How AI Automation Projects Are Implemented

Project phases

1. Discovery: process map, volumes, systems, owners and pain points.
2. Business case and success criteria agreed with the process owner.
3. Data access and test set: real, permitted examples including difficult cases.
4. Prototype and evaluation against the test set.
5. Integration into source and target systems.
6. Pilot with full human review and defined stop conditions.
7. Rollout, monitoring and handover of responsibility.

Integration with existing systems

Automations are only as useful as their connection to the systems where work happens: email and collaboration tools, CRM, ERP, ticketing, document management and data warehouses. Prefer official APIs over screen automation, define which system holds the authoritative data and plan who maintains connectors when systems are updated.

Security, data protection and governance

Before the prototype, clarify which data may be processed by which model and where, how long inputs and outputs are stored, and who may change prompts, rules or permissions. Log model versions and approvals so that results remain explainable. For personal data, involve your data protection officer early.

Operating AI automation

After go-live, quality can drift when inputs change. Keep the test set current, sample results regularly, track costs per processed item and agree on a change process. Assign an internal owner even if an agency operates the solution.

Checklist for selecting an AI automation agency

• References you can verify for comparable processes
• Evaluation method with your own data
• Clear model and vendor strategy, including exit options
• Documented permissions, approvals and logging
• Transparent pricing for build and operation
• Knowledge transfer to your team