Originally published on Medium.
The short version
Software has gone through three eras: Systems of Record (Salesforce, SAP, Oracle), Systems of Engagement (Slack, Gmail, Notion), and now Systems of Work, where the software does the work itself. This is Service-as-Software. The AI doesn't assist a human; the AI is the workforce. Seven shifts come with it: tools to agents, license-based to outcome-based pricing, siloed systems to knowledge graph hubs, human workforce to AI workforce management, skepticism to trust-building in autonomy, point solutions to strategic integration, feature parity to quality and alignment. If you're building software, ask: can this be autonomous? What would change if you marketed it as "hire this agent instead of this person"? The shift is happening now.
The Evolution of Software
Software has gone through three major eras:
Systems of Record - databases and ERP systems that hold the truth about your business. Salesforce. SAP. Oracle.
Systems of Engagement - tools that let humans interact with those records. Slack. Gmail. Notion.
Systems of Work - software that does the work itself. This is Service-as-Software.
What Changes in Service-as-Software
In traditional SaaS, an AI assistant helps a human. The human makes decisions. The human is responsible.
In Service-as-Software, the AI worker executes and completes tasks autonomously. You still have oversight, but the work happens without continuous human involvement. Your AI employee submits expense reports. Files contracts. Responds to support tickets. Manages inventory.
The AI doesn't assist a human using software. The AI is the workforce.
Why It Changes the Game
Fewer humans overseeing more work. Instead of hiring five account managers to manage accounts, you hire one person to oversee ten AI agents managing accounts.
Quality and alignment matter more. When software does the work, accuracy becomes critical. Governance becomes critical. Trust becomes critical.
Streamlined oversight. You don't micromanage AI workers like you micromanage humans. You set goals, monitor outcomes, and intervene when needed.
Connecting Systems of Record to Systems of Work
This only works if your AI agents have access to real-time data. Your Systems of Record need to become open. API-first. Real-time.
You need a knowledge graph - a unified view of your business data that agents can reason about. Products. Customers. Transactions. Relationships.
Monetizing the Shift
SaaS monetizes through seats and licenses. "Pay per user."
Service-as-Software monetizes through outcomes. "Pay for results delivered."
Fewer licenses. Same or more value. That's the power of outcome-based pricing.
AI Workforce Management
Here's the next frontier: workforce management for AI.
You'll need "Lattice for AI workers" - platforms to:
- Define roles and responsibilities for agents
- Track performance and productivity
- Manage learning and capability development
- Handle career progression (more capable agents get bigger tasks)
- Enable team collaboration between agents
Building a Culture of Rapid Change
We're moving from "tools to agents." Like Tesla moved from human-driven cars to autonomous vehicles. The transition is the hard part.
You need:
- Clear goals for what agents should accomplish
- Metrics for success
- Regular retraining and updates
- Psychological safety to experiment and fail
- Leadership that embraces change
Seven Major Shifts
1. From Tools to Agents. Humans use software. Software uses humans.
2. From License-Based to Outcome-Based Pricing. Pay for what gets done, not who's using it.
3. From Siloed Systems to Knowledge Graph Hubs. All data flows through a unified intelligence layer.
4. From Human Workforce to AI Workforce Management. You need new processes and platforms to manage AI capability.
5. From Skepticism to Trust-Building in Autonomy. Leaders need frameworks for trusting AI to make decisions without human approval.
6. From Point Solutions to Strategic Integration. Every tool needs to connect. Every agent needs context.
7. From Feature Parity to Quality and Alignment Focus. Quantity of features matters less than reliability and accuracy of execution.
What This Means for You
If you're building software, you need to think about Service-as-Software. Can your product be autonomous? What would change if you marketed it as "hire this agent instead of this person"?
If you're leading an organization, you need to prepare for a workforce that includes AI agents. What governance do you need? What skills does your team need to develop?
The shift from SaaS to Service-as-Software isn't coming in a few years. It's happening now. The companies that understand this and adapt first will win.