Hire AI Automation Developers.

• Real experience connecting to production systems (CRM, ERP, ticketing platforms) through documented APIs, not just demos against sandbox data.
• A track record of building evaluation suites, not just shipping a working prototype and calling it done.
• Comfort designing for failure: what happens when an API call times out, when a classification is wrong, when a case doesn't fit any known pattern.
• Ability to explain a system's decision path in plain language to a non-technical stakeholder, since that's what an audit or a dispute will eventually require.

• "Walk me through how you'd design the exception path for a case that doesn't match any known pattern." A vague answer here usually means the judgment layer will be designed reactively after launch.
• "How do you decide what needs human approval versus full automation?" Listen for a permissions-based answer, not a confidence-threshold-only one.
• "How would I audit what the system did on a specific case from three months ago?" Tests whether replay and logging are treated as core design, not an afterthought.
• "What's the first thing you'd build, and why?" The integration layer should come before the judgment layer in a good answer.

• A proposal that's almost entirely about the AI model choice, with little detail on integration or verification.
• No mention of shadow-mode testing before going live, running alongside the existing process first is standard practice, not optional.
• Reluctance to commit to a measurable accuracy or exception rate before launch.
• A cost estimate with no line item for evaluation or ongoing monitoring, that work doesn't disappear, it either happened or it's a risk being inherited later.

• In-house — best when automation is a sustained, ongoing priority across many workflows and the team can justify dedicated headcount for integration and evaluation work.
• Agency — best for a first project or a handful of well-scoped workflows, brings established evaluation practices and integration experience across multiple systems without a long ramp-up.
• Freelancer — workable for a narrow, well-defined single workflow with low integration complexity, riskier for anything touching several production systems or requiring ongoing evaluation after launch.

See our AI automation guide for the architecture whoever you hire should be building toward, and our AI automation cost guide for what a realistic budget looks like.