Bain forecasts US$100 billion SaaS market in agentic AI automation

Bain & Company has estimated a $100 billion market in the U.S. for SaaS companies leveraging agentic AI. The firm said this market stems from automating coordination tasks within enterprise systems.

This estimate comes from the second installment in Bain’s five-part series on the software industry in the age of AI. The report explores where agentic AI could open new software markets and how SaaS providers can capture them.

Coordination work in enterprise systems

According to Bain, the market arises from manual tasks employees perform across different enterprise applications. These workflows often involve ERP, CRM, and support systems, as well as vendor management tools and email.

Such tasks include pulling data from one system and cross-referencing it with another, interpreting unstructured messages, and deciding whether to approve, respond, escalate, or wait.

Bain noted that rules-based automation and robotic process automation have limitations when workflows involve ambiguity and information scattered across multiple systems. Agentic AI can interpret data from diverse sources, coordinate actions across systems, and operate within policy guardrails.

The report argues that agentic AI is not primarily about replacing SaaS platforms; instead, the market opportunity lies in converting labor-intensive coordination work into software spending.

Bain estimates that vendors are already capturing $4 billion to $6 billion of the U.S. market, with more than 90% remaining untapped.

Outside the U.S., Bain projects that Canada, Europe, Australia, and New Zealand could collectively add a similar-sized market, bringing the total in those regions and the U.S. to roughly $200 billion.

Market size by function

The market is not evenly distributed across enterprise functions. Bain estimates that sales represents the largest single share at about $20 billion, driven mainly by the number of sales employees rather than unusually high automation potential.

Cost of goods sold and operations account for approximately $26 billion. The large operational workforce means even modest automation rates translate into a sizable addressable market. R&D and engineering, customer support, and finance each represent roughly $6 billion to $12 billion in addressable market size. These functions have large workforces and higher automation potential in specific workflows.

Customer support and R&D/engineering have the highest automation potential, with roughly 40% to 60% of workflow tasks automatable. Bain noted that both areas involve structured data, standardized processes, and clearer output signals. 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 judgment.

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 necessitate tighter oversight.

Bain’s automation factors

The report identifies six factors that determine how much of a workflow an AI agent can realistically handle. These include output verifiability, consequence of failure, digitized knowledge availability, and process variability. Bain said workflows with clear verification signals are easier to automate than those involving subjective judgment. Examples include compiling code, reconciling invoices, and resolving support tickets.

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

Bain also highlighted digitized knowledge availability as a constraint. Agents need access to structured data and documented context, as well as machine-readable inputs, including decision logic that often resides informally with experienced employees.

Integration complexity affects automation when workflows traverse multiple systems and APIs. Authentication layers and exception-handling processes add further complexity, making these workflows harder to automate end-to-end than those contained within a single platform. The highest-value areas are concentrated where no single system of record controls the full outcome, often spanning ERP, CRM, and support systems, the company says.

David Crawford, chairman of Bain’s global technology and telecommunications practice, said SaaS companies have spent the past two decades building positions around systems of record, and the next source of advantage will be “cross-workflow decision context” — the ability to interpret and act within workflows that move through multiple systems.

Company examples and adjacent workflows

The report cited Cursor, Sierra, Harvey, Glean, Salesforce, ServiceNow, and Workday in its discussion of agentic AI adoption. According to Bain, Cursor has surpassed $16.7 million in average monthly revenue after doubling in a single quarter. Sierra has crossed $150 million per annum, Harvey passed $190 million per annum, and Glean $200 million per annum.

The report also pointed to GitHub as an example of a company using data from an existing core workflow to expand 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. The first is automating core workflows, where they already have domain knowledge and customer trust. Existing system integrations can support automation of core workflows. The second is automating adjacent workflows that the company does not currently serve directly. These areas can be harder to identify because they require detailed mapping of customer workflows and the underlying data that supports decisions.

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, contrasting 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 also advised companies to evaluate the quality of their data. Bain said relevant factors include whether the data is comprehensive, tied to outcomes, and usable for automation.

Bain said companies could close ability gaps through internal development, acquisitions, or partnerships. 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 stressed 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.

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

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