technical hurdles. High computational costs and unpredictable pricing models can strain budgets, especially as AI demands grow. Enterprises must ensure data accuracy and address integration obstacles to effectively deploy AI solutions within their existing environments.

Technical and integration barriers

Organizational readiness and security concerns

Beyond technical issues, organizational readiness is vital for the successful adoption of AI agents. Resistance to new technologies and skills shortages can impede progress.

Navigating this landscape requires a keen strategic focus. Organizations must evaluate their technology frameworks to identify areas where AI can deliver maximum impact. For example, ServiceNow announced it would acquire AI agent platform Moveworks for $2.85 billion in March 2025, signaling that legacy software incumbents are making massive strategic bets on agent technology as core to their future platform strategy. Key considerations include:

The future landscape: AI agents and SaaS coexistence

The integration of AI agents into the SaaS ecosystem marks a pivotal evolution in enterprise technology. Emerging models are transforming SaaS platforms into dynamic orchestration hubs, focusing on maintaining data integrity while enabling seamless interactions across diverse systems.

Evolving models and market trends

New models are reshaping how businesses utilize software, blending AI capabilities with existing infrastructures. This evolution empowers organizations to enhance efficiency without overhauling legacy systems. By streamlining operations, these models ensure that businesses remain agile and responsive to changing demands.

Market trends show a significant shift towards integrated platforms. Traditional vendors are incorporating advanced AI features to enhance functionality and drive innovation. This movement towards comprehensive solutions reflects a broader industry trend — offering enterprises versatile tools that cater to various operational needs. New market entrants are seizing this opportunity by developing agent-centric architectures, emphasizing agility and forward-thinking design.

Strategic focus for enterprises

Navigating this landscape requires a keen strategic focus. Organizations must evaluate their technology frameworks to identify areas where AI can deliver maximum impact. Key considerations include:

As AI agents and SaaS platforms continue to evolve, embracing these changes will enable organizations to lead in innovation. By leveraging the combined strengths of AI and SaaS, businesses can achieve new levels of efficiency and productivity in an increasingly automated world.

What businesses should consider When evaluating AI agents

When considering AI agents, businesses should begin by identifying processes ripe for automation. This involves a detailed analysis of existing workflows to pinpoint where AI can deliver the most substantial benefits.

Evaluating vendor strategies and data management

Assessing vendor strategies is crucial for successful integration. Companies should explore vendors’ AI development plans and ensure they align with strategic objectives. This forward-looking approach guarantees that the chosen solutions will evolve alongside the organization’s needs.

Data management must be a priority. Organizations should clarify how data will be handled, ensuring it meets compliance requirements and aligns with internal standards for security and accessibility.

Effective implementation practices

Introducing AI agents requires a well-thought-out plan. Pilot programs offer a controlled environment to test AI solutions, allowing businesses to gather insights and refine deployment strategies.

Metrics should extend beyond cost reductions to include improvements in operational efficiency and decision-making capabilities. These comprehensive measures provide a clearer picture of AI's value across the enterprise.

Fostering a culture of innovation is key. By providing employees with the necessary training and resources, businesses can enhance their teams' ability to effectively utilize AI technologies, ensuring seamless integration into work processes.

Preparing your organization for the AI agent era

Transitioning to the AI agent era requires a solid infrastructure. Ensuring uniformity in data formats facilitates integration across various systems, reducing the risk of incompatibility. This harmonization supports efficient data flow and enhances the precision of AI-generated insights.

Identity and access management

Securing sensitive information and ensuring compliance demands robust identity management. An effective system governs the access that AI agents have, ensuring they function within established boundaries. This strategy prevents unauthorized access and maintains the integrity of AI operations.

Governance frameworks

Instituting governance frameworks is key for managing AI rollout. These frameworks set the parameters for ethical AI use, data management, and adherence to legal standards. They also create accountability structures that build trust in AI deployments.

Future-proofing the technology stack

Organizations must prioritize adaptable platforms that offer flexibility and scalability. Moving beyond isolated solutions enables better responsiveness to technological evolution. Facilitating data transfer across systems also encourages innovation and operational flexibility.

Remaining agile is essential as AI capabilities evolve. Businesses should remain receptive to technological advancements and be ready to incorporate new developments into their operations. This readiness positions organizations to leverage AI advancements effectively and sustain competitiveness.

The shift from traditional SaaS to AI agents represents the most significant transformation in enterprise technology since cloud computing emerged. Organizations that act decisively now — building the right infrastructure, partnerships, and governance frameworks — will define the next era of workplace productivity. Ready to see how AI agents can revolutionize your workflows? Request a demo to explore how Glean and AI can transform your workplace and discover what's possible when we bring the future of work to your organization today.