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

Bain & Company has estimated a US$100 billion market in the US for SaaS firms utilizing agentic AI. The agency stated the market is tied to automating coordination work in enterprise techniques.

The estimate comes from the second report in Bain’s five-part collection on the software program business in the age of AI. The report examines the place agentic AI may create new software program markets and the way SaaS firms can seize them.

Coordination work in enterprise techniques

Bain stated the market lies in the handbook work workers carry out between enterprise functions. These workflows usually span ERP, CRM and assist techniques. They might also contain vendor administration instruments and e-mail.

That work consists of pulling information from one system and checking it in opposition to one other supply. It also can contain deciphering unstructured messages and deciding whether or not to approve, reply, escalate, or wait.

Bain stated rules-based automation and robotic course of automation are restricted in workflows involving ambiguity and data unfold in a number of techniques. Agentic AI can interpret data from totally different sources, coordinate actions in techniques, and function in coverage guardrails.

The report argues that agentic AI shouldn’t be primarily a alternative for SaaS platforms, however that the market comes from changing labour-intensive coordination work into software program spending.

It estimates distributors are already capturing US$4 billion to US$6 billion of the US market. More than 90% stays untapped, in accordance with the agency.

Outside the US, Bain estimated that Canada, Europe, Australia, and New Zealand may add a similar-sized market. That would carry the whole in these areas and the US to about US$200 billion.

Market dimension by perform

The market shouldn’t be evenly distributed in enterprise features. Bain estimates that gross sales represents the most important single share at about US$20 billion. This is especially as a result of variety of gross sales workers, not unusually excessive automation potential.

Cost of products offered and operations account for about US$26 billion. The giant dimension of the operational workforce means even modest automation charges can translate into a big addressable market. R&D and engineering, buyer assist, and finance every symbolize about US$6 billion to US$12 billion in addressable market dimension. These features have sizeable workforces and better automation potential in particular workflows.

Customer assist and R&D or engineering have the very best automation potential, with roughly 40% to 60% of workflow duties automatable. Bain stated each areas have structured information, standardised processes, and clearer output indicators. Finance and human assets fall in the 35% to 45% vary. The report stated accounts payable and payroll have larger automation potential, whereas monetary planning and worker relations contain extra judgement.

Sales and IT sit at 30% to 40%. Bain pointed to relationship nuance, deal-by-deal variation, and the unpredictable nature of safety incidents as limits on automation in these areas. Legal has decrease general automation potential, at 20% to 30%. Bain stated contract overview and compliance are repeatable, however the penalties of errors create a necessity for tighter oversight.

Bain’s automation elements

The report identifies six elements that decide how a lot of a workflow can realistically be dealt with by an AI agent. They embrace output verifiability, consequence of failure, digitised data availability, and course of variability. Bain stated workflows with clear verification indicators are simpler to automate than work involving subjective judgement. Examples embrace compiling code, reconciled invoices, and resolved assist tickets.

Workflows involving regulatory or monetary threat require nearer human supervision, even the place brokers are technically succesful, in accordance with the report. These embrace tax filings, authorized compliance, and safety incident response.

Bain additionally recognized digitised data availability as a constraint. Agents want entry to structured information and documented context. They additionally want machine-readable inputs, together with choice logic that usually sits informally with skilled workers.

Integration complexity impacts automation when workflows move by way of a number of techniques and APIs. Authentication layers and exception-handling processes add additional complexity, and these workflows are more durable to automate end-to-end than workflows contained in a single platform. The highest-value areas are concentrated the place no single system of file controls the complete final result. These workflows usually span ERP, CRM and assist techniques, the corporate says.

David Crawford, chairman of Bain’s world know-how and telecommunications follow, stated SaaS firms have spent the previous twenty years constructing positions round techniques of file with the following supply of benefit being “cross-workflow choice context,” which is outlined as the flexibility to interpret and act in workflows that transfer by way of a number of techniques.

Company examples and adjoining workflows

The report cited Cursor, Sierra, Harvey, Glean, Salesforce, ServiceNow, and Workday in its dialogue of agentic AI adoption. Cursor has surpassed US$16.7 million in common month-to-month income, in accordance with Bain, after doubling in a single quarter. Sierra has crossed US$150 million each year, Harvey handed US$190 million pa, and Glean US$200 million pa.

The report additionally pointed to GitHub for instance of an organization utilizing information from an current core workflow to maneuver into adjoining work. GitHub’s core enterprise is developer collaboration and supply management, however its repository and workflow information helped assist growth into AI-assisted developer productiveness and safety automation.

Bain stated SaaS firms can increase by way of two sorts of workflow automation. The first is automating core workflows, the place they have already got area data and buyer belief. Bain stated current system integrations can assist automation of core workflows. The second is automating adjoining workflows that the corporate doesn’t presently serve immediately. These areas will be more durable to determine as a result of they require detailed mapping of buyer workflows and the underlying information that helps selections.

Pricing fashions can change when brokers ship accomplished outcomes. Bain stated outcome- and use-based pricing can turn out to be extra related when brokers resolve points or course of invoices. The report contrasts this with conventional pricing primarily based on seats and logins.

Bain’s suggestions for SaaS firms

Bain advisable that SaaS firms start by figuring out which buyer workflows at the moment are automatable with agentic AI. The agency stated firms ought to assess automation on the subprocess degree not treating whole features as equally automatable.

The report additionally stated firms ought to assess the standard of their information. Bain stated related elements embrace whether or not the info is complete, tied to outcomes, and usable for automation.

Bain stated firms may shut skill gaps by way of inside improvement, acquisitions, or partnerships. The report cited AppLovin’s in-house improvement of its Axon platform, ServiceNow’s acquisition of Moveworks, and Salesforce’s partnership with Workday as examples of various approaches.

The agency additionally pointed to the necessity for AI engineering expertise, cloud-native structure for multi-agent orchestration, and funding for mannequin coaching and inference. It stated firms ought to align pricing and gross sales incentives with AI-driven outcomes not legacy seat-based fashions.

Bain stated SaaS firms will even want information and product foundations designed for agentic workflows, together with machine-readable hand-offs and techniques that seize selections and outcomes from every workflow run.

Crawford stated the timeframe for SaaS firms is “measured in quarters, not years,” as AI-native firms collect extra deployment information with every buyer workflow they automate.

(Photo by engin akyurt)

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