Bain’s Forecast: Agentic AI Automation to Dominate US$100 Billion SaaS Market

Bain & Company’s Estimated US$100 Billion Market for SaaS Companies Using Agentic AI

Bain & Company has projected a significant US$100 billion market in the United States for SaaS companies leveraging agentic AI. This market is focused on streamlining coordination work within enterprise systems.

These insights are derived from the second installment of Bain’s five-part series exploring the software industry’s evolution in the AI era. The report delves into the potential for agentic AI to open up new software markets and how SaaS firms can capitalize on them.

The Role of Coordination Work in Enterprise Systems

Bain highlights the manual tasks carried out by employees across different enterprise applications as the core of this market. These tasks involve activities spanning ERP, CRM, support systems, vendor management tools, and email.

Typical tasks include data extraction, cross-referencing, interpreting unstructured data, and making decisions such as approval, response, escalation, or delay.

While rules-based automation and robotic process automation have limitations in complex workflows, agentic AI can excel in interpreting data from diverse sources, coordinating actions, and adhering to policy guidelines.

The report emphasizes that agentic AI complements rather than replaces SaaS platforms, with the market evolving from transforming labor-intensive coordination work into software-based solutions.

Current estimates suggest that vendors are tapping into a US$4 billion to US$6 billion segment of the US market, leaving over 90% untapped for further growth opportunities.

Outside the US, Bain anticipates similar market potential in Canada, Europe, Australia, and New Zealand, potentially expanding the total market size in these regions and the US to around US$200 billion.

Market Segmentation by Function

The market distribution across enterprise functions varies significantly. Sales activities are projected to dominate, accounting for approximately US$20 billion, largely due to the extensive sales workforce rather than exceptionally high automation potential.

Cost of goods sold and operations represent a combined value of about US$26 billion, given the sizable operational workforce where even modest automation rates can translate into substantial market share. R&D, engineering, customer support, and finance each present addressable market sizes ranging from US$6 billion to US$12 billion.

Customer support and R&D/engineering demonstrate the highest potential for automation, with 40% to 60% of tasks considered automatable. Finance and HR fall in the 35% to 45% range, with variations based on workflow complexities.

Automation potential is lower in sales and IT (30% to 40%), primarily due to relationship nuances and deal-specific variations. Legal processes exhibit the lowest automation potential at 20% to 30% due to the stakes involved in errors.

Factors Influencing Automation Success

The report identifies six key factors determining the feasibility of AI agents handling various workflows effectively. These include output verifiability, consequence of failure, availability of digitized knowledge, and process variability.

Workflows with clear verification signals are easier to automate, while those involving regulatory or financial risks require closer human oversight. Access to structured data and documented context is crucial for AI agents, along with integration complexities.

Integration complexities arise when workflows traverse multiple systems and APIs, making end-to-end automation challenging. The highest value areas are where workflows span across different enterprise systems.

Examples of Company Success and Expansion into Adjacent Workflows

The report highlights successful companies like Cursor, Sierra, Harvey, Glean, Salesforce, ServiceNow, and Workday that have leveraged agentic AI. Notably, GitHub utilized its repository data to expand into AI-assisted developer productivity and security automation.

SaaS companies can achieve growth by automating core workflows where they possess domain expertise and customer trust. Additionally, automating adjacent workflows, not directly served by the company, presents opportunities for expansion.

Pricing models may evolve towards outcome- and use-based structures, especially as AI agents deliver results. This contrasts with traditional seat-based pricing models.

Recommendations for SaaS Companies

Bain advises SaaS companies to identify automatable customer workflows with agentic AI, focusing on subprocess-level assessments rather than broad function-based evaluations.

Quality data assessment is crucial, considering factors like comprehensiveness, outcome relevance, and suitability for automation.

Companies can bridge capability gaps through internal development, acquisitions, or partnerships, as evidenced by industry examples like AppLovin, ServiceNow, and Salesforce.

AI engineering talent, cloud-native architecture, and funding for model training are essential for success in agentic workflows. Pricing and sales strategies should align with AI-driven outcomes rather than legacy models.

Ultimately, SaaS companies need foundational structures designed for agentic workflows, enabling seamless hand-offs and decision capture in each workflow run.

As AI-native companies accumulate deployment data, the timeline for SaaS firms to adapt is compressed into quarters rather than years, according to David Crawford, Bain’s chairman of global technology and telecommunications.

(Image by engin akyurt)

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