How long does AI implementation take?

AI implementation timeline depends on three factors: project scope, data readiness, and integration complexity. Here is what to expect at each stage.

Typical Timeline Breakdown

• Discovery and audit (weeks 1-2) -- workflow mapping, data assessment, compliance review, written scope and fixed-price estimate
• Data preparation (weeks 2-4) -- the most unpredictable phase; clean and structured data compresses this to days, messy legacy data can stretch it to 4-6 weeks
• Build and integration (weeks 4-8) -- AI model selection or fine-tuning, integration with existing systems, UI build if required
• Testing and rollout (weeks 8-10) -- parallel run against existing process, edge case testing, staff onboarding

What Slows Implementation Down

• Data in multiple disconnected systems with no unified schema
• Stakeholder decision delays on scope and edge cases
• Legacy software with no API requiring custom integration middleware
• Regulated environments requiring compliance sign-off between phases

How to Accelerate Your Timeline

Start with an AI workflow audit -- a 2-week structured assessment that identifies data issues and integration blockers before any build cost is spent. Projects that complete a discovery audit first consistently launch 30-40% faster than those that skip directly to development. Contact Code and Trust to scope your project.