Agentic AI Automation Market to Reach 100 Billion Dollars Says Bain Report

Bain & Company has estimated a US$100 billion market in the US for SaaS companies using agentic AI. The firm said the market is tied to automating coordination work in enterprise systems.

The estimate comes from the second report in Bain's five-part series on the software industry in the age of AI. The report examines where agentic AI could create new software markets and how SaaS companies can capture them.

Coordination Work in Enterprise Systems

Bain said the market lies in the manual work employees perform between enterprise applications. These workflows often span ERP, CRM and support systems. They may also involve vendor management tools and email.

That work includes pulling data from one system and checking it against another source. It can also involve interpreting unstructured messages and deciding whether to approve, respond, escalate, or wait.

Bain said rules-based automation and robotic process automation are limited in workflows involving ambiguity and information spread in multiple systems. Agentic AI can interpret information from different sources, coordinate actions in systems, and operate in policy guardrails.

The report argues that agentic AI is not primarily a replacement for SaaS platforms, but that the market comes from converting labour-intensive coordination work into software spending.

It estimates vendors are already capturing US$4 billion to US$6 billion of the US market. More than 90% remains untapped, according to the firm.

Outside the US, Bain estimated that Canada, Europe, Australia, and New Zealand could add a similar-sized market. That would bring the total in those regions and the US to about US$200 billion.

Market Size by Function

The market is not evenly distributed in enterprise functions. Bain estimates that sales represents the largest single share at about US$20 billion. This is mainly due to the number of sales employees, not unusually high automation potential.

• 📊 Cost of goods sold and operations: approximately US$26 billion addressable market
• 🔬 R&D and engineering: US$6 billion to US$12 billion market size
• 💬 Customer support: US$6 billion to US$12 billion addressable market
• 💰 Finance: US$6 billion to US$12 billion market potential

The large size of the operational workforce means even modest automation rates can translate into a large addressable market.

Finance and human resources fall in the 35% to 45% range. The report said accounts payable and payroll have higher automation potential, while financial planning and employee relations involve more judgement.

Sales and IT sit at 30% to 40%. Bain pointed to relationship nuance, deal-by-deal variation, and the unpredictable nature of security incidents as limits on automation in those areas. Legal has lower overall automation potential, at 20% to 30%. Bain said contract review and compliance are repeatable, but the consequences of errors create a need for tighter oversight.

Bain's Automation Factors

The report identifies six factors that determine how much of a workflow can realistically be handled by an AI agent. They include:

1. Output verifiability
2. Consequence of failure
3. Digitised knowledge availability
4. Process variability
5. Integration complexity
6. System interdependencies

Bain said workflows with clear verification signals are easier to automate than work involving subjective judgement. Examples include compiling code, reconciled invoices, and resolved support tickets.

Workflows involving regulatory or financial risk require closer human supervision, even where agents are technically capable, according to the report. These include tax filings, legal compliance, and security incident response.

Bain also identified digitised knowledge availability as a constraint. Agents need access to structured data and documented context. They also need machine-readable inputs, including decision logic that often sits informally with experienced employees.

Integration complexity affects automation when workflows pass through several systems and APIs. Authentication layers and exception-handling processes add further complexity, and these workflows are harder to automate end-to-end than workflows contained in a single platform.

The highest-value areas are concentrated where no single system of record controls the full outcome. These workflows often span ERP, CRM and support systems, the company says.

Company Examples and Adjacent Workflows

The report cited several companies in its discussion of agentic AI adoption:

• Cursor: surpassed US$16.7 million in average monthly revenue after doubling in a single quarter
• Sierra: crossed US$150 million per annum
• Harvey: passed US$190 million per annum
• Glean: reached US$200 million per annum

The report also pointed to GitHub as an example of a company using data from an existing core workflow to move into adjacent work. GitHub's core business is developer collaboration and source control, but its repository and workflow data helped support expansion into AI-assisted developer productivity and security automation.

Bain said SaaS companies can expand through two types of workflow automation:

Pricing models can change when agents deliver completed outcomes. Bain said outcome- and use-based pricing can become more relevant when agents resolve issues or process invoices. The report contrasts this with traditional pricing based on seats and logins.

Bain's Recommendations for SaaS Companies

Bain recommended that SaaS companies begin by identifying which customer workflows are now automatable with agentic AI. The firm said companies should assess automation at the subprocess level not treating entire functions as equally automatable.

The report cited AppLovin's in-house development of its Axon platform, ServiceNow's acquisition of Moveworks, and Salesforce's partnership with Workday as examples of different approaches.

The firm also pointed to the need for AI engineering talent, cloud-native architecture for multi-agent orchestration, and funding for model training and inference. It said companies should align pricing and sales incentives with AI-driven outcomes not legacy seat-based models.

Bain said SaaS companies will also need data and product foundations designed for agentic workflows, including machine-readable hand-offs and systems that capture decisions and outcomes from each workflow run.

Crawford said the timeframe for SaaS companies is "measured in quarters, not years," as AI-native companies gather more deployment data with each customer workflow they automate.

(Photo by engin akyurt)

See also:

Google tests Remy AI agent for Gemini as focus turns to user control