AI consulting in Spain: from promising pilot to production system

Why pilots fail to reach production

• The pilot runs outside the real stack: demo-ready but fragile in day-to-day operations.
• No clear business owner and no process to validate outcomes.
• Critical integrations (ERP, CRM, support) are postponed until too late.
• Security, cost, and quality guardrails are not defined upfront.

4-phase pilot-to-production playbook

• Phase 1 — Operational discovery (1-2 weeks): use case, baseline, and success criteria.
• Phase 2 — Architecture and integration (2-3 weeks): data, permissions, observability, and fallback.
• Phase 3 — Controlled rollout (3-6 weeks): team-based deployment with human validation.
• Phase 4 — Scale (ongoing): prompt optimization, per-task costs, and new workflows.

Minimum governance to reduce risk

• Approved-use catalog: what AI can automate vs. what requires human review.
• Data policy: classification, retention, and response traceability.
• Quality alerts: error thresholds, incident handling, and rollback protocol.
• Monthly cost-per-process review to protect operating margins.

KPIs that actually convince leadership

• Average task time before/after (initial target: -25% to -40%).
• Reduction in rework and operational errors by team.
• Commercial capacity increase without proportional headcount growth.
• Use-case payback with sub-9-month target.

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