Deploy AI agents that show up on the P&L.

Most companies are still pitching AI agents in a deck. Hayat Amin embeds them into live workflows in finance, legal, and go-to-market — then measures the cost-to-serve reduction and revenue lift on the next month's P&L. The work runs alongside the fractional CFO function so the finance number proves the AI number.

What an AI agent operator actually does

• Maps the workflow in detail — every handoff, every rule, every exception.
• Picks the smallest agent that solves the highest-leverage step.
• Builds it on the model and infrastructure stack you already pay for.
• Sets up evaluation gates: accuracy, latency, hallucination rate, cost per task.
• Wraps governance — audit logs, role-based access, kill switches.
• Measures the ROI on month 1, month 3, and month 6.

Where AI agents earn their keep first

1. Finance: month-end close, AR/AP, expense classification, board-pack generation.
2. Legal: contract abstraction, redline review, IP filing prep, diligence-room Q&A.
3. GTM: outbound research, lead qualification, sales-call coaching, post-call CRM hygiene.

The thesis: agents that touch revenue or cost lines win

Most AI agent deployments fail because they're built around tasks that don't appear on the P&L. Hayat only deploys agents into workflows where success or failure shows up in next month's numbers. That makes the ROI argument self-evident — and the kill-switch decision easy when an agent underperforms.

What you walk away with

• A working agent in production, behind your auth.
• Evaluation harness with weekly accuracy and cost reports.
• A governance brief your board and audit committee can sign off.
• A documented playbook for the next 3 agents.
• A finance-grade ROI calculation showing payback period in days.

Companion reading

• Agentic AI business strategy — what actually scales
• AI cost optimisation strategy
• AI governance framework for SMEs