Before creating a new headcount for administrative or repetitive work, conduct a thought experiment: can an AI agent or an automation workflow fulfill these duties? This approach can reduce overhead and force a re-evaluation of how tasks are accomplished.

Run HR, finance, and legal using AI agents that operate based on codified rules. This creates an autonomous back office where human intervention is only required for exceptions, not routine patterns. The mantra is: "patterns deserve code, exceptions deserve people."

For TeamShares' portfolio of durable, "evergreen" small businesses, the primary impact of AI isn't a threat to their core revenue. Instead, AI is a massive efficiency lever in the back office, reducing tasks like audit-related bank reconciliations from a six-week process to mere minutes, dramatically cutting operational overhead.

One person now manages RevOps, enablement, data analysis, and CRM administration—functions that previously required 10-15 people—by orchestrating AI agents. This demonstrates a massive leap in productivity and operational leverage made possible by AI.

Flexport uses AI agents for tasks that were previously skipped because they were too costly for human employees, like calling warehouses to confirm addresses. This shows that AI's value isn't just in replacing existing work, but in performing new, marginally valuable tasks at a scale that is finally economical.

Curative deployed an AI agent, Gwen, that handles the entire sales workflow for smaller provider contracts—from research and outreach to negotiation and signing. It took the company from signing 100 contracts a week with a full team to 100 per day, with the agent alone signing more contracts in 8 weeks than the team did in a year.

The true power of an AI agent is its capacity to handle the mundane, repetitive work that humans—both internal teams and external agencies—often neglect or de-prioritize. SaaStr couldn't find people willing to consistently manage hundreds of follow-ups, a task their AI now handles flawlessly.

Instead of hiring a traditional Head of Ops, Chapter hired an AI engineer for the role. This led to automating complex compliance and licensing workflows across 50 states, allowing the company to handle a workload that typically requires 50-60 people with a team of just one and a half.

In a striking case study of AI efficiency, portfolio company Trace used AI co-agents to automate sales and customer service roles. This allowed them to reduce headcount from 40 SDRs and CSRs to just two, while simultaneously achieving profitability and increasing revenue by 50%.