We’re entering the age of the System of Work: software that doesn’t just manage data, it does the work. These AI-native systems generate the actual deliverables: the compliance document, the diagnosis summary, the customer reply, and the inspection report. They collapse the distance between insight and action.

This isn’t a UX upgrade. It’s a paradigm shift in how work happens.

What is a System of Work?

A System of Work will autonomously perform productive tasks. It’s not a place you go to see what needs doing, it’s the system that actually does it. That might look like:

• An AI agent that resolves a customer ticket end-to-end
• A generated legal contract tailored to a specific jurisdiction
• A clinical platform that drafts summaries and actions from messy encounter notes

Where legacy tools required human intervention to interpret and act, Systems of Work incorporate agentic behaviour: they act on their own, within guardrails. They represent a new kind of software stack: data, model, workflow and output, all in one loop.

These systems are defined by a few key traits.

• Agentic behaviour: They initiate actions rather than waiting for human prompts.
• Embedded actions: They do not just suggest what to do. They complete the task directly within the system.
• Workflow ownership: They become the go-to place where work is created, reviewed, and shipped.
• Outcome-based value: They are priced and evaluated based on the results they deliver, not just on features or access.

Why the record → work shift matters

In a world of Systems of Work, the traditional axis of influence in software is beginning to lose relevance. The classic “source of truth” may still exist somewhere in the stack. However, the source of productivity, the system where outcomes are created, will shift.

Once a system starts producing core business outputs, it becomes exponentially more valuable and much harder to displace. This creates a new kind of competitive advantage. Systems of Work do not only benefit from access to data. They generate new, proprietary data through use. They encode human expertise, automate repeatable decisions, and improve over time. These effects compound, and become self-reinforcing advantages that deepen with every interaction.

This shift also changes the economics of software adoption.

• From insight to execution: No more offline handoffs. Work gets done where the data lives and the value is immediate and obvious.
• From user-as-operator to user-as-editor: Humans go from clicking buttons and entering data to reviewing AI-generated output.
• From seat-based pricing to outcome-based monetisation: Business models shift toward per-task or per-output models, unlocking new budget lines tied to productivity.
• From “jobs to be done” to “roles to be done”: AI systems take over full professional roles, not just isolated tasks enabling full workflow ownership.
• From software tools to workflow axis of influence: These systems become the gravitational centre of daily operations. Frontline teams feel the value directly, not just IT buyers.

How Vertical AI is powering this shift

This transition is most visible in Vertical AI: AI products built specifically for the needs of one industry. These aren’t generic LLM wrappers. They embed into frontline workflows and solve the hard, boring, expensive problems unique to sectors like healthcare, logistics, construction, and financial services.

The most advanced Vertical AI systems:

• Combine deep domain knowledge with powerful models
• Build proprietary data moats by capturing interaction-specific context
• Deliver real-world value through actionable outputs, not just dashboards

And crucially, they don’t just make software smarter, they replace entire layers of human effort.

Industries will not just adopt AI

Industries will be restructured by AI

Systems of Work will drive the biggest changes in industries where work is still manual, complex, or repetitive.

Winning without replacing

Smart AI startups are using over-the-top (OTT) strategies to enter these workflows. Rather than attempting to displace existing systems of record, they adopt a wedge strategy to layer in value → starting with simple value-additive propositions like:

• A co-pilot layered on top of an EHR system
• A transcription layer that starts automating follow-up actions
• A reporting tool that gradually becomes the system of work for compliance

Once embedded, these tools don’t just assist, they absorb the workflow. Once the workflow is absorbed, the product has direct access to the tacit knowledge that the human uses to complete a task end-to-end. Access to this ‘grey’ data area helps feed and improve an autonomous neural network, which becomes an expert on the specific task required.

Final thought

The System of Record defined the last era of enterprise software. The System of Work will define the next. We don’t know what that means for Systems of Record, maybe some of them will be able to participate in the System of Work revolution as well. We are focused on finding the founders who are willing to tackle the ‘work’ problem from scratch. The pathways to achieving a System of Work are complex and multi-layered. The optimal way to build toward a System of Work will vary by use case, user archetype, and industry.

Join us from the leading edge of Vertical AI startups, as we publish our live thinking on building Systems of Work.