Short answer
A practical AI roadmap has six stages: AI literacy, AI user, AI builder, AI engineer, agentic AI and production AI. Most professionals and business owners get the most value from the first three: understanding AI, using it well daily, and building no-code workflows and automation. The later stages — Python, APIs, RAG, agents and deployment — are for those pursuing technical AI careers.
By Chandan Maheshwari, AI mentor & consultant, founder of School of AI · Last updated
Key takeaways
- Six stages: Literacy → User → Builder → Engineer → Agentic → Production.
- Most non-technical learners should master stages 1–3.
- Engineering stages need Python and software fundamentals.
- Your target outcome decides how far to go.
Stages in detail
What each stage involves.
- 1. AI literacy — concepts, vocabulary, limitations, safe use
- 2. AI user — prompting, research, content and analysis in daily work
- 3. AI builder — no-code workflows, automation, simple assistants
- 4. AI engineer — Python, APIs, embeddings, RAG, evaluation
- 5. Agentic AI — tool calling, multi-step agents, memory
- 6. Production AI — deployment, monitoring, security, governance
Becoming an AI engineer
A typical technical path is Python, software fundamentals, machine learning basics, deep learning and transformers, LLM APIs, RAG and vector databases, tool calling and agents, then evaluation and deployment. Maths — linear algebra, probability and statistics — matters more as you move toward ML research. School of AI programs focus on stages 1–3 and applied automation, not engineering careers.
Which stages to aim for
| Learner | Aim for |
|---|---|
| Student (any stream) | Stages 1–3 |
| Working professional | Stages 1–3 |
| Business owner / entrepreneur | Stages 1–3, plus automation |
| Aspiring AI engineer | Stages 1–6 |
Learning this with School of AI
GenAI Foundation Program
Live, mentor-led and online, open to learners anywhere in India.