AI Project Timelines: How Long Things Actually Take

Every AI project takes longer than the initial estimate. Here are honest timelines from dozens of production deployments.

Simple Chatbot (FAQ/Support)

• Quoted: 1–2 weeks
• Actual: 2–4 weeks
• Why it takes longer: the chatbot works in demo. Then edge cases appear. Customers ask questions nobody predicted. The knowledge base needs restructuring. Integration with the existing helpdesk takes a week by itself

RAG System (Document Q&A)

• Quoted: 2–4 weeks
• Actual: 4–8 weeks
• Why it takes longer: document parsing is the bottleneck. PDFs with tables, images, and mixed formatting break every parser. Chunking strategy requires experimentation. Retrieval accuracy needs tuning. The difference between “it works on 5 test docs” and “it works on 5,000 production docs” is enormous

Data Pipeline Automation

• Quoted: 1–3 weeks
• Actual: 2–5 weeks
• Why it takes longer: data quality issues. The source data is messier than anyone admits. Error handling for edge cases. Rate limits on APIs. The “last 5%” of data that doesn’t fit the pattern

Custom Fine-Tuned Model

• Quoted: 4–8 weeks
• Actual: 8–16 weeks
• Why it takes longer: training data preparation takes 50% of the total time. Multiple training runs to get quality right. Evaluation requires domain expertise. Deployment infrastructure needs building

Why Estimates Are Always Wrong

• The demo works on curated data. Production data is messy
• Integration with existing systems always has surprises
• Stakeholder feedback changes requirements mid-project
• LLM behavior is non-deterministic — edge cases emerge over time