How to become AI Advisor in 2026: i run a $60-70k/mo distribution agency and do AI advising in it daily without AI advising i'd never close the high-ticket clients here's the full 6-stage roadmap: STAGE 1: pick one industry before you pick any model - spend your time learning which process bleeds money - pick the vertical you already have context in - learn their margins, their tools, their vocabulary - specialists charge 20-30% over generalists for identical work STAGE 2: automate your own operations first - do it on your real business, end to end - write down what broke, what you abandoned, what each run costs - that write-up becomes your portfolio and it outsells any certificate STAGE 3: do 3 audits for free - 2 hours inside the business, watching how work actually moves - time and cost every repetitive process you see - hand back one page, cuts ranked by payback period - that one page is the skill they pay for later STAGE 4: turn the audit into a product with tiers - discovery, 1-2 weeks, 3-5 stakeholder interviews and a readiness scorecard, $1.5-3k - comprehensive, 2-4 weeks, diagnostic across data, infra, talent and governance plus a prioritized use case matrix, $5-15k - strategy and roadmap, 4-8 weeks, implementation plan, vendor evaluation, budget, $15-50k - tiered packages convert 35-40% better than hourly proposals STAGE 5: ship one of your own recommendations - keep your hands in the build, month two is when that shows - take the smallest cut on your own map and implement it - measure hours and dollars before and after, in writing STAGE 6: treat the assessment as the front door to the real contract - a $10k assessment is paid discovery for a $150k implementation - advisory retainer after that runs $1-3k/mo, more in regulated verticals - price on deliverables and you move past $200/hr HOW TO LAND THE FIRST CLIENT: - start growing your X & LinkedIn, it's literally the best selling channel you have - publish one audit a week in your socials and let the buyers identify themselves - join their trade groups and industry slacks, in AI rooms you only meet competitors - offer their bookkeeper, agency and lawyer 10-20% to make the introduction - spot a company hiring someone to do reports by hand, then message the manager - record a 5 minute teardown of their process and send it with no pitch - charge small for a 90 minute workshop and audit whoever stays after - if they loved the audit, send a detailed proposal and wait for the yes soon i'll prepare detailed long-form tweet or article on how to attract buyers into your B2B business which tricks are working right now, which are not really efficient and better to avoid them ai advising is not theoretical stuff... you should build, show, prove your efficiency you should spend a lot of time for warming up the clients, since usually buyers are ready to pay highly learn it, implement it daily and THE MAIN, show in public, share what you do and give a chance to your buyers to see potential output before sell gl

20 things to know about AI Engineering before reading Advanced course: - context engineering (prompt caching, KV cache, context rot) - structured outputs (JSON schema, constrained decoding) - tool calling & MCP - agent loops, handoffs, termination conditions - orchestration (LangGraph, checkpointing, HITL) - workflow patterns (chaining, routing, parallelization) - document parsing (OCR, layout, tables) - chunking (recursive, semantic, overlap) - embeddings (bi-encoders, dimensionality, MTEB) - vector indexes (HNSW, IVF, pgvector, Qdrant) - hybrid search (BM25 + vector, metadata filters) - query transformation (HyDE, step-back, multi-query) - reranking (cross-encoders) - GraphRAG & knowledge graphs - evals (golden sets, LLM-as-judge, Ragas) - observability (tracing, OpenTelemetry, Langfuse) - guardrails (OWASP LLM01, least privilege, sandboxing) - caching & routing (semantic cache, cascades, fallbacks) - fine-tuning (LoRA, QLoRA, DPO, distillation) - inference serving (vLLM, quantization, continuous batching) i am going to release this new article approximately on Monday/Tuesday spent there already ~10 days of deep research, collected 112 resources (it's not finalized even on half yet kek) and already 8,000 words so, everybody who is interested to figure out in AI Engineering as me, turn on your notis for the next week 🔔