AI Operations · Role
AI Operations Manager
The AI Operations Manager owns getting a company's AI agents and automations into production, working and measured. They don't research models — they run the function.
What they do
- Prioritise which processes to automate first, and at what ROI.
- Ship agents to production and give each one an owner and an SLA.
- Measure cost, reliability and savings, and iterate on what underperforms.
Skills it needs
Salary by country and seniority
| Country | Seniority | P25 | Median | P75 | |
|---|---|---|---|---|---|
| Germany | All | €65.922 | €68.948 | €71.974 | Schätzung aus öffentlichen Quellen |
| Spain | All | €45.358 | €55.216 | €65.074 | Schätzung aus öffentlichen Quellen |
| France | All | €26.813 | €26.813 | €26.813 | Schätzung aus öffentlichen Quellen |
| Italy | All | €28.358 | €28.358 | €28.358 | Schätzung aus öffentlichen Quellen |
| Portugal | All | €41.500 | €41.500 | €41.500 | Schätzung aus öffentlichen Quellen |
| United States | Mid | €101.200 | €101.200 | €101.200 | Schätzung aus öffentlichen Quellen |
| United States | Senior | €149.349 | €181.218 | €181.218 | Schätzung aus öffentlichen Quellen |
| United States | All | €114.275 | €136.589 | €158.904 | Schätzung aus öffentlichen Quellen |
Figures in euros, gross annual — each with its source.
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The market uses several titles for the same role:
How it changes by seniority
- Junior — Runs already-defined automations and supports the ones in production.
- Mid — Owns an automation end to end: prioritises, ships and measures it.
- Senior — Runs a portfolio of automations with owners and SLAs, and owns their ROI.
- Lead — Leads the team and the automation roadmap; owns aggregate impact.
When to hire
When you have AI pilots and none of them reach production or get measured.
When not to
If you don't have a clear candidate process yet: start with an assessment, not a hire.
Interview questions
1. You have three AI pilots that work in a demo and none in production. How do you decide which to ship first?
What to look for: Prioritises by return and risk, not technical flash; asks for process data before choosing.
2. A production agent starts failing 15% of the time. What are your first three steps?
What to look for: Goes to observability (what changed, which cases), scopes the impact and decides whether to escalate to a human while investigating.
3. How do you prove to leadership that an automated process is genuinely saving?
What to look for: Talks baseline before vs. after and includes the cost of supervision, not just gross savings.
4. A team wants to automate a process you think should be redesigned first. How do you handle it?
What to look for: Separates automating from redesigning; can say "not yet" with data without killing the team’s momentum.
Scorecard
- ROI prioritisation — Chooses what to automate by return and risk, with process data, not technical novelty.
- Operating in production — Assigns owner and SLA, watches cost/reliability and acts when something degrades.
- Impact measurement — Ties each agent to a business number with a baseline and total cost included.
- Human-agent coordination — Designs the hand-off: what escalates, when and to whom; neither all-manual nor all-automatic.
Red flags
- — Talks models and prompts but never owner, SLA or cost.
- — Measures success by pilots launched, not processes in production.
- — Wants to review 100% of outputs "just in case" and doesn’t see it kills the saving.
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