Use cases
AI employees for the back office, hired on probation, not on faith.
The back office runs on the same loop every week: pull the data, update the records, draft the report, chase the calendar. The roles are hard to fill and dull to keep filled. That work is now hireable as software. We onboard AI employees to your tools, run them through probation, and measure them in cost per task. Humans keep the judgment calls.
What are AI employees for the back office?
AI employees are autonomous agents onboarded like staff: a defined role, their own accounts, least-privilege tool access, and measurable output. In the back office they take on research, reporting, CRM hygiene, scheduling, document preparation and support triage. Each starts on probation, with a human reviewing every output until accuracy holds a threshold, then works autonomously within its scope, with escalation rules and audit logs. Cost is tracked per task, so the comparison with a hire is a number. extendfuture sets up one role in a scoped AI Employee Pilot.
Who this is for
Funded startups and $10M to $100M companies where operations headcount is the bottleneck. The pattern is consistent: the req has been open for months, the work is repetitive judgment layered over tools you already run, and a senior person is covering it in the gaps. These are the roles that work first:
- Research and monitoring: markets, competitors and prospects, briefed on a schedule
- Reporting: the weekly numbers assembled, checked against source systems, and sent
- CRM hygiene: records deduplicated, enriched and kept current
- Scheduling and coordination: calendars, follow-ups and reminders handled end to end
- Document preparation: drafts, summaries and packs built from your templates
- Support triage: first-line sorting, tagging and routing, with humans on the hard ones
How the system works
An AI employee is run like a hire, not installed like software. Security comes before the first task: least-privilege access, personal data masked before models see it, an audit log on every action, and human approval gates on anything sensitive. The whole setup is designed to support DPDP, GDPR, HIPAA and SOC 2 expectations. Then the role follows the same arc as a human hire:
- Define the role: a real job description with tasks, tools, boundaries and escalation rules
- Onboard: accounts, permissions, and your actual work as context
- Probation: every output reviewed by a human, corrections fed back, accuracy tracked to a threshold
- Full duties: autonomous within scope, human sign-off beyond it, monthly performance reports
The right first workflow
Start where volume is high, the rules are mostly written down, and a mistake is cheap to catch. Internal reporting and CRM hygiene usually make the strongest first hires: output can be checked against source systems, and nothing irreversible happens without a person. Just as important is what not to automate first:
- Not first: outbound messages to customers. Client-facing work comes after probation is passed internally
- Not first: anything irreversible, such as payments, deletions or contract commitments
- Not first: judgment-heavy exceptions your own team still debates
- Not first: work a regulator reads, until approval gates and audit trails have proven themselves
Success metrics
The role is instrumented from day one, so performance is a report, not an impression. The AI Employee Pilot runs on one role and ends with the numbers a manager would ask for:
- Cost per task, tracked against what the same work costs a person
- Accuracy against the threshold agreed during probation
- Escalation rate: how often it hands work to a human, and why
- Throughput: tasks completed per day, around the clock
- Hours returned to the people who used to do this work manually
Related proof
This is not a concept page. The mechanics described here, defined roles, probation, escalation, audit logs, run in production systems we operate today:
- An autonomous newsroom we operate researches, writes and publishes every day at under $2 per article, with humans on editorial judgment
- An insurance advisory session that took about an hour now runs in under fifteen minutes, fully automated and audit-traceable
- A SaaS revenue team lifted qualified-lead conversion by roughly a quarter with predictive sales analytics
- Under NDA: strata-report and contract intelligence, and high-throughput transcription, doing document work at volume
Which back-office roles should an AI employee take first?
High-volume, rule-plus-judgment roles: research and monitoring, reporting, CRM and data hygiene, scheduling and coordination, document preparation, and first-line support triage. Internal roles come first because output is easy to check against source systems. Client-facing work follows once the AI employee has passed probation.
What is the probation model?
The same idea as a human hire. During probation a person reviews every output, corrections are fed back into the system, and accuracy is tracked against a threshold you agree up front. Only when it holds that threshold does the AI employee work autonomously, and even then only within its defined scope. Everything beyond scope routes to a human approval gate.
How is the cost measured against hiring?
In cost per task, instrumented from day one. Setup is a fixed project and the running cost is dominated by model usage, so you compare the AI employee to a hire with a number, not a hope. The performance dashboard shows output, accuracy and cost side by side.
What should we not automate first?
Anything irreversible, anything a regulator reads, outbound messages to customers, and exceptions your own team still debates. Start internal, prove accuracy through probation, then widen the scope deliberately. Picking the wrong first workflow is how most AI pilots die.
Is our data safe with an AI employee inside our tools?
The AI employee gets the minimum access the role needs, every action lands in an audit log, and personal data is masked before models see it. Data residency is honored, and human approval gates sit on sensitive steps. The design is built to support DPDP, GDPR, HIPAA and SOC 2 expectations.
How do we start?
Book a founder-led 30-minute call, or go straight to the AI Employee Pilot: scoped, one role. We onboard the AI employee, run its probation, and hand you the performance numbers. If the role is not a fit for automation yet, we say so plainly.
Talk to the people who build.
One call. An honest read on what AI can do for this, and the number it has to beat.