Every enterprise revolution has been powered by a new data layer:

ERP integrated core business operations and financial data (SAP, Oracle)
CRM illuminated customer relationships and revenue data (Salesforce, HubSpot)
Analytics revealed digital product and user behavior (Segment, Amplitude)

Each made the invisible measurable. Yet one dataset has never been captured at scale:

Work itself.

The Data Blindspot

Enterprises know everything about their customers and finances but not how work actually gets done. Ask any leader how projects move from idea to execution, or why some teams consistently move faster than others, and you’ll get anecdotes - not data.

Work, the real execution behind outcomes, remains invisible. When key people leave, their know-how disappears with them. The organization loses operational intelligence it never recorded. Despite drowning in data, enterprises are blind to their most important question: how does workflow through the business?

Why Work Data Never Existed Until Now

Work happens everywhere: email, Slack, Excel, Salesforce, Zoom. Legacy methods like process mining only see structured systems; activity monitoring creates noise. Neither can interpret intent, why someone acts, not just what they do.

Large language models changed that. LLMs can now understand the context and purpose behind digital activity, distinguishing between productive analysis and distraction, creative problem-solving and rework. For the first time, it’s possible to capture and structure work data automatically, across every tool, in real time.

What Work Data Unlocks

1. Measurable AI Impact

Most AI initiatives track adoption, not outcomes. A rollout may show 90% usage but zero productivity gain. Work data closes this gap; it measures whether workflows actually changed. Enterprises can finally see which use cases improve outcomes and which waste time, proving AI ROI and stopping bad projects early.

2. Visible Performance Gaps

Top performers use better workflows, but those methods stay invisible. Work data surfaces how they work, the shortcuts, sequencing, and focus that drive results. Organizations can replicate excellence systematically instead of relying on tribal knowledge.

3. Process Reality vs. Process Theory

Documented SOPs rarely match how work happens. Work data exposes the real execution path, where teams deviate, where improvements emerge, and which unofficial workarounds actually add value. Leaders can automatically update processes based on evidence, not assumptions.

4. The Foundation for Autonomous Operations

Automation fails when AI can’t see real workflows. With work data, AI agents gain the context needed to orchestrate entire processes, understanding exceptions, regional variations, and human intent. This shifts automation from brittle scripts to adaptive systems that truly understand operations.

The Next Data Revolution

CRM revealed who customers are. ERP tracked what money moves. Analytics showed how products are used. Work data reveals how everything actually happens.

ERP revealed how the business runs. CRM showed who the customer is. Analytics exposed how products are used. Work data reveals how everything actually gets done.

It is the connective tissue between every system, process, and outcome, turning work itself into an intelligent, measurable dataset. Enterprises that harness it will transform faster, deploy AI smarter, and operate with a compounding advantage.

The future of enterprise performance will be written in the data of work itself.