The rise of agentic AI is rapidly reshaping the future of enterprise software, and according to Bain & Company, the transformation could unlock a massive US$100 billion software market in the United States alone. The consulting firm believes the opportunity lies not in replacing existing SaaS platforms, but in automating the coordination work that happens between enterprise systems every day.
This projection was revealed in Bain’s second report from its five-part research series focused on the future of the software industry in the AI era. The report explores how agentic AI can create entirely new software categories while enabling SaaS vendors to expand their market share through intelligent automation.
Understanding the New Era of Agentic AI
Traditional enterprise software has long focused on storing information and managing workflows. Systems such as ERP, CRM, HRM, finance, and customer support platforms are already deeply integrated into modern organizations. However, despite years of digital transformation, a significant amount of work inside enterprises is still performed manually.
Employees often spend hours moving information between systems, verifying data accuracy, responding to messages, escalating issues, or coordinating approvals across departments. These repetitive but complex tasks create inefficiencies that standard automation tools struggle to solve.
This is where agentic AI changes the game.
Unlike traditional robotic process automation (RPA), which follows predefined rules, agentic AI can understand context, interpret unstructured information, make decisions within policy limits, and coordinate actions across multiple systems. Instead of simply automating clicks and repetitive inputs, these AI agents can handle dynamic workflows that require reasoning and adaptability.
According to Bain, this capability creates a major commercial opportunity for SaaS providers.
Why Coordination Work Represents a Massive Market
Bain’s report highlights that the real value of agentic AI lies in automating “coordination work” inside enterprises. This includes all the behind-the-scenes operational activities employees perform while navigating different software systems.
For example, a finance employee may need to:
- Pull invoice data from an ERP system
- Verify details against vendor records
- Review email communications
- Approve or escalate discrepancies
- Update payment systems
Similarly, customer support teams constantly switch between CRM systems, support tickets, chat histories, and internal documentation to resolve customer issues.
These workflows are time-consuming because information is scattered across platforms, and many decisions require contextual understanding.
Bain argues that agentic AI can bridge these gaps by:
- Interpreting data from multiple systems
- Understanding unstructured communications
- Executing actions across platforms
- Operating within company-defined guardrails
- Reducing dependency on manual intervention
The consulting firm estimates that the US market for this type of AI-driven automation could reach US$100 billion. Currently, only US$4 billion to US$6 billion of that market has been captured, meaning more than 90% of the opportunity still remains untapped.
Beyond the United States, Bain believes regions including Canada, Europe, Australia, and New Zealand could contribute another US$100 billion market, taking the combined opportunity close to US$200 billion.
Which Business Functions Have the Highest Automation Potential?
The report explains that automation opportunities vary significantly across enterprise departments. Some workflows are easier to automate because they rely on structured processes and clear outcomes, while others still require human judgment.
Sales Leads the Market Opportunity
Bain estimates that sales functions account for roughly US$20 billion of the total addressable market. This is primarily due to the large number of employees working in sales operations globally.
However, sales workflows are not necessarily the easiest to automate. Human relationships, negotiation dynamics, and deal-specific variations limit full automation potential.
Even so, AI agents can assist with:
- Lead qualification
- CRM updates
- Proposal generation
- Customer follow-ups
- Sales forecasting
- Meeting summaries
Operations and Cost of Goods Sold
Operations and cost-of-goods-sold functions together represent nearly US$26 billion in potential value. Large operational workforces mean even partial automation can generate significant savings.
Agentic AI can streamline:
- Vendor coordination
- Procurement workflows
- Supply chain monitoring
- Inventory tracking
- Exception handling
- Logistics communication
Customer Support and Engineering Show High Automation Rates
Among all functions, customer support and engineering demonstrate the highest automation potential, ranging between 40% and 60%.
These areas often involve:
- Structured datasets
- Standardized workflows
- Clear output expectations
- Repeatable decision-making
For customer support, AI agents can analyze tickets, recommend solutions, respond to customers, escalate critical issues, and close cases automatically.
In engineering, AI systems can assist with:
- Code reviews
- Documentation
- Security scanning
- Testing automation
- Workflow coordination
Finance and Human Resources
Finance and HR functions show automation potential between 35% and 45%.
In finance, repetitive processes such as payroll and accounts payable are ideal for AI-driven automation. However, areas involving strategic planning or judgment-heavy decisions still require human oversight.
Human resources workflows that can benefit include:
- Resume screening
- Employee onboarding
- Policy management
- Payroll coordination
- Internal ticket handling
Legal and IT Have More Limitations
Legal functions remain among the hardest areas to automate fully, with only 20% to 30% automation potential.
Although contract reviews and compliance checks can be partially automated, legal errors often carry serious financial and regulatory consequences. This creates a greater need for human supervision.
Similarly, IT operations face challenges because cybersecurity incidents and infrastructure issues are often unpredictable.
The Six Factors Determining AI Automation Success
Bain identified six key factors that determine whether a workflow is suitable for agentic AI automation.
1. Output Verifiability
Tasks with clearly measurable outcomes are easier to automate. Examples include:
- Compiled code
- Reconciled invoices
- Resolved support tickets
AI systems perform better when success criteria are objective and easy to validate.
2. Consequences of Failure
Workflows involving regulatory or financial risk require stricter human oversight.
Processes such as:
- Tax filing
- Legal compliance
- Security incident management
cannot yet rely entirely on AI due to the high cost of errors.
3. Availability of Digitized Knowledge
AI agents require structured and machine-readable information. Organizations that still rely heavily on undocumented employee knowledge face greater automation barriers.
4. Process Variability
Highly variable workflows are harder to automate because the AI must adapt to different situations continuously.
5. Integration Complexity
Many enterprise workflows involve multiple systems, APIs, authentication layers, and exception-handling rules. This complexity increases implementation difficulty.
6. Decision Logic Accessibility
Organizations often fail to document how experienced employees make decisions. Without this information, AI agents cannot fully replicate operational processes.
Why SaaS Companies Are Perfectly Positioned
Bain believes SaaS providers already hold a strategic advantage because they control many of the systems where enterprise data resides.
Over the past two decades, SaaS companies have built dominant positions around systems of record such as:
- CRM platforms
- ERP systems
- HR software
- Support platforms
- Collaboration tools
Now, the next competitive advantage will come from “cross-workflow decision context,” which refers to understanding workflows that move across multiple systems.
David Crawford, chairman of Bain’s global technology and telecommunications practice, explained that the companies capable of interpreting and coordinating actions between systems will dominate the next generation of enterprise software.
Examples of Companies Already Winning in Agentic AI
Bain highlighted several companies experiencing rapid growth due to AI-powered workflow automation.
Cursor
Cursor reportedly surpassed US$16.7 million in average monthly revenue after doubling revenue within a single quarter. Its AI-assisted development platform is gaining rapid adoption among software engineers.
Sierra
Sierra has reportedly crossed US$150 million in annual revenue by focusing on AI-powered customer engagement solutions.
Harvey
Harvey has exceeded US$190 million annually by providing AI tools tailored for legal professionals and compliance workflows.
Glean
Glean has crossed US$200 million annually through AI-powered enterprise search and workflow intelligence.
GitHub
Bain also referenced GitHub as a strong example of expanding from core workflows into adjacent AI opportunities. GitHub originally focused on source control and developer collaboration, but its massive repository data enabled expansion into AI coding assistance and security automation.
The Shift From Seat-Based Pricing to Outcome-Based Pricing
One of the biggest changes highlighted in Bain’s report involves SaaS pricing models.
Traditional SaaS platforms typically charge customers based on:
- Number of users
- Seats
- Logins
- Licenses
However, agentic AI changes the economics because customers increasingly pay for completed outcomes instead of software access.
For example:
- A customer support AI agent may charge per resolved ticket
- A finance automation tool may charge per invoice processed
- A sales AI system may charge based on conversions or leads handled
This transition toward outcome-based pricing could fundamentally reshape SaaS business models over the coming years.
How SaaS Companies Can Capture the Opportunity
Bain recommends that software companies move quickly because AI-native competitors are already scaling aggressively.
Focus on Workflow-Level Automation
Instead of treating entire departments as automatable, companies should evaluate individual subprocesses.
This approach helps identify:
- High-value workflows
- Fast implementation opportunities
- Lower-risk automation areas
Improve Data Foundations
AI systems depend heavily on clean, connected, and outcome-linked data.
Organizations must ensure their data is:
- Comprehensive
- Structured
- Machine-readable
- Integrated across platforms
Invest in AI Engineering Talent
The report emphasizes the growing importance of:
- AI engineers
- Cloud-native architecture
- Multi-agent orchestration systems
- Model training infrastructure
Companies lacking internal expertise may struggle to compete.
Expand Through Partnerships or Acquisitions
Bain pointed to several strategic approaches already being used in the market.
Examples include:
- AppLovin developing its Axon AI platform internally
- ServiceNow acquiring Moveworks
- Salesforce partnering with Workday
These examples demonstrate how companies are accelerating AI capabilities through multiple paths.
The Competitive Window Is Closing Quickly
Bain warns that SaaS companies do not have years to prepare for this transformation.
According to David Crawford, the timeframe is “measured in quarters, not years.”
AI-native companies gain advantages every time they automate another customer workflow because they continuously collect more operational data, improve decision-making models, and refine automation performance.
As more organizations adopt agentic AI, the competitive gap between AI-first platforms and traditional SaaS vendors could widen rapidly.
Final Thoughts
Bain’s report presents a compelling picture of how agentic AI may redefine the future of enterprise software. Rather than replacing SaaS systems entirely, AI agents are expected to unlock enormous value by automating the coordination work that connects those systems together.
With an estimated US$100 billion opportunity in the United States and nearly US$200 billion globally across key markets, the potential for SaaS companies is enormous. Businesses that successfully combine workflow automation, cross-system intelligence, and outcome-based pricing could emerge as the next generation of software leaders.
At the same time, the report makes it clear that speed matters. Companies that delay investments in AI infrastructure, workflow automation, and data integration risk falling behind fast-moving AI-native competitors already reshaping the enterprise software landscape.
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