Bain & Company has projected a US$100 billion market in the United States for software-as-a-service (SaaS) companies that integrate agentic artificial intelligence (AI) into their platforms. The estimate, published in the second report of Bain’s five-part series on the software industry in the age of AI, identifies automation of coordination work across enterprise systems as the primary revenue opportunity.
The report defines coordination work as the manual tasks employees perform between enterprise applications, including enterprise resource planning (ERP), customer relationship management (CRM), and customer support systems, as well as vendor management tools and email. Examples include pulling data from one system to verify against another, interpreting unstructured messages, and making decisions to approve, respond, escalate, or wait.
According to Bain, rules-based automation and robotic process automation (RPA) are insufficient for workflows involving ambiguity and information spread across multiple systems. Agentic AI can interpret data from various sources, coordinate actions across systems, and operate within policy guardrails, making it suited for these complex tasks.
The report argues that agentic AI is not primarily a replacement for existing SaaS platforms. Instead, the market emerges from converting labor-intensive coordination work into software spending. Bain estimates that vendors are currently capturing only US$4 billion to US$6 billion of the US market, with over 90% untapped.
Outside the United States, Bain estimated that Canada, Europe, Australia, and New Zealand could represent a similar-sized market, bringing the combined total in those regions and the US to roughly US$200 billion.
Market Size by Enterprise Function
The market opportunity is not evenly distributed. Bain estimates sales represents the largest single share at about US$20 billion, primarily due to the number of sales employees rather than unusually high automation potential. Cost of goods sold (COGS) and operations account for about US$26 billion, driven by a large operational workforce where even modest automation rates translate into a significant addressable market.
Research and development (R&D) and engineering, customer support, and finance each represent between US$6 billion and US$12 billion in addressable market size. These functions have sizeable workforces and higher automation potential in specific workflows.
Customer support and R&D or engineering have the highest automation potential, with roughly 40% to 60% of workflow tasks automatable. Bain said both areas feature structured data, standardized processes, and clearer output signals. Finance and human resources fall in the 35% to 45% range, with accounts payable and payroll showing higher automation potential, while financial planning and employee relations involve more judgment.
Sales and information technology (IT) sit at 30% to 40%. Bain pointed to relationship nuance, deal-by-deal variation, and the unpredictable nature of security incidents as limiting automation in those areas. Legal has the lowest overall automation potential at 20% to 30%, as contract review and compliance are repeatable but error consequences require tighter oversight.
Factors Determining Automation Feasibility
The report identifies six factors that determine how much of a workflow can be handled by an AI agent: output verifiability, consequence of failure, digitized knowledge availability, integration complexity, process variability, and the presence of clear decision logic.
Bain said workflows with clear verification signals, such as compiling code, reconciling invoices, and resolving support tickets, are easier to automate than those involving subjective judgment. Workflows involving regulatory or financial risk, such as tax filings, legal compliance, and security incident response, require closer human supervision even where agents are technically capable.
Digitized knowledge availability is a constraint, as agents need access to structured data and documented context, including decision logic that often resides informally with experienced employees. Integration complexity arises when workflows pass through multiple systems and application programming interfaces (APIs), with authentication layers and exception-handling processes adding further difficulty.
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.
Company Examples and Adjacent Workflows
The report cited several companies in its discussion of agentic AI adoption, including Cursor, Sierra, Harvey, Glean, Salesforce, ServiceNow, and Workday. Cursor has surpassed US$16.7 million in average monthly revenue after doubling in a single quarter. Sierra has crossed US$150 million in annual revenue, Harvey passed US$190 million, and Glean US$200 million.
Bain also pointed to GitHub as an example of a company using data from an existing core workflow to move into adjacent workflows.
David Crawford, chairman of Bain’s global technology and telecommunications practice, stated that SaaS companies have spent the past two decades building positions around systems of record. He said the next source of advantage is cross-workflow decision context, defined as the ability to interpret and act in workflows that move through multiple systems.
The remaining three reports in Bain’s series are expected to explore additional aspects of the software industry’s transformation in the age of AI, including further market sizing and strategic implications for vendors.
