The Rise of Agentic Enterprise Software: What Comes After SaaS?
For more than a decade, SaaS changed the way businesses use software.
Companies moved from installing software on individual machines to accessing cloud based applications through a browser. CRM, ERP, accounting, HR, project management and collaboration tools became services that businesses could subscribe to and use from anywhere.
But enterprise software is entering another transition.
The next shift is not simply from on premise software to cloud software.
It is from software that people operate to software that can increasingly perform work on their behalf.
This is where agentic enterprise software comes into the picture.
Gartner estimates that up to $234 billion of enterprise application software spending could be exposed to agentic AI by 2030, representing roughly 20% of enterprise SaaS spending. Gartner describes this as a shift where AI agents can complete tasks across multiple systems instead of requiring users to interact with each application individually.
So, what actually comes after SaaS?
What Is Agentic Enterprise Software?
Traditional enterprise software works primarily through human interaction.
A user opens an application, searches for information, enters data, makes a decision and performs an action.
Agentic software changes this workflow.
Instead of simply responding to a command, an AI agent can understand a goal, access relevant information, reason through a task and perform permitted actions across connected systems.
For example, consider a project manager who wants to know which projects are at risk.
With traditional software:
Open ERP → check projects → review budgets → compare expenses → create report → contact the team
With an agentic system:
Ask the system → agent analyses project data → identifies risks → prepares the findings → recommends or performs permitted actions
The difference is not simply better software.
It is a different way of interacting with software.
SaaS Is Not Disappearing
The phrase "What Comes After SaaS?" should not be interpreted as "SaaS is dead."
That would be an oversimplification.
Gartner's current research suggests SaaS will continue to remain important, particularly as a system of record. The change is that AI agents may increasingly operate across those systems, reducing how often people need to interact directly with every individual application.
Think of it this way:
SaaS provides the systems.
APIs connect the systems.
AI agents can increasingly operate across the systems.
This could create a new layer between people and enterprise applications.
From Applications to Outcomes
Traditional software is often purchased based on features.
How many users?
How many modules?
How many dashboards?
How many integrations?
Agentic software introduces a different question:
What outcome can the system deliver?
For example, instead of purchasing software simply to manage expenses, a business could increasingly expect its software to:
• Monitor expenses
• Identify unusual transactions
• Compare spending against budgets
• Prepare reports
• Route exceptions for approval
• Notify the appropriate person
The application still exists.
But the value increasingly comes from what the software can accomplish.
Gartner describes this as a shift from interface based value toward outcome based value as agentic AI becomes more capable.
The Interface May Become Less Important
For years, businesses competed on software interfaces.
Better dashboards.
Better navigation.
Better mobile applications.
Better reporting screens.
These will still matter.
But AI agents introduce another possibility.
Users may not always need to navigate through several applications to complete a task.
An employee could potentially ask an agent to gather information from multiple business systems and complete a workflow according to predefined rules.
Gartner predicts that by 2028, a third of user experiences could shift from traditional application interfaces toward agentic front ends.
This does not mean applications disappear.
It means the way users interact with them could change significantly.
The Rise of Multi Agent Workflows
The next stage may involve more than one AI agent.
Imagine a construction company managing a large project.
A project agent monitors progress.
A finance agent monitors project expenses.
A procurement agent tracks purchase requirements.
A reporting agent prepares management reports.
Instead of each agent operating independently, they could eventually coordinate parts of the same workflow.
For example:
Project Agent → detects cost issue
Finance Agent → analyses spending
Procurement Agent → checks purchase data
Reporting Agent → prepares summary
Human Manager → reviews the recommended action
Gartner expects enterprise AI to evolve toward collaborative agents and eventually agent ecosystems operating across multiple applications and business functions.
But There Is a Problem
Agentic software cannot work effectively without a strong software foundation.
An AI agent needs access to business context.
That means businesses need:
• Reliable data
• Connected systems
• APIs
• Structured workflows
• Business rules
• Authentication
• Permission controls
• Audit trails
• Monitoring
If customer information is in one system, project information is in another, financial data is in spreadsheets and approvals happen through messaging applications, an AI agent has very little reliable context to work with.
This is why the move toward agentic software is not simply an AI implementation project.
It is also a software architecture and data integration challenge.
Why Custom Software Becomes More Important
This is where custom software development can play an important role.
A business does not necessarily need to replace every existing SaaS application.
Instead, it may need a connected software layer that brings together its important business processes and data.
For businesses with specialized workflows, custom software can be designed around those processes from the beginning.
Consider a construction business.
Its software might need to connect:
Projects → Site Visits → Quotations → Purchases → Expenses → Milestones → Payments → Documents
Once these processes are connected, future AI agents have a much better foundation to work with.
The objective is no longer simply:
"Build software that manages the business."
It becomes:
"Build software that can eventually help the business manage itself."
What Businesses Should Do Now
Businesses do not need to make their entire organization autonomous overnight.
A more practical approach is to identify repetitive workflows where AI can provide measurable value.
For example:
Start with information
Use AI to summarize reports, documents, customer activity and project information.
Move to recommendations
Allow AI to identify anomalies, risks, opportunities and required actions.
Introduce controlled automation
Allow agents to perform predefined actions when specific conditions are met.
Keep humans involved
Important financial, operational and strategic decisions should have appropriate human oversight.
This creates a gradual transition from:
Human does everything
to:
Human + software
to:
Human + AI agents + software
The New Software Architecture
The enterprise software stack could increasingly look something like this:
Business Applications
ERP
CRM
HRMS
Project Management
Finance
↓
APIs and Integration Layer
↓
Business Data and Context
↓
AI Agent Layer
↓
Automated Workflows
↓
Human Oversight
The exact architecture will vary from business to business.
But the direction is becoming clearer.
AI cannot simply sit on top of disconnected systems and magically understand how a business operates.
The underlying software needs to provide the right context, permissions and workflows.
What Comes After SaaS?
SaaS changed software from something businesses installed into something they subscribed to.
Agentic enterprise software could take the next step by changing software from something businesses operate into something that increasingly performs work.
That does not mean SaaS disappears.
It means SaaS could become part of a much larger architecture where applications provide data and capabilities while AI agents coordinate actions across them.
The future may therefore look less like:
People → Applications → Tasks
and more like:
People → Goals → AI Agents → Applications → Actions
The companies preparing for this shift will not necessarily be the ones using the most AI tools.
They will be the ones building software with connected data, clear workflows, secure integrations and the flexibility to add intelligence where it actually creates business value.
For businesses, the next question is no longer simply:
"Which SaaS application should we buy?"
It may increasingly become:
"What work should our software be capable of doing for us?"