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.
\nThe estimate comes from the second report in Bain\u2019s 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.
\nCoordination work in enterprise systems
\nBain 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.
\nThat 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.
\nBain 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.
\nThe 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.
\nIt 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.
\nOutside 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.
\nMarket size by function
\nThe 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.
\nCost of goods sold and operations account for about US$26 billion. The large size of the operational workforce means even modest automation rates can translate into a large addressable market. R&D and engineering, customer support, and finance each represent about US$6 billion to US$12 billion in addressable market size. These functions have sizeable workforces and higher automation potential in specific workflows.
\nCustomer support and R&D or engineering have the highest automation potential, with roughly 40% to 60% of workflow tasks automatable. Bain said both areas have structured data, standardised 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 judgement.
\nSales 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.
\nBain\u2019s automation factors
\nThe report identifies six factors that determine how much of a workflow can realistically be handled by an AI agent. They include output verifiability, consequence of failure, digitised knowledge availability, and process variability. 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.
\nWorkflows 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.
\nBain 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.
\nIntegration 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.
\nDavid Crawford, chairman of Bain\u2019s global technology and telecommunications practice, said SaaS companies have spent the past two decades building positions around systems of record with the next source of advantage being \u201ccross-workflow decision context,\u201d which is defined as the ability to interpret and act in workflows that move through multiple systems.
\nCompany examples and adjacent workflows
\nThe report cited Cursor, Sierra, Harvey, Glean, Salesforce, ServiceNow, and Workday in its discussion of agentic AI adoption. Cursor has surpassed US$16.7 million in average monthly revenue, according to Bain, after doubling in a single quarter. Sierra has crossed US$150 million per annum, Harvey passed US$190 million pa, and Glean US$200 million pa.
\nThe report also pointed to GitHub for example of a company using data from an existing core workflow to move into adjacent work. GitHub\u2019s 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.
\nBain 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. Bain said 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.
\nPricing 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.
\nBain\u2019s recommendations for SaaS companies
\nBain 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.
\nThe report also said companies should assess the quality of their data. Bain said relevant factors include whether the data is comprehensive, tied to outcomes, and usable for automation.
\nBain said companies could close ability gaps through internal development, acquisitions, or partnerships. The report cited AppLovin\u2019s in-house development of its Axon platform, ServiceNow\u2019s acquisition of Moveworks, and Salesforce\u2019s partnership with Workday as examples of different approaches.
\nThe 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.
\nBain 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.
\nCrawford said the timeframe for SaaS companies is \u201cmeasured in quarters, not years,\u201d as AI-native companies gather more deployment data with each customer workflow they automate.
\n(Photo by engin akyurt)
\nSee also: Google tests Remy AI agent for Gemini as focus turns to user control
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\nThe post Bain sees US$100 billion SaaS market in agentic AI automation appeared first on AI News.<\/p>\n","protected":false},"excerpt":{"rendered":"
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\u2019s five-part series on the software industry in the age of AI. The report examines [\u2026]<\/p>\n
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