The Rise of Agentic Work: Can AI Replicate a Team, Not Just a Person?

Why It Matters

Agentic AI transforms productivity by turning multi‑person workflows into scalable, repeatable operations, creating new sources of intellectual property and cost savings for enterprises.

Key Takeaways

• AI agents now target end‑to‑end business workflows, not isolated tasks
• Success hinges on codifying SOPs into programmatic, repeatable rules
• Early adopters include e‑commerce COO functions, family‑office finance, insurance underwriting
• Properly legible processes become valuable intellectual property for firms
• Organizations must embed human guardrails and confidence thresholds in AI agents

Pulse Analysis

The rise of generative AI has already reshaped how knowledge workers handle routine outputs, but the frontier now lies in extending those capabilities to whole business processes. Unlike a personal workflow that lives in a single screen, a corporate process spans multiple departments, data sources, and approval layers. To move AI from a clever assistant to a true "agent" requires translating the tacit knowledge embedded in spreadsheets, SOPs, and experienced staff into explicit, machine‑readable instructions.

Venture‑backed startups are tackling this challenge across three verticals. In e‑commerce, AI agents are being trained to mimic a chief operating officer, handling product selection, creative design, inventory logistics, and vendor coordination. Family‑office firms are experimenting with agents that manage capital calls, idle‑cash treasury, and forecasting reports. Insurance companies, guided by a recent McKinsey study, are decomposing underwriting into discrete AI roles—data gathering, risk profiling, pricing, compliance checks, and feedback loops—turning hidden expert judgments into codified rules. These pilots illustrate that the bottleneck is not model performance but the rigor of process documentation.

For enterprises, mastering agentic work promises two strategic advantages. First, it unlocks efficiency gains by automating repetitive handoffs and reducing reliance on scarce human expertise. Second, the act of making processes legible creates a new asset: a repository of institutional knowledge that can be protected, licensed, or leveraged for competitive differentiation. Companies that invest in structured workflow design, embed confidence thresholds, and retain human oversight will be positioned to reap the productivity and IP benefits of AI‑driven automation. The shift from digitising containers to digitising the work itself marks a pivotal evolution in enterprise technology.